bluuebunny/crossref_metadata_2025_split huggingface dataset:
Signal Detection Theory
Authors: Wixted John T. 
Published: September 2014

Abstract

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

Abstract
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

AbstractBecause 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

Abstract
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

Abstract

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.
Resentful Demoralization
Authors: Onghena Patrick 
Published: September 2014

Abstract
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.
Counterfactual Reasoning
Authors: Holland Paul W. 
Published: September 2014

AbstractCounterfactual 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

AbstractProbability 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

Abstract
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

AbstractIntegrated 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

Abstract
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

Abstract
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

AbstractThis 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

Abstract

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

Abstract

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

AbstractThe “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

AbstractStepped 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

AbstractIn 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

AbstractIn 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

AbstractOutcomes 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

AbstractFisher'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

AbstractAsian‐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

AbstractIn 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.