bluuebunny/crossref_metadata_2025 huggingface dataset:
Fisherian Tradition in Behavioral Genetics
Authors: Dickins David
Published: September 2014

R.A. Fisher did much to bring about the ‘Modern Synthesis’ of Mendelian genetics with Darwinian natural selection into a theory of evolution that worked. This was the fruit of his dual talent as a mathematician (particularly, but not exclusively, as a statistician) and a biologist. Though his training also in physics would have led him to welcome the subsequent transformation of parts of biology to ‘hard science’ status in the field of molecular and developmental genetics, his insights were in the domain of transmission genetics, acting in populations, from which he derived a cluster of fecund hypotheses that have been particularly influential in the field of behavioral biology/evolutionary psychology.
Free Response Data Scoring
Authors: Clauser Brian E. , Margolis Melissa J.
Published: September 2014

Computers offer the potential to extend assessment by delivering complex interactive item formats that require examinees to respond in ways that more closely approximate the ‘real world’ criterion behaviors of interest. However, for this expansion of assessment to be practically useful, it is necessary for the computer to both deliver and score the assessments. During the last two decades, these procedures have been the focus of considerable effort by numerous researchers. This entry describes the state of the art for computerized scoring of complex item formats. It begins with a presentation of the types of procedures currently in use. The entry then discusses the strengths and weaknesses of differing approaches, focusing on conceptual distinctions between approaches. The discussion concludes with comments about the future of automated scoring.
Cultural Transmission
Authors: Hershberger Scott L.
Published: September 2014

Cultural transmission refers to the transfer of the knowledge, values, and ideas within a population. It is also a nongenetic form of natural selection in which behavioral adaptations that arise from cultural influences are passed from one generation to the next. Importantly, cultural transmission occurs between an individual and the individual's conspecifics, both between generations and within generations. ‘Cultural transmission’ is not synonymous with ‘environmental influence’ – only ‘individuals’ are the relevant part of the environment critical to cultural transmission. The transfer of information is based on social learning specifically, and not on other types of learning such as behavioral reinforcement. Mathematical models of cultural transmission have been developed that emphasize either the similarities or the differences among individuals within a population. Cultural transmission may also be an important cause of correlated genotypes and environments.
Signal Detection Theory
Authors: Wixted John T.
Published: September 2014

Signal‐detection theory is a framework for understanding the process of decision making under conditions of uncertainty. Its essential claim is that subjective evidence for the presence or absence of a stimulus is a continuous variable. That is, evidence comes in degrees and is never entirely present or entirely absent even though the stimulus itself is. Although the average amount of subjective evidence is higher on trials in which the stimulus actually is present, trial‐to‐trial variability is such that no evidence value perfectly distinguishes stimulus‐present trials from stimulus‐absent trials. As such, the observer is assumed to select a criterion evidence value above which the stimulus is declared to be present and below which it is declared to be absent. This way of thinking sheds light on an extremely wide range of decision problems ranging from auditory detection to the detection of a psychiatric condition, and it offers a measure of discrimination (
d
′) that, unlike percent correct, is theoretically uncontaminated by response bias.
Social Interaction Models
Authors: Hewitt John K.
Published: September 2014

Social interaction is modeled in behavior genetics as the influence of one individual's phenotype on another. The consequences of this for phenotypic variances and covariances among relatives of different degrees of relationship can be predicted from an extension to the usual path models. Social cooperation (or imitation) and social competition (or contrast) result in different patterns of variation and resemblance that depend on the prior similarity of the interacting pair. Although of considerable theoretical interest, the behavior genetics literature does not offer many convincing examples of the empirical effects of such social interactions.
Children of Twins Design
Authors: D'Onofrio Brian M.
Published: September 2014

Because parents provide the environmental context for the family and transmit genetic makeup to their offspring, the genetic and environmental processes responsible for associations between family risk factors and offspring adjustment are confounded. Social scientists typically use statistical controls to account for ‘third’ variables that influence both characteristics, but family studies cannot account for unmeasured genetic or environmental confounds. The Children of Twins (CoT) Design can separate co‐occurring genetic and environmental developmental mechanisms because the design utilizes control groups that vary in genetic and environmental risk. As a result, the design can delineate intergenerational associations into (a) environmental processes specifically related to the risk factor, (b) genetic factors that influence the risk factor and offspring characteristic, and (c) common environmental factors that vary between families. The CoT approach offers advantages over traditional family studies that assume genetic factors are not important; rather, the genetically informed design explores the manner in which genetic and environment processes act to influence offspring adjustment.
Heritability: Overview
Authors: Rose Richard J.
Published: September 2014

Heritability, a statistical measure originating in quantitative genetics, is an estimate of the contribution of genetic differences to the differences observed in a measured variable (e.g., some dimension of behavior) in a given population at a given time. Heritability is a population concept. It is an estimate, typically made from the resemblance observed among relatives. It does not describe individuals; it refers only to the underlying differences between people.
ACE
Model
Authors: Maes Hermine H.
Published: September 2014

The ACE model refers to a genetic epidemiological model that postulates that additive genetic factors (A) (
see
Additive Genetic Variance
), common environmental factors (C), and specific environmental factors (E) account for individual differences in a phenotype (P) (
see
Genotype
) of interest. This model is used to quantify the contributions of genetic and environmental influences to variation and is one of the fundamental models of basic genetic epidemiology.
Panel Study
Authors: Billiet Jaak
Published: September 2014

Panel surveys are most suitable for the study of attitude change over time and for arriving to causal conclusion about phenomena in the social world. The opportunities and main weaknesses of panel studies are introduced. A selected bibliography may help the reader to gain more in‐depth knowledge about this issue.
Resentful Demoralization
Authors: Onghena Patrick
Published: September 2014

Resentful demoralization is a threat to the construct validity of the treatment in intervention studies. This threat may occur in intervention studies in which comparison groups not obtaining a desirable treatment become discouraged or retaliatory and, as a result, perform worse on the outcome measures. Some strategies to avoid, minimize, or deal with resentful demoralization are suggested and illustrated.
Teaching Statistics: Sources
Authors: Beins Bernard C.
Published: September 2014

Seven books, six journals, and a web page devoted to material on teaching statistics are reviewed. Sources are ordered from general to technical, and examples illustrative of the content are provided.
Counterfactual Reasoning
Authors: Holland Paul W.
Published: September 2014

Counterfactual conditional statements underlie definitions of causation. The substitution of factual, observed data in control groups for counterfactual information about treatment groups is the basis of causal inference in randomized experiments and observational studies. Formal models that give causal interpretations to statistical calculations and that involve counterfactual quantities were introduced as early as 1923.
Probability: Foundations of
Authors: Macdonald Ranald R.
Published: September 2014

Probability and probability models are mathematical representations of uncertainties. They can be seen either as analogies with urn models or as a logical system which conforms to a set of probability laws or axioms. It is argued that if uncertainties conform to an urn model or to the probability axioms and the probability space is dense enough then there is a correct probability associated with every uncertainty. Because probability laws place constraints on uncertainties there are disputes over where probabilities apply. One view is that probabilities are relative frequencies. This means that probabilities do not apply to unique events as it might be necessary to break down any relative frequency further before it can be applied to a particular event. Nevertheless some conformity between people's judgments and the frequency view of probability are reported. An alternative subjective approach takes probabilities to be measures of belief but it is unclear then why they should conform to probability laws. A partial answer is that if a person X bets in accordance with subjective probabilities which violate these laws then X can be made to lose regardless of the state of the world. Problems with taking probabilities to be measures of belief are discussed and it is concluded that neither the frequency nor the subjective approaches provide absolute standards against which people's uncertainty judgments should be compared. Theorists have attempted to identify objective probabilities but it is unclear how their existence could ever be established. Nevertheless the search for objective probabilities is defended.
Computer‐Assisted Data Collection
Authors: Gabel Tim J. , Pedrazzani Susan D. , Day Orin A.
Published: September 2014

Computer‐assisted data collection methods have been widely used in the government survey and market research industries but have not been as prominent in the clinical research setting, which relies heavily on the use of paper‐based case report forms. In many clinical studies, respondents typically use paper forms to provide self‐reported data for the study. These forms are then converted to electronic format by data‐entry or clinical data management systems suitable for electronic data capture. Methods such as audio computer‐assisted self‐interviewing (A‐CASI) and telephone audio computer‐assisted self‐interviewing (T‐ACASI) may be particularly helpful in collecting high‐quality patient‐reported data, especially in Phase IV and postmarketing surveillance studies. This article introduces the primary considerations for using A‐CASI or T‐ACASI and provides insights into the management of data collected with these methods. It describes some of the advantages and data management efficiencies inherent in computer‐assisted interviewing systems and concludes with specific examples of successful use of these approaches in clinical studies.
Integrated Database
Authors: Kenny Susan J.
Published: September 2014

Integrated databases contain information collected from more than one data source. In the process of combining different data sources, individual data elements often need to be transformed or harmonized so that they represent a consistent or standard context. This transformation must be done without the loss of data integrity and interpretation. Combining data from multiple sources yields increased statistical power to identify important relationships between data elements. Without the increased sample size of an integrated database, the relationship between infrequently occurring rare events with some dependent factor, such as treatment regimen, may be difficult to identify. Integrated databases are used by research institutes, pharmaceutical companies, and regulatory agencies during clinical development and during postmarketing surveillance. Integrated databases have been used for many years, but in recent years, the use of integrated databases has expanded beyond the classic application to clinical trial data to include applications to image, genomic, and other digital data. More advances in data warehousing, enterprise software applications, and efforts in standardization of clinical information have influenced the construction and use of integrated databases.
Query Management: The Route to a Quality Database
Authors: Hauser Larry A. , Morrow Ellen
Published: September 2014

The success of a clinical trial depends on the quality and integrity of the results captured in the database. If data stored in the database are incorrect, conclusions of analyses may also be incorrect. The purpose of the query management process is to achieve a high‐quality database to be used for additional analysis and reporting, whether for a regulatory filing or a peer‐reviewed publication. This task is accomplished by first identifying any data that seem questionable for accuracy or completeness and then taking steps to address the inconsistency. This article focuses on query management for a paper‐based study processed from a case report form, as well as data captured at the investigator site via an electronic data capture (EDC) system. EDC offers several key advantages, with potential for improved efficiencies and reduced time to database lock. Therefore, it is at a tipping point in its adoption, with nearly half of all new Phase I–III studies forecast to use EDC by the end of 2007. Future trends are projected to take the technology a step further with eSource (without a paper source), which results in even greater changes from the traditional roles that Data Management, the clinical monitor, and the investigator staff have historically played.
Data Standards
Authors: Helton Edward , Kush Rebecca
Published: September 2014

The “globalization” of clinical research over the past two decades has stressed routine paper‐based processes and the associated regulatory guidelines. A slow but steady trend has occurred toward electronic clinical trials within the industry and electronic regulatory submissions by regulatory authorities. This trend has in turn encouraged the development and adoption of data standards to facilitate exchange of clinical research information among partners (i.e., interoperability among computer systems). Standards to support clinical research from protocol development through analysis, reporting, and archive of electronic data are now available through the Clinical Data Interchange Standards Consortium (CDISC). A concurrent commitment has been made to harmonize these clinical research standards with those healthcare standards through Health Level Seven (HL7) since 2001; the rationale is to pave the way for the use of electronic health records for research and safety reporting, in addition to patient care, to improve the workflow associated with clinical research for investigative site personnel. CDISC standards are now being implemented and adopted globally. In addition, the FDA has participated in pilot projects with CDISC to identify best practices for implementation. More work must be done to augment the base standards; all stakeholders in clinical research are encouraged to become involved in influencing and extending this collaborative work.
Bootstrap with Examples
Authors: Hesterberg Tim
Published: September 2014

This article provides an introduction to the bootstrap. The bootstrap provides statistical inferences—standard error and bias estimates, confidence intervals, and hypothesis tests—without assumptions such as normal distributions or equal variances. As such, bootstrap methods can be remarkably more accurate than classic inferences based on normal ortdistributions. The bootstrap uses the same basic procedure regardless of the statistic being calculated, without requiring the use of application‐specific formulae.

This article may provide two big surprises for many readers. The first is that the bootstrap shows that commontconfidence intervals are woefully inaccurate when populations are skewed, with one‐sided coverage levels off by factors of two or more, even for very large samples. The second is that the number of bootstrap samples required is much larger than generally realized.
Sensitivity, Specificity and Receiver Operator Characteristic (
ROC
) Methods
Authors: Kraemer Helena Chmura
Published: September 2014

Three of the most common problems in medical research are as follows: (
1
) evaluating a medical test against a diagnostic criterion in a population, (
2
) choosing the best among available tests, and finally (
3
) developing the best combination of medical tests. In 1979, a
Lancet
editorial commented that
“The assessment of diagnostic methods belongs to the backwoods of clinical research. New treatments are submitted to thorough testing and books are written on the methodology of the controlled therapeutic trial; but no standards have been fixed for the assessment of diagnostic methods.”
Since 1979, some progress has been reflected in the Standards for Reporting of Diagnostic Accuracy (STARD) guidelines, but statistical methods remain much less developed than the methods for the assessment of treatments in randomized clinical trials. As a result, medical tests often enter general clinical use with little or no convincing documentation of their accuracy, which then has major repercussions on patient health and medical costs.
Confounding
Authors: Greenland Sander
Published: September 2014

The word
confounding
has been used to refer to at least three distinct concepts. In the oldest usage, confounding is a
bias
in estimating causal effects (
see
Causation
). This bias is sometimes informally described as a mixing of effects of extraneous factors (called
confounders
) with the effect of interest. This usage predominates in nonexperimental research, especially in epidemiology and sociology. In a second and more recent usage, confounding is a synonym for noncollapsibility (
see
Collapsibility
), although this usage is often limited to situations in which the parameter of interest is a causal effect. In a third usage, originating in the
experimental‐design
literature, confounding refers to inseparability of main effects and
interactions
under a particular design. The term
aliasing
is also sometimes used to refer to the latter concept; this usage is common in the
analysis of variance
literature.
Risk Assessment
Authors: Armitage Peter
Published: September 2014

The “risk” of a potentially dangerous hazard may be measured either by the probability of its occurrence (perhaps per unit time or unit of exposure), or by combining this with a measure of its severity. Risk assessment may involve estimation of these quantities, evaluation of their significance for individuals and communities, the extent to which individuals may differ in their perceptions of risks, and the measures taken by individuals and communities to reduce the impact of risks. These aspects are considered in turn.
Stepped Wedge Design
Authors: Hughes James P.
Published: September 2014

Stepped wedge trials are randomized trials in which each randomization unit first receives the control condition and then crosses over to receive the intervention. However, the time at which the intervention is provided to each unit is randomized. Stepped wedge trials may be either individually or cluster‐randomized. The stepped wedge design is particularly useful when it is not feasible to provide the intervention to everyone or every community at once, and for evaluating the effectiveness of interventions that have been shown to be efficacious in a more limited, research setting and are now being scaled up to the community level. This design is also useful for evaluating temporal changes in the intervention effect.
Application of New Designs in PhaseITrials
Authors: Garrett‐Mayer Elizabeth
Published: September 2014

In recent years, novel designs have been introduced for dose‐finding studies in phase I clinical trials. Most of these are based on mathematical models of the relationship between dose and toxicity. Though these may appear more technical than more commonly used algorithmic designs, the novel designs tend be better for many reasons including smaller sample size, more precise and accurate estimate of the desired dose, fewer patients treated at unsafe levels, and more patients treated at efficacious doses. Several of these novel designs are discussed. The continual reassessment method is an adaptive approach that uses the information gathered on early patients in the trial to make decisions about the dose for patients treated later in the trial. Adaptive Bayesian designs behave in a similar way, but the framework in which they are developed differs. A design in which efficacy and toxicity are both used to determine an optimum dose is also discussed.
Group Sequential Designs
Authors: Wassmer Gernot
Published: September 2014

In clinical research, a great interest in interim analyses exists for ethical, economical, and organizational or administrative reasons. Interim analyses are performed withinsequential designsthat offer the possibility to stop a trial early with a statistically significant test result. This trial will possibly need less patients than the trial with a fixed sample size in which a test decision can be made only at the end of the trial. A therapy that was shown to be superior can be applied earlier and the inferior therapy can be replaced by the better one. Furthermore, in interim analyses, the quality of the performance of the trial can be assessed and possibly be improved when necessary. Interim analyses are also carried out for the assessment of the safety of the treatments under consideration, and the observation of serious adverse events can lead to an early stopping of the trial.
Proportional Odds Model: Overview
Authors: Liu Ivy , Mukherjee Bhramar
Published: September 2014

Outcomes recorded on an ordinal scale, such as severity of migraine, stages of progression of disease, or degree of relief caused by treatment, occur commonly in clinical trials. Questionnaire data, which are often collected at each visit to gauge patient condition, routinely contain responses on an ordinal scale. Naïve dichotomization of the full ordinal scale leads to loss of information and efficiency while analyzing such outcomes. The proportional odds model is a popular choice for analyzing such ordinal outcomes. The current article provides a concise introduction to the proportional odds model and proceeds to review some more recent work related to this model. Specifically, we discuss the problem of analyzing stratified ordinal data in which observations from the same strata are correlated. Such correlated ordinal data may develop in multicenter clinical trials or with ordinal responses measured repeatedly on the same subject in longitudinal studies. Certain computing tools for fitting such models are discussed.
Fisher's exact test including power and mid‐P value
Authors: Routledge Rick
Published: September 2014

Fisher's exact test can be used to assess the significance of a difference between the proportions in the two groups. In particular, it provides a widely applicable way to assess the results of simple, completely randomized experiments leading to two‐by‐two contingency tables with small frequency counts, However, it has low power, especially when the standardP‐value is used. The power can be increased considerably through either using the mid‐Pvalue, or carefully constructing a test based, at least in part, on a binomial model.
Community‐Based Breast and Cervical Cancer Control Research in Asian Immigrant Populations
Authors: Taylor Victoria M. , Hislop T. Gregory , Yasui Yutaka , Tu Shin‐Ping , Jackson J. Carey
Published: September 2014

Asian‐American women are less likely to receive mammography and Papanicolaou (Pap) testing than women from other racial/ethnic groups. Screening rates are particularly low among Asian women who are not proficient in English. This article focuses on community‐based breast and cervical cancer control research in Asian populations. First, the authors address community participatory research principles and several important program evaluation issues: sampling, survey methods, recruitment and retention, translation, data quality, and control group “contamination.” Second, the authors summarize community‐based studies that aimed to increase mammography or Pap testing levels among women of Asian descent. Overall, 9 relevant studies are identified that targeted Asian communities in Canada, the United States, and England. Overall, three of the studies randomized individual women to intervention or control status, two randomized groups of women, and four used a quasi‐experimental (two‐community) study design. These studies, which reported mixed results, highlight the methodological challenges in conducting evaluative public health research, particularly in racial/ethnic minority communities.
TNTTrial
Authors: LaRosa John C. , Grundy Scott M. , Waters David D.
Published: September 2014

In the Treating to New Targets (TNT) study, 10,001 patients with stable CHD and LDL‐C levels <130 mg/dL (3.4 mmol/L) were randomized to double‐blind therapy with either atorvastatin 10 or 80 mg/day and were followed for a median of 4.9 years. The primary endpoint was an occurrence of a first major cardiovascular event, defined as CHD death, nonfatal, non‐procedure‐related myocardial infarction, resuscitated cardiac arrest, and fatal or nonfatal stroke. Mean LDL‐C levels achieved during treatment were 77 mg/dL (2.0 mmol/L) with atorvastatin 80 mg and 101 mg/dL (2.6 mmol/L) with atorvastatin 10 mg. A primary event occurred in 434 patients (8.7%) receiving atorvastatin 80 mg and 548 patients (10.9%) receiving atorvastatin 10 mg, representing a 2.2% absolute reduction in risk and a 22% reduction in risk of major cardiovascular events (HR = 0.78; CI = 0.69, 0.89;P < 0.001) in the high‐dose group. In conclusion, intensive lipid‐lowering therapy with atorvastatin 80 mg in patients with stable CHD provided significant additional clinical benefit beyond therapy with atorvastatin 10 mg.
UGDPTrial
Authors: Meinert Curtis
Published: September 2014

The UGDP (University Group Diabetes Program) was a multicenter, randomized, placebo‐controlled trial aimed at assessing the efficacy of antidiabetic treatments in delaying or preventing the long‐term complications of adult‐onset diabetes (type‐2 diabetes). The trial was in the carly vanguard of trials aimed at assessing efficacy of approved treatments for chronic conditions—phase IV trials in the vernacular of trials. The trial involved two oral agents, tolbutamide and phenformin, and matching placebos and two insulin‐assigned treatment groups (insulin dosage as determined by body surface and insulin dosage as needed to “normalized” blood glucose levels). The trial generated a firestorm of controversy in 1970 with presentation and publication of results suggesting that a widely used oral agent—tolbutamide (Orinase, Upjohn, Kalamazoo now part of Pfizer)—was of no value in reducing the risk of morbiarty or in prolonging life. The FDA used its results to revise the label for the drug to include a special warning concerning the possibility cardiovascular risks associated with the drug, but only after a prolonged battle that raged on for more than a decade—a battle that ultimately ended up in front of the U.S. Supreme Court.
Coronary Drug Project
Authors: Canner Paul L.
Published: September 2014

The Coronary Drug Project, which was conducted during 1966 to 1974, was a randomized, double‐blind, placebo‐controlled trial of five lipid‐modifying agents—estrogens at two dosage levels, dextrothyroxine, clofibrate, and niacin—in 8341 men with previous myocardial infarction. The primary efficacy outcome was all‐cause mortality for the entire follow‐up period. The first three named regimens were terminated early because of adverse effects. Only niacin significantly reduced the risk of (1) cardiovascular events during a mean follow‐up of 6.2 years and (2) all‐cause mortality during 6.2 years with study treatment plus an additional 9 years of post‐trial follow‐up. Analyses of treatment–placebo differences with respect to all‐cause mortality, subdivided by level of adherence to the study prescription and by amount of change in serum cholesterol level, could not be properly interpreted because reasons for good/poor adherence and for cholesterol changes in the placebo group were not well defined.
Cox Proportional Hazard Model
Authors: Cai Jianwen , Zeng Donglin
Published: September 2014

In this article, we provide an overview on the Cox proportional hazards model. We discuss the partial likelihood approach for parameter estimation and inference. Some practical issues in using the Cox regression model are discussed, including how to handle tied data, incorporate time‐dependent covariates, and fit the Cox regression model in stratified population. The article also reviews some practical ways for assessing proportional hazards assumption; one authentic example is used to illustrate data analysis using the Cox regression model. Finally, we list a few extensions of the Cox regression model.
Logrank Test
Authors: Hopkins Alan
Published: September 2014

The nonparametric log‐rank test compares two or more independent estimated time‐to‐event curves (survival functions) based on censored data. Right‐censored data originates in clinical trials when measuring time‐to‐an‐event. Time‐to‐infection, time‐to‐disease progression, and time‐to‐death are examples of clinical endpoints that may not be observed on all subjects randomized in a clinical trial because of a fixed follow‐up period or dropout. We shall refer to time‐to‐event data with right censoring as survival data. The log‐rank test is the most commonly used test for comparing survival functions among a class of weighted log‐rank statistics. The log‐rank test is the most powerful nonparametric test to detect proportional hazards alternatives. It is closely related to the Cox regression model.
Intraclass Correlation Coefficient with Emphasis on Clinical Trials
Authors: Campbell Marion K. , Grimshaw Jeremy M. , Piaggio Gilda
Published: September 2014

Increasingly researchers are recognizing that in many situations, using a cluster‐randomized trial (in which groups of individuals are randomized rather than individuals directly) may be more appropriate than an individually randomized trial. Cluster‐randomized trials are more complex to design, however, primarily because observations on individuals within the same cluster tend to be correlated. The statistical measure of this correlation within clusters is known as the intraclass or intracluster correlation coefficient (ICC). In this article, we discuss the measurement and impact of the ICC on the design and analysis of cluster‐randomized trials. In particular we outline how an ICC can be calculated, the likely magnitude of ICCs in specific settings, the factors that influence the ICC estimate, how an ICC should be reported, and the impact of the ICC on sample‐size requirements and analysis.
Aligned Rank Test
Authors: McNeil Rebecca B. , Woolson Robert F.
Published: September 2014

Aligned rank tests are an intuitive rank‐based approach to the evaluation of an experimental treatment effect in blocked or stratified data. Their interblock ranking foundation yields an efficiency advantage relative to comparable tests constructed from intrablock rankings such as Friedman's test. Both exact and asymptotic tests of the null hypothesis are available with this approach. We present a general overview of the history and construction of aligned rank tests, and provide an example in selected data from a clinical trial of high‐dose vitamin D supplementation.
Nonparametric Methods
Authors: Wolfe Douglas A.
Published: September 2014

The motivation behind distribution‐free hypothesis tests and confidence intervals based on signs and ranks is discussed and standard text references are provided. Distribution‐free test and confidence interval procedures for the one‐sample, two‐sample, one‐way analysis of variance, two‐way analysis of variance, and independence settings are presented. Point estimation techniques, including Hodges–Lehmann estimators and contrast estimation, and multiple comparisons procedures are described. Basic references for censored data approaches and bootstrapping are provided.
Generalized Estimating Equations: Introduction
Authors: Hardin James W. , Hilbe Joseph M.
Published: September 2014

Correlated datasets develop when multiple observations are collected from a sampling unit (e.g., repeated measures of a bank over time, or hormone levels in a breast cancer patient over time), or from clustered data where observations are grouped based on a shared characteristic (e.g., observations on different banks grouped by zip code, or on cancer patients from a specific clinic). The generalized linear model framework for independent data is extended to model correlated data via the introduction of second‐order variance components directly into the independent data model's estimating equation. This generalization of the estimating equation from the independence model is thus referred to as a Generalized Estimating Equation (GEE). This article discusses the foundation of GEEs as well as how user‐specified correlation structures are accommodated in the model‐building process. This article also discusses the relationship and similarity to the underlying generalized linear model framework and we point out alternative approaches to GEEs for modeling correlated data such as fixed‐effects models and random‐effects models.
Over the Counter (
OTC
) Drugs
Authors: Brass Eric P.
Published: September 2014

Regulatory decision making and public health efforts are dependent on clinical research to define the ability of consumers to self‐manage treatment regimens with over‐the‐counter drugs. Label compression studies evaluate the ability of consumers to understand the key communication messages of a prototype drug label. Self‐selection studies assess the ability of consumers to use the label to determine whether use of the proposed drug is appropriate for them based on their individual health status. Actual use studies allow assessment of consumers' ability to decide whether to use a proposed over‐the‐counter drug, and they evaluate how consumers manage the treatment regimen. These clinical research methods pose unique challenges as they balance the need for rigorous assessments with efforts to facilitate unbiased participant decision making to understand how the drug would be used in the open marketplace without professional supervision.
Dermatology Trials
Authors: Feldman Steven R. , Clark Adele R.
Published: September 2014

One characteristic of many skin disease clinical trials is that they involve topical treatments. The direct application of potent topical medications can have excellent efficacy while minimizing the potential for systemic side effects. A limitation of trials in dermatology is that topical treatment tends to be more difficult for patients to use than oral medications. An advantage of dermatologic trials is that the outcomes are readily visible on the skin. Investigator and patient assessed outcomes can be used. A trial of a topical clobetasol illustrates the rapid effects of the drug and the relative safety over the short term.
Central Nervous System (CNS)
Authors: Sommer David B. , Goldstein Larry B.
Published: September 2014

Modern clinical trials have revolutionized the practice of neurology. This introduction examines some of the issues and challenges involved in therapeutic trials for six common conditions that affect the central nervous system (ischemic stroke, Alzheimer's disease, migraine headache, epilepsy, Parkinson's disease, and multiple sclerosis) and explores how trials in these areas influence clinical practice. Examples of ongoing trials in each of these areas are also identified.
Update in Hyperlipidemia Clinical Trials
Authors: Chien Kuo‐Liong
Published: September 2014

Current large‐scale clinical trial evidence clearly demonstrates that statins can reduce cardiovascular mortality and morbidity in hyperlipidemic patients. These clinical trial results clearly indicate that cholesterol reduction can reduce cardiovascular risks significantly. However, several issues are still under debate, such as subgroup benefits, lipid subtypes, and new outcome measures. Progress on study design, surrogate endpoints, outcomes measures, and new drugs improve the knowledge of hyperlipidemia treatment. In this review, the recent advances on clinical trials of hyperlipidemia will be discussed and some hints on recent updated information will be provided.
Informed Consent Process, Forms, and Assent
Authors: Glantz Leonard H.
Published: September 2014

Informed consent is a foundational principle for the ethical conduct of research with human subjects. It is essential for researchers to understand the purposes of the doctrine to enhance their ability to obtain both ethically and legally valid informed consent.
“Patients” vs. “Subjects”
Authors: Glantz Leonard H.
Published: September 2014

The terms “patient” and “subject” are often used interchangeably when proposing, conducting, or reporting on research. This error is not just a semantic one but also a conceptual one. Proper use of these terms is essential for all parties involved in the research to understand clearly their roles, expectations, and obligations.
Institutional and Independent Review Boards
Authors: Fish Susan S.
Published: September 2014

Institutional Review Boards (IRBs), or Independent Ethics Committees (IECs) as they are known around the world, are a cornerstone of the system of protections human research subjects. An IRB is a committee that functions as a regulatory and ethics review board for research that involves human subjects. In countries that regulate research in humans, IRBs/IECs are required to approve research studies prior to performing any research intervention, which includes screening, on any subject. In countries without research regulations, IRBs/IECs may also review research. This article focuses mainly on IRBs that are regulated by U.S. agencies.
Ethical Challenges Posed by Cluster Randomization
Authors: Klar Neil , Donner Allan
Published: September 2014

The purpose of this article is to discuss the ethical challenges posed by cluster randomized trials, which is done by revisiting the development of ethical guidelines that have been largely developed in the context of individually randomized clinical trials. A recurring theme underlying the article is that the relative absence of ethical guidelines for cluster randomized trials appears to have created a research environment in which the choice of randomization unit may determine whether informed consent is deemed necessary before random assignment. It seems questionable, on both an ethical level and a methodological level, whether the unit of randomization should play such a critical role in obtaining informed consent. Editors should require all reports of randomized trials to include mention of Institutional Review Board (IRB) approval and to discuss subject consent. For cluster randomized trials, however, additional discussion of procedures used to obtain informed consent is warranted.
Clinical Trial Misconduct
Authors: Rennie Drummond
Published: September 2014

This article reviews the history and impact of research misconduct. It outlines some of the early cases that prompted standards and legislation. Finally, it discusses recent research abuses within and outside the United States.
Multistage Genetic Association Studies
Authors: Thomas Duncan , Conti David
Published: September 2014

The practice of individualized medicine for therapy or prevention could be informed by studies of genetic modifiers of treatment effects. This special case of gene–environment interaction studies offers several unique opportunities by exploiting random assignment of treatments. Two basic approaches are possible, one based on genetic studies added to a preexisting randomized controlled trial, the other based on targeted intervention studies added to a preexisting genetic cohort study. We focus in particular on multistage sampling designs that exploit the prior information from the parent study to improve the statistical efficiency of the study of genetic modifiers of treatment effects. In particular, we discuss different contexts in which either design might be used—a study of a single candidate gene, an entire pathway, or a genome‐wide association scan.
Longitudinal Data
Authors: Li Hung‐Ir
Published: September 2014

Longitudinal data have been the focus in various research such as economy, social science, public health, and biomedical research in the past few decades. These data comprise outcome measures or responses prospectively collected or naturally accrued repeatedly from the same subjects across multiple time points along with subject characteristic measures. In some observational studies in epidemiology/public heath, the data accrued over a long period of time as recorded in medical charts and are retrospectively retrieved. The data are used to answer questions regarding the change or progression of the disease over time and/or potential risk factors for the disease. Although in other studies, the existing data could be used to identify subgroups with different background characteristics or traits. The identified groups are then followed up for their health conditions for a certain period of time prospectively. The data from these groups are then compared with answer questions set out for the studies. In the clinical research and development settings, most of the longitudinal data are collected via prospectively designed clinical trials in order to assess the changes in efficacy and/or safety of certain medical intervention, drug, biologics, or devices. The longitudinal data from clinical trials in phase III of product development are intended to confirm that the clinical benefit or desired changes over time are primarily due to the target product, can be maintained for an adequate amount of time, and are applicable to a well‐defined population before regulatory approval is granted and the product is registered for public use. This article provides a summary of the definitions, features, and various types of longitudinal data resulting from clinical trials in phase III medicinal product development. The respective characteristics and the related issues along with the rules of thumb in the selection of the statistical methods for the analyses will also be provided.
Confidence Intervals in Clinical Trials
Authors: Reiczigel Jeno
Published: September 2014

A confidence interval (CI) or interval estimate for an unknown parameter θ is a “from‐to” range calculated from the sample so that it contains θ with a high—usually 90%, 95%, or 99%—probability. CIs can be given for unknown population parameters like the mean, variance, median, proportion, or for model parameters like a regression coefficient, odds ratio, correlation coefficient, or for functions of the above‐mentioned parameters like, for example, the variance/mean ratio, the difference of two proportions or two means, and so on. In clinical studies, CIs are typically given for the mean or median treatment effect, proportion of cure, difference in these outcome measures comparing two treatments, and so on. Practically for anything for which an estimate from the sample is meaningful at all, a CI is also meaningful. For curves like a regression line or a ROC curve, interval estimation results in a so‐called confidence band.
Categorical Response Models
Authors: Tutz Gerhard
Published: September 2014

This article discusses category response models, with a focus on binary response models with covariates, ordinal responses, and nominal responses. Inferences are provided for multicategorical responses and developments and examples are reviewed.
Discriminant Analysis with Biomedical Examples
Authors: Wernecke Klaus‐D.
Published: September 2014

This article describes discriminant analysis. First, a brief overview is provided followed by discussions of allocation rules and their error rates, Bayes allocation rules, discrimination using normal models, nonparametric discrimination, logistic discriminant analysis, future methods, and judging reliability. Finally, a Section on software packages concludes the article.
Frailty Models
Authors: Wienke Andreas
Published: September 2014

The notion of frailty provides a convenient way to introduce random effects, association, and unobserved heterogeneity into models for survival data. In its simplest form, a frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or related individuals. In essence, the frailty concept goes back to the work of Greenwood and Yule on “accident proneness.” The term frailty itself was introduced by Vaupel et al. in univariate survival models, and the model was substantially promoted by its application to multivariate survival data in a seminal paper by Clayton (without using the notion “frailty”) on chronic disease incidence in families.
Generalized Linear Models: Overview
Authors: Kauermann Göran , Norrie John
Published: September 2014

Generalized linear models (GLMs) provide a flexible and commonly used tool for modeling data in a regression context. The approach has had a major impact on statistical modeling technology with research on a wide range of extensions continuing today.
Interval Censored: Introduction
Authors: Gerds Thomas A , Meier‐Hirmer Carolina
Published: September 2014

In survival or time‐to‐event analysis, interval censoring occurs if the time of an event is theendpointof interest that cannot be observed exactly for each patient but occurred chronologically between two follow‐ups. Information of the event status is typically collected by periodically performing clinical examinations or laboratory tests. Events of interest can be the onset of a disease, infection, the recurrence of a tumor, and other changes in the course of a disease. For example, in aclinical trial/study, the progression of a tumor can only be assessed at periodic examination times, e.g., every 6 months. The true time‐to‐progression is hidden somewhere between two adjacent examination times, e.g., between 12 and 18 months. Another typical example is the infection time, e.g., of HIV, when for the diagnosis the result oflaboratory testsis needed. The time‐to‐infection is included in an interval defined by the dates of acquisition. Two sources of censoring can be allocated. External censoring is due to the study design, which prohibits instantaneous access of the event of interest because the patient is not all the time medically supervised. An example for externally introduced censoring is the arrangement of yearly instead of monthly or daily examinations. On the other hand, the censoring can be due to the natural limits of the experiment. For instance, if the event of interest is the onset of a tumor, or anadverse drug reactionbecoming visible only after a certain limit of toxication is exceeded.
Kaplan‐Meier Plot
Authors: Holländer Norbert
Published: September 2014

The Kaplan–Meier plot or Kaplan–Meier curve is the standard method to describe and compare graphically the overall survival of groups of patients. This method is applied to survival time, which is defined as time from treatment start or date of randomization to death or last date of follow up, and, more general, to any type of time‐to‐event data, such as time‐to‐disease progression, time‐to‐detoriation, or time‐to‐first toxicity. Sometimes, the time‐to‐event is also called failure time. In the sequel, the term “survival time” is used as a synonym for any time‐to‐event.