bluuebunny/crossref_metadata_2025_split huggingface dataset:
Women's Health Initiative: Statistical Aspects and Selected Early Results
Authors: Prentice Ross L. , Anderson Garnet L. 
Published: January 2014

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

Abstract

WHO has responsibility for the global epidemiologic surveillance of disease, receiving information on outbreaks of communicable disease and distributing this information throughout the world. WHO is also responsible for the
International Health Regulations
, the International Classification of Diseases, and other standards, which together make international epidemiologic comparisons possible. WHO also publishes
World Health Statistics Quarterly
,
World Health Statistics Annual
,
World Health Report
. The thirtieth World Health Assembly decided that the main social target of governments and WHO in the coming decades should be “health for all” by the year 2000. Member states committed themselves to monitoring progress toward, and evaluating the attainment of, this common goal. The health‐for‐all monitoring and evaluation process is intended to establish a baseline of current health and socioeconomic conditions, against which progress toward defined targets and objectives can be measured. Periodic measurement should establish trends that will permit anticipation of future conditions, and enable planning for them. The Health Situation and Trend Assessment Program is at present responsible for global health situation analysis and projection; strengthening of country health information; and partnerships and coordination of epidemiology, statistics, and trend assessment. Global epidemiologic surveillance is under the responsibility of the Emerging and other Communicable Diseases Surveillance and Control Program
Assay Sensitivity
Authors: Chuang‐Stein Christy 
Published: January 2014

Abstract
The assay sensitivity of a clinical trial is defined as the trial's ability to distinguish an effective treatment from a less effective or an ineffective one. It is an important consideration when designing, conducting, and analyzing a clinical trial. The lack of adequate assay sensitivity has different implications for superiority trials and noninferiority trials. This article discusses factors that could impact assay sensitivity. It also discusses the increasing use of a three‐arm design to help evaluate assay sensitivity of a trial.
Bayesian Dose‐Finding Designs in Healthy Volunteers
Authors: Zhou Yinghui 
Published: January 2014

Abstract
In the majority of phase I clinical trials, experimental drugs are administered via predefined dose‐escalation schemes to healthy volunteers to explore the relationship between the dose administered and the primary responses of interest. The responses might be binary outcomes such as adverse events; quantitative summary measures concerning pharmacokinetics (PK), such as the average or maximum plasma concentration over time; or pharmacodynamic (PD), such as heart rate or blood pressure changes from baseline. Conventional dose‐escalation designs can take a long time to complete to investigate every available dose. Also, conventional statistical analyses are not accurate due to small sample sizes: 20 to 80 volunteers are typically used in phase I trials. Adaptive designs have been studied as they can potentially speed up dose‐escalation and have better properties in terms of avoidance of unsafe doses and accuracy of estimation when compared with conventional designs. This review of state‐of‐the‐art adaptive designs illustrates how such a design can be implemented and discusses some practical issues met during implementation.
Change/Percent Change From Baseline
Authors: Vickers Andrew J. 
Published: January 2014

Abstract
Many clinical trials involve baseline and follow‐up assessment of a continuous outcome, such as pain, blood pressure, or weight. Different methods are available to analyze the results of such trials, which include between‐group two‐sample comparisons of the follow‐up measure, change score or percentage change, and linear regression of treatment on the follow‐up measure with the baseline measure as a covariate [analysis of covariance (ANCOVA)]. Each method provides a statistic that may have greater or less clinical applicability, depending on the particular trial involved. ANCOVA has greatest statistical power irrespective of the correlation between baseline and follow‐up measure, the nature of the treatment effect (shift or ratio), whether the control group also experiences change, and the distribution of the follow‐up measure. ANCOVA has additional advantages including control for chance baseline differences and incorporation of randomization strata as covariates. If trials results are best expressed in terms other than ANCOVA, such as a percentage change, then the preferred approach is to analyze the trial using ANCOVA and then convert the results into statistics that can be readily understood by clinicians.
Chemistry, Manufacturing and Controls (
CMC
)
Authors: Schmuff Norman R. , Lin David T. 
Published: January 2014

Abstract

This article describes general expectations and issues related to the Quality (also known as Chemistry, Manufacturing and Controls) section of Investigational New Drug (IND) (see Investigational New Drug Application (IND) article) applications filed with the U.S. Food and Drug Administration's (FDA's) Center for Drug Evaluation and Research (CDER) (see CDER article). Only brief mention will be made to FDA's Center for Biologics Evaluation and Research (CBER) (see CBER article) and other regulatory authorities (see Ministry of Health and Welfare (Japan, now known as the MHLW),
Medicines Control Agency
(United Kingdom, now known as the MHRA), and
EMEA
(European Union) articles). For determining whether INDs are required for clinical studies, see the Investigational New Drug Application (IND) article.
Clinical Trials, Early Cancer and Heart Disease
Authors: Schneiderman Marvin A. , Gehan Edmund A. 
Published: January 2014

AbstractEarly developments in controlled clinical trials at the National Institutes of Health (NIH) took place mainly at the National Cancer Institute (NCI) and what was then the National Heart Institute (NHI) (subsequently the National Heart, Lung, and Blood Institute (NHLBI))beginning in the 1950s. This article reviews the developments from the early 1950s to the late 1960s at both institutes, summarizing the early efforts in clinical trials, the organizations set up to conduct and monitor the clinical trials, and the developments in statistical methodology that have formed the basis for conducting many of the present day randomized controlled trials. The early history of clinical trials at these institutes has been reviewed in more detail at NCI by Gehan & Schneiderman and at NHLBI by Halperin et al.
Flexible Designs
Authors: Posch Martin , Bauer Peter , Brannath Werner 
Published: January 2014

AbstractFlexible designs allow for mid‐trial design adaptations in an adaptive interim analysis based on all information collected while controlling the type I error rate. Examples of such adaptations are, for example, sample size reassessment, addition or dropping of interim analysis, or dropping of treatment arms. The crucial feature of the adaptive procedure is that the adaptation rules need not be specified in advance.
Global Assessment Variables
Authors: Tilley Barbara , Huang Peng , O'Brien Peter C. 
Published: January 2014

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

AbstractIn 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.
Lan‐DeMetsAlpha‐Spending Function
Authors: DeMets David L. , Lan K. K. Gordon 
Published: January 2014

AbstractRandomized clinical trials are typically monitored periodically for early evidence of convincing benefit or of potential harm for both scientific and ethical reasons by a data monitoring committee. After increments in the number of patients randomized or in the number of observed events, interim statistical analyses may be conducted to evaluate emerging results. This process may be referred to as a group sequential design. However, repeated analysis of accumulating data with conventional statistical testing can lead to substantially increased Type I or false positive error rates. The alpha spending function provides a flexible approach to control the Type I error while allowing the data monitoring committee to review the data as needed without having to specifya prioriwhen and how many such interim analyses will be conducted. Other earlier group sequential methods had these somewhat restrictive requirements. This alpha spending function approach can be applied to several spending functions that approximate some of these earlier methods.
Longitudinal Data
Authors: Li Hung‐Ir 
Published: January 2014

Abstract
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.
Min Test
Authors: Laska Eugene M , Meisner Morris J 
Published: January 2014

AbstractThe Min test is a statistical tool used to test a null hypothesis such asHoi: νi≤ 0 for at least oneiversus the alternative H1: νi > 0 fori = 1, 2,…,K. This is sometimes called the sign‐testing problem. If every α‐level test of Hoi: νi≤ 0 individually rejects thei‐th null hypothesis at the α level, then the global null hypothesis is rejected according to the Min test. No adjustment for multiplicity is required. Such hypotheses arise in many clinical trials when it is desired to establish that a set of inequalities on the parameters simultaneously holds. Examples include testing whether an identified treatment is best on single or multiple endpoints; whether one treatment is equivalent to or noninferior to a control; and whether a dose of a combination is synergistic.
Compliance and Survival Analysis
Authors: Goetghebeur Els 
Published: January 2014

Abstract
Partial compliance with assigned treatment can act as both cause and effect of survival experiences in randomized clinical trials. Structural failure time models have been developed for causal inference on the effect of resulting exposure patterns on survival time. In this setting, we review randomization based estimation methods that protect the standard α level. We also point to alternative methods relying on the assumption of no unmeasured time‐dependent confounders.
Control Groups
Authors: Green Stephanie 
Published: January 2014

AbstractUse of appropriate control groups is a critical principle in clinical research methods. The modern randomized controlled trial is the most convincing and reliable method for minimizing bias and demonstrating effectiveness of treatment. Control groups for randomized trials may be patients randomized to no treatment, a placebo, or standard active therapy with or without a placebo. Due to practical and ethical constraints not all clinical questions can be addressed with a well‐controlled randomized trial. Non‐randomized trials can have specific comparator groups or be controlled in the sense that carefully chosen historical information from the literature is used for reference.
Good Clinical Practice (
GCP
)
Authors: Barr David B. 
Published: January 2014

Abstract
Good Clinical Practices (GCPs) are international ethical and quality standards for the design, conduct, performance, monitoring, auditing, analysis, recording, reporting, and regulation of clinical investigations. Clinical investigations are experiments in which a medical product such as a drug is administered or dispensed to, or used involving, one or more human subjects. These standards provide assurance that the rights and confidentiality of the trial subjects are protected and that the data and reported results are credible and accurate.
Allocation Concealment
Authors: Schulz Kenneth F. 
Published: January 2014

AbstractAllocation concealment, as an integral part of randomization, implements an allocation sequence. Inadequate allocation concealment can lead to exaggerated estimates of treatment effect, but with scope for bias in both directions. Without adequate allocation concealment, even proper random assignment sequences can be subverted. Investigators must recognize the inquisitiveness and ingeniousness of human nature and “immunize” trials against selection and confounding biases with proper allocation concealment.
Composite Endpoints in Clinical Trials
Authors: O'Brien Peter C. , Tilley Barbara C. , Dyck Peter J. 
Published: January 2014

Abstract

The primary analysis plays a key role in clinical trials to determine efficacy. By providing a single
P
‐value, the primary analysis provides the basis for controlling the overall type I error rate. However, it is often desirable to consider multiple important patient outcomes in the primary analysis in order to use the most comprehensive and most clinically meaningful assessment of the patient. These properties also tend to increase the power of the resulting statistical tests. Composite endpoints are an effective way of combining the information from multiple endpoints when identification of a single primary outcome is not possible. We discuss a variety of ways for forming such composites. The ideal is an existing composite that has been extensively studied, validated, and used in clinical practice, and where a meaningful effect size has been identified. Statistical alternatives that may be useful when such an existing composite is not available include a rank sum approach and least squares methods and other methods described in global assessment variables.
Ionospheric corrections for GPS time transfer
Authors: Rose Julian A. R. , Watson Robert J. , Allain Damien J. , Mitchell Cathryn N. 
Published: January 2014

AbstractA real‐time ionospheric mapping system is tested to investigate its ability to compensate for the ionospheric delay in single‐frequency Global Positioning System (GPS) time transfer over Europe. This technique is compared with two other single‐frequency systems: one that does not incorporate any ionospheric correction and one that uses the broadcast Klobuchar model. A dual‐frequency technique is also shown as a benchmark. A period in March 2003, during a solar maximum, has been used to display results when the ionospheric delays are large and variable. Data from two European GPS monitoring centers were used to test the time‐transfer methods. For averaging times between several minutes and a few hours, the instabilities in the time transfers were dominated by ionospheric effects. The instabilities at longer averaging times were found to be due to clock noise and hardware instabilities. Improvements in time‐transfer instabilities are shown by using the ionospheric tomography system.
Chemiluminescence, Electrogenerated
Authors: Nepomnyashchii Alexander B. , Bard Allen J. , Leland Jonathan K. , Debad Jeff D. , Sigal George B. , Wilbur James L. , Wohlstadter Jacob N. 
Published: January 2014

Electrogenerated chemiluminescence (
ECL
) is the process in which electrogenerated species undergo electron transfer reactions to form excited states that emit light. Many molecules have the potential to produce
ECL
; however, Ru(bpy)
3
2 +
(bpy = 2,2′‐bipyridine) is the most common emitter used for analytical applications. Ru(bpy)
3
2 +
is water soluble and can provide high‐intensity signals in aqueous solutions under physiological conditions (
pH
= 7.2–7.6). Application of a voltage to an electrode in the presence of an emitter induces light production and allows for the detection of the emitter at very low concentrations. Advantages over other analytical methods include low backgrounds, precise spatial and temporal control over the emission process, and capability for signal amplification. Commercial systems exist that use
ECL
to detect numerous clinically relevant analytes with high sensitivity using a variety of assay formats.