bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physicians' Health Study (PHS)
Authors: Gaziano J. Michael , Hennekens Charles H. 
Published: September 2014

AbstractThe United States Physicians' Health Study I was a landmark randomized trial of primary prevention because it was conducted entirely by mail, used a prerandomization run‐in, collected prerandomization blood specimens, used a 2 × 2 factorial design to test two hypotheses, had very high compliance to the assigned treatments, and had virtually complete morbidity and mortality data after 12 years of treatment and follow‐up. PHS I was funded by the National Institutes of Health and enrolled 22,071 male physicians aged 40 to 84 years at baseline in 1982. This randomized, double‐blind, placebo‐controlled trial tested primarily the effects of aspirin on cardiovascular disease and beta carotene on cancer. After 5 years of treatment and follow‐up, based on the unanimous recommendation of the independent Data and Safety Monitoring Board, the randomized aspirin component was terminated early mainly because of the emergence of a statistically extreme (P < 0.00001) 44% reduction in first myocardial infarction among those assigned to aspirin. After 12 years of treatment and follow‐up, the beta carotene arm ended on schedule and produced no significant overall evidence of benefit or harm either on cancer or cardiovascular disease.
Multiple Risk Factor Intervention Trial (MRFIT)
Authors: Eberly Lynn E. , Stamler Jeremiah , Kuller Lewis H. , Neaton James D. 
Published: September 2014

AbstractThe Multiple Risk Factor Intervention Trial (MRFIT) was a randomized controlled trial on primary prevention of coronary heart disease (CHD) death in men with elevated blood pressure (BP), serum cholesterol, and/or smoking habit. In all, 361,662 men were screened in 18 U.S. cities; 12,866 men were randomized to multifactor Special Intervention (SI) or Usual Care (UC). With a 7 year average follow‐up, the CHD death rate was 7.1% lower for SI than UC [relative difference in proportion dead; 90% CI (−15%, 25%)]. UC men made more extensive risk factor reductions than anticipated in trial design, and the number of UC deaths was substantially smaller than expected. This result led to an underpowered comparison. Post‐hoc assessments showed significantly lower rates of combined morbidity/mortality outcomes for SI than UC. Other findings from MRFIT substantially changed the treatment of hypertension, and long‐term epidemiologic follow‐up of all 361,662 screenees yielded important findings of great impact on major public health and medical care issues.
Women's Health Initiative: Statistical Aspects and Selected Early Results
Authors: Prentice Ross L. , Anderson Garnet L. 
Published: September 2014

Abstract
An overview is provided of the women's health initiative clinical trial and observational study of 161 808 postmenopausal women in the age range 50 to 79. The clinical trial includes 68 132 women in a partial factorial design having four intervention comparisons: a low‐fat eating pattern among 48 835 women; a hormone therapy trial of 0.625 mg per day of conjugated equine estrogen among 10 739 women who were posthysterectomy at baseline; a hormone therapy trial of this same estrogen plus 2.5 mg per day of medroxyprogesterone acetate among 16 608 women with uterus at baseline; and a calcium (1000 mg/day) plus vitamin D (400 international units/day) trial among 36 282 women. Each CT component had its specified primary and secondary hypotheses, along with an overriding emphasis on overall health benefits versus risks. A so‐called global index was defined for each CT component as the earliest occurrence of a set of clinical outcomes plausibly affected by the intervention, or death from other causes. Specialized data management communication, quality assurance, and data monitoring procedures were established to support this large and complex research program. Some detail is provided on the results of the combined hormone trial component, which was stopped early in 2002 when the external Data and Safety Monitoring Board judged that overall health risks exceeded benefits over an average follow‐up period in excess of five years.
Wilcoxon‐Mann‐Whitney Test: Definition and Example
Authors: Moses Lincoln E. 
Published: September 2014

AbstractThis is a method of comparing the size of observations in two groups by ranking all the observations and comparing the average ranks of the two groups. The distribution theory is described. The test has high efficiency for the comparison of two normal distributions with the same variance, and is much better than thettest for some nonnormal distributions. Topics discussed include adjustment for tied data and the calculation of confidence limits for a difference in translation between the two distributions.
Interaction Model: Overview
Authors: Kreiner Svend 
Published: September 2014

AbstractLoglinear models that describe association patterns among categorical variables are sometimes calledinteraction models. Graphical interaction models are useful for portraying conditional independence and indicating when an association remains the same after collapsing over a variable. This article discusses a “Separation theorem” that describes how conditional independence patterns are maintained by conditioning on a certain subset of variables as indicated by an interaction graph. Collapsibility issues are also discussed.
Global Assessment Variables
Authors: Tilley Barbara , Huang Peng , O'Brien Peter C. 
Published: September 2014

AbstractMultiple outcomes are often collected for an evaluation of a treatment's effect on disease or quality of life or other underlying condition. When multiple outcomes are being assessed, and no predefined and validated composite outcome (summary measure) exists, two classes of statistical tests have been developed that address two different scientific questions. Tests in one class are designed to test whether a treatment effect is observed for any of the outcomes. Tests in the other class are designed to test whether a treatment confers an overall benefit (or harm) to patients. We provide a brief survey of global statistical tests (GST) of the second class. Each GST is discussed in terms of its motivation, approach to conducting the test, and some basic properties of the test. To facilitate the interpretation of the GST, we use the concept of a global treatment effect (GTE) to quantify the treatment's overall effect on multiple outcomes. We provide examples of the application of GSTs to clinical trials along with recommendations for choosing outcomes to include in the GST.
Disease Trials in Reproductive Diseases
Authors: Johnson Julia V. 
Published: September 2014

AbstractHistorically, studies in reproductive medicine have been of limited quality mainly because of the challenging group of subjects, the limited effective study design, and the lack of standards advised for clinical trials. In addition, reproductive medicine has classically received limited funding for the National Institute of Child Health and Human Development (NICHD) at the National Institutes of Health (NIH). Because of the importance of reproductive health issues, the NIH has developed programs to increase translational and clinical research. In addition, research has encouraged standards for reproductive research to assure high‐quality clinical trials. This article will review the classic limitations of studies in reproductive medicine, the standards that have improved the quality of research, and it will describe NIH funded programs to promote knowledge and optimize patient care.
Microarray
Authors: Page Grier P. , Cui Xiangqin 
Published: September 2014

AbstractMicroarray technology had a troubling beginning, but many problems resulted from the growing pains of this technology that, like many new things, was initially more promise than delivery. However, the promise has finally come to fruition, and microarray technology has developed into a mature technology that can generate valid and useful results in basic, translational, clinical trials, and now clinical practice. Much maturity resulted from the realization that microarrays are not some magic tool, but rather are like any other measurement device and that unless microarray experimentation is coupled with good experimental practices, it will not yield valid results or, worse yet, may lead to misleading results.
Wei‐Lin‐Weissfeld Method for Multiple Times to Events
Authors: Signorovitch James E. , Wei Lee‐Jen 
Published: September 2014

AbstractSubjects followed in a longitudinal study can often experience multiple clinical events of interest at different times. The events could be of different types, for example, the onset times for various opportunistic infections in HIV‐infected patients, or multiple recurrences of the same type of event, such as recurrent superficial bladder tumors in cancer patients. This article describes the Wei–Lin–Weissfeld (WLW) method for analyzing multivariate failure time data that develop in such settings.
Hazard Ratio
Authors: Schmoor Claudia , Graf Erika 
Published: September 2014

AbstractHazard ratios are useful measures to compare groups of patients with respect to a survival outcome. In the context of clinical trials “survival outcome” or “survival time” are used generically to denote the time “survived” from a specified time origin, like diagnosis of cancer, start of treatment, or first infarction, to a specific event of interest, like death, recurrence of cancer, or reinfarction. Broadly speaking, the hazard rate quantifies the risk of experiencing an event at a given point in time. The hazard ratio at that time is the ratio of this risk in one population divided by the risk in a second population chosen as reference group. The hazard ratio between two populations can vary over time. For a given point in time, hazard ratios below 1.0 indicate that the risk of the event is lower in the first group than the risk in the reference group, whereas a hazard ratio of 1.0 indicates equal risks, and values above 1.0 imply a higher risk in the first group compared with the reference population.
Cost Effectiveness in Health Economics
Authors: Leese Morven 
Published: September 2014

AbstractCost‐effectiveness analysis is a method for comparing the costs and consequences of competing courses of action. In the context of evaluating health technologies, these might include the delivery of pharmaceutical products, psychological interventions, or particular ways of organizing health services. It is similar to cost benefit analysis except that benefit is measured in monetary terms and therefore can be set directly against cost, whereas the consequences in a cost‐effectiveness analysis are measured in nonmonetary terms, such as years of life saved, quality of life, quality‐adjusted years of life (QALYs), or symptom reduction. Issues in cost effectiveness that arise in clinical trials are as follows: (i) the economic perspective to be adopted, for example, that of the patient, health provider, or society as a whole; (ii) the design of the trial so as to include all the appropriate costs and economically relevant outcomes and to provide adequate statistical power for the economic evaluation; (iii) the choice of summary statistics to measure cost effectiveness, for example, the incremental cost effectiveness ratio or, as now recognised to be analytically more convenient, the net monetary benefit; (iv) presentational methods, for example, the cost‐effectiveness plane and the cost‐effectiveness acceptability curve; (v) the inclusion of uncertainty, both stochastic and deterministic, directly in the main analyses and also through subsidiary sensitivity analyses; and (vi) the generalization of results to time frames or geographical areas other than that of the trial and to different patient groups, using modeling techniques such as Markov modeling and regression analysis.
Subgroup Analysis
Authors: Simon Richard M. 
Published: September 2014

AbstractSubgroup analysis refers to the practice of attempting to determine whether and how treatment effects vary among subgroups of subjects who are studied in intervention studies. This article reviews the variety of statistical methods that have been used and developed for subgroup analyses. These methods include testing null hypotheses of no treatment effects within subgroups,P‐value adjustments for multiplicity, confidence intervals for subgroup‐specific treatment effects, interaction tests, qualitative interaction tests, and empirical Bayes and fully Bayesian methods. The article addresses the distinction between preplanned subgroup analyses in which experiment‐wise type I error rate is controlled and an unplanned data‐dredging exercise for the purpose of generating hypotheses to be tested in subsequent studies. The article emphasizes the importance of restricting inferential methods to a very limited number of prespecified subgroups based on strong biological rationale or reports from other clinical trials. The article discusses the central nature of a limited number of planned highly focused subgroup analyses for pharmacogenomic clinical trials. The article also addresses the limited reliably evaluating consistency of treatment effects among subgroups in positive clinical trials, including center effects in multi‐center trials, and the article discusses limitations imposed by sample size for subgroup analyses and the role of meta‐analysis in subgroup analyses.
Treatment‐by‐Center Interaction
Authors: Gallo Paul 
Published: September 2014

AbstractIn multicenter clinical trials, in addition to the main study objectives that address effects of the therapies being evaluated, it is important to consider the level of consistency of treatment effects that is observed across the centers. Investigating whether evidence of treatment‐by‐center interaction exists and, if so, gaining greater understanding of what has led to this nonhomogeneity of results across sites can be an important aspect of most fully comprehending the trial results. This understanding may shed light, for example, on conditions under which the trial therapies can be used most effectively or may help identify characteristics of patients most likely to benefit. Important questions meriting additional clinical investigation may be identified. Considering treatment‐by‐center interaction may also impact the strength of the overall conclusions to be drawn from the trial and the extent to which its results can be generalized.
Pharmacovigilance
Authors: Waller Patrick , Evans Stephen 
Published: September 2014

AbstractPharmacovigilance is considered here to include all safety‐related activity beyond the point at which humans are first exposed to a new medicinal drug. It is a process of clinical risk management involving the identification, investigation, and minimization of the adverse effects of medication, in particular those that have serious clinical consequences. This article considers the purposes of pharmacovigilance, methodologies, and systems and processes in place to prevent adverse drug reactions (ADRs), which includes the reporting to regulatory authorities of serious, unexpected, and suspected adverse reactions that occur in clinical trials. We also cover some historical aspects, consider the limitations of existing approaches, and provide pointers for the future development of this discipline.
PhaseIVTrials
Authors: Day Simon 
Published: September 2014

AbstractThis article places phase IV trials in context with other phases (most typically phase III). The design and objectives of phase IV trials may often be very similar to those of phase III trials, but some much broader aims and objectives such as postmarketing surveillance, quality of life, broad acceptability of treatments, and health economics may also be studied. Trials with different purposes may need different design considerations.
Algorithm‐Based Designs
Authors: He Weili , Liu Jun 
Published: September 2014

Abstract
First, we describe the traditional algorithm‐based A + B designs and the modifications of A + B designs, along with the advantages and drawbacks of these designs. Accelerated designs and a modification of the accelerated designs are delineated in the next section, then we introduce a model‐based approach in the estimation of the maximum tolerated dose following an algorithm‐based design. Finally, design properties for commonly used and less commonly used algorithm‐based designs are provided.
Identifying the Most Successful Dose (
MSD
) in Dose‐Finding Studies
Authors: O'Quigley John , Zohar Sarah 
Published: September 2014

Abstract
From a range of candidate doses, the MSD (most successful dose) is the dose at which the overall success rate—the product of the rate of seeing nontoxicities together with the rate of tumor response—is the highest. Among different algorithms designed to achieve this goal, the one closest to optimal (SIM) is the approach described in O'Quigley et al. (2001). This approach has been extended to the cancer setting by Ivanova (2003) and Zohar and O'Quigley (2006).
Stability Analysis
Authors: Tsong Yi , Chen Wen Jen 
Published: September 2014

Abstract
Shelf life of a drug product is defined as the length of time under the specific conditions of storage that the product will remain within acceptance criteria established to ensure its identity, strength, quality, and purity. Regulatory agencies request that an adequate stability study be performed by an applicant to collect the chemical data of the batch at a prespecified time point in order to provide supporting evidence of product stability and establish a proposed shelf life (1–7).
Block Randomization
Authors: McEntegart Damian 
Published: September 2014

Abstract
Simple unconstrained randomization is the yardstick for randomization techniques in clinical trials as it is completely unpredictable and provides an unambiguous basis for inference from statistical analysis. But simple randomization is rarely used because of the need to protect the trial from imbalance in the overall numbers allocated to treatment and from temporal bias in the allocations. For these reasons the randomization procedure is often restricted by some form of blocking, the most common being random permuted blocks. The methodology, issues and practical considerations relating to the use of blocked randomization are described. Particular attention is paid to the issue of potential selection bias, which may arise from the predictability of tail allocations towards the end of any block of treatment allocations if the past allocations in the block are known or can be guessed. The merits of using unequally sized blocks to avoid selection bias are discussed in detail. Inference following blocked randomization is also discussed. Alternative forms of blocking are described; these include lists generated from the maximal procedure, the use of rows from Hadamard matrices, Zelen's balanced blocks procedure and mixed randomization. Practical considerations relating to the use of blocked randomization are described including software used to generate random blocks. Also covered are considerations relating to the use of electronic systems to automatically perform allocation from a pre‐defined randomization list; the use of such systems is becoming increasingly common with the advent of telephone interactive voice response (IVR) and web based randomization systems.
Interim Analyses
Authors: Todd Susan 
Published: September 2014

Abstract

Recruitment of patients to a clinical trial usually occurs over time, resulting in the steady accumulation of data throughout the trial's duration. Yet, if traditional statistical methods are implemented, the sample size of the trial should be determined in advance, and data collected on all subjects should occur before an analysis proceeds. For ethical and economic reasons, techniques of sequential testing incorporating
interim analyses
have been developed to enable the valid examination of accumulating data. The aim is to stop a study as soon as sufficient evidence exists to reach a firm conclusion. Here, the advantages and disadvantages of conducting interim analyses in phase III clinical trials are presented and the development of methodology for interim analyses is chronicled and briefly described. Examples are given of completed trials, which have been carried out incorporating interim analyses, and references to relevant software and literature are provided.
Methods for Conduct of Rigorous Group‐Randomization
Authors: V. Peterson Arthur 
Published: September 2014

Abstract

A group‐randomized trial (GRT) has two defining characteristics: (
1
) it investigates the effectiveness of a
group
‐delivered intervention, and (
2
) it includes at least one intervention condition and at least one control condition to which
groups
are
randomly
assigned.
1
The groups can be, for example, schools, worksites, clinics, neighborhoods, or even entire towns or cities. Such group‐delivered interventions have advantages of practicality and cost‐efficiency in reaching large numbers of people and, once proven effective, can be important contributions to public health strategy and policy.
Run‐In Period
Authors: Berger Vance W. , Durkalski Valerie L. 
Published: September 2014

AbstractDefining the study population is a critical step in clinical trial design. Ideally, the goal should be to choose a population who best represent the target population in a clinical setting, which in turn should be that segment of the population most likely to derive benefit. Because prespecified criteria cannot always predict which patients will and will not derive benefit, run‐in periods, which may be used for other reasons as well, are often incorporated into the design to enrich the sample by detecting and excluding a subgroup of the study population that is less likely to respond to the intervention. We review the definition, objective, and impact of run‐in periods on clinical trial conduct and results.
Futility Analysis
Authors: Freidlin Boris 
Published: September 2014

AbstractMost randomized clinical trials are designed to demonstrate benefit of experimental versus standard treatment. For ethical and economic reasons, it is important to stop a trial early for “futility” if there is no longer a reasonable chance that the experimental arm can be shown to be definitively better than the standard arm. Futility monitoring has become a widely accepted component of clinical trial methodology. We provide a review of commonly used futility procedures and discuss practical aspects of their implementation.
Intention‐to‐Treat: Overview
Authors: Lachin John M. 
Published: September 2014

AbstractThe intention‐to‐treat principle developed from concerns that analysis of a subset of subjects into randomized a clinical trial may provide a severely biased comparison of the study treatments, and it may not describe the expected outcome in all subjects for whom a treatment might be administered. Theintention‐to‐treat analysisincludes all subjects randomized into the study to provide a pragmatic assessment of the effects of therapy in general use, regardless of other considerations such as drug tolerability or compliance. This approach is contrasted with subset analyses, such as the “efficacy subset” that only includes the subset that tolerated the assigned agent, who were largely compliant and who completed follow‐up.However, a true intention‐to‐treat analysis requires that all those randomized should complete follow‐up and outcome assessments to the extent possible, even if no longer taking the drug, and that their outcome data should be employed in the analysis. This design is termed anintention‐to‐treat design.Many statistical methods provide an unbiased comparison of treatment effects when all missing observations are ignorable, assuming that no bias is introduced. However, this assumption is inherently unverifiable. Thus, the only incontrovertibly unbiased study is one in which all randomized patients are evaluated and included in the analysis, assuming that other features of the study are also unbiased. This design is the essence of the intent‐to‐treat philosophy.