bluuebunny/crossref_metadata_2025_split huggingface dataset:
Guidelines On Statistical Methods in Clinical Trials
Authors: Facey Karen M. , Lewis John A. 
Published: September 2014

Abstract
This article provides an overview of the International Conference on Harmonization (ICH) and its work in producing regulatory guidelines that are of interest to medical statisticians. The scope and key issues of the ICH E9 Guideline on Statistical Principles in Clinical Trials are outlined. This includes topics related to minimizing the bias in trial design, analysis and reporting, and includes specific issues such as multicenter trials, interim analysis, analysis sets, and subgroups. Other regulatory guidelines that include statistical issues are mentioned and the manner in which guidelines should be used is debated.
Medical Devices
Authors: Campbell Gregory , Irony Telba Z. , Lao Chang S. , Li Heng , Pennello Gene , Vishnuvajjala R. Lakshmi , Yue Lilly Q. 
Published: September 2014

AbstractA medical device is any item that treats or diagnoses a health condition but whose action is primarily not chemical or biological. The main focus concerns the evaluation of clinical studies to establish the safety and effectiveness of different kinds of medical devices. The design and analysis of medical device studies pose unique statistical challenges. Both nondiagnostic and diagnostic devices are considered. Nondiagnostic devices include therapeutic ones and implants, among others. Statistical issues for these kinds of devices include the placebo effect, sham controls, inability to perform blinded studies, noninferiority, survival analysis, repeated measures, and historical controls. Diagnostic devices pose a very diverse set of challenges, with markedly different design and analysis considerations. Special attention is devoted here to microarrays in particular. Bayesian approaches to medical device studies are discussed.
Missing Data in Clinical Trials
Authors: Murray Gordon D. 
Published: September 2014

AbstractMissing data is an almost universal problem in clinical trials, and substantial bias can arise if the statistical analysis does not take proper account of the issue. The article reviews the different mechanisms that can lead to missing data, and describes appropriate approaches to the statistical analysis. As in so many aspects of clinical trials, the correct approach depends crucially on what question is being asked, and, in particular, whether the trial design is explanatory or pragmatic.
Multicenter Trials: Rationale and Examples
Authors: Bryant John , Cronin Walter , Wieand Sam 
Published: September 2014

Abstract
The chapter deals with multicenter clinical trials, which are defined as studies involving two or more field sites conducted according to a common protocol that uses a single data coordinating center to receive, process, and analyze study data. Several examples are provided. Organizational, structural, and analytic aspects of trials, and guidelines for the conduct of such studies are discussed.
Quality of Life
Authors: Fairclough Diane L. , Ganz Patricia A. 
Published: September 2014

AbstractIn the health sciences, assessment of quality of life and other patient‐reported outcomes are now an integral component of patient‐focused research. Health‐related quality of life (HRQoL) is a multidimensional construct that assesses the extent to which one's usual or expected physical, emotional, and social well‐being are impacted by a medical condition or its treatment. HRQoL may be assessed using generic or disease‐specific instruments. These instruments consist of a set of questions, method of administration, instructions for administration, method of scoring and analysis, and interpretation. Validation of these instruments includes assessment of content validity, internal consistency, test–retest reliability, and discriminant and predictive validity. Some of the statistical issues that arise in the analysis of studies with HRQoL assessments are multiple endpoints, missing data (informative dropout), and interpretation (statistical versus clinical significance).
Sample Size Determination in Clinical Trails Including Adjustments for Noncompliance, Loss to Follow‐Up and Groups Sequential Analysis
Authors: Lakatos E. 
Published: September 2014

AbstractConceptual issues in sample size calculation for clinical trials are discussed. Each parameter and related issues are highlighted: type I and II errors and the balance between them, one versus two‐sided testing, the smallest clinically meaningful difference versus the anticipated treatment effect, endpoint selection, multiple comparisons, missing data, unequal allocation, repeated measures, equivalence, and group sequential trials. For survival trials, conceptual issues are discussed in further detail, including the binomial versus log rank statistic, exponential versus proportional and nonproportional hazards, and adjustment for treatment lag, staggered entry, noncompliance, drop‐in, loss‐to‐follow‐up and competing risks, and duration of treatment effect.
Time‐Varying Treatment Effect
Authors: Lavori Philip W. , Dawson Ree 
Published: September 2014

AbstractThis entry is concerned with assessing the effect of treatments that vary in time on the outcomes for individual patients. For example, levels of compliance with therapy may vary over time among participants in a clinical trial. Assessment of treatment effects in this situation is complicated by the fact that the relevant comparison may not be between randomized groups of patients. The so‐called “ignorability of treatment” condition is discussed, relating to the requirement that treatment actually received should be independent of potential patient responses to treatment. Other issues relating to the observational comparisons in medicine and adjustment for confounders are reviewed, together with a brief mention of recent developments in structural equation modeling of treatment effects in this context.
Vaccine Studies
Authors: Halloran M. Elizabeth 
Published: September 2014

AbstractThis entry begins with a brief overview of the biological background for vaccine action, and then describes the various phases of vaccine development, including early phases of identification of candidate vaccines, establishment of safety and immunogenicity in animals, safety and immunogenicity in humans, field efficacy trials, and post licensure studies. Measures of vaccine efficacy are described, and certain potential study biases are identified.
Interim Analysis of Censored Data
Authors: Tsiatis A. A. 
Published: September 2014

AbstractMany clinical trials of chronic diseases are monitored periodically and interim analyses are performed, both for reasons of “safety”, that is, to monitor possible side effects, and to avoid continuing a trial from which no more convincing evidence can be expected. The adequate handling of this monitoring requires close collaboration between the Data and Safety Monitoring Committee and statistical expertise.Statistical methods have been developed for respecting basic rules of hypothesis testing when multiple views are involved. Usually some form ofgrouped sequentialapproach fits best to the practical situation. A complication is that often the entry of study subjects isstaggeredover time, so that time on trial does not correspond to calendar time.
Medical Ethics and Statistics
Authors: Hutton J. L. 
Published: September 2014

AbstractEthical choice and behavior in health care practice and research require an understanding of uncertainty and the contribution that can be made by statistical methods. Various criteria for judging what is “best” are discussed in relation to routine medical practice, the perception of “normality”, screening for disease, epidemiologic research, and the communication of uncertainty. Attention is given to the ethics of medical research in developing countries, informed and uninformed consent, and cluster randomized trials. Guidelines provided by various national and international organizations are described.
Medical Research Council Streptomycin Trial
Authors: Sutherland I. 
Published: September 2014

Abstract
This is an outstanding early example of a rigorously planned clinical trial with concurrent controls. Only a limited supply of the drug was available. Patients with acute bilateral pulmonary tuberculosis were allocated by a random process to treatment either with bed rest alone or to streptomycin plus bed rest, and precautions were taken to avoid bias in their management and assessment. The streptomycin patients showed early radiologic improvement compared with the controls, but resistant strains of the bacilli emerged rapidly and there was much vestibular toxicity.
The trial provided an explicit model for subsequent tuberculosis research and, more importantly, served as a catalyst for the scientific investigation of other treatments and interventions throughout clinical and preventive medicine. The introduction in this trial of an allocation scheme based on random sequences represents the most important single advance in the evolution of the clinical trial in the twentieth century.
Pharmaceutical Industry, Statistics in: Including Two Examples
Authors: Senn Stephen 
Published: September 2014

Abstract
The role of the statistician is traced from early, basic research through the various stages of drug development leading to phase III clinical trials and phase IV postmarketing surveillance. Specific areas of activity, and statistical topics of particular interest, are listed. Examples are taken from the development of drug for treating asthma, and work on project prioritization. Future trends will be affected by financial constraints, by a need to specialize that may lead to work being done by contract research organizations, by the need for speed, and by the need for marketing to be more “evidence‐based”.
Algorithm ‐ Some Theory
Authors: Maindonald John H. 
Published: September 2014

AbstractThe term algorithm is generally used to describe a series of logical steps that, when implemented within a computer program, will perform a desired computational task. The task must be defined precisely. Issues that are generally important in finding or choosing such an algorithm include: computational cost, computer memory or storage requirements, accuracy and precision, simplicity of description, and the tuning of the algorithm to computer architecture and organization.
Computer Algebra
Authors: Kendall Wilfrid S. 
Published: September 2014

AbstractThis article begins by presenting basic common features, and by listing and giving references for current commercial computer algebra packages. There follow two simple introductory examples of the use of computer algebra in statistical contexts, and then a discussion of some typical constraints and features of computer algebra systems: it is important to be aware of these, since in this area, unrealistic expectations lead all too rapidly to frustration and disappointment. Finally, we list a few representative examples of research uses of computer algebra in statistical science, and conclude with suggestions for further reading.
Computer Architecture and Organization
Authors: Nielsen Ole M. 
Published: September 2014

Abstract
Computer technology has made amazing progress since the first general‐purpose electronic computer was created half way through the last century. This rapid rate of improvement is mainly driven by advances in the technology used to build computers but is also (and increasingly so) due to innovations in computer design needed to harness the tremendous capabilities of integrated circuits. Examples are memory hierarchies, pipelining, and parallelism.
This article gives an overview of modern computer architectures and outlines programming practices that will lead to efficient use of the hardware we are likely to encounter in biostatistics. The article concludes with qualified guesses as to the nature of computing in the future.
Computer Languages and Programs
Authors: Lawrence Catherine , Maindonald John H. 
Published: September 2014

Abstract

Computer languages are artificial languages that enable humans to give instructions to computer systems. Texts written in these languages are called
computer programs
or
code
. Influences that explain differences between languages include differences of historical development, of conceptual framework, and of intended use. The use of computer languages for writing large and complex programs raises major issues of program design. The use of computer languages for statistical analysis raises further issues that have been the subject of extensive discussion and research.
Optimization and Nonlinear Equations
Authors: Smyth Gordon K. 
Published: September 2014

Abstract
Optimization means finding that argument which minimizes or maximizes a given function such as a likelihood function or a sum of squares. Many optimization algorithms are derived from algorithms that solve the nonlinear equations defined by setting the derivative of the objective function equal to zero. This article discusses a number of methods for unconstrained optimization, including bisection and golden search in the univariate case and Newton's method and quasi‐Newton algorithms in the multivariate case. Applications to maximum likelihood estimation, Fisher's method of scoring, nonlinear regression, and generalized linear models are described. Restricted step modifications are discussed for preventing divergence of Newton‐type algorithms. A number of derivative‐free methods are discussed, including the Nelder–Mead simplex method, methods with numeric derivatives, conjugate methods, and the EM algorithm.
Polynomial Approximation
Authors: Smyth Gordon K. 
Published: September 2014

Abstract
Polynomials are very simple mathematical functions, which have the flexibility to represent very general nonlinear relationships. Approximation of more complicated functions by polynomials is a basic building block for many numerical techniques. This article considers two distinct but related applications. The first is polynomial regression in which polynomials are used to model a nonlinear relationship between a response variable and an explanatory variable. The advantages of using orthogonal polynomials as predictor variables are illustrated using a data set on the height and age of preadult girls. The second problem is that of approximating a difficult to evaluate function, such as a density or a distribution function, with the aim of fast evaluation on a computer. The use of Chebyshev polynomials is illustrated for the purpose of obtaining a uniformly accurate approximation to a function over a finite interval.
R
Authors: Venables W. N. , Maindonald John H. 
Published: September 2014

Abstract
The R system is an independent, open source, freely available implementation of a computer language that is well adapted to the development of scientific calculations, data analysis, and graphics. It has gained particular importance for developing and promulgating new statistical, data analytical, or graphical techniques. Versions of R are available for Microsoft Windows, Unix, Linux, and MacOS 8 or later.
Software for Sample Survey Data, Misuse of Standard Packages
Authors: Brogan Donna 
Published: September 2014

Abstract
Many health science researchers conduct primary or secondary analyses with data from complex sample surveys but do not realize that specialized statistical software needs to be used. This article compares analyses of survey data conducted with survey software and with standard statistical software. The numerical example illustrates that the inappropriate use of standard statistical software for sample survey data can result in: (a) biased point estimates of population parameters, (b) underestimation of standard errors, (c) confidence intervals that are too wide, and (d) misleading tests of statistical significance. Weighted analyses with standard statistical software, with regular or normed weights, can yield correct point estimates of population parameters but still yield incorrect results for estimated standard errors and statistical tests. Data analysts are advised to use appropriate software for the analysis of survey data.
Magic Square Designs
Authors: Martin Richard J. 
Published: September 2014

Abstract

An
n
by
n
magic square with entries 1 to n
2
has all row and column sums equal. If
n
has
k
factors, then a factorial design with n
2
runs and up to 2
k
factors can be obtained from this for which the main effects and at least some of the two‐factor interactions are linear‐trend‐free (their estimates are unaffected if there is a straight line trend over time). An unconnected experimental design called a
magic Latin square
is a Latin square with additional blocking obtained from structured rows and columns.
Branching Processes
Authors: Jagers Peter 
Published: September 2014

Abstract
Branching processes are stochastic population models based on explicit descriptions of individual life and reproduction. The basic assumption is that individuals live and multiply independently. This can, however, be relaxed. Individuals may be of various types and have varying life spans. In the simple branching processes, there is only one type of individual and life span is determistic (one year or season) or exponentially distributed, yielding Markov processes, and as simple special cases, birth‐and‐death processes. But more general branching processes allow complex reproduction patterns as well as arbitrary life distributions.
The main results are that no finite population can persist forever. It either dies out or it grows, usually at the exponential rate postulated by Euler and Malthus. However, recent results show that if population size can influence individual reproduction, other growth modes, like linear, can also occur. In exponential growth, the age‐distribution, and population composition generally, stabilize.
Brownian Motion and Diffusion Processes
Authors: Chiang Chin Long 
Published: September 2014

AbstractA brief review of the one‐dimensional Brownian motion in terms of three perspectives. (1) As the limiting case of a random walk, the total displacement has a normal distribution. (2) The mathematical model leads to a partial differential equation, which is known as Fokker–Planck diffusion equation. And (3) as a stochastic process, both the time element and the displacement are continuous variables. The solution of the partial differential equation for the transition density function also has the normal distribution. Wiener–Levy process is a special case.The Ornstein–Uhlenbeck process, which describes the velocity of the movement of a particle in a Brownian motion is also mentioned.
Clinical Signals
Authors: Campbell Michael J. 
Published: September 2014

AbstractClinical signals are obtained when human activity is monitored, usually electronically. Typical examples are brain activity via the EEG, and heart activity from the ECG. Other signals include blood pressure and measures of lung function. The signals can be sampled at short time intervals, such as 0.1 s for measuring autonomic function, or a longer periods such as every 2 hrs for measuring circadian activity. Techniques of analysis include spectral analysis, and time‐varying spectral analysis. Examples are given for heart rate and blood pressure variability, the EEG, and human luteinizing hormone levels.
Image Analysis and Tomography
Authors: Glasbey C. A. , Berman M. , Hudson H. M. 
Published: September 2014

AbstractImage analysis is the use of computers to extract quantitative information from images, such as those that arise from microscopes, medical scanners, electrophoresis gels, and illuminated objects. We give examples of such images, an overview of the different types of digital image, and the eclectic collection of methodologies that have been proposed to analyse them. We then consider the tomographic reconstruction of images from projections, and the three major components of image analysis: enhancement, segmentation, and taking measurements. We illustrate the methods using the examples.
Ion Channel Modeling
Authors: Hawkes Alan G. 
Published: September 2014

AbstractElectrical activity in the nervous system is regulated by the passage of minute electrical currents due to charged ions passing though ion channels in cell membranes. Studying them increases our understanding of normal physiology and the effect of drugs and toxins.Stochastic modeling of ion channel mechanisms is needed to infer the nature of the mechanism from observation of ionic currents, and to estimate the parameters involved. The models are Markov or semi‐Markov processes where, typically, states are aggregated into groups with the same conductance level because we can see what level it is at, not what state it is in.Many distributions can be obtained, such as open times or shut times, and the likelihood can be calculated for a record of sojourns at the various levels that are first extracted from observational noise. Hidden Markov methods have also been applied.
Multistage Carcinogenesis Models
Authors: Sherman Claire D. , Portier Christopher J. 
Published: September 2014

Abstract
Biologically‐based mathematical models of carcinogenesis have been used for quantifying cancer risks for approximately fifty years. These models are developed to understand the mechanisms of carcinogenesis as well as to determine reasonable margins of safety for exposure to carcinogens. It is important to capture the biological aspects of carcinogenesis within the framework of the mathematical model to help provide a better understanding of the carcinogenesis process. In addition, it is important to have an accurate model when extrapolating to exposures (doses) observed in the human population since predictions from these models may vary dramatically.
Point Processes
Authors: Milne Robin K. 
Published: September 2014

AbstractThis article is a review of the theory of point processes that aims to introduce the reader to the wider literature. It provides a relatively nontechnical summary of foundational ideas, an introduction to the rich variety of point process, and stochastic process models that can be built from more basic point processes such as Poisson and renewal processes, and discusses some aspects of statistical inference for point process data. An annotated bibliography offers guidance to the literature.
Serial‐Sacrifice Experiments
Authors: Dewanji A. 
Published: September 2014

AbstractSimple survival experiments provide little information to study the progress of different disease(s) and possible interaction between them. In serial sacrifice experiments, individual animals are randomly selected and killed (sacrificed) at fixed or adaptively selected time points, allowing the examination of presence or absence of the different diseases. Therefore, a serial sacrifice experiment, with interim observation on some individuals before natural death occurs, allows one to probe into the disease processes and the complex interrelationships between different diseases. Data from the sacrificed animals provides an estimate for prevalencep𝒜(t), the probability being that an alive animal has a particular disease combination 𝒜 at timet. On the other hand, death data provides an estimate of probability of death with the particular disease combination 𝒜, given by μ𝒜(t)p𝒜(t), where μ𝒜(t) denotes the mortality rate from disease combination 𝒜. In animal carcinogenicity experiments with serial sacrifice, in particular, the tumor occurrence time distribution is estimable. Design issues regarding the optimal sacrifice schedule are of some interest, but not much work has been done on this. There are few examples of serial sacrifice experiments, the ED01 study with 2‐acetylamino‐fluorene (2‐AAF) conducted by the US national center for toxicological research (NCTR) being probably the largest.