bluuebunny/crossref_metadata_2025 huggingface dataset:
Generalizability Theory: Estimation
Authors: Sanders Piet F.
Published: September 2014

Generalizability theory is a conceptual and statistical framework for the analysis and construction of measurement instruments. Among the most important concepts of generalizability discussed in the first section of this entry are the universe of admissible observations, universe and observed scores, random and fixed facets, crossed and nested designs, variance components, generalizability studies, decision studies, and generalizability coefficients. A discussion of the results of a generalizability study and a decision study of a crossed one‐facet random effects design and of a two‐facet crossed one‐facet random effects design is presented in the two following sections. A number of other designs that illustrate the versatility of generalizability theory are presented in the final section.
Educational Psychology: Measuring Change Over Time
Authors: Kulikowich Jonna M.
Published: September 2014

Educational psychologists use repeated measures analyses to study changes over time that reflect academic, cognitive, emotional, and social development of students. The purpose of this chapter is to review the basic principles and procedures of repeated measures in statistics, and, based on these principles and procedures, to describe important considerations of educational psychologists as they attempt to answer research questions. Testing of assumptions, estimating effect sizes and power, and current statistical advances in the study of how individuals' affect, behavior, and cognition change are discussed.
Teaching Statistics to Psychologists
Authors: Spilich George
Published: September 2014

The introduction of computerized statistical packages over the past three decades has dramatically altered graduate education in psychology for better and for worse. Students now are more broadly trained in technique but this improvement has occurred at the expense of training in design. The availability of statistical packages in the hands of those without professional training has implications for psychology as a profession.
Twin Designs
Authors: Spinath Frank M.
Published: September 2014

The classical twin study compares the phenotypic resemblances of identical or
monozygotic
(MZ) and fraternal or
dizygotic
(DZ) twins. MZ twins derive from the splitting of one fertilized zygote and, therefore, inherit identical genetic material. DZ twins are first‐degree relatives because they develop from separately fertilized eggs and are 50% genetically identical on average. It follows that a greater within‐pair similarity in MZ, compared to DZ twins suggests that genetic variance influences the trait under study.
Reactivity
Authors: Onghena Patrick
Published: September 2014

Reactivity is a threat to the outcomes or treatments construct validity of observational and intervention studies that refers to the unintended reaction of participants on being observed or on being in a study. It is a threat to the
outcomes construct validity
if participants behave differently because they are aware that some aspect of their functioning is being observed or measured. It is a threat to the
treatments construct validity
if participants behave differently because they interpret the research or treatment setting in a way that makes the actual treatment different from the treatment as planned by the researcher. Because of the inevitable tendency of human participants to interact and give meaning to settings and their own and other's behavior, reactivity is a considerable risk to invalidity of both quasi‐ and randomized experiments in behavioral science.
Observational Study:Definition and Examples
Authors: Rosenbaum Paul R.
Published: September 2014

An observational study is an empiric investigation that attempts to estimate the effects caused by a treatment when it is not possible to perform an experiment. Random assignment of subjects to treatment or control in an experiment ensures that comparable groups of subjects are compared under alternative treatments. Without random assignment, in an observational study, the treatment and control groups may not be comparable prior to treatment, so that differing outcomes after treatment may not be effects caused by the treatment. Pretreatment differences that have been accurately measured are calledovert biases; they may often be controlled using analytical adjustments, such as matching, stratification, or covariance adjustment. Pretreatment differences that were not recorded are called hidden biases, and they pose substantial problems that must be addressed by other means. A well designed observational study permits detection of one or two of the most plausible hidden biases. The analysis of an observational study can measure the degree of sensitivity of conclusions to hidden biases of plausible magnitudes. Design strategies involving pattern specificity, such as Campbell's ‘multiple operationalism’ or Hill's dose‐response, may reduce sensitivity to hidden bias, and this possibility may be appraised using the design sensitivity of competing designs.
Internet Research Methods
Authors: Stewart David W.
Published: September 2014

This article provides an introduction to the use of the Internet as a research tool. Methods for searching the Internet for secondary sources of information are explained and representative search tools and information resources are identified. The use of the Internet as a tool for gathering primary data is also discussed. Issues related to the evaluation of information obtained from the Internet are identified.
Gene‐Environment Correlation
Authors: Dick Danielle M.
Published: September 2014

Genetic and environmental effects are not independent sources of influence on behavior, but rather, genotype can influence an individual's exposure to certain environments, how that individual experiences the environment, and the degree of influence that certain environments exert. This phenomenon is called
gene‐environment correlation
. Three types of gene‐environment correlation are discussed: passive, evocative, and active. Examples of each type of gene‐environment correlation, and support for these effects from the literature are presented.
M
endelian Inheritance and Segregation Analysis
Authors: Sham P.
Published: September 2014

This entry examines the basic Mendelian model of inheritance. The model is based upon the familiar inheritance ratios of 1:1 and 3:1, which are known as ‘Mendelian segregation ratios’. The entry then discusses classical segregation analysis and then complex segregation analysis.
Dominance
Authors: Evans David M.
Published: September 2014

Dominance refers to the nonlinear interaction between alleles at the same locus. In the case of a discrete trait, if one allele masks the effect of the other allele at the same locus, we say that the first allele displays dominance over the second allele. In the case of a quantitative trait, the amount of dominance at a locus may be quantified by the dominance parameter
d
, which equals the deviation of the heterozygote's phenotypic value from the midpoint of the two homozygotes' phenotypic values. In the case of a single biallelic locus in Hardy–Weinberg equilibrium, the dominance variance is given by the formula (2
pqd
)
2
where
p
and
q
are the allele frequencies at the locus. The amount of dominance variance affecting a trait can be estimated using the classical twin design.
Evaluation Research
Authors: Lipsey Mark W. , Tidd Simon T.
Published: September 2014

Quantitative methods are widely used in the evaluation of social programs. The most methodologically challenging type of evaluation attempts to determine if a program actually has the intended effects on the social conditions it addresses. Variations on experimental and quasi‐experimental research designs are used for this purpose, but their application to social programs involves many distinctive issues, problems, and pitfalls.
Shared Environment
Authors: Dick Danielle M.
Published: September 2014

The shared environment (also called common environment) refers to environmental influences that have the effect of making siblings more similar to one another. Shared environmental influences can include shared family experiences, shared peer groups, and sharing the same school and community. In general, there has not been strong evidence for shared environmental effects on many behaviors, particularly those measured in adults. Possible reasons for this are discussed. Shared environmental effects are evident in children and adolescents, but these effects generally decrease across the life span. New developments in behavior genetic methods have made it possible to specify shared environments of importance and to tease apart familial and nonfamilial sources of shared environmental influence.
Test Translation
Authors: Hershberger Scott L. , Fisher Dennis G.
Published: September 2014

Semantic and conceptual equivalence should characterize tests translated from one language to another. Semantic equivalence concerns the degree to which the ideas implied by the words and phrases of the original test have been captured by those in the test's translation. Various methods of confirming semantic equivalence include direct translation, translation/back‐translation, the ultimate test, the parallel blind technique, and the random probe technique. Semantic equivalence is most likely when the two test versions have been developed in tandem. Conceptual equivalence concerns the similarity of the construct measured by the two test versions. Methods for confirming conceptual equivalence tend to be more statistical than those methods used to confirm semantic equivalence, and include exploratory and confirmatory factor analyses, and item response theory (IRT)
Tree Models
Authors: Daniels Richard , Shi Dailun
Published: September 2014

Tree models are statistical tools which allow for the estimation of hypothetical parameters that represent probabilities of unobservable events. They are appropriate for analyzing categorical data, where each observation belongs to one of a finite set of categories. We describe the structure and characteristics of multinomial tree models, and provide an illustrative example.
Social Psychology
Authors: Judd Charles M. , Muller Dominique
Published: September 2014

In part due to the wide array of their theoretical interests, social psychologists use, and sometimes develop, a similarly wide array of statistical tools. These tools range from basic analysis of variance, with factors either between and/or within participants, to more integrative general linear models, and from multilevel modeling procedures to structural equation modeling.
INUSConditions
Authors: Horsten Leon , Weber Erik
Published: September 2014

We give a concise summary of Mackie's theory of causation, which can be seen as a sophisticated variant of the analysis of the notion of causation in terms of necessary and sufficient conditions. Special emphasis is placed on the notion ‘INUS condition’, which occupies a central place in Mackie's account.
Overdispersion
Authors: Dean Charmaine B.
Published: September 2014

Count data analyzed under a Poisson assumption or data in the form of proportions analyzed under a binomial assumption often exhibit overdispersion, where the empirical variance in the data is greater than that predicted by the model. This entry illustrates how overdispersion may arise and discusses the consequences of ignoring it, in particular, the underestimation of standard errors of covariate effects. Straightforward methods for ascertaining whether overdispersion is evident are presented. Simple methods for incorporating overdispersion within the framework of quasi‐likelihood estimation are reviewed as well as generalized linear mixed models, which form a broad scheme for model‐based analysis when overdispersion is present.
Covariates
Authors: Sauerbrei Willi
Published: September 2014

The central role of covariates for several issues in clinical research and patient management is discussed. In clinical trials covariates are used to define the patient population, to allocate treatment in a randomized trial, to adjust the estimate of a treatment effect, and to define subgroups for the investigation of interactions. Furthermore, we will discuss the issue of categorization or determination of the functional relationship to describe the influence of a continuous covariate on an outcome of interest. Some issues of reporting and of the summary assessments of prognostic markers are discussed in the last section. (The issues of analyzing a prognostic marker study are discussed elsewhere.) To further increase and improve the use of covariate data of individual patients, it is important to have reliable measurements and to analyze the data and their interrelationships with the most suitable statistical approach. Often, this will require a close collaboration between clinicians and statisticians.
Subgroup
Authors: Wittes Janet
Published: September 2014

Randomized clinical trials are usually designed to show an effect of treatment in the study population at large. Trials rarely have sufficient statistical power to measure reliably the effect of a treatment within a subgroup of that population. They are even less likely to provide reliable information on whether the effect of a treatment differs from subgroup to subgroup. This article defines various terms related to analysis of subgroups. It distinguishes between subgroups defined on the basis of baseline variables from “improper” subgroups defined on the basis of variables measured after randomization. It also discusses hypotheses defined a priori and those identified after the results have emerged. It emphasizes that random variation arising from small sample sizes can lead to unrealistically large or small observed effects and warns investigators to interpret findings from subgroups with caution.
Sequential Analysis ‐ Recent Advances
Authors: Lai T. L.
Published: September 2014

This article gives an overview of sequential analysis and its applications to data and safety monitoring of clinical trials, determination of median effective dose and maximum tolerated dose, fixed‐width confidence intervals, and adaptive design of biomedical studies.
Mother to Child Human Immunodeficiency Virus Transmission Trials
Authors: Shapiro David E.
Published: September 2014

Mother‐to‐child transmission (MTCT) of human immunodeficiency virus type 1 (HIV) can occur through three major routes: through the placental barrier during gestation, via contact with maternal bodily fluids during labor and delivery, and by ingestion of breast milk after delivery; most MTCT is believed to occur close to the time of or during childbirth. Prevention of MTCT (PMTCT) is one of the most successful areas in HIV clinical research. Interventions to prevent MTCT have been developed for two different settings: the United States and other developed countries with access to medications, medical infrastructure, and safe replacement feeding so that breastfeeding can be discouraged; and resource‐limited countries with limited access to medications and clean water, and where breastfeeding is critical. This article describes four important PMTCT randomized trials, including two trials of key interventions used in developed countries and two trials of interventions feasible in resource‐limited settings.
Physicians' Health Study (PHS)
Authors: Gaziano J. Michael , Hennekens Charles H.
Published: September 2014

The 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

The 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

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.
Hazard Rate
Authors: Gail Mitchell H.
Published: September 2014

The hazard rate is defined and related to other quantities used in epidemiology and survival analysis.
Wilcoxon‐Mann‐Whitney Test: Definition and Example
Authors: Moses Lincoln E.
Published: September 2014

This 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

Loglinear 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

Multiple 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

Historically, 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.
Incompetent Subjects and Proxy Consent
Authors: Glantz Leonard H.
Published: September 2014

Research with incompetent subjects presents the most serious ethical challenges than any other category of research. Special limitations are set on the types of research that can be conducted with this population. Both researchers and those who provide surrogate consent have obligations to protect the rights and welfare of this population that exceed those duties owed to competent subjects.
Microarray
Authors: Page Grier P. , Cui Xiangqin
Published: September 2014

Microarray 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

Subjects 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

Hazard 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

Cost‐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

Subgroup 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

In 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

Pharmacovigilance 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.
Prognostic Variables in Clinical Trials
Authors: Berger Vance W. , Sankoh A. J.
Published: September 2014

Identifying and making appropriate use of influential prognostic factors in the design and analysis of clinical trials is important. The discussion focuses on the nature and validity of covariates, and some statistical considerations for the selection of and adjustment for important prognostic factors.
PhaseIVTrials
Authors: Day Simon
Published: September 2014

This 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

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

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

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

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

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

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

Defining 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

Most 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.
Gold Standard
Authors: Agin Marilyn A.
Published: September 2014

In economics, the gold standard refers to a monetary standard under which the currency of a country is equal in value to and redeemable by a certain quantity of gold. Today, informal usage of the term “gold standard” has expanded beyond economics to denote a practice or principle that is a model of excellence with which things of the same class can be compared.
Intention‐to‐Treat: Overview
Authors: Lachin John M.
Published: September 2014

The 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.
Sample Size Calculation for Comparing Variabilities
Authors: Wang Hansheng , Chow Shein‐Chung
Published: September 2014

In clinical research, in addition to comparing mean responses between treatment groups, it is also of interest to compare variabilities of responses between treatment groups. In practice, it is a concern that (1) larger variability may result in a worse safety profile and (2) larger variability may decrease the probability of reproducibility of the observed clinical results. Besides, efficacy and safety assessment are often done based on mean responses by assuming that the associated variabilities are the same, which may not be true in many cases. As a result, comparing variabilities of responses between treatment groups has received much attention lately. In order to achieve a desired statistical power when comparing variabilities of responses between treatment groups, commonly used procedures or formulas, which are derived based on comparing mean responses, cannot be applied directly and are necessarily modified.
Predicting Random Effects in Community Intervention
Authors: J. Stanek Edward
Published: September 2014

The main objective in a group randomized trial is a comparison between treatments. Often, interest exists in the expected response for a particular group receiving a treatment. As a random sample of groups is included in the trial, the parameter for such a group is represented as a random effect. In this context, predicting the expected response for a group requires predicting a random effect. Proposed solutions to this problem are reviewed focusing on differences in the model assumptions in a simple setting. The author concludes that although popular computing solutions exist, fundamental open questions remain that impact interpretation.
Combination Therapy
Authors: Darbyshire Janet
Published: September 2014

Combination therapy regimens most commonly combine two or more drugs which have additive or synergistic action to treat a disease more effectively, frequently infections or cancer, with the aim of producing more effective regimens and preventing the emergence of resistance of the micro‐organism or the tumour to the drugs. However combinations of drugs are widely used to treat many other diseases such as hypertension, asthma, diabetes and arthritis. Combinations of drugs and other modalities, such as surgery or radiotherapy, are also used in certain disease areas, such as cancer.
Multiple Endpoints
Authors: Bretz Frank , Branson Michael
Published: September 2014

The analysis of multiple endpoints is common practice in pharmaceutical research. A vast amount of literature exists on the design and the analysis of clinical trials with multiple endpoints. Related statistical approaches can roughly be classified into endpoint‐specific multiple testing methods and multivariate global tests. This article briefly summarizes the main approaches, describes their advantages and disadvantages, and provides some recommendations on practical issues.
SAS
Authors: Littell Ramon C.
Published: September 2014

The SAS® System is an integrated system of software for data management, analysis, and presentation.
Nested Experimental Designs
Authors: Shah Kirti R. , Sinha Bikas K.
Published: September 2014

Nested experimental designs form an important subclass of experimental designs wherein some factors are found to be naturally nested within the ‘levels’ of other factors. In a study to examine differential effects of several methods of reducing water pollution, for example, water samples may be taken from different counties and in each county possibly from different water sources, say wells. These samples are then treated with the available methods (for reducing water pollution) and the resulting data analyzed. In this case we may consider the counties as being the levels of the first fac
Spatial Design, Optimal
Authors: Zimmerman Dale L.
Published: September 2014

Statistical inferences from spatial data are affected substantially by the spatial configuration of the sites where those data are observed. Thus, it is natural to try to select a configuration that optimizes, in some sense, the quality of the desired inference(s). This article lays out the classical framework for optimal model‐based spatial sampling design. Prediction error variance design criteria are featured, but optimality criteria for estimating model parameters are also mentioned. Optimal designs may be built “from scratch” or by expanding or contracting an existing design.
Weighted Distributions
Authors: Patil G.P.
Published: September 2014

Traditional environmetric theory and practice have been occupied largely with randomization and replication. But in environmental and ecological work, observations also fall in the nonexperimental, nonreplicated, and nonrandom categories. The problems of model specification and data interpretation then acquire special importance and great concern. The theory of weighted distributions provides a unifying approach for these problems. Weighted distributions take into account the method of ascertainment, by adjusting the probabilities of actual occurrence of events to arrive at a specification of the probabilities of those events as observed and recorded. Failure to make such adjustments can lead to incorrect conclusions.
Image Analysis
Authors: Horgan Graham , Glasbey Chris
Published: September 2014

Image analysis is the process of extracting information from digital images. We outline some of the main ideas and illustrate with an application in mapping forestry using satellite images.
Nelder Plots
Authors: Barry Ronald P.
Published: September 2014

A Nelder plot, also called a Nelder Wheel or Nelder Fan, is a systematic planting design in which plants or trees are planted at the intersection of circular arcs and linear spokes. In general, Nelder plots allow many different planting densities to be examined in a single plot. This is frequently more efficient than planting a different plot for each planting density. Nelder plots can be constructed that allow the effect of different planting geometries to be examined in a single plot. This article discusses the construction of Nelder plots and the analysis of data derived from Nelder plots.
Effect Size
Authors: Umbach David M.
Published: September 2014

Broadly construed, effect size is the degree to which one distribution differs from another. While the term itself has connotations of cause and effect, the underlying idea applies to any comparison between two populations without implying causality. Suppose that one seeks to compare two populations with respect to a specified outcome. Regard the populations as a control and an exposed population. The outcome under study will have some distribution across control individuals and, if the exposure has any effect at all, a different distribution among exposed individuals. Differences between the two distributions could be assessed using any one of a number of criteria or even using several criteria simultaneously. A desire for parsimony suggests the use of a single quantity for summarizing distributional differences. Generically, such a quantity assesses the magnitude of the exposure effect, that is, the effect size.
Frequency Curves
Authors: Hosking J.R.M.
Published: September 2014

The fundamental property of a random quantityXis the frequency with which its possible values occur. A frequency curve is the graphical representation of this distribution of frequencies.