Download or read book A Nonparametric Test for Treatment Effects when a Covariate Has Been Used to Assign Treatments written by Kenneth Earl Stanley and published by . This book was released on 1974 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nonparametric Tests to Detect Relationship Between Variables in the Presence of Heteroscedastic Treatment Effects written by Siti Tolos and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical tools to detect nonlinear relationship between variables are commonly needed in various practices. The first part of the dissertation presents a test of independence between a response variable, either discrete or continuous, and a continuous covariate after adjusting for heteroscedastic treatment effects. The method first involves augmenting each pair of the data for all treatments with a fixed number of nearest neighbors as pseudo-replicates. A test statistic is then constructed by taking the difference of two quadratic forms. Using such differences eliminate the need to estimate any nonlinear regression function, reducing the computational time. Although using a fixed number of nearest neighbors poses significant difficulty in the inference compared to when the number of nearest neighbors goes to infinity, the parametric standardizing rate is obtained for the asymptotic distribution of the proposed test statistics. Numerical studies show that the new test procedure maintains the intended type I error rate and has robust power to detect nonlinear dependency in the presence of outliers. The second part of the dissertation discusses the theory and numerical studies for testing the nonparametric effects of no covariate-treatment interaction and no main covariate based on the decomposition of the conditional mean of regression function that is potentially nonlinear. A similar test was discussed in Wang and Akritas (2006) for the effects defined through the decomposition of the conditional distribution function, but with the number of pseudo-replicates going to infinity. Consequently, their test statistics have slow convergence rates and computational speeds. Both test limitations are overcome using new model and tests. The last part of the dissertation develops theory and numerical studies to test for no covariate-treatment interaction, no simple covariate and no main covariate effects for cases when the number of factor levels and the number of covariate values are large.

Download or read book Analysis of Clinical Trials Using SAS written by Alex Dmitrienko and published by SAS Institute. This book was released on 2017-07-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of Clinical Trials Using SAS®: A Practical Guide, Second Edition bridges the gap between modern statistical methodology and real-world clinical trial applications. Tutorial material and step-by-step instructions illustrated with examples from actual trials serve to define relevant statistical approaches, describe their clinical trial applications, and implement the approaches rapidly and efficiently using the power of SAS. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials. Commonly used methods are covered, including dose-escalation and dose-finding methods that are applied in Phase I and Phase II clinical trials, as well as important trial designs and analysis strategies that are employed in Phase II and Phase III clinical trials, such as multiplicity adjustment, data monitoring, and methods for handling incomplete data. This book also features recommendations from clinical trial experts and a discussion of relevant regulatory guidelines. This new edition includes more examples and case studies, new approaches for addressing statistical problems, and the following new technological updates: SAS procedures used in group sequential trials (PROC SEQDESIGN and PROC SEQTEST) SAS procedures used in repeated measures analysis (PROC GLIMMIX and PROC GEE) macros for implementing a broad range of randomization-based methods in clinical trials, performing complex multiplicity adjustments, and investigating the design and analysis of early phase trials (Phase I dose-escalation trials and Phase II dose-finding trials) Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience and the ready-to-use SAS macros compiled in this book.

Download or read book Randomization Tests written by Eugene S. Edgington and published by . This book was released on 1980 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Random assignment; Calculating significance values; One-way analysis of variance and the independent t test; Repeated-measures analysis of variance and the correlated t test; Factorial designs; Multivariate designs; Correlation; Trend tests; One-subject randomization tests.

Download or read book Statistical Methods for Testing Treatment covariate Interactions in Cancer Clinical Trials written by Shifang Liu and published by . This book was released on 2011 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Treatment-covariate interaction is often used in clinical trials to assess the homogeneity of treatment effects over these subgroups defined by a baseline covariate, which is frequently conducted after primary analysis including all patients is completed. When the endpoint is the time to an event, as in the cancer clinical trials, the Cox proportional hazard model with an interaction term has been used exclusively to test the significance of treatment-covariate interaction in oncology literature. But the proportional hazards assumption may not be satisfied by the data from clinical trials. Although there are several procedures proposed in statistical literature to assess the interaction based on a nonparametric measure of interaction or nonparametric models, some of these procedures do not take into the account of the nature of the data well, while some are very complicated which may have limited their applications in practice. In this thesis, a non-parametric procedure based on the smoothed estimate of Patel-Hoel measure is first derived to test the interaction between the treatment and a binary covariate with censored data. The theoretical distribution of the test statistic of the proposed procedure is derived. The proposed procedure is also evaluated through Monte-Carlo simulations and applications to data from a cancer clinical trial. Jackknifed versions of two test statistics based on nonparametric models are then derived by simplifying these test statistics and applying the jackknife method to estimate their variances. These jackknifed tests are also compared with the smoothed test and other related tests.

Download or read book The Analysis of Covariance and Alternatives written by Bradley Huitema and published by John Wiley & Sons. This book was released on 2011-10-24 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments Power analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs Measurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials Thoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering.

Download or read book Analysis of Observational Health Care Data Using SAS written by Douglas E. Faries and published by SAS Press. This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data. This book is part of the SAS Press program.

Download or read book ANALYSIS OF NON LINEAR COVARIA written by Jiajun Xu and published by Open Dissertation Press. This book was released on 2017-01-26 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Analysis of Non-linear Covariates Effects and Temporal Treatment Effect in Cox-type Models" by Jiajun, Xu, 徐家俊, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: This thesis focuses on the statistical analysis of time to event data that the effects of one or more continuous explanatory variables are not linear or the treatment effect varies over time, say the waning efficacy of some chemoprevention interventions. Standard Cox proportional hazards model cannot be applied to those situations. Modifications of the standard Cox model to accommodate the above situations are proposed. Motivated by a breast cancer data set, we first consider the estimation of the potentially non-linear age effect based on the generalized partly linear survival models. Appropriate adjustment of the non-linear age effects is warranted to ensure a valid statistical inference on other fixed effects. A simple and efficient sieve maximum likelihood estimation method that can be implemented easily using any standard statistical software is proposed. A data-driven algorithm to determine the optimal number and location of the knots in the estimation of the non-linear age effect is adopted. This algorithm is able to identify some possible change points where the investigated covariate effect is very different before and after these points. The performance of the proposed method is evaluated by simulation studies. For illustration purpose, the method is applied to the breast cancer data set from the public domain to study the non-linear effects of age-at-onset on the disease free survival of the patients. The next problem considered is the estimation of a time-varying treatment effect probably due to waning of the treatment efficacy. Two special features are attached to this special problem. The first one is the possibility of multiple episodes of the disease from the same subject over time, leading to recurrent events nature of the data. The second one is that the treatment is administered intermittently to the subjects to offer protection for the control of infectious disease. The continual administration of the treatment is mainly because of the waning efficacy of the treatment. The primary goal of this study is to estimate the time-varying treatment effect, which generally declines over time. One main objective of this study is to provide a method to choose the optimal interval between two consecutive supplementary treatments (boosters) to maintain a high level of protection to the subjects at all time. Another important question is to determine whether the intervention will have a harmful effect to the subjects in the long run. Both the fully parametric time-varying treatment effect and the fully nonparametric treatment effect are considered based on the Andersen-Gill type Cox model for recurrent data. The partial likelihood approach is applicable to estimate the parameters. Furthermore, intra-class or within-subject correlation may not be ignorable in clinical studies with recurrent event or clustered data. The marginal approach is considered. To ensure a valid statistical inference on the fixed effects, robust variance estimate is proposed to adjust for the dependent nature of the recurrent event data. The method is applied to data from a phase III clinical trial for malaria control. Subjects: Failure time data analysis Survival analysis (Biometry)

Download or read book Robust Tests for Treatment Effect in Survival Analysis Under Covariate adaptive Randomization written by Ting Ye and published by . This book was released on 2019 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covariate-adaptive randomization is popular in clinical trials with sequentially arrived patients for balancing treatment assignments across prognostic factors which may have influence on the response. However, existing theory on tests for treatment effect under covariate-adaptive randomization is limited to tests under linear or generalized linear models, although covariate-adaptive randomization has been used in survival analysis for a long time and its main application is in survival analysis. Often times, practitioners would simply adopt a conventional test such as the log-rank test or score test to compare two treatments, which is controversial since tests derived under simple randomization may not be valid under other randomization schemes. In this article, we prove that the log-rank test valid under simple randomization is conservative in terms of type I error under covariate-adaptive randomization, and the robust score test developed under simple randomization is no longer robust under covariate-adaptive randomization. We then propose a calibration type log-rank or score test that is valid and robust under both simple randomization and a large family of covariate-adaptive randomization schemes. Furthermore, we obtain Pitman's efficacy of log-rank and score tests to compare their asymptotic relative efficiency. Lastly, we provide theoretical guarantee for treatment effect inference under covariate-adaptive randomization when there is no model misspecification. Extensive simulation studies about the type I error and power of various tests are presented under several popular randomization schemes.

Download or read book Nonparametric Tests of Conditional Treatment Effects written by Sokbae Lee and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nonparametric Tests for Treatment Effect Heterogeneity written by and published by . This book was released on 2006 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: A large part of the recent literature on program evaluation has focused on estimation of the average effect of the treatment under assumptions of unconfoundedness or ignorability following the seminal work by Rubin (1974) and Rosenbaum and Rubin (1983). In many cases however, researchers are interested in the effects of programs beyond estimates of the overall average or the average for the subpopulation of treated individuals. It may be of substantive interest to investigate whether there is any subpopulation for which a program or treatment has a nonzero average effect, or whether there is heterogeneity in the effect of the treatment. The hypothesis that the average effect of the treatment is zero for all subpopulations is also important for researchers interested in assessing assumptions concerning the selection mechanism. In this paper we develop two nonparametric tests. The first test is for the null hypothesis that the treatment has a zero average effect for any subpopulation defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, in other words, that there is no heterogeneity in average treatment effects by covariates. Sacrificing some generality by focusing on these two specific null hypotheses we derive tests that are straightforward to implement

Download or read book Methods and Applications of Longitudinal Data Analysis written by Xian Liu and published by Elsevier. This book was released on 2015-09-01 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: - descriptive methods for delineating trends over time - linear mixed regression models with both fixed and random effects - covariance pattern models on correlated errors - generalized estimating equations - nonlinear regression models for categorical repeated measurements - techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. - From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis - Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection - Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.

Download or read book Nonparametric Tests for Censored Data written by Vilijandas Bagdonavicius and published by John Wiley & Sons. This book was released on 2013-02-07 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.

Download or read book Matched Sampling for Causal Effects written by Donald B. Rubin and published by Cambridge University Press. This book was released on 2006-09-04 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.

Download or read book Covariate Adjustment in Experiments with Matched Pairs written by Yuehao Bai and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies inference on the average treatment effect in experiments in which treatment status is determined according to “matched pairs” and it is additionally desired to adjust for observed, baseline covariates to gain further precision. By a “matched pairs” design, we mean that units are sampled i.i.d. from the population of interest, paired according to observed, baseline covariates and finally, within each pair, one unit is selected at random for treatment. Importantly, we presume that not all observed, baseline covariates are used in determining treatment assignment. We study a broad class of estimators based on a “doubly robust” moment condition that permits us to study estimators with both finite-dimensional and high-dimensional forms of covariate adjustment. We find that estimators with finite-dimensional, linear adjustments need not lead to improvements in precision relative to the unadjusted difference-in-means estimator. This phenomenon persists even if the adjustments are interacted with treatment; in fact, doing so leads to no changes in precision. However, gains in precision can be ensured by including fixed effects for each of the pairs. Indeed, we show that this adjustment is the “optimal” finite-dimensional, linear adjustment. We additionally study two estimators with high-dimensional forms of covariate adjustment based on the LASSO. For each such estimator, we show that it leads to improvements in precision relative to the unadjusted difference-in-means estimator and also provides conditions under which it leads to the “optimal' nonparametric, covariate adjustment. A simulation study confirms the practical relevance of our theoretical analysis, and the methods are employed to reanalyze data from an experiment using a “matched pairs” design to study the effect of macroinsurance on microenterprise.

Download or read book Notices of the American Mathematical Society written by American Mathematical Society and published by . This book was released on 1976 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains articles of significant interest to mathematicians, including reports on current mathematical research.

Download or read book Handbook of Parametric and Nonparametric Statistical Procedures Fifth Edition written by David J. Sheskin and published by CRC Press. This book was released on 2020-06-09 with total page 1388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Following in the footsteps of its bestselling predecessors, the Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Edition provides researchers, teachers, and students with an all-inclusive reference on univariate, bivariate, and multivariate statistical procedures.New in the Fifth Edition:Substantial updates and new material th