bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drift (For Interim Analysis)
Authors: Reboussin David M. 
Published: September 2014

AbstractDrift, 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

AbstractThis 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

Abstract
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

Abstract
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

Abstract

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

AbstractBiological 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

AbstractBenefit/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

AbstractRandomized 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

AbstractThis 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

AbstractPharmacoepidemiology 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

Abstract
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

Abstract
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

AbstractPublication 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

Abstract
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.
Coherence in Phase
I
Clinical Trials
Authors: (Ken) Cheung Ying Kuen 
Published: September 2014

Abstract
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

AbstractThe 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

AbstractIn 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

Abstract
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

Abstract
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

AbstractThe 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

Abstract
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

Abstract
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.