Submitting Clinical Trial 
Datasets and Documentation  
for Clinical Outcome 
Assessments Using  
Item Response Theory  
 
 
Guidance for Industry 
Technical Specifications Document 
 
 
 
 
For questions regarding this technical specifications document, contact 
CDER at cder-edata@fda.hhs.gov. 
 
 
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
 
November 2023 
Technical Specifications Document 
   
Revision History 
 
Date  Version  Summary of Revisions 
November 2023  1.0  Initial Version 
     
     
     
 
  Contains Nonbinding Recommendations 
Table of Contents 
 
1.0  Introduction .................................................................................................................... 1 
1.1   Purpose ............................................................................................................................ 1 
1.2  Scope ................................................................................................................................ 2 
1.3  Relationship to Other Documents ................................................................................. 2 
2.0  Relevant Acronyms ........................................................................................................ 3 
3.0  Documentation Specifications ....................................................................................... 4 
3.1  Contents of Covered COA Documentation .................................................................. 4 
3.2  Timing of Covered COA Documentation ..................................................................... 5 
4.0  Overview of Dataset Specifications .............................................................................. 5 
4.1  SDTM Specifications ...................................................................................................... 6 
4.1.1  Item Dataset ............................................................................................................. 6 
4.1.2  Questionnaires Dataset ........................................................................................... 8 
4.1.2.1  General Considerations ................................................................................... 8 
4.1.2.2  Handling of Missing Data .............................................................................. 10 
4.1.2.3  Handling of Data Not Collected due to Use of Skip Logic or Computerized 
Adaptive Testing ............................................................................................. 11 
4.1.3  Trial Summary Dataset ........................................................................................ 12 
4.2  ADaM Specifications .................................................................................................... 12 
4.2.1  General Considerations ................................................................................. 12 
4.2.2  Handling of Missing Data .............................................................................. 14 
5.0  Appendix ....................................................................................................................... 15 
5.1  Example Item Dataset .................................................................................................. 15 
5.2  Example SDTM Questionnaires Dataset.................................................................... 16 
5.3  Example ADaM Questionnaires Analysis Dataset .................................................... 18  Contains Nonbinding Recommendations 
Submitting Clinical Trial Datasets and Documentation for Clinical 
Outcome Assessments Using Item Response Theory  
 
Guidance for Industry 
Technical Specifications Document1 
 
 
 
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 office responsible for this guidance as listed on the 
title page.  
 
 
 
 
1.0  INTRODUCTION 
 
1.1   Purpose 
 
This document provides technical specifications for the submission of clinical outcome 
assessment (COA) data that use Item Response Theory (IRT) and supplements the FDA Center 
for Drug Evaluation and Research (CDER) Patient-Focused Drug Development (PFDD) 
Methodological Guidance Series.2 As described in the Biomarkers, EndpointS, and other Tools 
(BEST) Resource glossary,3 a COA refers to the assessment of a clinical outcome made through 
a report by a clinician, a patient, a non-clinician observer, or through a performance-based 
assessment; thus, there are four types of COAs: clinician-reported outcome (ClinRO), patient-
reported outcome (PRO), observer-reported outcome (ObsRO), and performance outcome 
(PerfO) measures.  
 
IRT is a family of mathematical models that describes the functional relationship between item 
performance, item characteristics, and the patients status on the construct being measured.4 
COAs that use IRT include static, fixed-form COAs that are developed and/or scored using IRT, 
 
1This guidance has been prepared by the Office of Biostatistics in the Center for Drug Evaluation and Research at 
the Food and Drug Administration. You may submit comments on this guidance at any time. Submit comments to 
Docket No. FDA-2018-D-1216 (available at https://www.regulations.gov/docket?D=FDA-2018-D-1216) (see the 
instructions for submitting comments in the docket). 
2More information is available at FDAs PFDD Guidance web page: https://www.fda.gov/drugs/development-
approval-process-drugs/fda-patient-focused-drug-development-guidance-series-enhancing-incorporation-patients-
voice-medical. 
3More information is available at NCBIs web page: https://www.ncbi.nlm.nih.gov/books/NBK338448/def-
item/glossary.clinical-outcome-assessment/. 
4American Educational Research Association, American Psychological Association, National Council on 
Measurement in Education, 2014, The Standards for Educational and Psychological Testing, Washington (DC): 
American Educational Research Association Publications. 
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or COAs that are administered using IRT-based Computerized Adaptive Testing (CAT). CAT is 
a sequential form of individual testing administered by a computer in which successive items in 
the COA measure are selected for administration based primarily on the items psychometric 
properties and content in relation to the patients responses to previous items5. COAs that use 
IRT can leverage an item bank for item selection. An item bank represents the total set of items 
from which a subset is selected during COA measure development or selected for the patient 
during adaptive testing.6  
 
1.2  Scope 
 
COAs covered in this guidance (referred to as covered COAs throughout this document) 
include (1) fixed-form (i.e., static, fixed-length) COAs that are developed and/or scored using 
IRT and (2) COAs administered using IRT-based CAT. This document provides specifications 
for the submission of Clinical Data Interchange Standards Consortium (CDISC) Study Data 
Tabulation Model (SDTM) and Analysis Data Model (ADaM) datasets. These technical 
specifications aim to provide general guidelines for standardized dataset content and structure, 
along with supporting documentation, to facilitate FDA review of the covered COA within the 
marketing application that the submitted data are intended to support. The SDTM and ADaM 
specifications outlined in section 4.0 Overview of Dataset Specifications are not prescriptive and 
do not include an exhaustive list of all datasets, variables, and controlled terminologies to be 
submitted for FDA review.  
 
These technical specifications provide an opportunity for dialogue between the sponsor and the 
Agency on issues related to trial design or conduct that may affect the content of these datasets. 
The specifications outlined in section 3.0 Documentation Specifications and section 4.0 
Overview of Dataset Specifications are pursuant to discussions with FDA and may vary by 
clinical drug development program and clinical trial therein. Agreement on the covered COA 
measure(s) used to collect trial data and analyses of the resulting COA data should be discussed 
with FDA as early as possible in a medical product development program; for example, prior to 
trial initiation. Sponsors are strongly encouraged to use the resources described in section 1.3 
Relationships to Other Documents and to seek Agency input for confirmation and clarification as 
needed.  
 
1.3  Relationship to Other Documents  
 
These technical specifications have been drafted in accordance with the business rules and 
assumptions outlined in the CDISC SDTM,7 the SDTM Implementation Guide8, the ADaM9, and 
the ADaM Implementation Guide (ADaMIG). As new versions of the models and 
implementation guides become available, these technical specifications may be updated 
accordingly to maintain alignment. In addition, the FDA Study Data Technical Conformance 
 
5American Educational Research Association, American Psychological Association, National Council on 
Measurement in Education, 2014, The Standards for Educational and Psychological Testing, Washington (DC): 
American Educational Research Association Publications. 
6Ibid. 
7More information is available at CDISCs SDTM web page: https://cdisc.org/standards/foundational/sdtm. 
8More information is available at CDISCs SDTMIG web page: https://cdisc.org/standards/foundational/sdtmig. 
9More information is available at CDISCs ADaM web page: https://cdisc.org/standards/foundational/adam. 
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Guide (sdTCG)10 provides general specifications and recommendations for submitting datasets 
using the SDTM and ADaM standards. Sponsors should review the FDA Data Standards 
Catalog11 to ensure data submissions follow FDA-supported standards. 
 
In addition, sponsors should reference the following:  
 
  FDA PFDD Guidance series12 
  CDISC Controlled Terminology13 
  CDISC Questionnaires, Ratings and Scales (QRS) Supplements and CDISC QRS Resources, 
which includes QRS Naming and Business Rules14 
 
 
2.0  RELEVANT ACRONYMS 
 
Abbreviation  Description 
ADaM  Analysis Data Model 
ADaMIG  Analysis Data Model Implementation Guide 
ADRG  Analysis Data Reviewers Guide 
BEST  Biomarkers, EndpointS, and other Tools 
BDS  Basic Data Structure 
CAT  Computerized Adaptive Testing 
CDISC  Clinical Data Interchange Standards Consortium 
CDER  Center for Drug Evaluation and Research 
ClinRO  Clinician-Reported Outcome 
COA  Clinical Outcome Assessment 
CRF  Case Report Form 
cSDRG  Clinical Study Data Reviewers Guide 
CSR  Clinical Study Report 
DDT  Drug Development Tool 
FDA  Food and Drug Administration 
IND  Investigational New Drug 
IRT  Item Response Theory 
NCBI  National Center for Biotechnology Information 
NCI EVS  National Cancer Institute Enterprise Vocabulary Services 
ObsRO  Observer-Reported Outcome 
PerfO  Performance Outcome 
PFDD  Patient-Focused Drug Development 
 
10More information is available at FDAs Study Data Standards Resources web page: 
https://www.fda.gov/industry/fda-resources-data-standards/study-data-standards-resources. 
11Ibid. 
12More information is available at FDAs PFDD Guidance series web page: 
https://www.fda.gov/drugs/development-approval-process-drugs/fda-patient-focused-drug-development-guidance-
series-enhancing-incorporation-patients-voice-medical. 
13More information is available at NCIs web page: https://datascience.cancer.gov/resources/cancer-
vocabulary/cdisc-terminology. 
14More information is available at CDISCs QRS web page: https://cdisc.org/standards/foundational/qrs. 
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PRO  Patient-Reported Outcome 
QRS  Questionnaires, Ratings and Scales 
SAP  Statistical Analysis Plan 
sdTCG  Study Data Technical Conformance Guide 
SDTM  Study Data Tabulation Model 
SDTMIG  Study Data Tabulation Model Implementation Guide 
SDTMIG-MD  Study Data Tabulation Model Implementation Guide for Medical Devices 
 
     
3.0  DOCUMENTATION SPECIFICATIONS 
 
This section details the content and timing of documentation to be submitted for covered COAs. 
Note the IRT terminology referenced within the submission should be explicitly defined in the 
study protocol and used consistently throughout the submission. 
 
3.1  Contents of Covered COA Documentation 
  
For a covered COA, the recommended documentation to be submitted to FDA is the following:  
 
  COA name, version, and copyright information (if applicable) 
 
  A copy of the measure with all items and response options for covered COAs that are 
administered as static, fixed-form. For covered COAs administered using CAT, a screenshot 
of each item in the item bank should be submitted 
 
  The item dataset described in section 4.1.1 Item Dataset 
 
  The COA measure instructions received by the reporter (e.g., patient, clinician, caregiver)  
 
  The user manual, including training materials and instructions for administration and scoring 
 
  Operational details (e.g., acceptable screen sizes, operating systems, and other specifications, 
as well as types of assistance provided, if applicable) 
 
  The handling and mitigation of server, power, and/or internet outage issues 
 
  Supporting software datasets and code 
 
  A detailed description of the calibration sample (i.e., the sample of respondents used to 
estimate the item parameters) 
 
  For covered COAs where IRT is used for scoring the following additional information should 
be submitted to FDA: 
 
o  Scoring details, including methods for generating scores (e.g., latent factor score (referred 
to as theta score throughout this document), scaled score) 
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  Contains Nonbinding Recommendations 
 
o  Conversion table(s) used to convert a theta score to other transformed scores (e.g., T-
score), if applicable 
 
o  Psychometric software (e.g., the software name and version) 
 
For covered COAs administered using CAT, it is recommended that the following 
documentation be submitted to FDA in addition to the documentation listed above: 
 
  Details of item selection or routing algorithm (e.g., the algorithm used to select the next item 
or sets of items for the patient with content constraints and/or item exposure control (if 
applicable)) 
 
  The starting criteria with justification 
 
  The termination criteria (i.e., the stopping rule) with justification 
 
Additional documentation can be requested to facilitate the Agencys review of the IRT analyses 
results. It is recommended that the sponsor consult with the review division to discuss the 
submission of documentation.  
 
3.2  Timing of Covered COA Documentation 
 
It is strongly recommended that the sponsor consult the Agency early during the IND phase to 
(1) determine the appropriate level of evidence to submit for the covered COA and (2) obtain 
agreement on the proposed fixed/static COA measure and/or item bank prior to the sponsor 
conducting the registration trial(s) (i.e., the trial(s) intended to contribute to substantial evidence 
of effectiveness). It is recommended that the agreed-upon COA evidence, including the item 
dataset described in section 4.1.1 Item Dataset, be submitted to FDA during the IND phase. If 
submission is not possible during the IND phase, the sponsor should consult with the review 
division. 
 
 
4.0  OVERVIEW OF DATASET SPECIFICATIONS 
 
Dataset specifications detail the CDISC datasets that are used to support covered COA data 
tabulation and analyses. Sponsors should implement the CDISC SDTM standard when 
submitting clinical tabulation data and the CDISC ADaM standard when submitting analysis 
data. This section supplements existing guidance by providing specifications for submitting 
covered COA data but does not encompass all data to be submitted for a clinical trial. As 
documented in the FDA sdTCG,15 SDTM and ADaM datasets should be accompanied by 
informative metadata provided in a compliant data definition file (i.e., Define-XML) and are 
expected to be accompanied by a Clinical Study Data Reviewers Guide (cSDRG) and an 
Analysis Data Reviewers Guide (ADRG), respectively. Standard CDISC Controlled 
 
15More information is available at FDAs Study Data Standards Resources web page: 
https://www.fda.gov/industry/fda-resources-data-standards/study-data-standards-resources. 
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Terminology16 developed and maintained by CDISC and National Cancer Institute Enterprise 
Vocabulary Services (NCI EVS) should be used where applicable. Sponsor extensions to 
extensible codelists and use of alternate (e.g., non-CDISC or sponsor-defined) terminologies 
should be indicated in the study metadata (e.g., Define-XML file, cSDRG, and other supportive 
documentation). In addition, software programs used to create ADaM datasets and analyze 
covered COA data should be submitted with the marketing application for FDA review.  
 
4.1  SDTM Specifications 
 
This section details the SDTM specifications for (1) the item dataset, (2) the Questionnaires (QS) 
dataset, and (3) the Trial Summary (TS) dataset.  
 
4.1.1  Item Dataset 
 
Sponsors should create and submit an item dataset (referred to as the ZQ dataset throughout this 
document) that provides all items, response options, and associated model parameters for a 
covered COA. The ZQ dataset does not contain individual patient data. When an item bank is 
used either during measure development or during measure administration, the ZQ dataset 
should contain information for all items within the item bank. When an item bank is not used, 
such as when IRT is only used in scoring, the ZQ dataset should contain information for all items 
within the fixed/static COA. Examples of ZQ dataset parameters (referenced as ZQPARM in 
Table 1) may include, but are not limited to, the following:  
 
  All possible numeric and/or character response values for items with categorical responses 
 
  Minimum and maximum numeric values for items using continuous scales by design 
 
  Character interpretation of minimum and maximum values for items using continuous scales 
by design 
 
  Item loading parameters, or equivalent, for items based on continuous response options  
 
  Item location and/or threshold parameters for items based on dichotomous or polytomous 
response options  
 
  Item intercept for an item using a count response scale 
 
  Item slope(s) (i.e., item discrimination parameter(s)) for each item 
 
The list of variables for the recommended item dataset is provided in Table 1, and an example 
ZQ dataset is provided in Appendix 5.1. Additional qualifiers such as units or subcategory (i.e., 
ZQSCAT) may be added if relevant for the covered COA. 
 
 
16More information is available at NCIs web page: https://datascience.cancer.gov/resources/cancer-
vocabulary/cdisc-terminology. 
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Table 1. Specifications for Item Variables 
Variable 
Variable Label  Type  Comments 
Name 
STUDYID  Study Identifier  Char  This is the Unique Identifier for a study. 
DOMAIN  Domain  Char  This is a two-character abbreviation for the domain 
(e.g., ZQ). 
RDOMAIN  Related QRS  Char  This is the related QRS domain in which the covered 
Domain  COA data resides within a study (e.g., FT, QS, RS). 
Abbreviation 
ZQSEQ  Sequence  Num  This is the sequence number assigned to ensure 
Number  uniqueness of records within the ZQ dataset. 
ZQCAT  Category of  Char  The measure name(s) and version number(s) should be 
Item  provided within ZQCAT. Controlled Terminology for 
FTCAT, QSCAT, or RSCAT should be implemented in 
ZQCAT if applicable; if not, ZQCAT should be 
constructed according to CDISC QRS Naming and 
Business Rules. The most common name that the 
measure is known by should be used; this could either 
be the complete name of the measure or an acronym. 
ZQTESTCD  Item   Char  A topic variable for ZQ and a short name for the item 
--TESTCD  name in ZQTEST. Controlled Terminology should be 
implemented if applicable; if not, construct these 
according to CDISC QRS Naming and Business Rules. 
ZQTEST  Item --TEST  Char  This is the long name of the item. SDTM Controlled 
name  Terminology should be implemented if applicable; if 
not, construct these according to CDISC QRS Naming 
and Business Rules. 
ZQPARMCD  Item Parameter  Char  This is the short name for the Item Parameter. Naming 
Short Name  conventions for ZQPARMCD should make it easy to 
discern if related parameters are grouped together (e.g., 
if multiple point estimates are present for threshold 
parameters as illustrated in Appendix 5.1). 
ZQPARM  Item Parameter  Char  This is the long name for the Item Parameter. 
ZQVALN  Numeric  Num  The numeric value corresponds to the parameter 
Parameter  identified by ZQPARMCD and ZQPARM. The sponsor 
Value  should consider the appropriate number of decimal 
places to be provided within ZQVALN given the 
parameter. 
ZQVALC  Character  Char  The character value corresponds to the parameter 
Parameter  identified by ZQPARMCD and ZQPARM. 
Value 
ZQSE  Standard Error  Num  This is the standard error for an estimated Numeric 
Parameter Value (ZQVALN). For example, ZQSE is 
provided when ZQVALN represents an estimated 
threshold parameter or item slope. Item slope may be 
estimated based on the IRT model implemented. 
 
 
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4.1.2  Questionnaires Dataset 
 
Covered COA data are typically represented in the SDTM Questionnaires (QS) dataset, the 
Functional Tests (FT) dataset, or the Disease Response and Clin Classification (RS) dataset. The 
dataset should be created using the assumptions and business rules provided within the SDTMIG 
and the FDA sdTCG. For brevity, the tabulation dataset containing covered COA data for 
individual patients is referenced as the QS dataset throughout the remainder of this document, 
though it is understood that other datasets may contain covered COA data (e.g., FT, RS). For 
select measures, CDISC publishes QRS Supplements17 that provide guidance on representing 
named COA measures in the SDTM. CDISC also provides submission values within the 
Controlled Terminology18 related to named COA measures. These resources should be consulted 
for guidance on a covered COA measure, in addition to the guidance provided in the SDTM and 
SDTMIG. 
 
4.1.2.1 General Considerations 
 
The QS dataset represents the covered COA data as collected for individual patients at distinct 
assessment timepoints. The subset of standard QS variables in Table 2 is included here to 
clarify how they should be completed for covered COA measures to foster consistency and 
standardization across industry. An example QS dataset is provided in Appendix 5.2.  
 
Table 2. Specifications for a Subset of QS Variables 
Variable 
Variable Label  Type  Comments 
Name 
QSCAT  Category of  Char  The measure name and version number should be 
Question  provided within QSCAT. Controlled Terminology 
should be implemented if applicable; if not, QSCAT 
should be constructed according to CDISC QRS 
Naming and Business Rules. The most common name 
that the measure is known by should be used, which can 
either be the complete name of the measure or an 
acronym. 
QSTESTCD  Question Short  Char  A topic variable for QS and short name for the value in 
Name  QSTEST. Controlled Terminology should be 
implemented if applicable; if not, construct the name 
according to CDISC QRS Naming and Business Rules. 
QSTESTCD should begin with a short code for the 
measure followed by the item number (e.g., ABC01). In 
cases where item numbers are absent, sequential 
numbers starting with 01 should be used, and can be 
shortened to 1 to accommodate the eight-character limit 
for values of QSTESTCD. The value in QSTESTCD 
 
17More information is available at CDISCs QRS web page: https://cdisc.org/standards/foundational/qrs. 
18More information is available at NCIs web page: https://datascience.cancer.gov/resources/cancer-
vocabulary/cdisc-terminology. 
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Variable 
Variable Label  Type  Comments 
Name 
should correspond to the value in ZQTESTCD to 
support traceability. See the example in Appendix 5.2. 
QSTEST  Question Name  Char  This is the name of the item or, if applicable, the source 
data summary score19 used to obtain the measurement 
or finding. QSTEST should start with a short code for 
the measure followed by a hyphen and a brief 
description. Controlled Terminology should be 
implemented if applicable; if not, construct according to 
CDISC QRS Naming and Business Rules. If QSTEST 
is > 40 characters in length, put meaningful text in 
QSTEST and describe the full-text in the study 
metadata (e.g., the annotated Case Report Form (CRF), 
the cSDRG, or the Define-XML). 
 
For each patient and assessment timepoint where the 
covered COA measure is planned to be administered 
per the protocol-defined schedule of assessments, a 
record should be provided for each item or source data 
summary score within the measure (See section 4.1.2.2 
Handling of Missing Data and section 4.1.2.3 Handling 
of Data Not Collected due to Skip Logic or 
Computerized Adaptive Testing). 
QSREFID  Reference  Char  Sequential ordering of all items the patient was 
Identifier  administered regardless of whether a response was 
provided. Recommended for covered COA measures 
that use CAT, since item selection and/or the order of 
item administration from the item bank can vary by 
patient and/or by assessment timepoint. 
QSSTAT  Completion  Char  This is populated as NOT DONE when an item score 
Status  (response) or source data summary score is empty/null. 
QSSTAT is empty/null if a result exists in QSORRES. 
QSREASND  Reason Not  Char  This is used in conjunction with QSSTAT when 
Performed  QSSTAT = NOT DONE to describe why an item 
score or source data summary score is empty/null (See 
section 4.1.2.2 Handling of Missing Data and section 
4.1.2.3 Handling of Data Not Collected due to Skip 
Logic or Computerized Adaptive Testing). 
 
 
The following are additional considerations: 
 
Additional content captured by the source should be submitted either as additional rows with 
relevant QSTEST and QSTESTCD values within the QS dataset or within a Supplemental 
 
19Source data summary scores are summary scores (e.g., total scores) that are source data (e.g., data reported within 
a CRF) and are submitted in the QS dataset. 
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Questionnaires (SUPPQS) dataset. Additional content is dependent on the covered COA measure 
administered, and if needed for analysis, should be copied into the applicable ADaM dataset. 
Example content captured within SUPPQS for each patient and assessment timepoint include the 
following: 
 
  Data Collection Mode: The mode of data collection used in the administration of the 
covered COA measure, if differing from the protocol and/or varying across patients, 
assessment timepoints, or sites (e.g., clinical trial site, home). Examples of data collection 
mode may include paper-based administration, handheld electronic device, computer web-
based application, or telephone-based administration. 
 
  Data Collector: In cases where a PRO measure is not self-administered (i.e., not 
independently completed by the patient without any assistance), the individual administering 
the PRO measure to the patient (e.g., caregiver, study staff member) by reading items to the 
patient and/or recording the patients responses. 
 
  Language: The language in which the measure was administered to the patient. 
 
  Response Time: The time taken to respond to each item administered within a covered COA 
measure and/or the time taken to respond to the total measure (used for assessing quality 
control).  
 
4.1.2.2 Handling of Missing Data 
 
Understanding the reasons for and prevalence of missing covered COA data are critical to 
support FDA review and regulatory decision-making. Missing data should be represented within 
the QS dataset with the reason for missingness captured under Reason Not Performed 
(QSREASND). The handling of missing data may differ depending on whether the covered COA 
is fixed/static or is administered using CAT. Item responses may be missing for one item, a 
subset of items, or all items within a covered COA, and Table 3 provides scenario-specific 
recommendations for displaying covered COA data that are missing at a planned (i.e., per 
protocol) assessment timepoint. Appendix 5.2 demonstrates scenarios for representing missing 
data within the QS dataset. CDISC QRS Supplements20 provide additional guidance on modeling 
missing data for named COA measures in SDTM datasets, including the modeling of timing 
variables. 
 
 
 
 
 
 
 
 
 
 
20More information is available at CDISCs QRS web page: https://cdisc.org/standards/foundational/qrs. 
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Table 3. Recommended QS Representation of Missing Data  
Scenario  Recommended Representation in QS Dataset 
The patient did not respond to an  The row for the missing item response should include: 
item administered within a covered   QSSTAT = NOT DONE 
COA.   QSREASND contains the reason the patient did not 
respond if known/collected. Otherwise, QSREASND is 
empty/null. 
A source data summary score  The row for the missing source data summary score should 
cannot be calculated per the scoring  include: 
algorithm based on the available   QSSTAT = NOT DONE  
item responses (e.g., due to   QSREASND is populated if known/collected (e.g., 
insufficient item response data).  QSREASND = NOT CALCULABLE). Otherwise, 
QSREASND is empty/null. 
For a fixed/static covered COA  The row for each missing item response and source data 
measure, the patient was not  summary score within the measure should include: 
administered the measure at a   QSSTAT = NOT DONE 
planned (per protocol) assessment   QSREASND contains the reason the measure was not 
timepoint.  administered if known/collected. Examples include, but 
are not limited to, patient was physically unable to 
complete the measure due to adverse event, patient 
refusal, patient did not provide measure, unable to 
contact, study site failed to administer or other site staff 
error, technological problems with a measure 
administered electronically, or patient was unable to 
attend a scheduled study visit due to hospitalization.  
For a covered COA measure  Given the exact items to be administered during CAT are 
administered using CAT, the patient  unknown, individual rows cannot be provided for each 
was not administered the measure  missing item. It is recommended that a row for each item 
for a planned (per protocol)  within the item bank not be submitted; rather, a single row 
assessment timepoint.  indicating the COA measure was not administered should 
be provided and include: 
 QSTESTCD = QSALL 
 QSTEST = All Questions 
 QSSTAT = NOT DONE 
 QSREASND contains the reason the patient was not 
administered the COA measure, if known/collected. 
 
 
4.1.2.3 Handling of Data Not Collected due to Use of Skip Logic or 
Computerized Adaptive Testing 
 
Separate from missing data, covered COA data may not be collected from the patient due to the 
use of CAT or skip logic to administer COA items. When the patient is not administered all 
items from an item bank due to the use of CAT, a row for each remaining unadministered item 
within the item bank should not be included within the QS dataset. Rather, only the administered 
items for CAT-administered measures should be submitted within the QS dataset as illustrated in 
Appendix 5.1. Conversely, when implemented, skip logic may be created based on the response 
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to certain items. When the patient is not administered an item within a covered COA measure 
due to the use of skip logic, the representation in the QS dataset should follow the guidance 
provided in the sdTCG.  
 
4.1.3  Trial Summary Dataset 
 
Data related to the trial summary should be stored in the TS dataset. Of particular interest to 
FDA is the frequency with which these technical specifications are used to create and submit 
trial data. Per the FDA sdTCG, sponsors may include the following parameters and associated 
values in the TS dataset to indicate that these technical specifications were used for the study: 
 
  TSPARAMCD = FDATCHSP 
  TSPARAM = FDA Tech Spec 
  TSVAL = IRT-Based COAs Technical Specifications Guidance v1.0 
 
4.2  ADaM Specifications 
 
This section provides specifications for the ADaM dataset containing analysis-ready covered 
COA data (referenced in this document as the ADQS dataset), which are derived from covered 
COA data in the SDTM QS dataset discussed in section 4.1.2 SDTM Dataset Containing 
Covered COA Data in conjunction with other SDTM and ADaM data. 
 
4.2.1  General Considerations 
 
The ADQS dataset described in this section follows the ADaM Basic Data Structure (BDS). In 
addition to variables for treatment assignment, stratification, subgrouping, and other covariates 
needed for analysis, the ADQS dataset should contain all individual items and summary scores 
(e.g., raw score, theta score, scale score (e.g., a standardized score such as T-score)) and 
associated standard errors.  
 
For CAT, additional information such as the number of items that were scored (i.e., scored 
count) and the number of items to which the patient responded (i.e., total item count) should be 
submitted to validate that the termination criteria for the CAT was met and that theta score was 
not calculated prematurely. Table 4 contains specifications for a subset of standard ADQS 
variables to clarify how they should be completed for covered COA measures to foster 
consistency and standardization across industry as well as traceability. Table 4 does not include 
all ADQS variables to be submitted, such as timing and treatment variables. An example ADQS 
dataset is provided in Appendix 5.3. 
 
 
 
 
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Table 4. Specifications for a Subset of ADQS Variables 
Variable 
Variable Label  Type  Comments 
Name 
PARCAT1  Parameter  Char  The measure name(s) and version(s) should be provided within 
Category 1  PARCAT1 for each item, summary score, or associated scoring 
parameter provided in PARAM to differentiate between 
measures administered during the study. The measure name may 
match the value stored in the variable QS.QSCAT from the input 
SDTM QS dataset. 
PARAM  Parameter  Char  The description of the analysis parameter, including individual 
items, summary scores (e.g., raw score, theta score, scale score 
(e.g., a standardized score such as T-score)), and associated 
standard errors. The value of PARAM may match the value 
stored in QS.QSTEST for parameters existing in the input 
SDTM QS dataset. Individual parameters are needed for each 
summary score. Documentation for derived parameters should 
be provided in submitted study metadata (e.g., the Define-XML 
file and the ADRG). 
PARAMCD  Parameter Code  Char  The short name of the analysis parameter in PARAM. The value 
of PARAMCD may match the values stored in QS.QSTESTCD 
for parameters existing in the input SDTM QS dataset. 
AVAL /  Analysis Value /   Num The analysis value for each parameter described by PARAM. 
AVALC  Analysis Value  /  For parameters existing in the input SDTM QS dataset, 
(C)  Char  quantitative values used for analysis and captured in AVAL may 
be copied from QS.QSSTRESN, and qualitative values used for 
analysis and captured in AVALC may be copied from 
QS.QSSTRESC. In some cases, it is acceptable to populate 
AVAL for qualitative values. Reference the ADaMIG for details 
around populating AVAL and AVALC. Study metadata should 
describe how AVAL and AVALC are calculated for derived 
parameters. 
DTYPE  Derivation Type  Char  Analysis value derivation method. DTYPE should be populated 
with the algorithm or statistical method used when AVAL or 
AVALC has been imputed or derived differently than the other 
analysis values within the parameter. Reference the ADaMIG 
for details around the submission of DTYPE. 
This document utilizes phantom records (i.e., where DTYPE = 
PHANTOM) to represent missing item scores or summary 
scores (See section 4.2.2 Handling of Missing Data and the 
examples within Appendix 5.3). Based on the study attributes, 
alternate DTYPE values may be used to handle missing data 
depending on the imputation method(s) implemented. When 
imputation is performed, imputation rules are included in the 
studys Statistical Analysis Plan (SAP). Imputation methods are 
not specified within this document for covered COAs. 
Variables in ADQS copied from input SDTM QS Dataset 
QSSEQ  Sequence  Num 
Number 
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Variable 
Variable Label  Type  Comments 
Name 
VISIT  Visit  Char  Sponsors should include any SDTM variables in the ADQS 
VISITNUM  Visit Number  Num  dataset needed to provide traceability to the source SDTM QS 
dataset. 
QSSTAT  Completion  Char  Sponsors should include SDTM variables that provide 
Status  explanations for missing item scores and missing source data 
QSREASND  Reason Not  Char  summary scores. See Comments for QSSTAT and QSREASND 
Performed  provided in Table 2. Specifications for a Subset of QS Variables. 
 
 
4.2.2  Handling of Missing Data 
 
As discussed in SDTM section 4.1.2.2 Handling of Missing Data and section 4.1.2.3 Handling of 
Data Not Collected due to Skip Logic or Computerized Adaptive Testing, understanding the 
reasons for and prevalence of missing COA data or COA data not collected are critical to support 
FDA review and regulatory decision-making. Approaches to represent missing covered COA 
data within the ADQS dataset include: 
 
  Copying records from the SDTM QS dataset, where QS.QSSTAT = NOT DONE and 
QS.QSREASND is populated with the reason the item(s) and/or summary score(s) are 
missing, or 
 
  Deriving phantom records in the ADQS dataset (e.g., where DTYPE = PHANTOM). 
 
When QS.QSREASND is populated within the SDTM QS dataset for a record, the value should 
be copied into the ADQS dataset within QSREASND. Phantom records should only be derived 
in the ADQS dataset when rows for the missing data do not exist in the SDTM QS dataset. For 
these records, phantom records should be derived for scenarios shown in Table 3: Recommended 
QS Representation of Missing Data. For example, if a patient was not administered a CAT 
measure due to skipping a visit and a row for the measure is not provided in the SDTM QS 
dataset, phantom records should be derived in the ADaM ADQS dataset for the measure. 
Deriving phantom records is one solution to represent missing data; however, based on the study 
attributes, values may be imputed instead. When applicable, if QS.QSREASND is not populated, 
a sponsor-defined analysis variable for Reason Not Performed (e.g., ADQS.AREASND) may be 
useful to indicate the reason (if known) that an item or measure was not completed, such as for 
phantom records. The example provided in Appendix 5.3 illustrates the representation of missing 
data.  
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5.0  APPENDIX 
 
5.1  Example Item Dataset 
 
Table A1 shows an example of the Study Data Tabulation Model (SDTM) item dataset that is recommended to be submitted for 
covered clinical outcome assessments (COAs). The parameters and their associated values are provided for illustrative purposes only; 
all parameters relevant to support the review should be submitted.  
 
Table A1, shown in two parts, illustrates the tabulation of one item containing polytomous response options from the item bank, which 
corresponds to a single ZQTEST value (i.e., Item 1):  
 
  Rows 1-5 represent the five discrete response options where ZQVALN represents the Numeric Parameter Value and ZQVALC 
represents the Character Parameter Value for Item 1. Both the numeric and character values should be submitted when applicable. 
When discrete response options are present for a given parameter, all possible response options should be submitted within 
ZQVALN and/or ZQVALC as shown in Table A1. 
 
  Rows 6-10 represent the discrete threshold parameters and item slope for the polytomous response options. Standard Error (ZQSE) 
is provided for each estimated threshold parameter. ZQSE should be provided when values are estimated, and Item Parameter 
(ZQPARM) values will differ based on the Item Response Theory (IRT) model used. 
 
Table A1. (Part 1) Subset of Sample ZQ 
Row  STUDYID  DOMAIN  RDOMAIN  ZQSEQ  ZQCAT  ZQTEST  ZQTESTCD 
1  StudyA  ZQ  QS  1  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
2  StudyA  ZQ  QS  2  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
3  StudyA  ZQ  QS  3  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
4  StudyA  ZQ  QS  4  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
5  StudyA  ZQ  QS  5  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
6  StudyA  ZQ  QS  6  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
7  StudyA  ZQ  QS  7  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
8  StudyA  ZQ  QS  8  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
9  StudyA  ZQ  QS  9  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
10  StudyA  ZQ  QS  10  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
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Table A1. (Part 2) Subset of Sample ZQ 
Row  ZQPARMCD  ZQPARM  ZQVALN  ZQVALC  ZQSE 
1  RESP  Item Response  1  Never   
2  RESP  Item Response  2  Rarely   
3  RESP  Item Response  3  Sometimes   
4  RESP  Item Response  4  Often   
5  RESP  Item Response  5  Always   
6  TPAR  Threshold Parameter  -1.2    0.29 
7  TPAR  Threshold Parameter  -0.6    0.14 
8  TPAR  Threshold Parameter  0.1    0.02 
9  TPAR  Threshold Parameter  0.8    0.13 
10  SLOPE  Item Slope  2.0    0.22 
 
 
5.2  Example SDTM Questionnaires Dataset 
 
Table A2 shows an example of tabulated item response scores using the Questionnaires (QS) domain specifications for a single patient 
who was administered a COA measure via CAT across three on-site assessment timepoints as specified by the protocol (not 
displayed), where the item bank (not displayed) contains eight questions. Where the measure was not completed as expected, Table 
A2 illustrates how missing data are submitted in the QS dataset for a subset of variables. Values within Numeric Finding in Standard 
Units (QSSTRESN) and Reason Not Performed (QSREASND) are provided for illustrative purposes; submitted values should be 
relevant in the context of the study. Scenarios displayed in Table A2, shown in two parts, include: 
 
  Rows 1-5 and rows 7-9 present the administered items during Visit 1 and Visit 3, respectively. Due to adaptive testing, the patient 
was administered items 1, 3, 4, 5, and 7 from the item bank during Visit 1. Thus, unadministered items 2, 6, and 8 are excluded 
from the QS dataset. During Visit 3, the patient was administered items 1, 5, and 8 from the item bank. Thus, unadministered items 
2, 3, 4, 6, and 7 are excluded.  
 
o  Reference Identifier (QSREFID) is populated for all administered items in the QS dataset in chronological order, even if the 
patient did not provide a response to an item.  
 
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  Row 2 presents the scenario where the patient did not provide a response to an administered item. Within CAT, this may occur 
when the measure allows a patient to skip an administered item by selecting the Next button to continue without selecting a 
response for the item.  
 
o  Completion Status (QSSTAT) = NOT DONE and QSREASND is populated with the reason when a patient did not provide a 
response to an item if known/collected (e.g., RESPONSE NOT PROVIDED). 
 
o  QSORRES, QSSTRESC, and QSSTRESN are empty/null. 
 
  Row 6 presents the scenario where the covered COA was not administered during Visit 2 due to a study site error. This scenario 
should be displayed as a single record in the QS dataset with the following:  
 
o  QSTEST = All Questions and QSTESTCD = QSALL.  
 
o  QSREFID, QSORRES, QSSTRESC, and QSSTRESN are empty/null. 
 
o  QSSTAT = NOT DONE and QSREASND is populated with the reason the COA measure was not administered if 
known/collected (e.g., STUDY SITE FAILED TO ADMINISTER).  
 
Table A2. (Part 1) Subset of Sample QS 
Row  USUBJID  VISIT  QSSEQ  QSCAT  QSTEST  QSTESTCD 
1  A_100_1001  VISIT 1  1  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
2  A_100_1001  VISIT 1  2  Example Item Bank v.1.0  EIB03-Item 3  EIB03 
3  A_100_1001  VISIT 1  3  Example Item Bank v.1.0  EIB04-Item 4  EIB04 
4  A_100_1001  VISIT 1  4  Example Item Bank v.1.0  EIB05-Item 5  EIB05 
5  A_100_1001  VISIT 1  5  Example Item Bank v.1.0  EIB07-Item 7  EIB07 
6  A_100_1001  VISIT 2  6  Example Item Bank v.1.0  All Questions  QSALL 
7  A_100_1001  VISIT 3  7  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
8  A_100_1001  VISIT 3  8  Example Item Bank v.1.0  EIB05-Item 5  EIB05 
9  A_100_1001  VISIT 3  9  Example Item Bank v.1.0  EIB08-Item 8  EIB08 
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Table A2. (Part 2) Subset of Sample QS 
Row  QSREFID  QSORRES  QSSTRESC  QSSTRESN  QSSTAT  QSREASND 
1  1  Often  Often  4     
2  2        NOT DONE  RESPONSE NOT PROVIDED 
3  3  Often  Often  4     
4  4  Always  Always  5     
5  5  Sometimes  Sometimes  3     
6          NOT DONE  STUDY SITE FAILED TO ADMINISTER 
7  1  Rarely  Rarely  2     
8  2  Never  Never  1     
9  3  Never  Never  1     
 
 
5.3  Example ADaM Questionnaires Analysis Dataset  
 
Table A3 shows an example ADQS dataset for the same patient and scenario descriptions as those provided in Appendix 5.2. Note 
that this table only shows a subset of variables to be submitted in the ADQS dataset; additional variables, such as population flags and 
treatment variables, are not provided as these depend on individual study needs. In this example, outputs of a psychometric software, 
including summary scores (e.g., raw score, theta score, T-score) and their associated standard errors, are derived in the ADQS dataset 
for each assessment timepoint. Values within Analysis Value (AVAL) are provided for illustrative purposes only. Scenarios displayed 
in Table A3, shown in two parts, include: 
 
  Rows 1-9 and 15-21 present records for Visits 1 and 3, respectively, where Visit 1 represents the baseline visit: 
 
o  Rows 1-5 and rows 15-17 present the administered items, copied from the SDTM QS dataset. 
 
o  Rows 6-9 and rows 18-21 present the summary scores and standard errors derived in the ADQS dataset. 
 
  Rows 10-14 present records for Visit 2: 
 
o  Row 10 presents the single record copied from the SDTM QS dataset where QSTESTCD = QSALL, provided for 
traceability.  
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o  Rows 11-14 present phantom records for each summary score and standard error derived in the ADQS dataset. For each row: 
 
  DTYPE = PHANTOM. 
 
  AVAL is empty/null. 
 
Table A3. (Part 1) Subset of Sample ADQS 
Row  USUBJID  AVISIT  PARCAT1  PARAM  PARAMCD 
1  A_100_1001  BASELINE  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
2  A_100_1001  BASELINE  Example Item Bank v.1.0  EIB03-Item 3  EIB03 
3  A_100_1001  BASELINE  Example Item Bank v.1.0  EIB04-Item 4  EIB04 
4  A_100_1001  BASELINE  Example Item Bank v.1.0  EIB05-Item 5  EIB05 
5  A_100_1001  BASELINE  Example Item Bank v.1.0  EIB07-Item 7  EIB07 
6  A_100_1001  BASELINE  Example Item Bank v.1.0  EIB-Raw Score  EIBRAW 
7  A_100_1001  BASELINE  Example Item Bank v.1.0  EIB-Theta Score  EIBTHETA 
8  A_100_1001  BASELINE  Example Item Bank v.1.0  EIB-T-Score  EIBTSCR 
9  A_100_1001  BASELINE  Example Item Bank v.1.0  EIB-Standard Error  EIBSE 
10  A_100_1001  VISIT 2  Example Item Bank v.1.0  All Questions  QSALL 
11  A_100_1001  VISIT 2  Example Item Bank v.1.0  EIB-Raw Score  EIBRAW 
12  A_100_1001  VISIT 2  Example Item Bank v.1.0  EIB-Theta Score  EIBTHETA 
13  A_100_1001  VISIT 2  Example Item Bank v.1.0  EIB-T-Score  EIBTSCR 
14  A_100_1001  VISIT 2  Example Item Bank v.1.0  EIB-Standard Error  EIBSE 
15  A_100_1001  VISIT 3  Example Item Bank v.1.0  EIB01-Item 1  EIB01 
16  A_100_1001  VISIT 3  Example Item Bank v.1.0  EIB05-Item 5  EIB05 
17  A_100_1001  VISIT 3  Example Item Bank v.1.0  EIB08-Item 8  EIB08 
18  A_100_1001  VISIT 3  Example Item Bank v.1.0  EIB-Raw Score  EIBRAW 
19  A_100_1001  VISIT 3  Example Item Bank v.1.0  EIB-Theta Score  EIBTHETA 
20  A_100_1001  VISIT 3  Example Item Bank v.1.0  EIB-T-Score  EIBTSCR 
21  A_100_1001  VISIT 3  Example Item Bank v.1.0  EIB-Standard Error  EIBSE 
 
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Table A3. (Part 2) Subset of Sample ADQS 
Row  AVAL  QSSEQ  VISIT  DTYPE  QSSTAT  QSREASND 
1  4  1  VISIT 1       
2    2  VISIT 1    NOT DONE  RESPONSE NOT PROVIDED 
3  4  3  VISIT 1       
4  5  4  VISIT 1       
5  3  5  VISIT 1       
6  12    VISIT 1       
7  2    VISIT 1       
8  70    VISIT 1       
9  1.8    VISIT 1       
10    6  VISIT 2    NOT DONE  STUDY SITE FAILED TO ADMINISTER 
11      VISIT 2  PHANTOM     
12      VISIT 2  PHANTOM     
13      VISIT 2  PHANTOM     
14      VISIT 2  PHANTOM     
15  2  7  VISIT 3       
16  1  8  VISIT 3       
17  1  9  VISIT 3       
18  16    VISIT 3       
19  2.5    VISIT 3       
20  75    VISIT 3       
21  3.2    VISIT 3       
 
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