bluuebunny/crossref_metadata_2025 huggingface dataset:
Bayesian Dose‐Finding Designs in Healthy Volunteers
Authors: Zhou Yinghui
Published: September 2014

In the majority of phase I clinical trials, experimental drugs are administered via predefined dose‐escalation schemes to healthy volunteers to explore the relationship between the dose administered and the primary responses of interest. The responses might be binary outcomes such as adverse events; quantitative summary measures concerning pharmacokinetics (PK), such as the average or maximum plasma concentration over time; or pharmacodynamic (PD), such as heart rate or blood pressure changes from baseline. Conventional dose‐escalation designs can take a long time to complete to investigate every available dose. Also, conventional statistical analyses are not accurate due to small sample sizes: 20 to 80 volunteers are typically used in phase I trials. Adaptive designs have been studied as they can potentially speed up dose‐escalation and have better properties in terms of avoidance of unsafe doses and accuracy of estimation when compared with conventional designs. This review of state‐of‐the‐art adaptive designs illustrates how such a design can be implemented and discusses some practical issues met during implementation.
Clinical Data Coordination
Authors: Kious Cheryl
Published: September 2014

This article provides an overview of the role of the Lead Data Manager, including best practices and considerations in the clinical data management process. The clinical data management process is responsible for taking the raw data provided by investigative sites and converting it into a high‐quality database “…enabling the data to be efficiently analysed and displayed.” The processes outlined are assumed for a regulatory study, and they are divided into the phases of study initiation, study conduct, and study closure.
Clinical Data Management
Authors: McBride Ruth
Published: September 2014

Clinical data management is data management systems and process focused on providing clean, current and reliable data to support clinical research, especially for clinical trials. Computerized systems have been used since the early 1960's. Current clinical data management systems vary. Many, but not all clinical trials use electronic data capture to collect data from clinical research sites. Efforts are underway to standardize the way clinical data are organized.
Censored Data and Clinical Trials
Authors: Andersen Per Kragh
Published: September 2014

The concepts of independent right censoring and noninformative right censoring in survival analysis are defined. Dependent censoring is a kind of selection bias and adds to biased inference for the survival time distribution. Informative censoring typically leads to inefficient inference. Examples are presented and relations between right censoring and competing risks are discussed. Other kinds of censoring, including interval censoring are touched upon.
Correlation, including intraclass
Authors: Armitage Peter
Published: September 2014

In a loose sense, two variables are correlated if changes in one tend to be accompanied by changes in the other. More specifically, correlation implies a linear relationship with superimposed random variation. The product–moment correlation coefficient was introduced by Galton and greatly exploited by Karl Pearson. Its square is the proportion of the variance of one variable “explained” by linear regression on a second variable. Standard tests are described in the article. An important extension is to intraclass correlation for measurements on individuals falling into groups, which measures the association between individuals in the same group.
Control Groups
Authors: Green Stephanie
Published: September 2014

Use of appropriate control groups is a critical principle in clinical research methods. The modern randomized controlled trial is the most convincing and reliable method for minimizing bias and demonstrating effectiveness of treatment. Control groups for randomized trials may be patients randomized to no treatment, a placebo, or standard active therapy with or without a placebo. Due to practical and ethical constraints not all clinical questions can be addressed with a well‐controlled randomized trial. Non‐randomized trials can have specific comparator groups or be controlled in the sense that carefully chosen historical information from the literature is used for reference.
Hypothesis
Authors: Rosenkranz Gerd
Published: September 2014

In the following article, the authors briefly review the role of hypotheses in science before statistical hypotheses and their relevance for clinical studies are investigated. The authors explain what statistical hypotheses have in common with scientific hypotheses and how they differ. In particular, testing statistical hypotheses is somewhat different from testing scientific hypotheses, although some general ideas apply to both. Eventually, the importance of formulating testable hypotheses for clinical studies are explored and some specific topics like superiority, equivalence, non‐inferiority, and multiple hypotheses are discussed.
Group‐Randomized Trials: Overview
Authors: Murray David M.
Published: September 2014

The purpose of this article is to put group‐randomized trials (GRTs) in context in terms of other kinds of designs and in terms of the terminology used in other fields, to summarize their development in public health, to characterize the range of public health research areas that now employ GRTs, to characterize the state of practice with regard to the design and analysis of GRTs, to consider their future in public health research, and to review the steps required to plan a new GRT.
Statins
Authors: F. Wilson Peter
Published: September 2014

The roles of statins to decrease blood cholesterol levels and to reduce risk for cardiovascular disease in the blood are described. Mechanism of action, safety, efficacy, drug interactions, and results from major randomized clinical trials are the major topics discussed. Recent data are summarized and provide an understanding of the clinical role of use of statins as agents for primary and secondary prevention of atherosclerotic cardiovascular disease. Recent comprehensive reviews have identified no relative risk differences for statins in the prevention of cardiovascular diseases for persons <65 years versus >65 years, men versus women, treated hypertension versus no hypertension, history of diabetes mellitus versus no diabetes mellitus, LDL‐C at baseline <135 mmol/L versus ≥135 mg/dl.
Diagnostic Studies
Authors: Schwenke Carsten
Published: September 2014

Diagnostic studies are performed to investigate the clinical usefulness of a new diagnostic procedure, for example, a new contrast agent for imaging or a new laboratory test to detect antibodies or antigens in blood. Sometimes, these studies are invented to substitute the standard procedure by an improved or less expensive procedure, which is not inferior in terms of efficacy and has some benefit compared with the standard procedure. Before starting a diagnostic study, first the unit for analysis should be defined in the terms “experimental unit” and “observational unit.”
Permutation Tests in Clinical Trials
Authors: Zucker David M.
Published: September 2014

This article describes the role of the permutation test as a mechanism for statistically testing treatment effects in a randomized trial without making statistical assumptions beyond those justified by the randomization.
Regression Models to Incorporate Patient Heterogeneity
Authors: O'Quigley John
Published: September 2014

Although we anticipate heterogeneity among patients included in a dose finding study, given the typically small number of patients available, it is common to group all the patients under a single heading. However, careful modeling can still enable us to make gains when dealing with clearly defined subgroups. Rather than carry out separate independent studies, it is possible via the use of simple regression models to include all patients under one heading while still allowing for the possibility that the groups behave differently. Different schedules of treatment may also lead to different behaviors and, under a working assumption that the groups behave similarly, it is important to have a mechanism that will indicate to us that the accumulating evidence suggests the groups are behaving differently. Regression models can enable us to do this. Certain studies involve more than a single grouping covariable, an example is the two prognostic groups based on heavily and lightly pretreated patients, together with a variable that labels the scheduling. For three different schedules and two prognostic groups, we have a total of six subgroups. Separate analyses are not feasible, and a regression model can be of great use in such situations.
Vaccine
Authors: F. Chan Ivan S.
Published: September 2014

Vaccines are primarily developed to prevent infectious diseases. They have been given to millions of people worldwide and contributed significantly to the improvement in public health. This article provides an overview of the clinical development of vaccines in terms of efficacy, immunogenicity, and safety assessment. The high expectation for the benefit‐to‐risk ratio required for developing preventive vaccines generates some unique challenges in the design and analysis of vaccine clinical trials. We will briefly discuss these challenges and illustrate with real examples.
Clinical Trials to Support Prescription to Over‐the‐Counter Switches
Authors: Brass Eric P.
Published: September 2014

In contrast to prescription drugs, over‐the‐counter (OTC) drugs require the consumer to make decisions about the use of the drug without the aid of a health‐care professional. Clinical research studies designed to assess the ability of consumers to use the OTC drug label to guide their treatment with the drug are an essential element of OTC regulatory decision making. Label comprehension studies test the label's communication of key messages to consumers. Actual use studies elucidate consumer behaviors when using the proposed OTC drug in a simulated OTC environment. Examples of how these studies can be used to inform program development and decision making are illustrated by case studies.
Disease Trials on Pediatric Patients
Authors: Bauchner Howard
Published: September 2014

Randomized clinical trials are the cornerstone of the Cochrane Collaboration and recommendations from the USPSTF. Data suggest that there are less RCTs involving children than adults. Unique obstracts that researchers face when conducting RCTs which include children as subjects include: limited number of children with major medical diseases; issues related to ethics, and measurable objective health outcomes.
Ethical Issues in International Research
Authors: Lie Reidar K.
Published: September 2014

This article discusses four recent controversies in international collaborative research: the standard of care controversy, the obligation to provide ancillary care to clinical trial participants, the obligation to continue to provide trial interventions after completion of the research, and the requirement to be responsive to the health care needs of the host country. In each case the central positions and arguments in the debate are presented.
The Belmont Report
Authors: Zucker Deborah
Published: September 2014

The Belmont Report summarizes the basic ethical principles and guidelines identified by the commission to assist in the protection of human subjects in research. The report focuses on three main areas, (
1
) the boundaries between research and practice, (
2
) the basic ethical principles that should underlie the conduct of research (autonomy, beneficence, justice) and the protection of human subjects, and (
3
) the application of these principles into practice. The report outlines general recommendations regarding obtaining informed consent, the assessment of risk and benefit, and the recruitment of participants. The Belmont Report lays out many of the ethical considerations that, to this day, are used in formulating regulations and in ensuring the protection of human study volunteers.
Gene Therapy
Authors: Ginn Samantha L , Alexander Ian E
Published: September 2014

Conducting a clinical trial poses a unique set of challenges that must be addressed to ensure the safety of human subjects. In this article, we discuss issues that relate in particular to gene therapy clinical trials. These issues include the choice of gene delivery system, the need for extensive preclinical testing to ensure the feasibility and safety of the approach, and careful monitoring of subjects for short‐ and long‐term toxicity associated with the gene transfer protocol.
Cell Line
Authors: Sivakumaran Theru A. , Iyengar Sudha K.
Published: September 2014

A cell line is obtained after the first subculture of primary cell culture that is established from freshly isolated tissues. Until the establishment of a primary culture, cells are usually heterogeneous and still closely represent the parent cell types and express tissue‐specific properties. After several passages, the cell line will die out because of senescence or escape senescence to become a continuous cell line, then to become a valuable resource, particularly if it is preserved after characterization and validation. Cell lines are used to study the normal physiology or biochemistry of the cells in disease and health, in drug screening as well as in studies of cell toxicity mechanisms. Today, cell lines may also assist stem‐cell therapy.
DNA
Bank
Authors: Sivakumaran Theru A , Iyengar Sudha K
Published: September 2014

Most diseases are multifactorial and are caused by a combination of genetic and environmental factors. Similarly, the way a person responds to a drug is also a complex trait that is influenced by many different genes. Paradigms to identify causative genetic variants responsible for disease or response to drugs are well described in the literature, and the following essential elements are required for successful completion of such studies: (
1
) rapid detection of genetic variation; (
2
) large number of samples of high quality with well‐characterized clinical data from participants, which includes longitudinal follow‐up for effect of treatment; (
3
) advanced informatics for storage and retrieval of samples and data; and (
4
) establishment of correlation between clinical and genetic data through modern statistical techniques. Although the basic conventions for sample collection in gene mapping were already established, it was only after completion of Human Genome Project and the initiation of the International HapMap Project that the need for large databases of well‐phenotyped study participants collected under unified protocols with DNA available was expressed. These endeavors resulted in the establishment of national and international DNA banks aimed initially at mapping specific disease entities, but later encompassing more global undertakings. Establishment of these DNA biobanks has not been without ethical, legal, and personal controversies, although their value to the biomedical research community is indisputable. The overall outcome of these initiatives is that the promise of personalized medicine is nearby, particularly for identification of the genetic variations responsible for drug response. We anticipate that clinical trials could perform genetic prescreening to exclude people who are unresponsive to treatment, which reduces side effects and increases the effectiveness of the drug for particular subgroups. This model is already being implemented for the P450 class of drugs, with the hope of more to come.
Flexible Designs
Authors: Posch Martin , Bauer Peter , Brannath Werner
Published: September 2014

Flexible designs allow for mid‐trial design adaptations in an adaptive interim analysis based on all information collected while controlling the type I error rate. Examples of such adaptations are, for example, sample size reassessment, addition or dropping of interim analysis, or dropping of treatment arms. The crucial feature of the adaptive procedure is that the adaptation rules need not be specified in advance.
Imputation
Authors: Cook Samantha R. , Rubin Donald B.
Published: September 2014

Missing data are a common problem with datasets in clinical trials. Imputation, or filling in the missing values, is an intuitive and flexible way to handle incomplete data. Here the authors present several imputation strategies and their theoretical background, as well as some current examples and advice on computation. Multiple imputation is a statistically valid strategy for handling missing data.
Regression to the Mean in Clinical Trials
Authors: Davis Clarence E.
Published: September 2014

When a person is selected for having an extreme measurement (e.g., high blood pressure), a second measure of the variable will, on average, be closer to the mean of the population distribution. This function is called regression to the mean. It is used as a justification for a randomized control group in clinical trials. It may also affect recruitment in clinical trials in which potential participants are selected because they hold an extreme value on some variable.
Multiple Comparisons ‐ Recent Advances
Authors: Proschan Michael A.
Published: September 2014

Because clinical trials are very expensive, investigators often want to make several comparisons in the same trial. But, performing several statistical tests at significance level α will increase the probability of at least one false positive declaration. The situation is analogous to throwing many darts at a target; even a completely unskilled dart thrower will eventually hit the bull's eye. Many multiple comparison adjustment methods control the probability of at least one false positive by “raising the bar” for each separate comparison. However, raising the bar increases the chance of missing a true effect and increases the sample size needed to maintain the same power. The tension between controlling the chance of at least one false positive and missing a true effect makes multiplicity adjustment a subject of great controversy in clinical trials. Although clinical trialists have differing opinions about how much adjustment is needed, certain settings exist most would agree that adjustment is needed and others in which most would agree that it is not. This article attempts to shed light on situations in which multiplicity adjustments are needed and the most common methods of adjustment in different clinical trial scenarios, which include multiple arms, multiple endpoints, subgroup analysis, and interim monitoring.
Lan‐DeMetsAlpha‐Spending Function
Authors: DeMets David L. , Gordon Lan K. K.
Published: September 2014

Randomized clinical trials are typically monitored periodically for early evidence of convincing benefit or of potential harm for both scientific and ethical reasons by a data monitoring committee. After increments in the number of patients randomized or in the number of observed events, interim statistical analyses may be conducted to evaluate emerging results. This process may be referred to as a group sequential design. However, repeated analysis of accumulating data with conventional statistical testing can lead to substantially increased Type I or false positive error rates. The alpha spending function provides a flexible approach to control the Type I error while allowing the data monitoring committee to review the data as needed without having to specifya prioriwhen and how many such interim analyses will be conducted. Other earlier group sequential methods had these somewhat restrictive requirements. This alpha spending function approach can be applied to several spending functions that approximate some of these earlier methods.
Protocol Violators
Authors: McMahon Alex D.
Published: September 2014

This article reviews the distinctions between protocol deviations and protocol deviators. A seriousness scale is introduced to assess the importance different types of deviations to the protocols.
Imaging Science in Medicine
Authors: Hendee William R.
Published: September 2014

The use of imaging technologies has expanded tremendously in medicine over the past two decades. This expansion reflects the evolution of new imaging tools that reveal human anatomy and physiology in ever‐increasing detail, as well as the development of digital data acquisition and processing techniques that extract information from human tissues in ever‐expanding degrees of subtlety. A further factor that is stimulating growth in imaging is the expansion of knowledge about biological systems at the cellular, molecular, and genetic levels. This factor is transforming medical imaging from a “technology‐push” discipline into one where fundamental biological questions, as well as important clinical issues, are the force driving development of new imaging technologies. This transformation demands enhanced knowledge about the interface between biological properties of tissues and the characteristics of imaging systems. Exploration of the existing state of knowledge about the interface, and what is needed for the immediate future, is the subject of this article.
Eligibility and Exclusion Criteria
Authors: Foulkes Mary A.
Published: September 2014

Design characteristics of a clinical trial, such as eligibility and exclusion criteria, are broadly influential in the conduct, interpretation, and impact of trials. The research objectives, the intended inference or generalization of results, the balance of factors affecting the trial recruitment and conduct, all impact the determination of eligibility and exclusion criteria. Clinical contraindications, regulatory and ethical considerations also relate to this determination.
Data Monitoring Committees including Operational Issues
Authors: Ellenberg Susan
Published: September 2014

Data Monitoring Committees, also known as Data and Safety Monitoring Boards, are often established as a component of clinical trial oversight to review interim data of the ongoing trial so that safety concerns or problematic operational issues can be identified rapidly and corrective measures can be undertaken. Such committees may be independent of the trial sponsor and investigator to ensure recommendations are as objective as possible and unaffected by conflicts of interest. Federal funding and regulatory agencies have issued policy documents concerning the establishment and operation of Data Monitoring Committees, but substantial variation exists in committee processes.
Preference Trials
Authors: Campbell Marion K. , Torgerson David J.
Published: September 2014

Although the randomized controlled trial is widely accepted as the design of choice for evaluating health‐care interventions, it requires that all participants are prepared to accept allocation to any of the interventions under study. When patients have a strong preference to receive one of the study interventions, this can lead to difficulties with compliance and potentially to biased assessment of outcomes. The patient preference trial has been proposed as a potential solution, with patients with strong preferences being allocated to their treatment of choice and those with no strong preference being randomized in the standard manner. All patients are followed up and outcomes assessed. We discuss the effects of patient preference and review the advantages and disadvantages of the patient preference design.
Optimal Biological Dose for Molecularly‐Targeted Therapies
Authors: Ahn Chul , Kang Seung‐Ho , Xie Yang
Published: September 2014

Phase I cancer clinical trials are small dose‐finding studies designed to rapidly identify the optimal dose of a new anticancer agent. The optimal dose has been defined as the maximum tolerated dose (MTD) for conventional cytotoxic anticancer drugs, and this dose is typically recommended as the phase II dose. In the past decade, a new approach for cancer treatment has emerged. This new approach, now widely referred to as “molecularly targeted therapy” is designed a priori, based on the knowledge of various physiological molecules that have been obtained by the development of molecular biology. Most molecularly targeted agents slow or stop the growth of tumors. Such agents are, in general, less toxic than conventional cytotoxic agents; as a result, the maximum therapeutic effect may occur at doses well below the MTD. With the emergence of a growing number of molecularly targeted agents, approaches to early drug development are changing. We present dose‐finding designs for phase I clinical trials of molecularly targeted drugs. There is an increasing need to develop designs to find the optimal biological dose for molecularly targeted agents, and the utility of designs needs to be prospectively evaluated in future clinical dose‐finding trials for a variety of molecularly targeted agents.
Optimizing Schedule of Administration in PhaseIClinical Trials
Authors: Braun Thomas M. , Thall Peter F.
Published: September 2014

Historically, phase I clinical trials of new agents have been designed to find a safe dose from a small set of possible doses. Typically, toxicity is defined based on one or possibly two cycles of chemotherapy so that it can be scored relatively quickly after the start of therapy. The study treats cohorts of patients according to an outcome‐adaptive statistical design or algorithm for choosing successive doses, and the resulting data are used to determine an acceptable or maximum tolerable dose (MTD). However, in most clinical settings, physicians administer multiple courses of an agent to a patient and monitor long‐term toxicity that may be related to the agent's cumulative effects. Furthermore, some patients may receive one or more modifications or delays of their planned treatment course due to low‐grade (non–dose limiting) toxicity from previous administrations. To address these situations, we have proposed a new paradigm for phase I clinical trials that determines a maximum tolerable schedule (MTS) of administration, rather than a conventional MTD (Braun, Yuan, and Thall, 14). The design is based on the time to toxicity of each patient rather than a binary indicator. The model and method account for the patient's entire sequence of administrations, with the overall hazard of toxicity modeled as the sum of a sequence of dose‐dependent hazards, each associated with one administration. As patients are enrolled, data monitoring and decision making are done continuously throughout the trial using an outcome‐adaptive Bayesian design that optimizes the schedule in terms of the overall risk of toxicity. We illustrate this new methodology with a clinical trial in allogeneic bone marrow transplantation.
Population Pharmacokinetic and Pharmacodynamic Methods
Authors: Bulitta Jürgen B , G Holford Nicholas H
Published: September 2014

Pharmacokinetic and pharmacodynamic models are widely used to provide the quantitative basis for understanding the clinical pharmacology of pharmaceuticals. These models are used to describe both the average behavior in the population and differences between individuals. Pharmacokinetic models describe the time course of absorption, distribution, and elimination of a drug and any metabolites. Pharmacodynamic models describe the relationship between drug concentration and biological response. The pharmacokinetic‐pharmacodynamic model often has to account for a delay between the time course of concentrations and the time course of pharmacodynamic effect. Statistical models can describe the predictable and the unpredictable variability between and within subject variability in response. Predictable variability is most commonly understood for drug elimination processes and typically uses subject factors such as weight, renal function, and other medications to account for between subject differences. Unpredictable variability is usually larger than predictable variability. A wide variety of software has been developed for implementing these pharmacological and statistical models; of these, NONMEM (GloboMax LLC, Hanover, MD) is the most widely used. Newer algorithms are actively being developed. Model building and parameter estimation are often computationally intensive. Population models are used by drug developers to create a rationale framework for development, by regulatory health authorities to evaluate clinical trials and enhance product labels and by clinical researchers to understand human biology and disease progression.
Robust Two‐Stage Model‐Guided Designs for Phase
I
Clinical Studies
Authors: Potter Douglas M.
Published: September 2014

The standard rule‐based cohorts‐of‐three phase I design, originally proposed for dose‐finding studies of chemotherapeutic agents in cancer patients, has well‐known limitations; these include treating a large fraction of the patients accrued at low, presumably ineffective doses. Model‐guided designs, such as the continual reassessment method, attempt to address some of these limitations. Such schemes can work well if the initially specified range of doses to be investigated brackets the true maximum tolerated dose (MTD); however, if this condition is not met, and additional doses must be added as the study progresses, these schemes can find an MTD in the absence of the observation of any dose‐limiting toxicity (DLT). In first‐in‐human studies, the latter situation is a particular concern. Robust two‐stage model‐guided designs use a rule‐based first stage that ends upon observation of the first DLT, and a second stage that uses a flexible model that is initialized with the initial DLT data. These designs have the robustness of rule‐based designs and the advantages of the model‐guided designs. Three such designs are reviewed and compared.
Maximum Tolerable Dose (
MTD
)
Authors: Chevret Sylvie
Published: September 2014

The “Maximum Tolerable Dose” (MTD), also known as the “Maximum Tolerated Dose” or the “Maximally Tolerated Dose”, is defined as the dose that produces an “acceptable” level of toxicity or that, if exceeded, would put animals or patients at “unacceptable” risk for toxicity. Besides determining animal toxicology, establishing the MTD is the main objective of Phase I clinical trials, mostly in cancer and HIV treatment in which relatively high doses of drugs are usually chosen to achieve the greatest possible beneficial antitumor effect. Definition of the MTD usually relies on the sample, as MTD is defined as the dose level at which more than two patients over six experienced dose‐limiting toxicity (DLT). More recently, the MTD has been defined as the dose that produces a certain frequency of DLT within the treated patient population. In this framework, the MTD is estimated from the data using Bayes or maximum likelihood inference. In all these designs, the MTD is established for one initial administration or treatment course of a cytotoxic experimental agent, ignoring efficacy. To address these issues, the maximum tolerated schedule and the most successful dose have been proposed to be used rather than a conventional MTD. Finally, the concept of MTD that uses toxicity as a surrogate endpoint for efficacy in cytotoxic Phase I trials has been also controversial. Interests in alternatives to MTD have gained recently when dealing with new cytostatic agents that may produce relatively minimal organ toxicity, compared with standard cytotoxics. New optimal doses should be defined in the near future.
Stability Study Designs
Authors: Lin Tsae‐Yun Daphne , Chen Chi Wan
Published: September 2014

A good stability study design is the key to a successful stability program. In this article, stability designs including full, bracketing, and matrixing designs will be exemplified. A full design involves complete testing of all factor combinations at all time points, which can be costly. The pharmaceutical industry would like to apply bracketing or matrixing design so that not every factor combination will be tested at every time point. The statistical aspects of these stability designs will be discussed in relation to the recent Internation Conference on Harmonization (ICH) guidelines. Several commonly used criteria for design comparison will be presented. The statistical and regulatory considerations on the selection of stability design will also be discussed. The stability study should be well designed so the shelf life of the drug product can be estimated with the high degree of accuracy and precision.
Sample Size Calculation for Comparing Proportions
Authors: Wang Hansheng , Chow Shein‐Chung
Published: September 2014

In practice, the objective of sample size calculation is to select the minimum sample size in such a way that the desired power can be obtained for detection of a clinically meaningful difference at the prespecified significance level. Therefore, the selection of the sample size depends on the magnitude of the clinically meaningful difference, the desired power, the prespecified significance level, and the hypotheses of interest under the study of choice (e.g., a parallel design or a crossover design).
Response Adaptive Randomization
Authors: Coad D.Stephen
Published: September 2014

When two or more treatments are being compared in a clinical trial, information accrues on the treatments as the trial proceeds. However, there may be evidence suggesting that some treatments are more promising than others, and consequently, there may be interest in applying response adaptive randomization, which uses the current data to assign more patients to the treatments that are performing better thus far in the trial. In this way, patients have a higher probability of being assigned to these treatments. Examples of this type of randomization are given, such as urn models and sequential maximum likelihood estimation rules, and some of their properties are described. The issue of inference after trials that use response adaptive randomization is then discussed. For example, this approach can lead to a loss in power when using standard tests and the problem of estimation can be considerably more complicated than in a trial that uses complete randomization. The use of stopping rules is also addressed, since, in practice, there may be interest in stopping a trial early if there is convincing evidence of a treatment difference or less promising treatments may be dropped from additional consideration. An indication is given of possible future work.
Dose Escalation and Up‐and‐Down Designs
Authors: Ivanova Anastasia
Published: September 2014

Up‐and‐down designs are dose‐finding designs that are easy to use and understand. This article describes four main classes of up‐and‐down designs: biased coin, group, escalation, and A + B designs. The attention is focused on statistical properties of these designs.
Minimum Effective Dose (MinED)
Authors: Thomas Neal , Ting Naitee
Published: September 2014

The minimum effective dose (MinED or MED) defines a lower bound for therapeutically useful doses. The MinED is important because most drugs have increasing potential for safety problems as dose is increased. The MinED is typically studied during phase II of drug development. A concise definition of the MinED from the International Conference on Harmonization (ICH) guidelines is “the smallest dose with a discernible useful effect.” We discuss methods to give operational meaning to the descriptors “useful” and “discernible.”
Dose Ranging Crossover Designs
Authors: Patterson Scott D. , Jones Byron , Zariffa Névine
Published: September 2014

Dose ranging trials are used frequently in drug development to learn about dose response for both desirable and undesirable effects and to provide sponsors with confidence that investment in confirmatory trials for market access is likely to be successful. The design of crossover studies and analysis of data that develops from such crossover trials are discussed.
Allocation Concealment
Authors: Schulz Kenneth F.
Published: September 2014

Allocation concealment, as an integral part of randomization, implements an allocation sequence. Inadequate allocation concealment can lead to exaggerated estimates of treatment effect, but with scope for bias in both directions. Without adequate allocation concealment, even proper random assignment sequences can be subverted. Investigators must recognize the inquisitiveness and ingeniousness of human nature and “immunize” trials against selection and confounding biases with proper allocation concealment.
Assessment Bias in Clinical Trials
Authors: Gøtzsche Peter C.
Published: September 2014

Assessment bias in a clinical trial occurs if bias in the assessment of the outcome exists. It is also called ascertainment bias, diagnostic bias, or detection bias. A major cause of assessment bias is lack of blinding. Other problems relate to differential identification of harmless or false‐positive cases of disease, bias in assessment of disease‐specific mortality, the use of composite outcomes, competing risks, timing of the assessments, and bias in assessment of harms.
Non‐Inferiority Trial
Authors: James Hung H. M. , Wang Sue‐Jane , O'Neill Robert
Published: September 2014

Noninferiority trials employ a selected active‐control treatment, the effect of which has been established in historical placebo‐controlled trials. The active control serves as a comparator for evaluating the effect of an experimental treatment. A placebo arm may be either present in or absent from the noninferiority trial, depending on whether use of the placebo arm is feasible. In contrast with showing superiority, it is much more challenging to design a “show noninferiority” trial. The notion of noninferiority is extremely difficult to define, and a threshold termed “noninferiority margin” must be determined to define the noninferiority hypothesis for testing. The margin determination depends on many key factors: the objective of the noninferiority trial, critical assumptions pertaining to the ability of a trial to distinguish between an effective and an ineffective treatments, and the applicability of the historical estimate of the active control's effect to the noninferiority trial when the placebo cannot be used, and so forth. This article is intended to pinpoint the factors critically needing consideration for the viability of using a noninferiority trial to evaluate the effect of an experimental treatment.
Masking
Authors: Joseph Donahue Rafe Michael
Published: September 2014

Blinding, along with allocation concealment and randomization, is a primary procedure used to control biases in clinical trials. Blinding may refer to both (1) the procedures that are used to keep persons associated with the trial (participants, investigators, and such) ignorant of the treatments to which the individual participants have been assigned and (2) the results of such efforts. Different individuals associated with a clinical trial bring different potential biases to the trial if they are not blinded. Blinding and allocation concealment both help to reduce biases in clinical trials, but allocation concealment seems even more important than blinding. Blinding may take place at varying levels from no one blinded (“open‐label”), to single‐blind, to double‐ and triple‐blind. The International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use guidelines and the Consolidated Standards of Reporting Clinical Trials guidelines encourage the reporting of details of blinding procedures used in clinical trials and the results they attained, including information about who may have been unblinded during the trials and what biases that may have generated therefrom.
Multinational (Global) Trial
Authors: Snapinn Steven M.
Published: September 2014

Multinational clinical trials are trials conducted in more than one country using a common protocol. They have become increasingly common and have many advantages, such as access to a large patient population and the potential to obtain a broadly generalizable conclusion. However, these trials also have a number of practical difficulties, such as differences among the countries in language, import/export procedures, and regulatory and legal requirements. In addition, country‐by‐treatment interactions have frequently been found in these trials, leading to difficulties in interpreting the results. For example, the MERIT‐HF trial, which evaluated metoprolol in patients with heart failure, found an overall hazard ratio for total mortality of 0.66, but a hazard ratio of 1.05 in the United States. There is particular concern that differences among countries hinder the ability to evaluate the cost effectiveness of an experimental treatment.
Assay Sensitivity
Authors: Chuang‐Stein Christy
Published: September 2014

The assay sensitivity of a clinical trial is defined as the trial's ability to distinguish an effective treatment from a less effective or an ineffective one. It is an important consideration when designing, conducting, and analyzing a clinical trial. The lack of adequate assay sensitivity has different implications for superiority trials and noninferiority trials. This article discusses factors that could impact assay sensitivity. It also discusses the increasing use of a three‐arm design to help evaluate assay sensitivity of a trial.
Superiority Trial
Authors: Sackett David L.
Published: September 2014

When busy clinicians encounter a new treatment, they ask themselves two questions. First, is itbetterthan (“superior to”) what they are using now? Second, if is not superior, is itas good aswhat they are using now (“noninferior”) and preferable for some other reason (e.g., fewer side effects or more affordable)? In statistical terms, these clinical questions are “one‐sided.” However, traditional statistical concepts call for two‐sided tests of statistical significance and require rejection of the null hypothesis. A solution to both this incompatibility (between one‐sided clinical reasoning and two‐sided statistical testing) and confusion (about the clinical interpretation of statistically nonsignificant results) is now gaining widespread recognition and application. It is encompassed by the concepts of “superiority” and “noninferiority,” and these inextricable concepts are the subjects of this entry.
Primary Efficacy Endpoint
Authors: Follmann Dean A
Published: September 2014

At its core, a clinical trial is designed to test a specific primary hypothesis. The primary efficacy endpoint in a clinical trial is a clinical or laboratory outcome measured in an individual after randomization that allows one to test the primary hypothesis and provides the means of assessing whether a therapy is effective compared with its control. A primary endpoint can be measured once or repeatedly over time, or may comprise several kinds of individual measurements. Once a primary endpoint is selected, statistical methods of analysis to test the primary hypothesis can be determined and sample size calculations can be performed to ensure the trial is properly powered. Because of the key role of the primary endpoint in the design and analysis of a trial, it is critical that it be chosen carefully. A primary efficacy endpoint must be precisely specified in advance and should(1)address the primary objective,(2)be ascertainable in all patients,(3)be “fair” to each study arm,(4)have demonstrated or accepted relevance for the population and intervention(s) of the trial, and(5)be sensitive to meaningful changes in a patient's health.
Enrichment Design
Authors: Fedorov Valerii V. , Liu Tao
Published: September 2014

Enrichment designs usually consist of two or three stages, where the first stage serves as a screening process for selecting a certain subpopulation, and the succeeding stages serve to distinguish the treatment effect from the placebo effect, within the selected (enriched) subpopulation. The efficiency of its efficacy (response) detection comes at the expense of the ability to make inferences for the general population; that is, the results are valid only for the selected subpopulation. At the same time, if a valid partitioning of the population is proven, then it might be viewed as evidence of “latent” classifiers, which can be targeted for a search. Statistical analysis commonly makes explicit use of the outcomes from only the selected subpopulation (final stage). Some simple models can take advantage of the outcomes from all stages. Most published studies using these designs have been conducted in the field of clinical oncology, but enrichment designs have become increasingly popular in various therapeutic areas.
Historical Control: Overview
Authors: Thomas Neal
Published: September 2014

Historical control refers to the practice of using data from past studies and administrative databases to estimate potential response to placebo or standard‐of‐care treatment among patients in an ongoing study. The historical control information may come from patient‐specific data or from aggregate published estimates. Studies that project response to a control regimen based on historical data rather than concurrently randomized patients are most common in settings where there are ethical barriers to the use of either placebo or an older treatment widely believed to have little benefit. Historical controlled trials (HCTs) are subject to several potentially important sources of bias. HCTs are examples from a broader class of nonrandomized comparative designs called observational studies. Numerous methods have been developed to reduce bias in observational studies, and these methods are sometimes applicable in HCTs, for example, covariate adjustment and matching [Cochran and Rubin (1)], and propensity score methods [Rosenbaum and Rubin (2)]. Bayesian statistical methods can be used to create hybrid designs that use some historical control data and some concurrently collected randomized control data. Times series are another source of historical control information commonly used in economics and marketing as well as, occasionally, in clinical research.
Equivalence Trials and Equivalence Limits
Authors: James Hung H. M.
Published: September 2014

An equivalence trial employs a selected active control treatment as a comparator for evaluating the effect of a test treatment. The notion of equivalence is that the difference between the test treatment and the active control is clinically insignificant with respect to therapeutic benefits or an adverse effect at issue. The threshold of clinical insignificance or indifference must be defined and is often termed “equivalence margin” or “equivalence limit.” In clinical trials, the study question that is more often of primary interest is the noninferiority hypothesis that the test treatment is clinically acceptably inferior to the control. Determination of the noninferiority hypothesis with a specified margin often requires special considerations pertaining to other study objectives, such as showing that the test treatment retains a certain fraction of the control's effect. These special considerations in defining a noninferiority margin may not be applicable to defining the superiority margin in the equivalence hypothesis.
Confirmatory Trials
Authors: Joseph Donahue Rafe Michael
Published: September 2014

Confirmatory clinical trials are defined by the ICH as adequately controlled trials in which the hypotheses are stated in advance and evaluated. They can be used to demonstrate or confirm efficacy, establish a safety profile, investigate benefit/risk, or establish dose‐response. They always have (at least) one prespecified primary objective. Confirmatory trials are not exploratory trials.
Biased‐Coin Randomization
Authors: Smith Mike D.
Published: September 2014

The biased coin randomisation is introduced as a procedure to minimize study bias whilst maintaining treatment balance throughout the trial. The range of biased coin randomizations are described for 2 and more than 2 treatment groups, and also for equal and unequal randomization. Biased coin randomizations are described which use both fixed and adaptive levels of coin ‘bias’. The well‐known urn model is described as a special representation of the biased coin randomization, and the link is discussed. Finally, the use of the biased coin randomization procedure for balance within covariate strata, and the application to response adaptive randomizations are described.