bluuebunny/crossref_metadata_2025 huggingface dataset:
Linear Model: Overview
Authors: Burkholder Iris , Edler Lutz
Published: September 2014

Linear Models for simple and multiple regression, Analysis of Variance (ANOVA), and Analysis of Covariance (ANCOVA) have broad and often direct application in clinical trials. Various extensions and generalizations of the Linear Model have been developed more recently often motivated by medical research problems. Most prominent are the loglinear models for the evaluation of count data; the logistic regression model for the evaluation of the influence of clinical factors on a dichotomous outcome as, for example, cancer incidence; and, last but not least, the Cox regression model for the analysis of survival data.
Designs with Randomization Following Initial Study Treatment
Authors: Dunger‐Baldauf Cornelia
Published: September 2014

Clinical study designs with randomization after the initial study treatment have been used in diverse disease areas such as arrhythmia, Alzheimer's disease, schizophrenia, migraine, headache, cancer, acquired immunodeficiency syndrome, and irritable bowel syndrome. They are useful in situations where the placebo‐controlled parallel group trial would be difficult to perform, such as in the investigation of long‐term efficacy of a drug when long‐term placebo treatment is not feasible, to address specific scientific questions, or to evaluate complex treatment schemes. Clinical studies with randomization after the initial study treatment are conducted in at least two stages. In the first stage, patients might either commence with a single treatment in an open‐label fashion, or they might be randomized to several treatments, possibly including a control. A treatment‐free interval might follow. Depending on the scientific question addressed by the trial, entry in the second trial stage might depend on the patient's benefit from the initial treatment. Further randomized trial stages can be added in the same fashion. The application of the approach will be discussed in detail for randomized withdrawal designs, randomized delayed start designs, and randomized retreatment designs.
N
of 1 Randomized Trial
Authors: Avins Andrew L. , Neuhaus John
Published: September 2014

“N‐of‐1 clinical trials” are randomized, multiple‐period crossover trials carried out in individual patients. Most clinical trials are conducted for the purpose of producing information generalizable to a larger population, but N‐of‐1 trials are usually conducted to inform a single patient and a clinical‐care provider about the effects of a specific therapy for that patient. Generalizing beyond that patient is usually not the intent of an N‐of‐1 study, though multiple N‐of‐1 trials may be combined to produce results with greater external validity. Like all crossover studies, there is a limited range of conditions and treatments that are amenable to investigation with an N‐of‐1 design, and practical barriers often exist to wider implementation of these studies in the typical clinical setting. Nevertheless, N‐of‐1 trials provide a potential means for providing more evidence‐based individualized therapeutic decision‐making in clinical practice.
Interrater Reliability
Authors: Kraemer Helena Chmura
Published: September 2014

Inter‐rater reliability indicates how interchangeable are the ratings from multiple raters in assessing the results of a randomized clinical trial. Lack of reliability results in attenuation of power in statistical testing, attenuation of effect sizes, and consequently the need for much larger sample sizes to achieve credible conclusions.
Min Test
Authors: Laska Eugene M , Meisner Morris J
Published: September 2014

The Min test is a statistical tool used to test a null hypothesis such asHoi: νi≤ 0 for at least oneiversus the alternative H1: νi > 0 fori = 1, 2,…,K. This is sometimes called the sign‐testing problem. If every α‐level test of Hoi: νi≤ 0 individually rejects thei‐th null hypothesis at the α level, then the global null hypothesis is rejected according to the Min test. No adjustment for multiplicity is required. Such hypotheses arise in many clinical trials when it is desired to establish that a set of inequalities on the parameters simultaneously holds. Examples include testing whether an identified treatment is best on single or multiple endpoints; whether one treatment is equivalent to or noninferior to a control; and whether a dose of a combination is synergistic.
Cronbach's Alpha
Authors: Kraemer Helena Chmura
Published: September 2014

Cronbach's alpha is a measure of internal consistency among m items that are summed or averaged to generate a score characterizing a subject (1), sometimes mistakenly reported as a measure of reliability. Cronbach's alpha is reported as background to the selection of outcome measures in randomized clinical trials.
Absolute Risk Reduction
Authors: Newcombe Robert G.
Published: September 2014

This article considers confidence interval methods for differences between proportions. In line with well‐known corresponding results for the single proportion, the simplest Wald confidence intervals perform poorly. Several better methods are described, and illustrated using data from two small clinical trials. Selected methods are readily implemented using Excel resources freely downloadable from the author's website.
Drift (For Interim Analysis)
Authors: Reboussin David M.
Published: September 2014

Drift, in the context of the design and analysis of group sequential plans for clinical trials, refers to the mean of a Brownian motion process describing the accumulating evidence on the treatment difference. Although the implementation of group sequential testing does not require explicit reference to Brownian motion process, some understanding of it simplifies the discussion of certain aspects, especially sample size determination, estimation, and stochastic curtailment.
Clinical Significance
Authors: Kamath Celia C. , Sloan Jeffrey A. , Cappelleri Joseph C.
Published: September 2014

This article defines clinical significance, as opposed to statistical significance, as it relates to health‐related quality of life measures used in clinical trials. It provides a brief history of the concept and addresses the different perspectives from which it is perceived and interpreted. It describes the current methods used to assess clinical significance and provides several examples of the different ways of measuring, validating, and using clinical significance. Recent developments on the concept are discussed. Finally, directions for future research are highlighted.
Good Clinical Practice (
GCP
)
Authors: Barr David B.
Published: September 2014

Good Clinical Practices (GCPs) are international ethical and quality standards for the design, conduct, performance, monitoring, auditing, analysis, recording, reporting, and regulation of clinical investigations. Clinical investigations are experiments in which a medical product such as a drug is administered or dispensed to, or used involving, one or more human subjects. These standards provide assurance that the rights and confidentiality of the trial subjects are protected and that the data and reported results are credible and accurate.
Overview of Anti‐Infective Drug Development
Authors: Barrett John F.
Published: September 2014

The identification and clinical development of an antibiotic as a new chemical entity (NCE) can take up to a decade to complete. The goal of this effort is to identify agents that inhibit and/or kill pathogenic bacteria. Outlined in this unit is a series of key “strategic protocols” utilized for drug development in this area. Included are the identification of the target and the hit/lead, as well as lead optimization for identifying a clinical candidate. The clinical development of a new antimicrobial agent can take 5 to 7 years to complete, depending on the clinical plan. This unit is designed to provide an overview of key aspects of antibacterial drug development and provide an introduction for subsequent units in this chapter that provide more detailed protocols for identifying and testing antimicrobial drugs.
Overview of Safety Pharmacology
Authors: Porsolt Roger D.
Published: September 2014

Safety pharmacology assesses the potential risks of novel pharmaceuticals for human use. ICH S7A guidelines recommend a core battery of studies on three vital systems: central nervous system (CNS), cardiovascular (CV) and respiratory. Primary CNS studies look for effects on general behavior, locomotion, neuromuscular coordination, the convulsive threshold, and vigilance. The primary CV test battery evaluates proarrhythmic risk using in vitro approaches (hERG channel and Purkinje fiber assays) and in vivo measurements in conscious animals via telemetry. Comprehensive cardiac risk assessment also includes full hemodynamic evaluation, most suitably conducted in the anesthetized large animal. Basic respiratory function can be approached via whole‐body plethysmography in conscious animals where sensitivity to respiratory‐depressant effects can be enhanced by exposure to increased CO
2
. Other aspects of safety pharmacology studies discussed are the timing of studies, ethical and animal welfare issues, and statistical evaluation.
Biological Assay, Overview
Authors: Sen Pranab K.
Published: September 2014

Biological essay (or “bioassay”), in its classical form, is concerned with the measurement of the concentration of an active ingredient in a test preparation, by estimation of its potency relative to that of a standard preparation. Assays, usually by animal experimentation, form an essential tool of biological standardization. Standard designs and methods of analysis are described here, including stochastic approximation, radioimmunoassays, semiparametric and nonparametric methods, and bioequivalence studies. In a broader sense, Bioassay refers to experiments, with biological units, to detect possible dose–response (or toxicity‐adverse effect) relationships. Underlying toxicodynamics (and/or pharmacodynamics) and toxicokinetics (and/or pharmacokinetics), possibly in conjunction with toxicogenomics (and/or pharmacogenomics) may render such studies statistically quite different from conventional biometric studies. Some relevant approaches are considered here.
Benefit/Risk Assessment in Prevention Trials
Authors: Costantino Joseph P.
Published: September 2014

Benefit/risk assessment (B/RA) is a mathematical procedure to estimate the probability of detrimental outcomes, beneficial outcomes and the net‐effect anticipated from exposure to a given agent or procedure. B/RAs of health‐related outcomes are used for public health planning, decision‐making regarding health care financing, and therapeutic decision‐making in clinical practice. Information obtained from B/RAs based on findings from controlled clinical trials, particularly those with double‐masking of treatment, are most informative for health care planning and decision making because such information is less likely to be bias than is information obtained from observational studies. Thus, B/RAs in prevention trials are an excellent source of information to use as the basis for health care planning and decision‐making. Applications of R/BA in prevention trials are performed as supplements for planning, monitoring and analyzing the trial. These applications can be categorized into three types of assessments. This article provides a description of the three types of assessment performed in prevention trials, defines the statistical algorithms used for each type of assessment and discusses several key methodological and practical issues involve with the application of B/RA to health‐related outcomes.
Prevention Trials
Authors: Prentice Ross L.
Published: September 2014

Randomized controlled trials provide the gold standard for assessing the health benefits and risks of preventive treatments or interventions. Such trials typically need to be very large‐scale and long‐term. Hence, cost and logistic considerations imply that few such trials can be conducted, and there is a need for careful hypothesis development and preliminary testing, prior to the initiation of a full‐scale disease prevention trial. Aspects of the design, conduct monitoring and analysis of prevention trials are reviewed, along with a discussion of differences from therapeutic trials, and of the interplay with observational studies. The National Institutes of Health‐sponsored Women's Health Initiative, which includes both a multi‐faceted prevention trial, and a large cohort study, among postmenopausal women, provides an illustration of prevention trial considerations and trade‐offs.
Screening, Models of
Authors: Stevenson Chris
Published: September 2014

This article describes models for disease screening and how they have developed in recent years. The discussion focuses on screening for cancer, because most of the methodological advances in screening design and evaluation have concerned cancer screening. The first part of the article describes the characteristics of these models and illustrates them with a discussion of a simple screening model. The second part describes the development, strengths, and weaknesses of the two main types of screening model—analytic and simulation models—with a particular focus on microsimulation models. The third part discusses model fitting and validation, and the final part briefly describes models for diseases other than cancer—in particular, models for screening for infectious diseases—and discusses the current state and possible future directions for models of disease screening.
Pharmacoepidemiology, Overview
Authors: Suissa S.
Published: September 2014

Pharmacoepidemiology is the study of patterns of medication use in the populations and their effects on diseases. Particular interest is focused on unexpected risks, especially events such as rare side effects. These can typically not be adequately assessed in the context of randomized clinical trials. This entry describes the use of Pharmacoepidemiology through the case‐crossover design, dealing with confounding in observational studies, the specification of appropriate risk functions over time, the probabilistic assessment of causality, and study methods based on the use of prescription data.
Regulatory Definitions
Authors: Traverso Kelly L.
Published: September 2014

Since President Roosevelt signed the Federal Food, Drug and Cosmetic law in 1938, drugs in clinical development must be tested for safety before they can be marketed. As a result, the role of safety in premarketing development is to determine that the product benefit exceeds the risks, which contributes in the efficacy of clinical drug development. This assessment is done throughout the phases of clinical development (See “Study Design: Phases I–V”). For the product benefit to be evaluated, safety information needs to be collected on the patients that are involved in the clinical studies. One of the markers that is evaluated is whether the patient had an adverse event (often referred as a side effect) while enrolled in the study. Sponsors of clinical studies are mandated by the Food and Drug Administration (FDA) and other global health authorities to report these adverse events (see also “Regulatory Issues”). These events are reported in timeframes outlined in the regulations and differ depending on the type of adverse event. This section will define what an adverse event is, the timeframe that sponsors have to report the event, and how the sponsor reports the information collected to the regulatory agencies.
Adverse Event Evaluation
Authors: A. McNair Lindsay
Published: September 2014

Although much of the focus in clinical trials is on the evaluation of the efficacy of an intervention or pharmaceutical product, the appropriate collection and assessment of safety data is a critical part of any clinical study. In early stage trials, the primary objective of the study may be the assessment of safety (or the identification of a maximum tolerated dose). For the assessment of safety data, several key factors must be collected for any adverse event. It is important to ensure that every adverse event—regardless of apparent association with the study or the intervention–is recorded. For each event, it is then important to collect the timing of the event, the relationship to the intervention, the severity, the expectedness, the intervention required, and the outcome. All of this information will initially be used to determine whether the event requires expedited reporting to regulatory agencies or safety monitoring committees, and later, it will be used to understand fully the safety profile of the intervention or investigational product.
Publication Bias
Authors: Rothstein Hannah R.
Published: September 2014

Publication bias is defined as the problem that results from systematic differences between the results of all the completed studies on a topic and the results of the subset of those studies that are published. The consequences of this problem include the danger that readers and reviewers will reach the wrong conclusion about what the evidence shows, leading at times to the use of unsafe or ineffective treatments. The role of meta‐analysis in highlighting the existence of publication bias is explained, after which a brief summary of the evidence that publication bias exists in the biomedical trials literature is provided. In addition to “classic” publication bias (bias due to selection on the basis of statistical significance), other potential information suppression mechanisms that represent a threat to the validity of research reviews are explained, including language, citation, availability, cost, outcome, and duplication biases. After this, ways of preventing publication bias are introduced, including universal prospective registration of trials, and design of prospective meta‐analyses. The role of thorough information search in reducing the impact of publication bias on meta‐analytic results is explained, and suggestions of what to search are made. The major procedures for assessing publication bias are summarized, including file drawer (failsafeN) analysis, the funnel plot, the Begg and Mazumdar test, the Egger test, and the trim and fill method. Differences in the various procedures' specific hypotheses about the distribution of study results as well as their limitations are noted. Finally, results of surveys of the impact of publication bias on health‐care meta‐analysis are described and their implications discussed.
Open‐Labeled Trial
Authors: Day Simon
Published: September 2014

This article discusses open‐labeled studies; that is, studies that are not blinded. It is usually assumed that lack of blinding is “total,” not just single‐blinded (either patient‐ or investigator‐blinded). Different levels and aspects of blinding can often be achieved so that trials may be only partially open label. In some cases, open‐label trials might be practically more acceptable, and in some studies, they may even be preferred to blinded studies.
PhaseITrials in Oncology
Authors: Ivanova Anastasia
Published: September 2014

This article outlines the goals and study design issues in phase I trials in oncology, with an emphasis on statistical design considerations. Traditional or 3 + 3 design is described. Statistical methods for phase I trials with long follow‐up periods, multiple agents, and ordinary toxicity outcome are reviewed.
Coherence in Phase
I
Clinical Trials
Authors: (Ken) Cheung Ying Kuen
Published: September 2014

This article reviews the coherence principles in phase I dose‐finding studies, in which several doses of a new treatment are tested for safety and the maximum dose exhibiting acceptable toxicity level is identified. Because most phase I designs are outcome‐adaptive, dose assignments for future patients depend on the previous observations in the trial. The coherence principles are ethical stipulations to ensure that dose escalation for a new patient will occur only when the most recent patient does not show sign of toxicity, and de‐escalation will occur only when a toxic outcome has just been reported. In this article, we give examples of coherent designs in the literature, and we discuss related concepts such as group coherence and real‐time coherence in more general clinical situations. We also review the application of the coherence concepts to the calibration of a two‐stage design.
PhaseI/IIClinical Trials
Authors: Zohar Sarah
Published: September 2014

The objective of dose‐finding phase I/II studies is to find an optimal dose level that corresponds to some given acceptable toxicity rate combined with a minimal response rate of a new drug or procedure. Most published phase I/II trials have been designed to determine the maximum tolerated dose during a phase I trial and subsequently to determine the response rate during a phase II trial. Recently, there has been increasing research in the development of dose‐finding methodologies, based on joint modeling of toxicity and efficacy outcomes in the same protocol that aim at minimizing the sample size in case of unacceptable high toxicity or unacceptable low efficacy. The available dose‐finding designs for phase I/II trials are presented with specific regard for new designs that jointly model toxicity and efficacy outcome.
Dose Escalation Guided by Graded Toxicities
Authors: O'Quigley John , Wages Nolan
Published: September 2014

In most cancer dose‐finding studies, the toxicities are graded typically from 0 (the absence of any side effects) to 4 based on the presence of serious and potentially life‐threatening toxicity. Usually, these grades are subsequently recoded whereby all toxicity grades between 0 and 3 are given the value 0 and are considered to be non‐dose‐limiting toxicities (DLT), and grade 4 (a dose‐limiting toxicity) is coded as 1. We describe here how greater use can be made of this information, which potentially leads to more efficient and effective dose‐finding studies. Our focus is on two‐stage continual reassessment method (CRM) designs. During this design, an initial escalation stage requires no use of a model until the first toxicity is encountered, at which time the second stage, based on a model, kicks in. In this context, two possible ways can be used to make use of information on toxicity grades. We can make use of this information in either one of these two stages in different ways. In the first stage, the ideas are usually straightforward that appeal mostly to common‐sense arguments. Nonetheless, it can be observed that use of graded toxicity information in the first stage alone can make an important contribution to increased efficiency. Use of graded toxicity information in the second stage requires an additional model to demonstrate the rate of toxicities. This method is described as well as the potential improvements in performance, which can be achieved by the use of such models.
Phase I Trials including Phase I Cancer Trials
Authors: Chevret Sylvie
Published: September 2014

Phase I trials, which are also called first‐into‐human studies, try to determine dosing, to document how a drug is metabolized and excreted, and to identify acute side effects. Usually, a small number of healthy volunteers (between 20 and 40) is used. They evaluate how well a new drug is tolerated, with the principal observations of interest being the pharmacokinetic and pharmacodynamic endpoints. Designs are often fixed, in which several dose levels and a placebo are randomly given, possibly in different periods, although some ascension is often conducted over the periods. At the other end of the severity spectrum, oncology Phase I trials are conducted in patients to determine the highest dosage that can be given safely, which is usually called the maximum tolerated dose (MTD). The designs are sequential and adaptive in some sense, because each patient is administered a dose level on the light of previous doses, and outcomes are observed in the trial. Two main adaptive designs are used, with dose allocation either algorithm‐ or model‐guided. They differ in terms of underlying interpretation of the MTD. The aim of this article is to summarize the most usual Phase I designs and analyses conducted in healthy volunteers and in oncology patients, separately. Differences between these approaches are stressed out, with some practical recommendations.
Therapeutic Dose Range
Authors: Sylvie Chevret
Published: September 2014

After preclinical assessments, the therapeutic dose range emphasizes the importance of the margin of safety that exists between the dose required for the desired effects of the drug and the dose that produces unwanted—potentially dangerous—side effects in determining the usefulness of a drug. The narrower the margin, the more likely it is that the drug will produce unwanted effects. This therapeutic index relies of the assessment of dose–response relationship. Traditional drug development consists of a sequence of independent trials organized in different phases. However, when establishing dose–response relationships and potential therapeutic dose range, over‐reliance on premarketing clinical trials has been pointed out. Assessment of dose–response relationships should occupy a prominent role in drug development with clinical studies designed to assess dose response as an inherent part of establishing the safety and effectiveness of the drug. This research relies on phase I studies in healthy volunteers with collected data on safety, tolerability, and effectiveness, as well as pharmacokinetics and pharmacodynamics. By contrast, drug development in life‐threatening diseases specifically uses the maximum tolerated dose as the only dose of interest. In all cases, note that the therapeutic dose range is based on small sample studies; thus, it makes it difficult or even impossible to individualize treatment based on patients' specific characteristics such as age, size, state of health, or use of concomitant medications. This discrepancy deserves additional attention.
Randomization Schedule
Authors: Kuznetsova Olga M.
Published: September 2014

The randomization schedule is used in a clinical trial to assign subjects randomly to one of the treatment regimens studied in the trial. Randomization helps to make the treatment groups comparable in baseline characteristics (both measured and not measured) and thus lessen the potential for bias in the evaluation of the treatments. It also allows causal inference with respect to intervention effects. The schedule is generated prior to the trial start and provides the list of treatment assignments by subject number (allocation number). In a stratified trial, a range of allocation numbers is designated for each stratum, and a separate randomization schedule is provided for each stratum. Subjects randomized into the trial are assigned allocation numbers in order of entry in their respective randomization stratum and receive the corresponding treatment.
Simple Randomization
Authors: Perevozskaya Inna T.
Published: September 2014

An overview of general concept of randomization is given, and its importance as a crucial part of the study design to avoid selection bias is stressed. Simple randomization is the first and most intuitive of the randomization methods. Other, more sophisticated, methods of randomization are highlighted. We discuss properties of the simple randomization and its relationship to other randomization methods. Concept and properties of stratified randomization are discussed as well.
Phase 2/3 Trials
Authors: Liu Qing
Published: September 2014

Seamless phase 2/3 combination designs (or phase 2/3 designs) hold great promise to bring optimal treatment to patients early and more efficiently. The concept is to combine traditional phase 2 and phase 3 trials seamlessly in operation and inferentially in statistical analysis. As phase 2/3 designs cover a large area of clinical applications, it is not possible to provide comprehensive details for all different clinical applications in this article. Rather, the focus is on providing general descriptions of phase 2/3 designs and their legal foundations under the framework of Title 21 Part 312 of Code of Federal Regulations and Section 355 (b) of the Federal Food, Drug, and Cosmetic Act. This legal framework also provides the basis for correcting misrepresentations of phase 2/3 combination designs found in the literature. Various aspects and underlying principles are discussed in depth with highlights on current difficulties and future challenges to statistical inference.
Dose Finding Studies
Authors: Bretz Frank , Pinheiro José C.
Published: September 2014

Determining an adequate dose level for a drug and, more broadly, characterizing its dose–response relationship are key objectives of clinical drug development. If the dose is set too high, safety and tolerability problems are likely to result, whereas selecting too low a dose makes it difficult to establish adequate efficacy in the confirmatory phase, possibly leading to a failed program. It is well known that many potentially useful drugs have been discarded, and much time and resources have been wasted by pharmaceutical companies because of incorrect dosing. In addition, many marketed drugs have needed to have their label changed due to inappropriate, generally excessive, initial dose recommendations. A variety of issues must be considered when planning and analyzing a dose‐finding study so as to ensure it produces useful information for decision making. We describe key statistical methodologies for designing and analyzing clinical dose‐finding studies. These methodologies can be classified into different categories: multiple comparison procedures (including trend tests), modeling techniques, hybrid methods combining the previous two approaches, and adaptive dose‐finding strategies.
PhaseIITrials
Authors: Keene Oliver N.
Published: September 2014

Phase II trials have two aims:(1)to provide a proof of concept that the drug is able to affect the disease it is designed to affect without overt safety problems and(2)to define the dose(s) to be taken forward to phase III. Trials that specifically address these issues are often referred to as phase IIa and phase IIb trials, respectively. Therefore, phase II trials provide the initial test of efficacy and reassurance on safety in patients and, if successful, define the dose or doses to be carried forward to be studied in the wider populations and longer‐term phase III trials.
Stratified Randomization
Authors: Perevozskaya Inna
Published: September 2014

This article outlines the situations where stratified randomization may be necessary and points out the reader to the “Randomization” article where the issue is discussed in detail.
Planning A Group‐Randomized Trial
Authors: Murray David M.
Published: September 2014

Planning a group‐randomized trial (GRT) is a complex process. This article discusses the process behind planning a GRT, including the research question, team, design, potential problems and variables, and intervention.
Multicenter Trial: Background and Examples
Authors: Meinert Curtis L.
Published: September 2014

This article defines and reviews the history of multicenter trials. Several key examples of multicenter trials are provided as well as same suggested readings.
Multiple Evaluators
Authors: Z. Liao Jason J. , Capen Robert C.
Published: September 2014

In medical and other related sciences, the clinical or experimental measurements obtained from multiple evaluators usually serve as a basis for diagnostic, prognostic, therapeutic, and performance evaluations. Thus, it is important to assess interevaluator agreement. The measurement scale can be continuous, binary, nominal, or ordinal. In this article, we review some commonly used approaches for both continuous and categorical outcomes.
Non‐Randomized Trial: A Tutorial
Authors: Li Zhengqing
Published: September 2014

To determine whether a new intervention has the postulated effect in a population of interest, one typically conducts a clinical trial comparing the intervention against a control. When assigning qualified subjects to the control or the intervention groups, a formal randomization process is preferred in order to remove the potential bias in subject allocation, to produce comparable groups in terms of both known and unknown prognostic factors at baseline, and to establish the validity of statistical tests of significance. This type of studies, so called randomized control study, is the standard by which all other studies are judged. Nonrandomized clinical trials arise from situations in which it is impossible or difficult to assign subjects to treatment by chance. Unlike randomized control studies, nonrandomized trials are vulnerable to bias because of the inability to control the balance of prognostic factors between the treatment groups. Because of this limitation, nonrandomized trials are primarily used for exploratory purpose. In this paper, a tutorial description of nonrandomized trials is provided. Basic features of study designs, statistical methods for analysis, and their limitations are discussed.
Composite Endpoints in Clinical Trials
Authors: O'Brien Peter C. , Tilley Barbara C. , Dyck Peter J.
Published: September 2014

The primary analysis plays a key role in clinical trials to determine efficacy. By providing a singleP‐value, the primary analysis provides the basis for controlling the overall type I error rate. However, it is often desirable to consider multiple important patient outcomes in the primary analysis in order to use the most comprehensive and most clinically meaningful assessment of the patient. These properties also tend to increase the power of the resulting statistical tests. Composite endpoints are an effective way of combining the information from multiple endpoints when identification of a single primary outcome is not possible. We discuss a variety of ways for forming such composites. The ideal is an existing composite that has been extensively studied, validated, and used in clinical practice, and where a meaningful effect size has been identified. Statistical alternatives that may be useful when such an existing composite is not available include a rank sum approach and least squares methods and other methods described in global assessment variables.
PhaseIIITrials
Authors: Mallinckrodt Craig , Whitmyer Virgil
Published: September 2014

The objective of phase III clinical trials is to confirm the preliminary but inconclusive evidence obtained from phase II studies that a drug is safe and effective for the intended indication in the relevant patient population. Phase III studies provide the primary basis upon which drugs are approved for use by the various regulatory agencies. Because no universally best approach to phase III research exists, it is important to tailor features of the study to the specific situation at hand. This article focuses on the key aspects of the design, conduct, and analysis of phase III trials and the trade‐offs between them that are necessary to consider when designing a trial intended to yield confirmatory evidence of a drug's effect. Nevertheless, even the best designed trials will have limitations. Any one trial may address its primary hypotheses with little or no ambiguity while other important questions remain unresolved. Therefore, information from the current trial, coupled with knowledge from earlier trials, is used to continually reevaluate what is known. With this knowledge one must reconsider what important questions remain unresolved, and continue to plan and conduct appropriate studies to address the unresolved questions.
Therapeutic Equivalence
Authors: Wiens Brian L.
Published: September 2014

We consider studies of therapeutic equivalence. Such studies aim to show that two treatments have similar efficacy. The context is of high importance, and situations in which therapeutic equivalence trials are appropriate are discussed. Choosing an equivalence limit is important to rule out the possibility that a placebo can be considered equivalent to a treatment with benefit. The two one‐sided tests approach is common and uses a Neyman‐Pearson framework for hypothesis testing although the test is commonly conducted by comparing the bounds of a confidence interval to the equivalence limit. Study conduct will be subjected to high scrutiny because poor study conduct can result in the perception of an increased chance of demonstrating equivalence. Some other aspects of a therapeutic equivalence trial are also discussed, including power and choice of an analysis set.
Sample Size Considerations for Morbidity/Mortality Trials
Authors: Shih Joanna H.
Published: September 2014

Sample size and power determination plays a crucial role in the design of morbidity/mortality clinical trials. To ensure that a trial is adequately sized so that the true treatment effect can be detected with a desired power, it is important to evaluate the assumptions used in the sample‐size calculation procedure. Various close‐form sample‐size formulas have been proposed for the analysis of morbidity/mortality trials with survival endpoints. However, most of them are formed based on restrictive assumptions and do not take into consideration factors that may affect the estimate of the treatment effect. These factors include drop‐in, drop‐out, staggered entry, loss to follow‐up, lags in treatment effects, and informative noncompliance. This article reviews the sample‐size calculation methods for testing the treatment effect in two‐arm survival trials. For each method considered, its basic features, underlying assumptions, and limitations are discussed. Presented first are the test statistics and associated methods that yield close‐form sample‐size formulas. Then attention will be focused on methods with aforementioned complexities in sample‐size calculation.
Alpha‐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.
Analysis Population
Authors: Mugglin Andrew S. , Connett John E.
Published: September 2014

Collecting data on humans in clinical trials is very complicated, for scientific, logistic, ethical, and financial reasons. Analyzing such data is no less complicated, and it is seldom a simple exercise to determine which data to analyze, let alone how to analyze them and report the findings. In this article, we seek to describe some issues faced by the data analyst in a clinical trial. We define several terms that are often used to describe succinctly a data set created according to certain principles. We identify several issues and themes that frequently develop when discussing the selection of data for analysis. We also provide some principles that we have found to be useful approaches to dealing with the difficulty of selecting data for analysis in a complex but real setting. Finally, we provide an illustrative example of how an analysis dataset was actually chosen.
Sample Size Calculation for Comparing Time‐to‐Event Data
Authors: Wang Hansheng , Chow Shein‐Chung
Published: September 2014

This article discusses three commonly employed testing procedures: the exponential model, Cox's proportional hazard model, and the log‐rank test. The sample size formula for testing equality, superiority, and equivalence are discussed for each method.
Stopping Boundaries
Authors: Leung Denis Heng‐Yan
Published: September 2014

To answer the ethical needs of stopping a clinical trial as soon as it is known that continuing the trial would be inappropriate or unnecessary, trial results are often monitored periodically during the trial. Stopping boundaries are conditions to indicate when results are sufficiently extreme to suggest stopping of the trial.
Double‐Dummy
Authors: Schulz Kenneth F.
Published: September 2014

Blinding embodies a rich history spanning several centuries. Double‐blinding usually reduces differential assessment of outcomes (information bias), but can also improve compliance and retention of trial participants while reducing biased supplemental care or treatment (sometimes called co‐intervention). With an active treatment control group, different options to double‐blinding present production obstacles, compliance impediments, and unblinding opportunities. To double‐blind in this situation, double‐dummy blinding nearly always becomes indispensable.
Secondary Efficacy Endpoints
Authors: Rockhold Frank , Segreti Tony
Published: September 2014

Effective and relevant secondary endpoints are a critical component of designing clinical trials. This article reviews the different techniques available for selecting secondary endpoints and their measurements.
Chemistry, Manufacturing and Controls (
CMC
)
Authors: Schmuff Norman R. , Lin David T.
Published: September 2014

This article describes general expectations and issues related to the Quality (also known as Chemistry, Manufacturing and Controls) section of Investigational New Drug (IND) (see Investigational New Drug Application (IND) article) applications filed with the U.S. Food and Drug Administration's (FDA's) Center for Drug Evaluation and Research (CDER) (see CDER article). Only brief mention will be made to FDA's Center for Biologics Evaluation and Research (CBER) (see CBER article) and other regulatory authorities (see Ministry of Health and Welfare (Japan, now known as the MHLW),
Medicines Control Agency
(United Kingdom, now known as the MHRA), and
EMEA
(European Union) articles). For determining whether INDs are required for clinical studies, see the Investigational New Drug Application (IND) article.
Hierarchical Bayes
Authors: Ghosh Malay
Published: September 2014

Dose–response studies of environmental toxins are designed to determine the detrimental effects associated with exposure to the toxin, over a particular range of exposures. To properly quantify the toxic effect of a toxin, thebackground riskof no exposure to the toxin, R(0), must first be specified. The additional risk incurred by exposure to a dose,d, of the toxin is then called the added risk (AR) at this dose and is given by AR(d) = R(d) –R(0).
Branch and Bound
Authors: Whitaker David
Published: September 2014

The branch and bound algorithm is mostly used to solve global optimization problems with integer variables (unknowns), linear objective and linear constraints (integer linear programming (LP) problems). The recent increase in the speed of computation on desktop computers has meant that the algorithm can be applied to large problems with several hundred integer variables, which only a decade ago were intractable to any true optimization algorithms. Although a number of algorithms have been proposed for the integer LP problem, the branch and bound technique is used in almost all of the available computer software. This technique has proven to be reasonably efficient for practical problems because it has the embedded advantage of solving continuous linear programmes as subproblems. Furthermore, in some situations the embedded subproblem is a special case of a LP problem (such as assignment and transportation problems), which have more efficient algorithms than the general LP algorithm. Branch and bound methods are also easy to implement on parallel processors. An increasing number of applications are currently forthcoming in this area.
Computer Intensive Sampling Methods in Ecology
Authors: Fortin Marie‐josée , Jacquez Geoffrey M. , Shipley Bill
Published: September 2014

Here, we focus on alternative significance tests for ecological data that often have skewed distributions, which impair the use of most parametric significance tests based on the normal distribution. Randomization tests have been proposed as an alternative to those classical significance tests where the observed data are repetitively reshuffled to generate a reference distribution that is then used to assess the significance of the statistic under study. Ecological data are also often correlated due to temporal autocorrelation, spatial autocorrelation, or phylogenetic structure, thereby violating assumptions of data independence of many randomization tests. In such circumstances, restricted randomization should be used. Alternatives to randomization tests such as bootstrap and jackknife are presented. Finally, we address the issue of multiple tests and show how the false discovery rate is more appropriate than Bonferroni correction and sequential methods. Copyright © 2015 John Wiley & Sons, Ltd.
Constrained Optimization
Authors: Stein Alfred
Published: September 2014

Constrained optimization aims to find an optimum of a function in the presence of constraints. Constrained optimization generally yields a lower optimum on the optimization function as compared with unconstrained optimization, but it honors the presence of constraints. A simple example concerns maximization of the production of a farm subject to constraints on the amount of fertilizers (say
n
kg per week) and man hours (
h
hours per day). Another example is minimization of leaching towards the environment, subject to constraints on soil parameters, groundwater table and other environmental variables.
EnvStats, anRPackage forEnvironmentalStatistics
Authors: Millard Steven P.
Published: September 2014

EnvStatsis a comprehensiveRpackage for environmental statistics and is the successor to theS‐PLUSmoduleEnvironmentalStats for S‐PLUS. EnvStatsprovides a set of powerful functions for performing graphical and statistical analyses of environmental data.
Jackknife Resampling
Authors: Friedl Herwig , Stampfer Erwin
Published: September 2014

The jackknife is a particular resampling method that aims primarily at the calculation of the bias and the variance of estimates, without making very restrictive distributional assumptions. It is nonparametric and easy to apply in quite general settings. As with other resampling techniques, the jackknife can be computationally very intensive. In the simplest case jackknife resampling is generated by sequentially deleting single cases from the original sample (delete‐one jackknife). A more generalized jackknife technique uses resampling that is based on multiple case deletion (delete‐djackknife). The main part of this article focuses on independently and (in the introductory part also) identically sampled data. It then discusses the basics of the jackknife and its applications to nonidentically distributed observations that can be described by linear, generalized linear, nonlinear, and Cox's regression models. Finally, the article gives an account of jackknife estimates for dependent data, e.g. in the context of generalized estimating equations or special time series models.
Kernel Density Estimation
Authors: Horová Ivana
Published: September 2014

The probability density function is a fundamental concept in statistics. Density estimation is the reconstruction of the density function from a set of observed data and it can give valuable indication of such features as skewness and multimodality in the data.