Patient-Focused Drug 
Development: Collecting 
Comprehensive and 
Representative Input
 
 
Guidance for Industry, Food and Drug 
Administration Staff, and Other 
Stakeholders 
 
 
 
 
 
 
 
 
 
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
Center for Biologics Evaluation and Research (CBER) 
 
 
June 2020 
Procedural 
 
   
Patient-Focused Drug 
Development: Collecting 
Comprehensive and 
Representative Input 
 
Guidance for Industry, Food and Drug 
Administration Staff, and Other Stakeholders 
 
 
Additional copies are available from: 
Office of Communications, Division of Drug Information 
Center for Drug Evaluation and Research 
Food and Drug Administration 
10001 New Hampshire Ave., Hillandale Bldg., 4th Floor 
Silver Spring, MD 20993-0002 
Phone: 855-543-3784 or 301-796-3400; Fax: 301-431-6353 
Email: druginfo@fda.hhs.gov 
https://www.fda.gov/drugs/guidance-compliance-regulatory-information/guidances-drugs  
and/or 
Office of Communication, Outreach and Development 
Center for Biologics Evaluation and Research 
Food and Drug Administration 
10903 New Hampshire Ave., Bldg. 71, Room 3128 
Silver Spring, MD 20993-0002 
Phone: 800-835-4709 or 240-402-8010 
Email: ocod@fda.hhs.gov 
https://www.fda.gov/vaccines-blood-biologics/guidance-compliance-regulatory-information-biologics/biologics-guidances 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
Center for Biologics Evaluation and Research (CBER) 
 
June 2020 
Procedural 
 
 
  Contains Nonbinding Recommendations 
 
TABLE OF CONTENTS 
 
  INTRODUCTION .......................................................................................................... 1 
  Overview of Guidance 1 ................................................................................................. 3 
  Patient Experience Data ................................................................................................ 4 
  GENERAL CONSIDERATIONS FOR COLLECTING PATIENT EXPERIENCE 
DATA .............................................................................................................................. 7 
  Overview ......................................................................................................................... 7 
  Defining the Research Objectives and Questions ........................................................ 8 
  Who to Collect Information From ................................................................................ 9 
  Defining the Target Population ....................................................................................... 9 
  Determining Who Will Be Providing Patient Experience Data....................................... 9 
  Determining the Study Design and Research Setting ............................................... 10 
  Sampling Methods .......................................................................................................... 10 
  Representativeness ......................................................................................................... 15 
  Sample Size .................................................................................................................... 16 
  Constructing a Sampling Frame ................................................................................. 17 
  Additional Considerations ........................................................................................... 18 
  Missing Data/Non-Response .......................................................................................... 18 
  Leveraging Existing Data .............................................................................................. 19 
  OPERATIONALIZING AND STANDARDIZING DATA COLLECTION AND 
DATA MANAGEMENT ............................................................................................. 19 
  Locating Patients/Sites ................................................................................................. 19 
  Human Subjects Protection ......................................................................................... 19 
  Sampling Strategy ........................................................................................................ 20 
  Collecting Data ............................................................................................................. 20 
  Interviews, Focus Groups, and Facilitated Discussions ............................................... 20 
  Observational Methods .................................................................................................. 21 
  Documents...................................................................................................................... 21 
  Survey Instruments ......................................................................................................... 21 
  Audiovisual Materials .................................................................................................... 21 
  Social Media and Verified Patient Communities ........................................................... 22 
  Recording Information ................................................................................................ 25 
  Resolving Site/Field Issues ........................................................................................... 25 
  Data Management ........................................................................................................ 26 
  Data Standards ............................................................................................................. 26 
  Monitoring and Quality Assurance ............................................................................ 27 
  Storing Data .................................................................................................................. 27 
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  Contains Nonbinding Recommendations 
 
  Confidentiality .............................................................................................................. 27 
  Data Analysis ................................................................................................................ 27 
  CONCLUSIONS........................................................................................................... 28 
  REFERENCES ............................................................................................................. 29 
  APPENDICES .............................................................................................................. 33 
Appendix 1. Standards and Requirements Pertaining to Submission of Data .................. 33 
Appendix 2. Glossary ............................................................................................................... 34 
 
 
ii 
  Contains Nonbinding Recommendations 
 
 
TABLE OF TABLES 
 
Table 1. Methodological Approaches for Collecting Patient Experience Data .............................. 5 
Table 2. Types of Sampling, Examples and Some Potential Limitations ..................................... 12 
Table 3. Common Methods for Gathering Patient Input Using Online Methods (Including Social 
Media) ........................................................................................................................................... 22 
 
TABLE OF FIGURES 
 
Figure 1. General Steps for Conducting Studies About Patient Experience................................... 8 
Figure 2. Factors to Consider To Achieve Sufficient Representation .......................................... 16 
 
 
 
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  Contains Nonbinding Recommendations 
 
Patient-Focused Drug Development: Collecting Comprehensive and 
Representative Input 
Guidance for Industry, Food and Drug Administration Staff, and 
Other Stakeholders1 
 
 
This guidance represents the current thinking of the Food and Drug Administration (FDA or Agency) on 
this topic.  It does not establish any rights for any person and is not binding on FDA or the public.  You 
can use an alternative approach if it satisfies the requirements of the applicable statutes and regulations.  
To discuss an alternative approach, contact the FDA staff responsible for this guidance as listed on the 
title page.   
 
 
 
 
  INTRODUCTION 
 
This guidance is intended to address both a statutory requirement under the 21st Century Cures 
Act of 2016 (hereafter referred to as Cures Act) Section 3002 (c) and a commitment made 
under the Prescription Drug User Fee Act (PDUFA) VI (authorized under the FDA 
Reauthorization Act of 2017 (FDARA), Title I) to issue methodological guidance to support 
patient-focused drug development. The guidance to be issued under Cures Act section 3002(c)(1) 
shall address: 
 
Methodological approaches that a person seeking to collect patient experience data for 
submission to, and proposed use by, the Secretary in regulatory decision making may use, that 
are relevant and objective and ensure that such data are accurate and representative of the 
intended population, including methods to collect meaningful patient input throughout the drug 
development process and methodological considerations for data collection, reporting, 
management, and analysis. 
 
In addition to this Cures Act provision, FDA committed to publish a series of guidances under 
Section I.J of the PDUFA VI Reauthorization Performance Goals for Enhancing the 
Incorporation of the Patients Voice in Drug Development and Decision-Making, and the first 
of the series (referred to as Guidance 1) covers the same methodological topics as Cures Act 
sections 3002(c)(1), with the additional commitment to include standardized nomenclature and 
terminologies.  
 
The Cures Act2 defines the term patient experience data to include data that: 
 
                                                 
1 This guidance has been prepared by the Office of New Drugs and the Office of Translational Sciences in the 
Center for Drug Evaluation and Research (CDER), in cooperation with the Center for Biologics Evaluation and 
Research (CBER), at the Food and Drug Administration.  
2 Patient experience data is defined for purposes of this guidance in Title III, Section 3001 of the 21st Century Cures 
Act, as amended by section 605 of the Food and Drug Administration Reauthorization Act (FDARA) of 2017. 
1  Contains Nonbinding Recommendations 
 
(1) are collected by any persons (including patients, family members and caregivers of 
patients, patient advocacy organizations, disease research foundations, researchers and 
drug manufacturers); and (2) are intended to provide information about patients 
experiences with a disease or condition, including (A) the impact (including physical 
and psychosocial impacts) of such disease or condition or a related therapy or clinical 
investigation; and (B) patient preferences with respect to treatment of the disease or 
condition.   
 
This expansive definition of patient experience data includes a wide range of opportunities for 
the collection of information that might be used to inform and provide a greater patient focus in 
medical product development.  
 
The range of patient experience data that would fit within the Cures Act statutory definition 
includes: patient registry data, natural history study data, patient focus group or meeting reports, 
patient survey data, clinical outcome assessment (COA) data collected during clinical trials, and 
elicited patient preference data. For the purposes of this guidance, some methodological 
considerations apply across the diversity of potential study objectives, but others will apply to 
only a subset of these. 
 
This guidance (Guidance 1) presents a general overview of methods and approaches for 
collecting patient experience data rather than focusing on methods for a specific, single purpose, 
e.g., to support collection of COA data or patient preference information. Specific issues related 
to COAs and patient preference information are addressed in other published FDA guidances.3  
 
FDA is publishing a series of guidances intended to facilitate the advancement and use of 
systematic approaches to collect and use robust and meaningful patient and caregiver input that 
can better inform medical product development and regulatory decision-making.  
 
The topics and questions that each guidance document will address are described below. 
 
Guidance 1: From whom do you get input, and why? How do you collect the information?  
 
Guidance 1 discusses sampling methods that could be used when planning a study to collect 
patient input. It also provides a general overview of the relationship between potential research 
question(s) and method(s) when deciding from whom to get input (including defining the target 
population and development of the sampling strategy).  
 
Guidance 2: What do you ask, and why? How do you ask non-leading questions that are well-
understood by a wide range of patients and other stakeholders?  
 
Guidance 2 will discuss methods for eliciting information from individuals identified in 
Guidance 1, gathering information about what aspects of symptoms, impacts of their disease, and 
                                                 
3 Issues related to COAs and patient preference information are addressed in the following guidance for industry: 
Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims and Patient 
Preference InformationVoluntary Submission, Review in Premarket Approval Applications, Humanitarian Device 
Exemption Applications, and De Novo Requests, and Inclusion in Decision Summaries and Device Labeling. 
2  Contains Nonbinding Recommendations 
 
other issues are important to patients. It will discuss best practices in conducting qualitative 
research and reference-related resources.  
 
Guidance 3: How do you decide what to measure in a clinical trial and select or develop fit-for-
purpose COAs ?  
 
Guidance 3 will address refining the list of concepts of interest important to patients for 
measurement. Given that not everything identified as important by patients, caregivers, and 
clinicians can be addressed by an investigational treatment or be measured, this guidance will 
address issues related to selecting what to measure in a medical product development program 
and identification or development of fit-for-purpose COAs to assess outcomes of importance to 
patients. 
 
Guidance 4: How do you incorporate a given COA tool or set of measures into a defined clinical 
study endpoint? How would you define a meaningful change in that endpoint?  
 
Guidance 4 will address topics related to incorporating COAs into endpoints for regulatory 
decision-making including COA-related endpoint development, defining meaningful within-
patient score changes, and collection, analysis, interpretation, and submission of data.  
 
In general, FDAs guidance documents do not establish legally enforceable responsibilities.  
Instead, guidances describe the Agencys current thinking on a topic and should be viewed only 
as recommendations, unless specific regulatory or statutory requirements are cited.  The use of 
the word should in Agency guidances means that something is suggested or recommended, but 
not required. 
 
  Overview of Guidance 1 
 
Section I.B discusses patient experience data, how they are collected, and the types of methods 
that may apply. 
 
Section II discusses general considerations for collecting patient experience data. It presents a 
logical sequence of questions for stakeholders to address to define the research questions of 
interest to them, the relevant study population, and considerations for the design of the study. 
Both quantitative and qualitative methods are presented to cover the range and diversity of study 
aims and approaches for collecting patient experience data. To satisfy the statutory requirement 
to address representative data collection in the context of both quantitative and qualitative 
methods, Guidance 1 also provides an overview of a range of potentially relevant sampling 
approaches of varying complexity.  
 
Section III discusses considerations related to collection and management of the data. Depending 
on the type of patient experience data and how the data will be used in medical product 
development, different content and formats may be appropriate for submission. When patient 
experience data are submitted to the Agency, the submitter should usually include the intended 
purpose of the patient experience data being submitted (i.e., how the data are intended for use in 
supporting medical product development and regulatory decision-making), and a study report 
3  Contains Nonbinding Recommendations 
 
and protocol from the research study, as well as additional information including the mode of 
primary data capture (see Appendix 1).  
 
Finally, some of the topics covered in the main body of Guidance 1 are further discussed in a 
series of appendices, including standards and requirements related to submission of data 
(Appendix 1) and the glossary of terms (Appendix 2). Hypothetical case examples are also 
included. To illustrate important concepts presented in this guidance, they are also posted on the 
CDER PFDD webpage.4 
 
  Patient Experience Data  
 
Patient experience data include the experiences, perspectives, needs, and priorities of patients 
related to:  
 
  The signs and symptoms patients experience and how these signs and symptoms affect 
their day-to-day functioning and quality of life 
 
  The course of their disease over time, including the effect the disease has on patients 
day-to-day function and quality of life over time, and the changes that patients experience 
in their symptoms over time 
 
  Patients experience with the treatments for their disease: the symptoms and burdens 
related to treatment 
 
  Patients views on potential disease or treatment outcomes and how they weigh the 
importance of different possible outcomes  
 
  How patients view the impact of the disease, treatment, and outcomes, and their view of 
potential tradeoffs between disease outcomes and treatment benefits and risks 
 
The patient experience in a medical product development context incorporates their journey 
throughout the course of their disease or condition, including patient views, feelings, needs, 
actions, preferences, and interactions (e.g., clinical trials, home life, social life) with respect to 
their disease and its treatment (Wolf et al. 2014; McCarthy et al. 2016).  
 
There are different parts of the patient experience to collect and/or measure. These may include: 
 
  Impact of the disease and its treatment on the patient 
 
-  Signs/symptoms of disease or condition 
-  Chief complaints (most bothersome signs/symptoms) 
-  Burden of living with or managing a disease or condition (including effect of the 
disease or condition on activities of daily living and functioning) 
                                                 
4 https://www.fda.gov/drugs/development-approval-process-drugs/fda-patient-focused-drug-development-guidance-
series-enhancing-incorporation-patients-voice-medical.  
4  Contains Nonbinding Recommendations 
 
-  Burden of treatment (including the effect of treatment on activities of daily living 
and functioning) 
-  Burden of participating in clinical studies 
 
  Patients perspectives about potential and current treatments 
 
-  Expectations of benefits 
-  Tolerance for harms or risks  
-  Acceptable tradeoffs of benefits and risks (i.e., patient preference) 
-  Attitudes towards uncertainty 
 
  Views on unmet medical needs and available treatment options 
 
  Enhanced understanding of the natural history of the disease or condition, including 
progression, severity, and chronicity 
 
Patient experience data may be collected throughout medical product development, beginning at 
the launch of a discovery program, or may be independent of any specific medical product 
development program. Data can be collected in a variety of settings, including clinical trials, 
observational studies, advisory boards, public meetings (such as Patient-Focused Drug 
Development (PFDD) meetings), and other novel settings (e.g., social media, online patient 
communities).  
 
Depending on study or research goals and the research questions, qualitative, quantitative, or 
mixed methods may be appropriate for collection of patient experience data. Table 1 presents 
some basic distinguishing features of qualitative, quantitative, and mixed methods research 
approaches. 
 
As noted earlier, Section II provides an overview of these methodological approaches and 
discusses factors to consider when selecting an appropriate methodological approach. 
 
Table 1. Methodological Approaches for Collecting Patient Experience Data 
  Research Approaches 
 
Study 
Qualitative Research  Quantitative Research  Mixed Methods Research 
Element 
Common  Description,  Numerical    Multiple objectives; 
research  understanding and  description, causal  provide complex and 
objectives  exploration/confirm explanation and  fuller explanation and 
ation  prediction  understanding; 
understand multiple 
perspectives 
5  Contains Nonbinding Recommendations 
 
  Research Approaches 
 
Study 
Qualitative Research  Quantitative Research  Mixed Methods Research 
Element 
Common  Attempt to  Study behavior    Study multiple contexts, 
study  understand  under controlled  perspectives or 
characteristics  participant views,  conditions; isolate  conditions; study 
perspectives and  the causal effect of  multiple factors as they 
meanings of  single variables  operate together 
concepts; study 
groups and 
individuals in 
natural or 
controlled settings 
Data  Qualitative data  Quantitative data    Both qualitative and 
collection  such as in-depth  generated using  quantitative data 
interviews,  structured data 
participant  collection 
observations, field  instruments 
notes and open-
ended questions 
Data analysis  Use descriptive  Identify statistical    Quantitative and 
analysis to identify  relationships  qualitative analysis, 
patterns, themes  among variables  used separately and in 
and holistic features  combination 
of qualitative data 
 
Examples  A group of patients  A group of    A group of patients are 
are interviewed to  patients are  given a survey 
describe their  surveyed about  instrument with both 
experience with the  their experience  open-ended and closed-
disease or condition  with the disease or  ended questions to 
condition with a  describe their experience 
survey instrument  with the disease or 
that uses closed- condition 
ended questions   
with distinct    A group of patients are 
response options  first surveyed about 
to quantify  their experience with the 
information  disease or condition 
with a survey instrument 
and then are later 
interviewed to obtain 
additional information 
 
 
6  Contains Nonbinding Recommendations 
 
 
  GENERAL CONSIDERATIONS FOR COLLECTING PATIENT EXPERIENCE 
DATA 
 
  Overview 
 
The selection of people from whom to collect input depends upon the specific questions and 
issues to be addressed (Figure 1). Thus, the selection process starts by considering the research 
question: what are the specific objectives to be addressed by collecting patient input? Are the 
objectives focused on understanding the most burdensome symptoms, the impact of current 
therapies, patients tolerance for risk, or the course of the disease over time? Each of these may 
require different approaches to patient selection and input collection. 
 
Some factors that are important to consider when selecting a research approach include: 
 
  Research goals or questions to be addressed 
 
  Target population and availability of people in that population 
 
  Most valuable information that should be generated through the study to achieve your 
research goals or answer your research questions 
 
  Feasibility of leveraging existing literature and data (See Section II.F.2) 
 
  Expected short-term and long-term impacts of the information you intend to gather 
through the study 
 
  Amount of time to conduct your studies 
 
  Study budget (including staffing, travel time, facilities costs, remuneration, data storage, 
management, and analysis) 
 
7  Contains Nonbinding Recommendations 
 
Figure 1. General Steps for Conducting Studies About Patient Experience 
1. Define the research objective(s) and questions
2. Determine the target patient population from whom to collect information
3. Determine the study design and research setting, including instruments
4. Determine which analyses are required to achieve the research objectives
5. Construct the study sample
6. Collect the data and perform data management tasks
7. Analyze and interpret the data
8. Report study results
 
 
  Defining the Research Objectives and Questions 
 
When formulating your research objectives, you should be specific. A broader research goal can 
be outlined by a set of specific research objectives, each reflecting a component of the broader 
goal. For example, the broader goal may be to evaluate how a treatment affects a particular 
symptom as well as the burden the administration of that treatment places on the patient. In this 
example each of these (the impact on the symptom, the burden on the patient) may be individual 
objectives of the research. Your research questions will further refine the research objectives into 
answerable questions. Your research objectives and questions should inform which 
methodological approaches you use in your research. 
 
You should examine previously conducted studies and other relevant research literature and 
consult subject matter experts (e.g., clinicians, social scientists, patients, advocates, caregivers) 
to help determine the most appropriate questions and to decide:  
 
  Which methods are better suited to meet your research goals and provide evidence to 
support your research questions 
 
  The design of study materials (e.g., study protocol, interview guides, coding dictionary; 
refer to Guidance 2 for more details). 
 
8  Contains Nonbinding Recommendations 
 
  Who to Collect Information From 
 
  Defining the Target Population 
 
The group of patients whose experience you wish to learn about is the target population. It is 
important that the study enrollment criteria (inclusion and exclusion criteria) are carefully 
designed to properly select the target patient population. For example, if there is interest in 
understanding the perspective of patients newly diagnosed with a particular disease, defining this 
population carefully (e.g., what are the criteria for newly diagnosed?, what diagnostic criteria 
are necessary to ensure patients have the target condition?) is important. If the target population 
is intended to represent the broad range of patients with a particular disease, the enrollment 
criteria and sampling plan should ensure that study enrollment is not limited to patients with 
recent onset, or longer-duration of disease, but is a sample of the full spectrum of patients with 
the disease. 
 
Example: If you wish to understand the views and preferences of all individuals with 
Parkinsons disease (PD) in the world, the target population could be defined as the set of all 
individuals who have been diagnosed with PD. If you are interested in a subset of PD patients, 
such as patients diagnosed within the last 5 years, the target population could be restricted 
accordingly. Note, however, that patient experience in a restricted target population might be 
different from that in a broader target population. For example, PD patients diagnosed within the 
last 5 years may have different views and preferences than the set of all PD patients, which 
includes those living with PD for over 10 years. 
 
  Determining Who Will Be Providing Patient Experience Data 
 
For the collection of patient experience data, FDA recommends direct reports from patients, 
unless they are unable to reliably report on the concept of interest (e.g., young children, 
individuals with cognitive problems). The ability to provide self-report also depends on the 
methods used to elicit the input and the complexity of the concepts. Methods of data collection 
can be tailored to specific situations and patient groups, for example by eliciting patient 
experience through play and drawings from young children. When collection of direct patient 
experience is limited, valuable, but distinct, information may still be obtained from caregivers, 
patient advocates, clinicians, and others. 
  
In situations where direct report from the patient is limited (e.g., patients with cognitive 
limitations), alternatives to patient-reported outcome (PRO) measures may be needed. Such 
measures can provide useful information on observable patient experience, such as signs of 
disease or condition and functioning (FDA, 2015). However, research has repeatedly 
demonstrated lack of agreement, often markedly so, on the severity and frequency of signs and 
symptoms when patient-reported and non patient-reported measures are compared. 
 
Factors to consider when deciding whether and how patient self-report may be used include: 
 
  Level of cognitive development, function, or mental status  
9  Contains Nonbinding Recommendations 
 
  Language skills 
  Numeracy skills (e.g., these are needed if a quantitative scale is to be assessed) 
  Health literacy (including basic literacy) 
  Health state 
  Co-morbidities 
 
Prior to study initiation, it is important to set the criteria for determining who the reporter is. For 
example: 
 
  To what extent can patients reliably and validly self-report? 
  What are the scenarios under which multiple reporters may be required? 
  How might this change over the duration of the study? 
 
The reporter should be recorded for each individual in your study and, if necessary, at each time 
point of the report. 
 
When there is the potential for patient report to be limited or compromised by any of the factors 
noted above, the researcher should consider engaging with subject matter experts (e.g., 
clinicians, social scientists, patients, advocates, caregivers) in the specific disease area of interest 
to determine the appropriateness of self-report in the target population. 
 
  Determining the Study Design and Research Setting 
 
The design of the patient experience study is largely determined by the study objectives (as well 
as resources and setting) and includes determination of a number of key features including: 
 
  Study type (e.g., a clinical trial/study, observational study, survey study) 
  Methodological approach (e.g., qualitative, quantitative, or mixed research methods) 
  Patient selection/sampling method 
  Sample size 
  Methods for diagnosis determination (e.g. self-report, clinician report, other source) 
  Subgroups  whether special considerations are needed for subgroups of interest 
 
  Sampling Methods 
 
An initial step is the approach to selecting the patient population that will participate in the study, 
referred to as the sampling scheme. As discussed previously, the research objective defines the 
appropriate population to be included (factors such as disease, stage of disease, treatment and 
treatment complications, disease status, disease complications), and selection of the appropriate 
population is essential to getting information relevant to addressing the research objectives.    
 
There are many sampling approaches, and they vary in complexity. Which approach you use 
may depend on your research objectives and resource constraints. You should engage with 
subject matter experts (e.g., statisticians, psychometricians) when determining which sampling 
methods to use. 
 
10  Contains Nonbinding Recommendations 
 
Table 2 provides a non-exhaustive listing of possible sampling approaches that can be used to 
obtain patient experience data. They can be classified under two broad categories: probability 
and non-probability methods. Studies using probability sampling use some version of random 
sampling (see Table 2) to select from a larger population to create a sample, for example to select 
who will participate in a survey. When probability sampling is used, the patients who participate 
in the study and the results of the study are more likely to reflect those in the target population. 
 
Non-probability sampling uses non-random processes to select the study sample, such that the 
selected sample may not be representative of the target population. When choosing a method, it 
is important to consider resource or other constraints and carefully consider how the sample 
selected may impact the interpretation of the patient experience data. 
 
Probability sampling includes the following important components: 
 
  A well-defined target population 
  Listing of individuals within the target population 
  Use of a random number generator 
 
The listing of individuals in the target population is referred to as the sampling frame. Ideally, 
the sampling frame should enumerate all individuals in the target population. Where this is not 
feasible, the sampling frame should be representative of the target population. A random number 
generator can be used to randomly sample individuals from the sampling frame, which in 
principle produces a sample of patients whose experiences can be interpreted as being 
representative of the target population. 
 
Example: Suppose the target population consists of 100,000 patients with Parkinsons Disease 
(PD) alive in the United States, and each individual is enumerated in a sampling frame with a 
label of 1 to 100,000. Using a random number generator, a sample of 2,000 patients is randomly 
selected from among the 100,000 patients, and their experiences are ascertained. Random 
sampling provides a mechanism for extending statements made about patient experience based 
on the individuals in the sample to the entire PD population. In practice, additional steps such as 
stratification may be needed to induce a sample having the desired characteristics.  
 
It is beyond the scope of this document to discuss sampling schemes in greater detail. However, 
more in-depth discussions with respect to advantages and disadvantages can be found in the 
literature (Rothenberg 1995; Heckathorn 1997; Korn and Graubard 1999; Fricker 2008; Groves 
et al. 2009; Levy and Lemeshow 2013; Valliant et al. 2013; Johnson and Christensen 2014; 
Johnson 2015). 
 
As noted, the appropriate sampling scheme is that which enables you to address your research 
objectives and can be implemented within the scope of your resource constraints.  
 
11  Contains Nonbinding Recommendations 
 
Table 2. Types of Sampling, Examples and Some Potential Limitations 
Potential 
Types of Sampling  Selection Strategy  Examples 
Limitations 
Probability Sampling 
Simple random  A sample drawn by a  A simple random sample is   Can be expensive 
sampling  procedure in which every  taken from a population of  or infeasible to 
member of the  patients admitted to a  conduct. 
population has an equal  hospital in the first six   
c hance of being selected.  months of 2015.   Samples can fail 
to reflect the 
heterogeneity in the 
target population. 
Stratified random  A sample drawn by  Population of patients with   Requires the 
sampling  dividing the population  a disease are stratified by  stratification factors 
into mutually exclusive  disease subtype and sex,  to be known for 
groups and then selecting  and a random sample is  each individual in 
a random sample from  taken within each  the population. 
w  ithin each group.  combination (stratum). 
Multiplicity  A sample drawn by first  Approach used to   The initial 
sampling  taking a probability  oversample those with rare  probability 
sample from the target  diseases when conducting a  sampling phase 
population followed by  household survey by  may not be feasible. 
drawing a sample from  including a question about   
the set of individuals who  people with a rare disease   Relies on the 
belong to the network of  who previously lived in the  initial respondents 
t hose initially sampled.   household.   to identify members 
in their network. 
Cluster sampling  A sample drawn by  A probability sample of   Often requires 
which clusters (i.e., a  hospitals in a state is taken,  information about 
collective type of unit  from which a probability  cluster size as 
that includes multiple  sample of patients from  selection 
elements, such as clinical  each hospital is taken.  probabilities can 
sites in different  depend on such 
geographic areas) are  information. 
randomly selected and   
either complete- or sub-   Units within 
sampling of individuals  cluster tend to be 
within the selected  homogeneous. 
c lusters are taken.   
12  Contains Nonbinding Recommendations 
 
Potential 
Types of Sampling  Selection Strategy  Examples 
Limitations 
 
 
 
  
  Generalization of cluster  CDC Medical Monitoring   Often requires 
Multistage  sampling to include  Project.   information about 
probability  multiple levels/stages of   Stage 1, a probability  cluster size as 
sampling  cluster sampling.  sample of states.   selection 
 Stage 2, a probability  probabilities can 
sample of facilities within  depend on such 
each sampled state.  information. 
 State 3, a probability   
sample of HIV patients   Units within a 
from each sampled facility.  cluster tend to be 
homogeneous. 
Non-Probability Sampling 
  
Clinical trials  A sample that consists of  Patients with iron   Trial results may 
patients who volunteer to  deficiency anemia are  not be generalizable 
participate in a clinical  recruited to participate in a  to the population of 
trial.  clinical study that  all iron deficiency 
compares the efficacy of an  anemia patients for 
experimental therapy  whom the therapy 
against a standard of care.  is indicated. 
Convenience  A sample drawn by  Patients who can travel to   Study results may 
sampling  including people who are  attend PFDD meetings.  not be generalizable 
available, volunteer, or  to the target 
can be easily recruited.  population.
  
 
 
 
 
Purposive  A sample drawn by  Researcher is interested in   Study results may 
sampling  which the researcher  studying adult females with  not be generalizable 
specifies the  acne and selects their  to the target 
characteristics of the  sample based on these  population.  
target population and  characteristics (i.e., adult   
locates individuals with  females with acne).   
those characteristics.   
   
 
13  Contains Nonbinding Recommendations 
 
Potential 
Types of Sampling  Selection Strategy  Examples 
Limitations 
Quota sampling  A non-random sample  Researcher chooses their   Study results may 
drawn by which the  sample to consist of 45%  not be generalizable 
researcher determines the  females and 55% males to  to the target 
appropriate sample sizes  maintain the correct  population. 
or quotas for the groups  proportions representative   
identified as important.  of the target population.   
   
Respondent-driven  Similar to snowball  A convenience sample of   Requires long 
sampling  sampling (see the next  individuals with substance  recruitment chain 
type in this list). The  use disorders is recruited.  and socially-
chain of referrals is often  Each individual in this  networked 
longer than snowball  initial sample is provided a  population. 
sampling and, under  fixed number of coupons   Study results may 
certain conditions,  that he/she uses to recruit  not be generalizable 
estimates can be  others in his/her network.  to the target 
generalizable to target  The second set of  population unless 
population.  individuals recruited via  assumptions, which 
coupons by the first set of  are not verifiable, 
individuals are also given a  are valid. 
fixed number of coupons   
that they use to recruit   
individuals in their   
network. This is repeated   
for a fixed number of    
cycles after which 
recruitment terminates. The 
coupons serve as financial 
incentives for the recruited 
to recruit others in the 
n etwork. 
Snowball sampling  A sample drawn by  Patients with sickle cell   Study results may 
which each research  disease participate in focus  not be generalizable 
participant is asked to  groups to discuss  to the target 
identify other potential  symptoms of the disease  population. 
research participants. The  and impacts of the 
initial sample of  medications taken. Focus 
individuals is often  group participants are 
obtained via non- asked to identify other 
probability sampling;  people they know with 
subsequent samples are  sickle cell disease who 
obtained by chained  may be potential research 
referrals from the  participants so study staff 
previous sample.  can invite them to join the 
  research study. 
14  Contains Nonbinding Recommendations 
 
Potential 
Types of Sampling  Selection Strategy  Examples 
Limitations 
Web-based  A sample drawn by using  Researcher selects patients   Limited by pre-
sampling  the web as the contact  from a web-panelist (e.g.,  registered panelists. 
mode; can involve  online polling panel) to   
multiple sampling  include in study.   Study results may 
strategies (e.g.,  not be generalizable 
systematic sampling,  to the target 
multiplicity sampling,  population. 
list-based, entertainment   
polls, un-restricted self-  Potential response 
selected surveys,  bias* (e.g., 
volunteer (opt-in) panel).  measurement error, 
  misclassification). 
* Although response bias such as measurement error or misclassification is listed as a limitation under web-based 
sampling, it applies to all types of study. 
  
  Representativeness 
 
When studying patient experience, it is important to obtain patient experience data that are not 
only relevant, objective, and accurate, but also representative of the target population. Sufficient 
representation may depend on the characteristics of the target population, the disease or 
condition under study, and the intended use of study results. In this document, the term 
representative or representativeness can be interpreted in the following ways.  
 
(1) A sample is representative of the target population if statements made about patient 
experience based on data from the sample of patients are generalizable to the target 
population. In principle, probability sampling schemes enable you to obtain such 
representative samples and often arise in the context of quantitative studies. However, if 
there are subgroups of patients from the target population that are not adequately 
represented in your study sample, your ability to generalize your research findings to the 
target population may be limited, even if you use a probability sampling scheme.  
 
(2) A sample is representative of the target population to the extent that patients in the study 
sample consist of individuals with various characteristics that approximate the 
heterogeneity of characteristics in the target population. If your sample does not reflect 
the broad range of patient characteristics of the target population, patient experience 
results may not be representative of the target population. Thus, statements made about 
patient experience based on data from the study are not necessarily generalizable to the 
target population. Whether this is acceptable depends on the research objectives. If your 
research objective is to generate hypotheses or tools to collect patient experience data, 
this extent of representativeness may be sufficient. It also may be possible to attain 
generalizability through weighting to account for the over-sampling (or under-sampling) 
of certain subpopulations if the sample is obtained with known sampling probabilities.  
 
Regardless of how individuals are selected for the study, it is important to ensure that patients in 
the study sample represent the target population, particularly with respect to the attributes that 
15  Contains Nonbinding Recommendations 
 
are associated with the endpoints of interest. Figure 2 provides some guidance regarding factors 
to consider to achieve sufficient representation. 
 
Figure 2. Factors to Consider to Achieve Sufficient Representation 
 
 
  Sample Size 
 
Important to the design of any study is sample size determination. Sample size estimates are 
driven by: 
 
  Research objectives 
  Type of outcomes/endpoints under consideration 
  Study design, including whether the study is quantitative or qualitative in nature  
  Planned methods of analysis 
 
Insufficient sample size may produce unreliable or imprecise results. FDA recommends that if 
the sample size is limited due to practical considerations (e.g., rare diseases), the research 
objectives and/or methods should be adjusted accordingly, and any limitations should be noted in 
the study report. Some practical considerations when determining sample size include: 
 
  Drop-out rates or non-response. Sample size may need to be adjusted upward to 
account for drop-outs or non-response. 
  Subpopulations. In cases where there is also interest in one or more subpopulations 
within the target population, sample size should be determined to ensure there is 
16  Contains Nonbinding Recommendations 
 
sufficient information to also learn about those subpopulations. Subgroup5 analyses of 
interest should be pre-specified at the study design stage whenever possible. Care should 
be taken with the number of subgroups being proposed for analysis and inference. 
Subgroups of interest may be based on reporter type (e.g., patients versus primary 
caregivers) and/or socioeconomic, demographic, cultural, linguistic, clinical or other 
factors pertinent to the disease/condition of interest.  
 
For qualitative studies, sample size determination is often based on concept saturation, which 
means the point at which no new important concepts relevant to the research question are 
emerging from iterative rounds of interviews and the group of patients thus far recruited appears 
to be representative (Sandelowski 1995; Francis et al. 2010; Dworkin 2012).  
 
For quantitative studies, sample size calculations for different sampling types, study types and 
data types can be found in the literature (Thompson 1987; Chow et al. 2008; Levy and 
Lemeshow 2013). For complex designs where sample size formulae are intractable to obtain, 
simulation could be used. 
 
  Constructing a Sampling Frame 
 
The existence of a sampling frame facilitates probability sampling. Without a sampling frame, it 
is difficult to randomly sample from the target population. To the extent that disease registries 
are inclusive and regularly updated, they may provide a natural sampling frame. The scope of 
registries may vary, with some defined at the regional level, sub-regional (e.g., state, province) 
level, or some level local to an organization such as a hospital or a chain of hospitals owned by a 
particular organization or part of a network. For an example of patient registries and related 
information, see https://ncats.nih.gov/radar. The following example discusses potential use of a 
clinician registry as a sampling frame.  
 
Example: In the United States, physician listings such as the AMA Masterfile or state 
licensing board files have the potential to be used to create a sampling frame for the target 
population in the sense that a sample of physicians from these sources may be used to elicit 
members of the target population, for example, patients who have been diagnosed with 
diabetes. Physicians who treat patients with diabetes may be sampled, and for each sampled 
physician, his or her patients are sampled.  
 
It is important to note that unless all relevant physicians are sampled, and all patients under the 
care of each sampled physician are identified, the resulting sampling frame may exhibit 
undercoverage in the sense that not every member of the target population is listed in the 
frame. 
 
                                                 
5 A subgroup is a part of the sample (e.g., in a clinical trial you look at the results only in the women in the trial, that 
is a subgroup analysis). A subpopulation is part of the whole population (e.g., if you only sample from women you 
are sampling from a subpopulation). We draw an inference from a subgroup to make generalizations about the 
subpopulation. 
17  Contains Nonbinding Recommendations 
 
Undercoverage, mentioned in the example above, occurs when a proportion of members of the 
target population is not included in the sampling frame. Undercoverage may not be problematic 
if members excluded from the frame could be reasonably viewed as not being substantially 
different from those within the frame. Problems occur when we do not know if patients in the 
frame are similar to those not in the frame, for example where patient experience data is 
collected using only an online community. Attempts to augment the sample from other patient 
populations found using some of the sampling methods mentioned in the table can help reduce 
undercoverage. 
 
  Additional Considerations 
 
  Missing Data/Non-Response 
 
Missing data are common in most types of studies. With respect to representativeness, 
individuals who were selected for the study may decline to participate in the study (unit non-
response), stop participating during the study (dropout), or may decline to answer some 
questions (item non-response) after consenting to participate in the study. Investigators should 
anticipate the types of missing data that are likely to occur given the study design, logistics, and 
the particular patient experience data that are being collected. 
 
The March 2019 Clinical Trials Transformation Initiative workshop on Enhancing the 
Incorporation of Patient Perspectives in Clinical Trials6 included discussion of how to 
collaboratively establish plans (in the study protocols) delineating strategies to minimize missing 
data, and where missingness cannot be avoided, to collect or determine the reasons for the 
missingness. Brick (2013), Calinescu et al. (2012), Levy and Lemeshow (2013), and Schouten et 
al. (2013) discuss design strategies for improving the response rate in the context of surveys. 
O'Neill and Temple (2012) and The National Research Council (2010) discuss design strategies 
for the prevention of missing data in clinical trials.  
 
To help better understand the extent and impact of missing data FDA recommends the following:  
 
  Provide a table summary of missing data; useful information includes frequencies, 
percentages, stratification by important subgroups, and reasons for missingness.  
 
  For longitudinal data, summarize missingness stratified by assessment visits or time 
points.  
 
In addition, methods for handling missing data in analysis should be addressed in the protocol. 
See The National Research Council (2010) and International Council on Harmonisation (ICH) 
E9(R1) for discussion of those methods. 
 
                                                 
6 https://www.ctti-clinicaltrials.org/news/recording-now-available-ctti-and-fda-hold-workshop-incorporating-
patient-perspectives-clinical  
18  Contains Nonbinding Recommendations 
 
  Leveraging Existing Data 
 
FDA encourages collaboration among multiple stakeholders and the use of methods to combine 
and leverage existing data (e.g., national registry data, archival databases, published literature) to 
fit the specific needs of the research questions and study goals. It is important to note that if you 
decide to explore the use of existing data, you should demonstrate the representativeness, 
methodological rigor of the data collection method and data integrity as outlined in Section III of 
this guidance. 
 
 
  OPERATIONALIZING AND STANDARDIZING DATA COLLECTION AND 
DATA MANAGEMENT 
 
Research to collect patient experience data that can be used in regulatory decision-making not 
only requires the study to be well-designed but also that the methods and standards for data 
collection and analysis meet Agency expectations for quality. Section III of this guidance 
provides an overview of standard approaches to consider regarding data collection and data 
management. 
 
You should standardize data collection activities and methods to support the best possible data 
quality. 
 
  Locating Patients/Sites 
 
A critical step in the process of data collection is to identify the appropriate sample and/or sites 
(e.g., medical practice/center, academic institution, research consortia) to study. Including 
patients from diverse sites helps to provide a representative patient sample. FDA acknowledges 
that for some conditions (e.g., rare diseases) a limited number of sites may be available for 
clinical trials and additional research. 
 
  Human Subjects Protection 
 
Research involving access to patient information or directly engaging with patients requires 
careful consideration of federal, state, and local laws, and institutional polices for the protection 
of human subjects. Because this guidance focuses on sampling methods for collecting patient 
experience data through a variety of research contexts (including, but not limited to, clinical 
trials, observational studies, advisory boards, public meetings) a full discussion of the laws that 
may apply to these collection methods is beyond its scope. Research subject to FDA regulations 
must satisfy the requirements for informed consent at 21 CFR part 50 and the Institutional 
Review Board (IRB) requirements at 21 CFR part 56.7,8 Research supported or conducted by the 
                                                 
7 Details on 21 CFR part 50 can be found at 
https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=50 
 
8 Details on 21 CFR part 56 can be found at 
https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=56  
 
 
19  Contains Nonbinding Recommendations 
 
Department of Health and Human Services must satisfy the requirements at 45 CFR part 46.9 
FDA recommends that researchers work with their IRBs and Health Insurance Portability and 
Accountability Act Privacy Boards to determine which laws may apply.  
 
FDA also recommends that research involving patient information be conducted in accordance 
with the principles of good clinical practice, including the ICH Guidelines. 
 
  Sampling Strategy 
 
Of importance to the data collection process is the determination of a strategy for the sampling of 
patients or sites. Refer to Section II.D.1 on the different types of sampling. 
 
  Collecting Data 
 
You should consider which data collection approach is most appropriate for their research 
objective. Data collection methods can include: 
 
  Interviews 
  Focus groups 
  Facilitated discussions at meetings 
  Observational methods 
  Documents (e.g., medical charts) 
  Survey instruments 
  Audiovisual materials 
  Social media and verified patient communities 
  Digital health technologies 
 
Each of these data collection methods generates different types of data, each of which has its 
own advantages and limitations. Additional details, including potential advantages and 
limitations of each method, are discussed below. 
 
  Interviews, Focus Groups, and Facilitated Discussions 
 
Different interview types are used to collect patient experience data, including one-on-one 
interviews (semi-structured, structured, or unstructured) or group interviews (focus groups, 
facilitated discussions at patient meetings). The method of interviewing (e.g., in person, 
telephone, or video chat) may vary depending on the goals of the interview. For example, if 
visual cues are important for the context of the research objective, an appropriate data collection 
method may be face-to-face interviews either in-person or by video chat instead of telephone 
interviews. Further details and considerations regarding the different interview methods will be 
provided in future guidance in this series.  
 
                                                 
9 Details on 45 CFR part 46 can be found at https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-
46/index.html  
20  Contains Nonbinding Recommendations 
 
  Observational Methods 
 
Observation can be a tool to collect patient experience data, and can include the observation of 
the interactions of a participant in particular setting, activity, or behavior (Creswell 2013). 
Observations are helpful in situations for individuals who have barriers to communicating their 
thoughts orally or in writing. Additionally, observations of individuals or groups often can be 
made to supplement interviews (individual or group) by documenting cues from the environment 
and behaviors such as facial expressions, gestures, tone of voice, and other non-verbal indicators. 
 
  Documents  
 
Various types of documents can be used to collect patient and/or caregiver input on burden of 
disease and treatment. These include: 
 
  Scientific publications  
  Public documents 
  Medical records, chart audits  
  Personal documents (e.g., patient journal) 
  Other printed materials (e.g., literature) 
  Graphics 
  Photo elicitation (participants take photographs or videotapes) 
  Archival records and physical artifacts. 
 
  Survey Instruments 
 
Survey instruments generally consist of a standard set of questions or items administered in the 
same order to each participant (Johnson and Christensen, 2017). They can be administered in 
both observational studies and clinical trials, for example, to collect PRO measures that are 
associated with study treatments. Data can be collected by survey instruments throughout the 
study or at the end of the study (e.g., exit surveys). Refer to Guidance 2 for details regarding 
survey instruments. 
 
If data obtained via survey instruments are intended to be a study endpoint in a clinical trial, 
FDA recommends that stakeholders adopt good measurement principles. Refer to subsequent 
guidance documents in this series for discussion of factors to consider when administering 
survey instruments in clinical trials. Refer to Appendix 1. Standards and Requirements 
Pertaining to Submission of Data regarding standards and requirements pertaining data 
submission to FDA. 
 
  Audiovisual Materials 
 
Audiovisual materials can be used to collect data in characterizing the patient experience. These 
may include recordings or photographs of individuals or groups, sounds (laughter or other 
vocalized expressions), email or discussion board messages with audiovisual attachments, and 
video chat/conferencing (e.g., Skype). 
 
21  Contains Nonbinding Recommendations 
 
Steps to consider when collecting audiovisual materials as data include: 
 
  Standardizing data collection (e.g., information that can be derived from photographs can 
vary based on lighting and camera settings) 
 
  Obtaining the required permissions needed to use materials, including informed consent 
 
  Obtaining permission to extract information from web content, if necessary (e.g., request 
permission to join online forums and inquire whether there are restrictions on use of 
information for research purposes) 
 
  Social Media and Verified Patient Communities 
 
Social media tools (e.g., medical community blogs, crowdsourcing, social media pages) may 
include information on patients perspectives regarding symptoms and impacts of a disease or 
condition. Targeted social media searches may be useful during the preliminary stages of a study 
to complement literature review findings, inform the development of research tools (e.g., 
qualitative study discussion guides), or as a supplement to traditional research approaches (e.g., 
literature, one-on-one interviews, focus groups, or expert opinion).  
 
Common methods for generating patient input using online methods, including social media,  
and their potential strengths and limitations are summarized in Table 3. 
 
Table 3. Common Methods for Gathering Patient Input Using Online Methods (Including 
Social Media) 
Social Media Qualitative 
Strengths   Limitations 
Research Methodology 
All social methodologies    May allow access to    Self-selection bias (e.g., social 
hard-to-reach populations  media participants may include 
  Cost and time saving for  a narrow band of patients with 
researchers  regard to clinical or 
  Relatively easy to  demographic characteristics or 
implement  willingness to participate in 
  Accurate and automatic  social media platforms) 
capture of data    Regulations and laws 
  Participant convenience  surrounding privacy and use of 
and comfort  public and private data 
 
  Might result in greater 
self-disclosure 
 
22  Contains Nonbinding Recommendations 
 
Social Media Qualitative 
Strengths   Limitations 
Research Methodology 
Asynchronous online    Can be conducted using    Lack of visual cues 
focus groups or interviews  email, discussion forums,    Underlying selection process 
(occur at different places,  and other forms of social  might be difficult, if not 
different times)  media  impossible, to quantify 
    Provide flexibility and    Representativeness might be 
  convenience of logging  questionable without strong 
in at time and place of  assumptions 
participants choosing   
  Lack of time pressure 
and greater reflection 
 
Synchronous online focus    Data captured in real-   Scheduling can be difficult; 
groups or interviews  time (synchronous)  must find a common meeting 
(among younger    Can be conducted using  time (for focus groups) 
participants; occur at  the phone (SMS/text    Requires a fast internet 
different places, same time  messages), chat methods,  connection, webcam/ 
[for focus groups])  video messaging  audio/video capabilities that 
    Interaction is often  some participants may not have 
  dynamic, immediate,  readily available  
conversational (similar to    Technology-rich interface can 
every-day interactions)  present more technical 
  Assessment of visual  difficulties 
cues (through video or    Moderation can be difficult with 
emotions conveyed  too many participants; 
through emoticon use)  sometimes participants have 
  Less threatening  trouble taking turns (for focus 
methodology for younger  groups) 
participants    Faster typing speed gives 
participants an advantage, and 
these participants can dominate 
the conversation (for focus 
groups) 
  Increased likelihood of passive 
participation (e.g., a participant 
logging on and observing but 
not participating) 
  Groups with more than five 
participants require two 
moderators (for focus groups) 
  Self-selection bias (social media 
participants may include a 
narrow band of patients with 
regard to clinical or 
demographic characteristics) 
23  Contains Nonbinding Recommendations 
 
Social Media Qualitative 
Strengths   Limitations 
Research Methodology 
 
Verified patient    Generated through    Must have authorization to 
communities10 and social  platforms such as online  obtain identifiable information 
media data collection  support groups and  (e.g., Protected Health 
    online educational groups   Information (PHI)) 
    Groups include   
identifiable patients and 
identifiable reporters 
  Helpful for:  
-  gathering information 
on health conditions 
(Prieto et al., 2014) 
-  sharing treatments 
and experiences of 
care (McGregor et 
al., 2014) 
-  recruiting research 
participants 
(O'Connor, Jackson, 
Goldsmith, & 
Skirton, 2014) 
 
Social media data    Generated through easily    Participants are unknown; 
collection (unelicited data)  accessible platforms  Respondent identification not 
    Low burden for people  verifiable 
  providing data     PHI not verifiable  
    Helpful for gathering    Underlying selection process is 
information on health  difficult if not impossible to 
conditions (Prieto et al.  quantify 
2014)    Representativeness is highly 
questionable without strong 
assumptions 
  Predominantly retrospective 
data 
 
 
Although social media tools can provide useful data, limitations related to sampling should be 
considered. With most social media sources, there is no mechanism for verifying patient identity 
or clinical and demographic characteristics; you are limited to relying on patient self-
identification and diagnosis, which can be inaccurate. Additionally, different demographic 
groups tend to use different types of social media. Based on this variability, it may be important 
                                                 
10 Communities that provide personal information to allow verification of personal characteristics, such as identity, 
diagnosis, or other patient characteristics. 
24  Contains Nonbinding Recommendations 
 
for you to use different social media tools to gather information from the demographic group(s) 
you are targeting. Likewise, when submitting information for regulatory review, you should 
demonstrate how the data collection methods used to generate data addresses these limitations 
and to ensure rigor in methodology and data integrity.  
 
Concerns around the lack of ability to confirm patient characteristics (e.g., diagnosis) can be 
mitigated in various ways. For example, to have a verified patient, there should be enough 
information to indicate the existence of a specific patient, including age (or age category, e.g., 
adolescent, adult, elderly), sex, gender, initials, date of birth, name, or patient identification 
number. In the most ideal case, clinical information would also be available, by permission, 
through a central database (e.g., for patients who are members of patient advocacy group 
message boards, social networking groups, or medical community blogs). An identifiable 
reporter can be a family member, doctor, other health care practitioner, or other individual who 
has sufficient information to indicate that they are an identifiable person who has knowledge 
about the patient. 
 
Refer to Guidance 2 for considerations for use of social media to collect patient experience data. 
 
  Recording Information 
 
You should develop, as a part of study protocols, written forms to collect patient experience data 
(e.g., a discussion guide or observational data collection form). A discussion guide or 
observation data collection form is a pre-designed form used to record information collected 
during an interview or observation (e.g., an interviewer may take notes on the discussion guide 
or observational protocol). Patient experience data can also be recorded through various forms, 
such as interview summaries and audio- and video-recordings.  
 
  Resolving Site/Field Issues 
 
Standardized training should be provided to the members of the research team to improve 
consistency of research. The roles and responsibilities of the team should be outlined in the 
research protocol. This will help to prevent many site issues. FDA encourages stakeholders to 
also have a troubleshooting guide. Researchers should anticipate and address site/field issues that 
might arise during data collection. Some issues to consider are listed below (Creswell (2013): 
 
Access to patients/sites 
 
  Patients willingness to participate in research 
  Patient responsiveness 
  Appropriateness of a site 
  Building of trust and credibility at the field site 
  IRB unfamiliar with certain methodologies 
 
Interviews, focus groups, facilitated discussions 
 
  Mechanics of conducting interviews and discussions (unexpected participant 
behaviors, sensitive issues, inexperienced researchers)  
25  Contains Nonbinding Recommendations 
 
 
Observational methods 
 
  Consistency in the role of observer 
  Mechanics of observing (remembering to take site notes) 
  Recording accurate quotes/notes 
  Managing information sufficiently at site 
 
Documents and audiovisual materials 
 
  Locating materials 
  Obtaining permission from the participant to use materials (e.g., audio/video-recorder) 
  Minimal noise disturbance  
  Best location for video recorder/camera 
 
Survey instruments  
 
  Paper-based administration: Quality control at the visit (e.g., administering correct 
version of the survey instrument, looking for non-response patterns such as not 
completing a particular section) 
  Electronic-based administration: Consistency in data monitoring procedures and 
follow-up (e.g., monitoring for timely completion and attrition) 
 
Ethical issues 
 
  Informed consent, if required 
  IRB oversight, if required 
  Conflicts of interest 
  Dishonest or hidden (secret) activities 
  Confidentiality and privacy considerations  
  Benefits and risks of research to participants 
 
  Data Management 
 
Data management considerations should be addressed in the early stages of a research study. 
Before initiating data collection, you should formulate a data management plan (DMP)  a 
written document that describes the data you expect to acquire or generate during your research 
study; how you intend to manage, describe, analyze, and store said data; and what mechanisms 
you will use at the end of your study to preserve and share your data (Stanford University 
Libraries n.d.). Creating a written DMP helps formalize the data management process, identify 
potential weaknesses in the DMP, and provide a record of what you intend to do.  
 
  Data Standards 
 
External stakeholders should use appropriate data standards when collecting, managing, and 
reporting patient experience data. When planning a study (including the design of case report 
26  Contains Nonbinding Recommendations 
 
forms, data management systems, and data analysis plans), you should determine which FDA-
supported standards to use. See Appendix 1. Standards and Requirements Pertaining to 
Submission of Data for some data standards resources.11 
 
While compliance with these standards may not be required for studies other than those 
conducted to support a regulatory medical product application (e.g., an Investigational New Drug 
(IND), New Drug Application (NDA) or Biologics License Application (BLA)) or medical 
product labeling language, we encourage researchers to, at a minimum, bear these standards in 
mind, because patient experience data that are ultimately intended for use in clinical trials would 
be subject to the applicable standards. 
 
  Monitoring and Quality Assurance 
 
FDA expects that external stakeholders will be responsible for monitoring the study, ensuring 
data integrity, and performing the data analysis. This includes assessing the quality of the design 
and performance of the studies. 
 
  Storing Data 
 
External stakeholders should plan how to store their data in advance of starting their study. 
Researchers should decide how data will best be stored so that the data can be easily retrieved 
and protected from any type of damage or loss. The approach to data storage should reflect the 
type of data collected. Regarding duration of data retention, researchers should comply with their 
IRB and applicable regulations. 
 
Principles to consider regarding data storage and handling data include the following (Creswell 
2013): 
 
  Create back-up copies of computer files 
  Use high-quality equipment for audio-recording information during interviews 
  Protect the anonymity of participants by de-identification 
  Create a data collection table or database to track and identify data 
  Maintain a list of types of data collected 
 
  Confidentiality 
 
All personal participant data collected and processed for research should be managed by the 
research team with adequate precautions to ensure confidentiality of the data in accordance with 
applicable national and/or local laws and regulations on personal data protection. 
 
  Data Analysis 
 
The approach taken to data analysis depends on the type of data, study conducted, and research 
questions and objectives. Sampling information should be taken in to consideration. Later 
                                                 
11 https://www.fda.gov/industry/fda-resources-data-standards/study-data-standards-resources 
27  Contains Nonbinding Recommendations 
 
guidance in this series will provide more discussion of potential data analysis methods used with 
different types of patient experience data.  
 
 
  CONCLUSIONS 
 
This document has provided an overview of methods to collect robust, meaningful, and 
sufficiently representative patient input to inform medical product development and regulatory 
decision-making. The proposed methods presented serve only as a basis for dialogue in the 
evolving and growing discipline of the science of patient input. If you are considering collecting 
patient experience data, FDA encourages you to have early interactions with FDA and obtain 
feedback from the relevant FDA review division on appropriate research design and any 
applicable regulatory requirements. 
28  Contains Nonbinding Recommendations 
 
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Calinescu, M, B Schouten, and S Bhulai, 2012, Adaptive survey designs that minimize 
nonresponse and measurement risk, Statistics Netherlands. 
Chow, SC, H Wang, and J Shao, 2008, Sample Size Calculations in Clinical Research, Boca 
Raton, FL: CRC Press. 
Creswell, JW, 2013, Qualitative Inquiry and Research Design: Choosing Among Five 
Approaches, Los Angeles, CA: SAGE Publications. 
Dworkin, SL, 2012, Sample size policy for qualitative studies using in-depth interviews, Arch 
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29  Contains Nonbinding Recommendations 
 
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Publications, Inc. 
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Information on Patient Preferences Regarding Benefit and Risk into Regulatory 
Assessments of New Medical Technology. 
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31  Contains Nonbinding Recommendations 
 
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32  Contains Nonbinding Recommendations 
 
  APPENDICES 
 
Appendix 1. Standards and Requirements Pertaining to Submission of Data 
 
Many existing FDA regulations, guidances, requirements, and other standards for data 
submissions apply to patient experience data. These pertain to the capture/collection, 
transmission, processing, storage, archiving, retention, and submission of data from clinical 
studies conducted to support a regulatory medical product application (e.g., IND, NDA, BLA) 
or medical product labeling language. The following is a partial list of such regulations, 
guidance(s), standards, and requirements:  
 
  FDA forms and submission requirements 
https://www.fda.gov/drugs/developmentapprovalprocess/formssubmissionrequirements/ 
 
  The International Council on Harmonisation (ICH) Guidelines12, such as ICH 
Harmonised Guideline for Good Clinical Practice: E6(R2) and the Electronic Common 
Technical Document (eCTD) 
 
  21 eCFR, Volumes 1  813 
 
  Guidance for Industry on Providing Regulatory Submissions In Electronic Format  
Standardized Study Data (FDA 2014b) 
 
  Guidance for Industry on Providing Regulatory Submissions in Electronic Format  
Submissions Under Section 745A(a) of the Federal Food, Drug, and Cosmetic Act (FDA 
2014a)  
 
  Guidance for Industry on Providing Regulatory Submissions in Electronic Format  
Certain Human Pharmaceutical Product Applications and Related Submissions Using 
the eCTD Specifications (FDA 2017b) 
 
  Guidance for Industry on Electronic Source Data in Clinical Investigations (FDA 2013) 
 
  The FDA Data Standards Catalog. 
 
For current and more detailed information on study data standards resources, please see: 
https://www.fda.gov/industry/fda-resources-data-standards/study-data-standards-resources.   
                                                 
12 https://www.fda.gov/RegulatoryInformation/Guidances/default.htm  
 
13 https://www.ecfr.gov/cgi-bin/text-idx?tpl=/ecfrbrowse/Title21/21tab_02.tpl  
33  Contains Nonbinding Recommendations 
 
Appendix 2. Glossary 
 
This glossary defines terms that will be used in the series of methodological Patient-Focused 
Drug Development FDA guidance documents that are required by the Cures Act, and that are 
part of the commitments made by FDA under PDUFA VI. The goal of this glossary is to 
provide standardized nomenclature and terminologies related to patient-focused medical product 
development. As appropriate, definitions from existing federal resources (e.g., BEST 
(Biomarkers, Endpoints, and Other Tools) Resource)14 have been incorporated. External 
resources were also used to define terms and have been cited.  
 
Attribute: A feature or characteristic of a medical product  such as efficacy or effectiveness, 
safety, means of administration, duration of effect, or duration of use  that may affect benefit-
risk considerations. 
 
Benefit: The favorable effects of a medical product. Types of benefits include clinical benefit 
(see clinical benefit). Benefits may also include important characteristics of the medical 
product, such as convenience (e.g., a more convenient dosing regimen or route of 
administration) that may lead to improved patient compliance, or benefits that affect those other 
than the patient. (Sources: International Council on Harmonisation (ICH) Guideline, Revision 
of M4E Guideline on Enhancing the Format and Structure of Benefit-Risk Information in ICH 
(Efficacy  M4E(R2) (International Conference on Harmonisation 2016); ANSI/AAMI/ ISO 
14971: 2007/(R)2016 Medical DevicesApplication of Risk Management to Medical Devices 
(American National Standards Institute 2016).)    
 
Benefit-Risk Assessment: Evaluation of the benefits and risks of a medical product and 
making a judgment as to whether the benefits outweigh the risks associated with specified 
conditions of use. 
 
Biomarker: A defined characteristic that is measured as an indicator of normal biological 
processes, pathogenic processes, or responses to an exposure or intervention, including 
therapeutic interventions. Molecular, histologic, radiographic, or physiologic characteristics are 
types of biomarkers. A biomarker is not an assessment of how an individual feels, functions, or 
survives. (Source: BEST (Biomarkers, EndpointS, and other Tools) Resource)  
 
Caregiver: A person who helps a patient with daily activities, health care, or any other 
activities that the patient is unable to perform because of illness or disability, and who 
understands the patients health-related needs. This person may or may not have decision-
making authority for the patient and is not the patients healthcare provider. 
 
Caregiver Preference: A statement of the relative desirability or acceptability to caregivers of 
attributes by which alternative health interventions may differ. 
 
Clinical Benefit: A positive, clinically meaningful effect of an intervention, e.g., a positive 
effect on how an individual feels, functions, or survives. (Source: BEST (Biomarkers, 
EndpointS, and other Tools) Resource) 
                                                 
14 Available at https://www.ncbi.nlm.nih.gov/books/NBK338448/. 
34  Contains Nonbinding Recommendations 
 
 
Clinical Outcome: An outcome that describes or reflects how an individual feels, functions, or 
survives. (Source: BEST (Biomarkers, EndpointS, and other Tools) Resource) 
 
Clinical Outcome Assessment: Assessment of a clinical outcome can be made through report 
by a clinician, a patient, a non-clinician observer, or through a performance-based assessment. 
Types of COAs include patient-reported outcome, clinician-reported outcome measures, 
observer-reported outcome, and performance outcome. (Source: BEST (Biomarkers, 
EndpointS, and other Tools) Resource) 
 
Clinical Relevance: The extent to which an endpoint can capture and measure an aspect of a 
potential clinical benefit (improvement in how the patient feels, functions, or survives) that is 
important from a clinical perspective and from the patients perspective.  
 
Clinician-Reported Outcome (ClinRO): A measurement based on a report that comes from a 
trained health-care professional after observation of a patients health condition. Most ClinRO 
measures involve a clinical judgment or interpretation of the observable signs, behaviors, or 
other manifestations related to a disease or condition. ClinRO measures cannot directly assess 
symptoms that are known only to the patient (e.g., pain intensity). (Source: BEST 
(Biomarkers, EndpointS, and other Tools) Resource) 
 
Concept (also referred to as Concept of Interest): In a regulatory context, the concept is the 
aspect of an individuals clinical, biological, physical, or functional state, or experience that the 
assessment is intended to capture (or reflect). (Source: BEST (Biomarkers, EndpointS, and 
other Tools) Resource) 
 
Data Analysis Plan: A roadmap for how the data will be organized and analyzed, and how 
results will be presented. A data analysis plan should be established when planning a research 
study (i.e., before data collection begins). Among other things, the data analysis plan should 
describe: (a) the data to be collected; (b) the analyses to be conducted to address the research 
objectives, including assumptions required by said analyses; (c) data cleaning and management 
procedures; (d) data transformations, if applicable; and (e) how the study results will be 
presented (e.g., graphs, tables).  
 
Data Management Plan (DMP): A written document that describes the data you expect to 
acquire or generate during the course of your research study; how you intend to manage, 
describe, analyze, and store said data; and what mechanisms you will use at the end of your 
study to preserve and share your data. (Source: Stanford University Libraries n.d.(b), Data 
Management Plans (DMPS) available at https://library.stanford.edu/research/data-
management-services/data-management-plans) 
 
Disease Burden: The impacts, direct and indirect, of the patients health condition that have a 
negative effect on his or her health, functioning, and overall well-being. Disease burden 
includes: the physical and physiologic impacts of the disease and its symptoms; co-morbidities; 
emotional and psychological effects of the disease, its management, or its prognosis; social 
impacts; effects on relationships; impacts on the patients ability to care for self and others; time 
35  Contains Nonbinding Recommendations 
 
and financial impacts of the disease and its management; and considerations on the impacts on 
the patients family. 
 
Endpoint: A precisely defined variable intended to reflect an outcome of interest that is 
statistically analyzed to address a particular research question. A precise definition of an 
endpoint typically specifies the type of assessments made, the timing of those assessments, the 
assessment tools used, and possibly other details, as applicable, such as how multiple 
assessments within an individual are to be combined. (Source: BEST (Biomarkers, EndpointS, 
and other Tools) Resource) 
 
Fit-for-Purpose: A conclusion that the level of validation associated with a medical product 
development tool is sufficient to support its context of use. (Source: BEST (Biomarkers, 
EndpointS, and other Tools) Resource) 
 
Generalizability: The extent to which study findings can be reliably extended to the target 
population of interest.  
 
Health Literacy: The degree to which individuals have the capacity to obtain, process, and 
understand basic health information and services needed to make appropriate health decisions. 
(Source: US Department of Health and Human Services (2000)) Health literacy also includes 
numeracy skillssuch as calculating cholesterol and blood sugar levels, measuring medication 
doses, and understanding nutrition labels  and knowledge of health topics. 
 
Literacy: A person's ability to read, write, speak, and compute and solve problems at levels 
necessary to: (a) function on the job and in society; (b) achieve one's goals; and (c) develop 
one's knowledge and potential. (Source: Public Law 102-73. The National Literacy Act of 1991 
(US Congress 1991))   
 
Methodologically Sound: Assurance that the methods and processes used to obtain and analyze 
patient experience data are rigorous, robust and adhere to scientifically established principles 
and best practices for method development or implementation. Evidence generated by 
methodologically sound methods and processes increases confidence that the results can be 
trusted, interpreted and support the intended regulatory uses. 
 
Mixed Methods Research: Research that uses both qualitative and quantitative research 
methods. See definitions for qualitative and quantitative research methods. 
 
Observer-Reported Outcome (ObsRO): A measurement based on a report of observable 
signs, events, or behaviors related to a patients health condition by someone other than that 
patient or a health professional. Generally, ObsROs are reported by a parent, caregiver, or 
someone who observes the patient in daily life. ObsROs are particularly useful for patients who 
cannot report for themselves (e.g., infants or individuals who are cognitively impaired). An 
ObsRO measure does not include medical judgment or interpretation. (Source: BEST 
(Biomarkers, Endpoints and Other Tools) Resource). Examples of ObsROs include a parents 
report of a childs vomiting episodes or a report of wincing thought to be the result of pain in 
patients who are unable to report for themselves.  
36  Contains Nonbinding Recommendations 
 
 
Patient: Any individual with or at risk of a specific health condition, whether or not the 
individual currently receives any therapy to prevent or treat that condition.  
 
Patient Advocate: An individual or group of individuals, who may or may not be part of the 
target population and who has a role in promoting an interest or cause to influence policy with 
respect to patients health or healthcare. 
 
Patient-Centered: See patient-focused 
 
Patient-Centered Outcome: An outcome that is important to patients survival, functioning, or 
feelings as identified or affirmed by patients themselves, or judged to be in patients best 
interest by providers and/or caregivers when patients cannot report for themselves. (Source: 
ISPOR Plenary, Patrick (2013)) 
 
Patient Engagement: Activities that involve patient stakeholders sharing their experiences, 
perspectives, needs, and priorities that help inform FDAs public health mission. Such activities 
may include testimony at Advisory Committee meetings, submission to a regulations.gov public 
docket, meetings attended by patients, FDA, and other stakeholders, other correspondence with 
FDA, interactions through social media, and interactions with or information from patient 
representatives or patient advocates. 
 
Patient Experience Data: Defined in Title III, section 3001 of the Cures Act, as amended by 
section 605 of the Food and Drug Administration Reauthorization Act of 2017,15 and includes 
data that are collected by any persons and are intended to provide information about patients 
experiences with a disease or condition. Patient experience data can be interpreted as 
information that captures patients experiences, perspectives, needs, and priorities related to: 1) 
the symptoms of their condition and its natural history; 2) the impact of the conditions on their 
functioning and quality of life; 3) their experience with treatments; 4) input on which outcomes 
are important to them; 5) their preferences for outcomes and treatments; and 6) the relative 
importance of any issue as defined by patients.  
 
Patient-Focused (also referred to as patient-centered): Ensuring that patients experiences, 
perspectives, needs, and priorities are meaningfully incorporated into decisions and activities 
related to their health and well-being.  
 
                                                 
15 For purposes of this section, the term patient experience data includes data that (1) are collected by any 
persons (including patients, family members and caregivers of patients, patient advocacy organizations, disease 
research foundations, researchers and drug manufacturers); and (2) are intended to provide information about 
patients experiences with a disease or condition, including (A) the impact (including physical and psychosocial 
impacts) of such disease or condition, or a related therapy, on patients lives; and (B) patient preferences with 
respect to treatment of such disease or condition. The definition is codified at section 569C(c)(4) of the Federal 
Food, Drug, and Cosmetic Act, and applies to section 3002 of the Cures Act, which directed FDA to issue certain 
guidance documents regarding the collection of patient experience data, see section 3002(b).  
37  Contains Nonbinding Recommendations 
 
Patient-Focused Drug Development (PFDD) (also referred to as patient-focused medical 
product development): A systematic approach to help ensure that patients experiences, 
perspectives, needs, and priorities are captured and meaningfully incorporated into the 
development and evaluation of medical products throughout the medical product life cycle. 
 
Patient Input: Information that captures patients experiences, perspectives, needs, and 
priorities. See also patient experience data.  
 
Patient Partner: An individual patient, caregiver, or patient advocacy group that engages other 
stakeholders to ensure the patients wants, needs, and preferences are represented in activities 
related to medical product development and evaluation. (Wilson et al. 2018) 
 
Patient Perspective: A type of patient experience data that specifically relates to patients 
attitudes or points of view about their condition or management of their condition. Patient 
perspectives may include perceptions, goals, priorities, concerns, opinions, and preferences. 
 
Patient Preference: A statement of the relative desirability or acceptability to patients of 
specified alternatives or choices among outcomes or other attributes that differ among 
alternative health interventions. (Source: FDA Guidance for Industry: Patient Preference 
Information Voluntary Submission, Review in Premarket Approval Applications, 
Humanitarian Device Exemption Applications, and De Novo Requests, and Inclusion in 
Decision Summaries and Device Labeling (FDA 2016)) 
 
Patient Preference Information (PPI): Assessments of the relative desirability or acceptability 
to patients of specified alternatives or choices among outcomes or other attributes that differ 
among alternative health interventions. The methods for generating PPI may be qualitative, 
quantitative, or mixed methods. (Source: FDA Guidance for Industry: Patient Preference 
Information Voluntary Submission, Review in Premarket Approval Applications, 
Humanitarian Device Exemption Applications, and De Novo Requests, and Inclusion in 
Decision Summaries and Device Labeling (FDA 2016))  
 
Patient-Provided Input: Patient experience data or other information that comes directly from 
patients. 
 
Patient-Reported Outcome (PRO): A measurement based on a report that comes directly from 
the patient (i.e., study subject) about the status of a patient's health condition without 
interpretation of the patient's response by a clinician or anyone else. A PRO can be measured by 
self-report or by interview, provided that the interviewer records only the patient's response. 
Symptoms or other unobservable concepts known only to the patient (e.g., pain severity or 
nausea) can only be measured by PRO measures. PROs can also assess the patient perspective 
on functioning or activities that may also be observable by others. (Source: BEST 
(Biomarkers, EndpointS, and other Tools) Resource) 
 
Patient Representative: An individual, who may or may not be part of the target population, 
who has direct experience with a disease or condition (e.g., a patient or caregiver) and can 
provide information about a patients experience with the disease or condition. 
38  Contains Nonbinding Recommendations 
 
 
Performance Outcome (PerfO): A measurement based on a standardized task(s) performed by 
a patient that is administered and evaluated by an appropriately trained individual or is 
independently completed. PerfOs require patient cooperation and motivation. These include 
measures of gait speed (e.g., timed 25-foot walk test), memory recall (e.g., word recall test) or 
other cognitive testing (e.g., digit symbol substitution test). (Source: BEST (Biomarkers, 
EndpointS, and other Tools) Resource) 
 
Qualitative Research Methods: Methods associated with the gathering, analysis, 
interpretation, and presentation of narrative information (e.g., spoken or written accounts of 
experiences, observations, and events). Qualitative research methods may also include direct 
observations (e.g., non-verbal communication and behaviors). 
 
Quantitative Research Methods: Methods associated with the gathering, analysis, 
interpretation, and presentation of numerical information.  
 
Real-World Data: Data relating to patient health status and/or the delivery of health care 
routinely collected from a variety of sources. (Source: FDA Guidance on Use of Real-World 
Evidence to Support Regulatory Decision-Making for Medical Devices (FDA 2017c)) 
 
Real-World Evidence: The clinical evidence regarding the usage and potential benefits or risks 
of a medical product derived from analysis of real-world data. (Source: FDA Guidance on Use 
of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices (FDA 
2017c)) 
 
Reporter: In research studies designed to collect patient experience data, the reporter is the 
individual, group of individuals, or entity providing patient experience data. Reporters may be 
patients, parents, sexual/romantic partners, caregivers, physicians, or other healthcare 
professionals. Selection of an appropriate reporter in a given research study will depend on the 
definition of the target population of interest. If a patient in the target population can be 
reasonably expected to reliably self-report, one would expect the patient herself/himself to be 
the reporter in that research study.  
 
Representativeness: Confidence that a sample from which evidence is generated is sufficiently 
similar to the intended population. In the context of patient experience data, representativeness 
includes the extent to which the elicited experiences, perspectives, needs, and priorities of the 
sample are sufficiently similar to those of the intended patient population. 
 
Research Protocol: A document that describes the background, rationale, objectives, design, 
methodology, statistical considerations, and organization of a clinical research project. (Source: 
University of California San Francisco (2017)) A research protocol guides the study and 
associated data collection and analysis in a productive and standardized manner.  
 
Risk: Risks are adverse events and other unfavorable effects associated with a medical product. 
Risks include drug interactions, risks identified in the non-clinical data, risks to those other than 
the patient (e.g., fetus, those preparing and administering the medical product), and risks based 
39  Contains Nonbinding Recommendations 
 
on pharmacologic class or current knowledge of the product. Factors such as potential misuse, 
abuse, or diversion of the product may also be considered. (Source: International Conference 
on Harmonisation (ICH) Guidelines  Efficacy M4E(R2))    
 
Risk Tolerance: The degree to which a patient would accept increased probability or severity 
of a harm in exchange for a specific expected benefit. (Source: Medical Device Innovation 
Consortium (2015)) 
 
Science of Patient Input: Methods and approaches of systematically obtaining, analyzing, and 
using information that captures patients experiences, perspectives, needs, and priorities in 
support of the development and evaluation of medical products.  
 
Social Media: Web-based tools that are used for communication. Social media may include 
blogs, microblogs, social networking sites, professional networking sites, thematic networking 
sites, wikis, mashups, collaborative filtering sites, media sharing sites, and others. (Source: 
Grajales et al. (2014)) 
 
Subgroup: A subset of the study population or study sample defined by specific baseline 
characteristics. For example, demographic subgroups are commonly defined by subject sex, 
race, and age. 
 
Surrogate Endpoint: A type of endpoint used in clinical trials as a substitute for a direct 
measure of how a patient feels, functions, or survives. A surrogate endpoint does not measure 
the clinical benefit of primary interest in and of itself but rather is expected to predict that 
clinical benefit or harm based on epidemiologic, therapeutic, pathophysiologic, or other 
scientific evidence. From a U.S. regulatory standpoint, surrogate endpoints and potential 
surrogate endpoints can be characterized by the level of clinical validation: (a) validated 
surrogate endpoints; (b) reasonably likely surrogate endpoints; and (c) candidate surrogate 
endpoints. (Source: BEST (Biomarkers, EndpointS, and other Tools) Resource) 
 
Target Population (also referred to as the target patient population, the underlying population 
or intended population): The group of individuals (patients) about whom one wishes to make 
an inference. 
 
Trade-off: The extent to which a change in the level of one or more attributes of a medical 
product is offset by a change in one or more other attributes of that product. (Source: Medical 
Device Innovation Consortium (2015)) 
 
Treatment Burden: The impacts of a specific treatment or treatment regimen that have a 
negative effect on the patients health, functioning, or overall well-being. Treatment burden 
includes: side effects, discomfort, uncertainty about treatment outcomes, dosing and route of 
administration, requirements, and financial impacts.  
 
Treatment Effect: The amount of change in a disease/condition, symptom, or function that 
results from a medical intervention (as compared with not receiving the intervention or 
receiving a different intervention). 
40  Contains Nonbinding Recommendations 
 
 
Treatment Outcome: The benefits or harms to a patient who receives an intervention; the 
impact on a patients health, function, or well-being  or on a clinical indicator thereof  that 
is assumed to result from an intervention. 
 
Unmet Medical Need: A condition whose treatment or diagnosis is not addressed adequately 
by available therapy. An unmet medical need includes an immediate need for a defined 
population (e.g., to treat a serious condition with no or limited treatment) or a longer-term need 
for society (e.g., to address the development of resistance to antibacterial drugs). (Source: FDA 
Guidance for Industry. Expedited Programs for Serious ConditionsDrugs and Biologics (FDA 
2017a)).  
41