bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dose Finding Studies
Authors: Bretz Frank , Pinheiro José C. 
Published: September 2014

AbstractDetermining an adequate dose level for a drug and, more broadly, characterizing its dose–response relationship are key objectives of clinical drug development. If the dose is set too high, safety and tolerability problems are likely to result, whereas selecting too low a dose makes it difficult to establish adequate efficacy in the confirmatory phase, possibly leading to a failed program. It is well known that many potentially useful drugs have been discarded, and much time and resources have been wasted by pharmaceutical companies because of incorrect dosing. In addition, many marketed drugs have needed to have their label changed due to inappropriate, generally excessive, initial dose recommendations. A variety of issues must be considered when planning and analyzing a dose‐finding study so as to ensure it produces useful information for decision making. We describe key statistical methodologies for designing and analyzing clinical dose‐finding studies. These methodologies can be classified into different categories: multiple comparison procedures (including trend tests), modeling techniques, hybrid methods combining the previous two approaches, and adaptive dose‐finding strategies.
PhaseIITrials
Authors: Keene Oliver N. 
Published: September 2014

AbstractPhase II trials have two aims:(1)to provide a proof of concept that the drug is able to affect the disease it is designed to affect without overt safety problems and(2)to define the dose(s) to be taken forward to phase III. Trials that specifically address these issues are often referred to as phase IIa and phase IIb trials, respectively. Therefore, phase II trials provide the initial test of efficacy and reassurance on safety in patients and, if successful, define the dose or doses to be carried forward to be studied in the wider populations and longer‐term phase III trials.
Multiple Evaluators
Authors: Z. Liao Jason J. , Capen Robert C. 
Published: September 2014

Abstract
In medical and other related sciences, the clinical or experimental measurements obtained from multiple evaluators usually serve as a basis for diagnostic, prognostic, therapeutic, and performance evaluations. Thus, it is important to assess interevaluator agreement. The measurement scale can be continuous, binary, nominal, or ordinal. In this article, we review some commonly used approaches for both continuous and categorical outcomes.
Non‐Randomized Trial: A Tutorial
Authors: Li Zhengqing 
Published: September 2014

Abstract
To determine whether a new intervention has the postulated effect in a population of interest, one typically conducts a clinical trial comparing the intervention against a control. When assigning qualified subjects to the control or the intervention groups, a formal randomization process is preferred in order to remove the potential bias in subject allocation, to produce comparable groups in terms of both known and unknown prognostic factors at baseline, and to establish the validity of statistical tests of significance. This type of studies, so called randomized control study, is the standard by which all other studies are judged. Nonrandomized clinical trials arise from situations in which it is impossible or difficult to assign subjects to treatment by chance. Unlike randomized control studies, nonrandomized trials are vulnerable to bias because of the inability to control the balance of prognostic factors between the treatment groups. Because of this limitation, nonrandomized trials are primarily used for exploratory purpose. In this paper, a tutorial description of nonrandomized trials is provided. Basic features of study designs, statistical methods for analysis, and their limitations are discussed.
Composite Endpoints in Clinical Trials
Authors: O'Brien Peter C. , Tilley Barbara C. , Dyck Peter J. 
Published: September 2014

AbstractThe primary analysis plays a key role in clinical trials to determine efficacy. By providing a singleP‐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.
PhaseIIITrials
Authors: Mallinckrodt Craig , Whitmyer Virgil 
Published: September 2014

AbstractThe objective of phase III clinical trials is to confirm the preliminary but inconclusive evidence obtained from phase II studies that a drug is safe and effective for the intended indication in the relevant patient population. Phase III studies provide the primary basis upon which drugs are approved for use by the various regulatory agencies. Because no universally best approach to phase III research exists, it is important to tailor features of the study to the specific situation at hand. This article focuses on the key aspects of the design, conduct, and analysis of phase III trials and the trade‐offs between them that are necessary to consider when designing a trial intended to yield confirmatory evidence of a drug's effect. Nevertheless, even the best designed trials will have limitations. Any one trial may address its primary hypotheses with little or no ambiguity while other important questions remain unresolved. Therefore, information from the current trial, coupled with knowledge from earlier trials, is used to continually reevaluate what is known. With this knowledge one must reconsider what important questions remain unresolved, and continue to plan and conduct appropriate studies to address the unresolved questions.
Therapeutic Equivalence
Authors: Wiens Brian L. 
Published: September 2014

Abstract
We consider studies of therapeutic equivalence. Such studies aim to show that two treatments have similar efficacy. The context is of high importance, and situations in which therapeutic equivalence trials are appropriate are discussed. Choosing an equivalence limit is important to rule out the possibility that a placebo can be considered equivalent to a treatment with benefit. The two one‐sided tests approach is common and uses a Neyman‐Pearson framework for hypothesis testing although the test is commonly conducted by comparing the bounds of a confidence interval to the equivalence limit. Study conduct will be subjected to high scrutiny because poor study conduct can result in the perception of an increased chance of demonstrating equivalence. Some other aspects of a therapeutic equivalence trial are also discussed, including power and choice of an analysis set.
Sample Size Considerations for Morbidity/Mortality Trials
Authors: Shih Joanna H. 
Published: September 2014

Abstract
Sample size and power determination plays a crucial role in the design of morbidity/mortality clinical trials. To ensure that a trial is adequately sized so that the true treatment effect can be detected with a desired power, it is important to evaluate the assumptions used in the sample‐size calculation procedure. Various close‐form sample‐size formulas have been proposed for the analysis of morbidity/mortality trials with survival endpoints. However, most of them are formed based on restrictive assumptions and do not take into consideration factors that may affect the estimate of the treatment effect. These factors include drop‐in, drop‐out, staggered entry, loss to follow‐up, lags in treatment effects, and informative noncompliance. This article reviews the sample‐size calculation methods for testing the treatment effect in two‐arm survival trials. For each method considered, its basic features, underlying assumptions, and limitations are discussed. Presented first are the test statistics and associated methods that yield close‐form sample‐size formulas. Then attention will be focused on methods with aforementioned complexities in sample‐size calculation.
Alpha‐Spending Function
Authors: DeMets David L. , Gordon Lan K. K. 
Published: September 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.
Analysis Population
Authors: Mugglin Andrew S. , Connett John E. 
Published: September 2014

AbstractCollecting data on humans in clinical trials is very complicated, for scientific, logistic, ethical, and financial reasons. Analyzing such data is no less complicated, and it is seldom a simple exercise to determine which data to analyze, let alone how to analyze them and report the findings. In this article, we seek to describe some issues faced by the data analyst in a clinical trial. We define several terms that are often used to describe succinctly a data set created according to certain principles. We identify several issues and themes that frequently develop when discussing the selection of data for analysis. We also provide some principles that we have found to be useful approaches to dealing with the difficulty of selecting data for analysis in a complex but real setting. Finally, we provide an illustrative example of how an analysis dataset was actually chosen.
Double‐Dummy
Authors: Schulz Kenneth F. 
Published: September 2014

Abstract
Blinding embodies a rich history spanning several centuries. Double‐blinding usually reduces differential assessment of outcomes (information bias), but can also improve compliance and retention of trial participants while reducing biased supplemental care or treatment (sometimes called co‐intervention). With an active treatment control group, different options to double‐blinding present production obstacles, compliance impediments, and unblinding opportunities. To double‐blind in this situation, double‐dummy blinding nearly always becomes indispensable.
Chemistry, Manufacturing and Controls (
CMC
)
Authors: Schmuff Norman R. , Lin David T. 
Published: September 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.
Hierarchical Bayes
Authors: Ghosh Malay 
Published: September 2014

AbstractDose–response studies of environmental toxins are designed to determine the detrimental effects associated with exposure to the toxin, over a particular range of exposures. To properly quantify the toxic effect of a toxin, thebackground riskof no exposure to the toxin, R(0), must first be specified. The additional risk incurred by exposure to a dose,d, of the toxin is then called the added risk (AR) at this dose and is given by AR(d) = R(d) –R(0).
Branch and Bound
Authors: Whitaker David 
Published: September 2014

Abstract
The branch and bound algorithm is mostly used to solve global optimization problems with integer variables (unknowns), linear objective and linear constraints (integer linear programming (LP) problems). The recent increase in the speed of computation on desktop computers has meant that the algorithm can be applied to large problems with several hundred integer variables, which only a decade ago were intractable to any true optimization algorithms. Although a number of algorithms have been proposed for the integer LP problem, the branch and bound technique is used in almost all of the available computer software. This technique has proven to be reasonably efficient for practical problems because it has the embedded advantage of solving continuous linear programmes as subproblems. Furthermore, in some situations the embedded subproblem is a special case of a LP problem (such as assignment and transportation problems), which have more efficient algorithms than the general LP algorithm. Branch and bound methods are also easy to implement on parallel processors. An increasing number of applications are currently forthcoming in this area.
Computer Intensive Sampling Methods in Ecology
Authors: Fortin Marie‐josée , Jacquez Geoffrey M. , Shipley Bill 
Published: September 2014

AbstractHere, we focus on alternative significance tests for ecological data that often have skewed distributions, which impair the use of most parametric significance tests based on the normal distribution. Randomization tests have been proposed as an alternative to those classical significance tests where the observed data are repetitively reshuffled to generate a reference distribution that is then used to assess the significance of the statistic under study. Ecological data are also often correlated due to temporal autocorrelation, spatial autocorrelation, or phylogenetic structure, thereby violating assumptions of data independence of many randomization tests. In such circumstances, restricted randomization should be used. Alternatives to randomization tests such as bootstrap and jackknife are presented. Finally, we address the issue of multiple tests and show how the false discovery rate is more appropriate than Bonferroni correction and sequential methods. Copyright © 2015 John Wiley & Sons, Ltd.
Constrained Optimization
Authors: Stein Alfred 
Published: September 2014

Abstract

Constrained optimization aims to find an optimum of a function in the presence of constraints. Constrained optimization generally yields a lower optimum on the optimization function as compared with unconstrained optimization, but it honors the presence of constraints. A simple example concerns maximization of the production of a farm subject to constraints on the amount of fertilizers (say
n
kg per week) and man hours (
h
hours per day). Another example is minimization of leaching towards the environment, subject to constraints on soil parameters, groundwater table and other environmental variables.
Jackknife Resampling
Authors: Friedl Herwig , Stampfer Erwin 
Published: September 2014

AbstractThe jackknife is a particular resampling method that aims primarily at the calculation of the bias and the variance of estimates, without making very restrictive distributional assumptions. It is nonparametric and easy to apply in quite general settings. As with other resampling techniques, the jackknife can be computationally very intensive. In the simplest case jackknife resampling is generated by sequentially deleting single cases from the original sample (delete‐one jackknife). A more generalized jackknife technique uses resampling that is based on multiple case deletion (delete‐djackknife). The main part of this article focuses on independently and (in the introductory part also) identically sampled data. It then discusses the basics of the jackknife and its applications to nonidentically distributed observations that can be described by linear, generalized linear, nonlinear, and Cox's regression models. Finally, the article gives an account of jackknife estimates for dependent data, e.g. in the context of generalized estimating equations or special time series models.
Parallel Computing: Statistical and Environmetric Uses
Authors: Kulperger Reg , Yu Hao 
Published: September 2014

Abstract
The common use of parallel computing has greatly evolved since the original encyclopedia article of 2001. Multicore processors are now quite common, so many computer users have this readily available on their desktop computers and laptops.
Now increasingly large datasets and simulation of complex statistical models are important in the study of many physical systems. Models in various areas, such as environment, biology, and other physical sciences, play an important role in prediction or detection of changes. All these require lots of computing power. Many of these computations can take advantage of parallel or distributed computing. This article discusses some of these ideas and then discusses how these are implemented in one specific language, R. In the present time, one generally no longer has to work at a low‐level programming language, as was the case a decade or two ago, but now certain types of parallel computations can be implemented at a relatively higher user‐friendly level, even with desktop computing.
Parallel computing consists of a computing environment connecting many processors. Instead of the previous generation where dedicated computer architecture was required, a more loose structure of distributed computing is now more common. This article is intended to give the reader an overview of the parallel computing environment, focusing on the statistical uses that can be made as opposed to a more detailed computing or engineering description.
Resampling Methods
Authors: Tu Wanzhu 
Published: September 2014

AbstractThis article presents an overview of existing resampling methods and their applications in parameter estimations and statistical inference. It describes the essential concepts and basic operating procedures of the most frequently used resampling methods, including the jackknife, bootstrap, permutation test, and cross‐validation. This article also discusses the interconnections among these methods. Finally, it briefly reviews the currently available software for implementation of the most commonly used resampling procedures.
S + SpatialStats
Authors: Vega Silvia C. , Kaluzny Stephen P. 
Published: September 2014

Abstract

S+SpatialStats is an add‐on module to the S‐Plus system for data analysis and graphics. S‐Plus is an environment for data analysis that provides an object‐oriented programming language and a multitude of functions for the statistical analysis of data. +patialStats provides a comprehensive suite of tools designed for the statistical analysis of
spatial
data. The current version, 1.5, features a graphical user interface (GUI), that is menus and dialogs designed for convenient access to the functionality described in this article.
Sensitivity Analysis of Computer Models
Authors: Fassò Alessandro 
Published: September 2014

Abstract
Sensitivity analysis of computer models has been developed over the last decades paralleling the development of massive computer simulations of engineering, chemical, and environmental systems.
Through extensive simulations, global sensitivity analysis is aimed at assessing the importance of each input to the model outputs by means of variance decomposition.
Depending on model complexity and simulation costs, various techniques are available ranging from design of experiments (DOE) to intensive Monte Carlo, metamodeling, and Bayesian inference.
Singular Value Decomposition and High Dimensional Data
Authors: Allen Genevera I. , Perry Patrick O. 
Published: September 2014

Abstract
The singular value decomposition and related principal component analysis are foundational multivariate methods used for dimension reduction and exploratory analysis of high‐dimensional data. Studying this decomposition using random matrix theory, however, reveals troubling behavior when the number of variables is larger than the number of samples. This article surveys random matrix theory results related to the singular value decomposition and high‐dimensional data as well as recent developments such as sparse principal component analysis. Software on the random matrix theory distributions related to the singular value decomposition can be found elsewhere; several software packages implementing methods for sparse principal component analysis are also available.
Uncertainty and Computer Models
Authors: Sain Stephan R. 
Published: September 2014

AbstractWith the explosion of computing power in recent years, computer models have become important tools for scientists to study the behavior of different phenomena as well as for prediction and decision making. However, there are many sources of uncertainty in computer models that can have significant impact on what is learned from such models. The field of uncertainty quantification, which combines elements from statistics, applied mathematics, and computational science, is emerging to provide tools to better understand, quantify, and mitigate the many uncertainties in computer models, and this article covers some of the key issues and problems facing researchers in this area.
Adaptive Designs
Authors: Flournoy Nancy , Oron Assaf P. 
Published: September 2014

AbstractThe designs described in this article involve treatment allocation probabilities that change according to prior data. Such designs are useful to protect from extreme results that might result from unrestricted randomization, to improve power (e.g., when group variances are unknown and unequal), when prior information is too limited to fix the sampling space a priori (e.g., regions of desirable efficacy and unacceptable toxicity are unknown). Procedures are described for studying K‐groups, but emphasis is placed on dose‐finding experiments. Some procedures involve randomization and some are deterministic given past data. Some are model based and others are nonparametric. Some procedures have a specific optimization criterion as a goal (e.g., maximize power or some other utility) and others are heuristically formulated, with large variation in ease of application and subsequent data analysis.