Statistical Inference

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ISBN 13 :
Total Pages : 223 pages
Book Rating : 4.:/5 (114 download)

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Book Synopsis Statistical Inference by : Andrew Ying

Download or read book Statistical Inference written by Andrew Ying and published by . This book was released on 2020 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Chapter 1, we consider the problem of detecting a sparse mixture as studied by Ingster (1997) and Donoho and Jin (2004). We consider a wide array of base distributions. In particular, we study the situation when the base distribution has polynomial tails, a situation that has not received much attention in the literature. Perhaps surprisingly, we find that in the context of such a power-law distribution, the higher criticism does not achieve the detection boundary. However, the scan statistic does. In Chapter 2, we derive the large-sample distribution of several variants of the scan statistic applied to a point process on an interval, which can be applied to detect the presence of an anomalous interval with any length. The main ingredients in the proof are Kolmogorov's theorem, a Poisson approximation, and recent technical results by [KW14]. In Chapter 3, we consider causal inference in survival analysis in the presence of unmeasured confounders. Instrumental variable is an essential tool for addressing unmeasured confounding in observational studies. Two stage predictor substitution (2SPS) estimator and two stage residual inclusion(2SRI) are two commonly used approaches in applying instrumental variables. Recently 2SPS was studied under the additive hazards model in the presence of competing risks of time-to-events data, where linearity was assumed for the relationship between the treatment and the instrument variable. This assumption may not be the most appropriate when we have binary treatments. We consider the 2SRI estimator under the additive hazards model for general survival data and in the presence of competing risks, which allows generalized linear models for the relation between the treatment and the instrumental variable. We derive the asymptotic properties including a closed-form asymptotic variance estimate for the 2SRI estimator. We carry out numerical studies in finite samples, and apply our methodology to the linked Surveillance, Epidemiology and End Results (SEER)-Medicare database comparing radical prostatectomy versus conservative treatment in early-stage prostate cancer patients. In Chapter 4, we investigate the causal effects of etanercept (trade name Enbrel) on birth defects, a pharmaceutical that treats autoimmune diseases and recently went through the US FDA revised labeling for use in pregnancy, as the proportion of liveborn infants with major birth defects was higher for women exposed to etanercept compared to diseased etanercept unexposed women. An outstanding problem, which was not addressed in the data analysis leading up to the FDA relabeling, is the missing birth defect outcomes due to spontaneous abortion since, in accepted standard practice an infant or a fetus is assumed not to be malformed unless a defect is found. This led to likely bias (and missing not at random) because, according to the theory of "terathanasia", a defected fetus is more likely to be spontaneously aborted. In addition, the previous analysis stratified on live birth against spontaneous abortion, which was itself a post-exposure variable showing higher rate of spontaneous abortion in the unexposed women, hence did not lead to causal interpretation of the stratified results. We aim to estimate and provide inference for the causal parameters of scientific interest, including the principal effects, making use of the missing data mechanism informed by terathanasia. During the process we also deal with complications in the data including left truncation, observational nature, and rare events. We report our findings which not only provide a more in-depth analysis than previously done on etanercept, but also shed light on how similar studies on causal effects of medication (or vaccine, other substances etc.) during pregnancy may be analyzed.

Survival Analysis and Causal Inference

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ISBN 13 :
Total Pages : 329 pages
Book Rating : 4.:/5 (126 download)

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Book Synopsis Survival Analysis and Causal Inference by : Denise Rava

Download or read book Survival Analysis and Causal Inference written by Denise Rava and published by . This book was released on 2021 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: In chapter 1 we study explained variation under the additive hazards regression model for right-censored data. We consider different approaches for developing such a measure, and focus on one that estimates the proportion of variation in the failure time explained by the covariates. We study the properties of the measure both analytically, and through extensive simulations. We apply the measure to a well-known survival dataset as well as the linked surveillance, epidemiology, and end results-Medicare database for prediction of mortality in early stage prostate cancer patients using high-dimensional claims codes. In chapter 2 we propose a new flexible method for survival prediction: DeepHazard, a neural network for time-varying risks. Prognostic models in survival analysis are aimed at understanding the relationship between patients' covariates and the distribution of survival time. Traditionally, semiparametric models, such as the Cox model, have been assumed. These often rely on strong proportionality assumptions of the hazard that might be violated in practice. Moreover, they do not often include covariates' information updated over time. Our approach is tailored for a wide range of continuous hazards forms, with the only restriction of being additive in time. A flexible implementation, allowing different optimization methods, along with any norm penalty, is developed. Numerical examples illustrate that our approach outperforms existing state-of-the-art methodology in terms of predictive capability evaluated through the C-index metric. The same is revealed on the popular real datasets as METABRIC, GBSG, ACTG and PBC. In chapter 3 we consider the conditional treatment effect for competing risks data in observational studies. While it is described as a constant difference between the hazard functions given the covariates, we do not assume the additive hazards model in order to adjust for the covariates. We derive the efficient score for the treatment effect using modern semiparametric theory, as well as two doubly robust scores with respect to both the assumed propensity score for treatment and the censoring model, and the outcome models for the competing risks. We provide the asymptotic distributions of the estimators when the two sets of working models are both correct, or when only one of them is correct. We study the inference based on these estimators using simulation. The estimators are applied to the data from a cohort of Japanese men in Hawaii followed since 1960s in order to study the effect of midlife drinking behavior on late life cognitive outcomes. In chapter 4 we consider doubly robust estimation of the causal hazard ratio in observational studies. The treatment effect of interest, described as the constant ratio between the hazard functions of thetwo potential outcomes, is parametrized by the Marginal Structural Cox Model. Under the assumption of no unmeasured confounders, causal methods, as Cox-IPW, have been developed for estimation of the treatment effect of interest. However no doubly robust methods have been proposed under the Marginal Structural Cox model. We develop an AIPW estimator for this popular model that is both model and rate-doubly robust with respect to the treatment assignment model and the conditional outcome model. The proposed estimator is applied to the data from a cohort of Japanese men in Hawaii followed since 1960s in order to study the effect of mid-life alcohol exposure on overall death.

Bayesian Causal Survival Analysis in Clinical Trials with Noncompliance

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ISBN 13 :
Total Pages : 248 pages
Book Rating : 4.:/5 ( download)

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Book Synopsis Bayesian Causal Survival Analysis in Clinical Trials with Noncompliance by : Fang Li

Download or read book Bayesian Causal Survival Analysis in Clinical Trials with Noncompliance written by Fang Li and published by . This book was released on 1999 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Survival Analysis: State of the Art

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Publisher : Springer Science & Business Media
ISBN 13 : 9401579830
Total Pages : 446 pages
Book Rating : 4.4/5 (15 download)

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Book Synopsis Survival Analysis: State of the Art by : John P. Klein

Download or read book Survival Analysis: State of the Art written by John P. Klein and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival analysis is a highly active area of research with applications spanning the physical, engineering, biological, and social sciences. In addition to statisticians and biostatisticians, researchers in this area include epidemiologists, reliability engineers, demographers and economists. The economists survival analysis by the name of duration analysis and the analysis of transition data. We attempted to bring together leading researchers, with a common interest in developing methodology in survival analysis, at the NATO Advanced Research Workshop. The research works collected in this volume are based on the presentations at the Workshop. Analysis of survival experiments is complicated by issues of censoring, where only partial observation of an individual's life length is available and left truncation, where individuals enter the study group if their life lengths exceed a given threshold time. Application of the theory of counting processes to survival analysis, as developed by the Scandinavian School, has allowed for substantial advances in the procedures for analyzing such experiments. The increased use of computer intensive solutions to inference problems in survival analysis~ in both the classical and Bayesian settings, is also evident throughout the volume. Several areas of research have received special attention in the volume.

Likelihood Method for Randomized Time-To-Event Studies with All-Or-None Compliance

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ISBN 13 : 9783668438637
Total Pages : 160 pages
Book Rating : 4.4/5 (386 download)

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Book Synopsis Likelihood Method for Randomized Time-To-Event Studies with All-Or-None Compliance by : Zhaojing Gong

Download or read book Likelihood Method for Randomized Time-To-Event Studies with All-Or-None Compliance written by Zhaojing Gong and published by . This book was released on 2017-04-28 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research Paper (postgraduate) from the year 2009 in the subject Statistics, grade: A, University of Canterbury (Department of Mathematics and Statistics), course: Statistics, language: English, abstract: Estimating causal effects in clinical trials often suffers from treatment non-compliance and missing outcomes. In time-to-event studies, it is more complicated because of censoring, the mechanism of which may be non-ignorable. While new estimators have recently been proposed to account for non-compliance and missing outcomes, few papers have specifically considered time-to-event outcomes, where even the intention-to-treat (ITT) estimator is potentially biased for estimating causal effects of assigned treatment. In this paper we develop a likelihood based method for randomized clinical trials (RCTs) with non-compliance for time-to-event data and adapt the EM algorithm according to derive the maximum likelihood estimators (MLEs). In addition, we give formulations of the average causal effect (ACE) and compliers average causal effect (CACE) to suit survival analysis. To illustrate the likelihood-based method (EM algorithm), a breast cancer trial data was re-analysed using a model, which assumes that the failure times and censored times both follow Weibull and Lognormal distributions, respectively (termed the NIGN-WW model and NIGN-LL model).

Causal Inference for Competing Risks and Semi-competing Risks Data

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ISBN 13 :
Total Pages : 135 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Causal Inference for Competing Risks and Semi-competing Risks Data by : Yiran Zhang

Download or read book Causal Inference for Competing Risks and Semi-competing Risks Data written by Yiran Zhang and published by . This book was released on 2022 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we utilize the novel statistical methods for obtaining causal effect under competing risks and semi-competing risks data in survival analysis. This dissertation is comprised of three main settings. In the first setting, we aim to assess the causal effect of mid-life alcohol exposure to the late life cognitive score which is related to Alzheimer's disease (AD) using a large scale longitudinal data. We applied the marginal structural model (MSM) with inverse probability weighted (IPW) to adjust for time-varying confounding. We found that there is a significant decline in cognitive scores among heavy drinkers compared always light drinker. However, since the cognitive scores also changes over time, learning the relationship of alcohol exposure and time to cognitive impairment is also worth to explore. In the second setting, we are interested in mid-life alcohol exposure to late life time to cognitive impairment which is also related to AD. Under this setting, as people are in their late-life stage, death prevents us from observing cognitive impairment. In survival analysis, death is considering as competing event. To estimate the causal effect of point treatment to time to event with the existence of competing event, we applied the MSM Cox proportional hazards model with IPW. Since hazard ratio is hard to interpret in medical research, we proposed predicted risk contrasts formula under the MSM Cox model. Observing the trend that people die quickly after experiencing cognitive impairment, in the third settings, we proposed a MSM illness-death to assess the causal effect for alcohol exposure to time to cognitive impairment, death and death after cognitive impairment. We considered two specific such models, the usual Markov illness-death structural model and the general Markov illness-death structural model which incorporates a frailty term. For interpretation purposes, risk contrasts under the structural models are defined. To accommodate the possibility of misspecification of propensity score model, we also derived the augmented IPW estimator under MSM illness-death usual Markov model.

Causal Inference

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Publisher : CRC Press
ISBN 13 : 9781420076165
Total Pages : 352 pages
Book Rating : 4.0/5 (761 download)

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Book Synopsis Causal Inference by : Miquel A. Hernan

Download or read book Causal Inference written by Miquel A. Hernan and published by CRC Press. This book was released on 2019-07-07 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

Survival Analysis Using S

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Publisher : CRC Press
ISBN 13 : 0203501411
Total Pages : 277 pages
Book Rating : 4.2/5 (35 download)

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Book Synopsis Survival Analysis Using S by : Mara Tableman

Download or read book Survival Analysis Using S written by Mara Tableman and published by CRC Press. This book was released on 2003-07-28 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.

Targeted Learning in Data Science

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Publisher : Springer
ISBN 13 : 3319653040
Total Pages : 655 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Targeted Learning in Data Science by : Mark J. van der Laan

Download or read book Targeted Learning in Data Science written by Mark J. van der Laan and published by Springer. This book was released on 2018-03-28 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.

Handbook of Survival Analysis

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Publisher : CRC Press
ISBN 13 : 146655567X
Total Pages : 635 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Handbook of Survival Analysis by : John P. Klein

Download or read book Handbook of Survival Analysis written by John P. Klein and published by CRC Press. This book was released on 2016-04-19 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians

Causal Inference in Statistics, Social, and Biomedical Sciences

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Publisher : Cambridge University Press
ISBN 13 : 0521885884
Total Pages : 647 pages
Book Rating : 4.5/5 (218 download)

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Book Synopsis Causal Inference in Statistics, Social, and Biomedical Sciences by : Guido W. Imbens

Download or read book Causal Inference in Statistics, Social, and Biomedical Sciences written by Guido W. Imbens and published by Cambridge University Press. This book was released on 2015-04-06 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

Randomization in Clinical Trials

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Publisher : John Wiley & Sons
ISBN 13 : 1118742249
Total Pages : 284 pages
Book Rating : 4.1/5 (187 download)

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Book Synopsis Randomization in Clinical Trials by : William F. Rosenberger

Download or read book Randomization in Clinical Trials written by William F. Rosenberger and published by John Wiley & Sons. This book was released on 2015-11-23 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition “All medical statisticians involved in clinical trials should read this book...” - Controlled Clinical Trials Featuring a unique combination of the applied aspects of randomization in clinical trials with a nonparametric approach to inference, Randomization in Clinical Trials: Theory and Practice, Second Edition is the go-to guide for biostatisticians and pharmaceutical industry statisticians. Randomization in Clinical Trials: Theory and Practice, Second Edition features: Discussions on current philosophies, controversies, and new developments in the increasingly important role of randomization techniques in clinical trials A new chapter on covariate-adaptive randomization, including minimization techniques and inference New developments in restricted randomization and an increased focus on computation of randomization tests as opposed to the asymptotic theory of randomization tests Plenty of problem sets, theoretical exercises, and short computer simulations using SAS® to facilitate classroom teaching, simplify the mathematics, and ease readers’ understanding Randomization in Clinical Trials: Theory and Practice, Second Edition is an excellent reference for researchers as well as applied statisticians and biostatisticians. The Second Edition is also an ideal textbook for upper-undergraduate and graduate-level courses in biostatistics and applied statistics. William F. Rosenberger, PhD, is University Professor and Chairman of the Department of Statistics at George Mason University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and author of over 80 refereed journal articles, as well as The Theory of Response-Adaptive Randomization in Clinical Trials, also published by Wiley. John M. Lachin, ScD, is Research Professor in the Department of Epidemiology and Biostatistics as well as in the Department of Statistics at The George Washington University. A Fellow of the American Statistical Association and the Society for Clinical Trials, Dr. Lachin is actively involved in coordinating center activities for clinical trials of diabetes. He is the author of Biostatistical Methods: The Assessment of Relative Risks, Second Edition, also published by Wiley.

Data Analysis with Competing Risks and Intermediate States

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Publisher : CRC Press
ISBN 13 : 1466570369
Total Pages : 278 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Data Analysis with Competing Risks and Intermediate States by : Ronald B. Geskus

Download or read book Data Analysis with Competing Risks and Intermediate States written by Ronald B. Geskus and published by CRC Press. This book was released on 2015-07-14 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analysis with Competing Risks and Intermediate States explains when and how to use models and techniques for the analysis of competing risks and intermediate states. It covers the most recent insights on estimation techniques and discusses in detail how to interpret the obtained results.After introducing example studies from the biomedical and

Statistical Modelling of Survival Data with Random Effects

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Publisher : Springer
ISBN 13 : 9811065578
Total Pages : 288 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Statistical Modelling of Survival Data with Random Effects by : Il Do Ha

Download or read book Statistical Modelling of Survival Data with Random Effects written by Il Do Ha and published by Springer. This book was released on 2018-01-02 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to researchers in medical and genetics fields, graduate students, and PhD (bio) statisticians.

Survival Analysis

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Publisher : Oxford University Press
ISBN 13 : 0195337514
Total Pages : 172 pages
Book Rating : 4.1/5 (953 download)

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Book Synopsis Survival Analysis by : Shenyang Guo

Download or read book Survival Analysis written by Shenyang Guo and published by Oxford University Press. This book was released on 2010-01-25 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival analysis is a class of statistical methods for studying the occurrence and timing of events. With clearly written summaries and plentiful examples, this pocket guide will put this important statistical tool in the hands of many more social work researchers than have been able to use it before.

Unified Methods for Censored Longitudinal Data and Causality

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Publisher : Springer Science & Business Media
ISBN 13 : 0387217002
Total Pages : 412 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Unified Methods for Censored Longitudinal Data and Causality by : Mark J. van der Laan

Download or read book Unified Methods for Censored Longitudinal Data and Causality written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2012-11-12 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fundamental statistical framework for the analysis of complex longitudinal data is provided in this book. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures. The techniques go beyond standard statistical approaches and can be used to teach masters and Ph.D. students. The text is ideally suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data.

Causal Modelling of Survival Data with Informative Noncompliance

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Publisher :
ISBN 13 :
Total Pages : 278 pages
Book Rating : 4.:/5 (768 download)

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Book Synopsis Causal Modelling of Survival Data with Informative Noncompliance by : Lang'O Taabu Odondi

Download or read book Causal Modelling of Survival Data with Informative Noncompliance written by Lang'O Taabu Odondi and published by . This book was released on 2011 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Noncompliance to treatment allocation is likely to complicate estimation of causal effects in clinical trials. The ubiquitous nonrandom phenomenon of noncompliance renders per-protocol and as- treated analyses or even simple regression adjustments for noncompliance inadequate for causal inference. For survival data, several specialist methods have been developed when noncompliance is related to risk. The Causal Accelerated Life Model (CALM) allows time-dependent departures from randomized treatment in either arm and relates each observed event time to a potential event time that would have been observed if the control treatment had been given throughout the trial. Alternatively, the structural Proportional Hazards (C-Prophet) model accounts for all-or-nothing noncompliance in the treatment arm only while the CHARM estimator allows time-dependent departures from randomized treatment by considering survival outcome as a sequence of binary outcomes to provide an 'approximate' overall hazard ratio estimate which is adjusted for compliance. The problem of efficacy estimation is compounded for two-active treatment trials (additional noncompliance) where the ITT estimate provides a biased estimator for the true hazard ratio even under homogeneous treatment effects assumption. Using plausible arm-specific predictors of compliance, principal stratification methods can be applied to obtain principal effects for each stratum. The present work applies the above methods to data from the Esprit trials study which was conducted to ascertain whether or not unopposed oestrogen (hormone replacement therapy - HRT) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. We use statistically designed simulation studies to evaluate the performance of these methods in terms of bias and 95% confidence interval coverage. We also apply a principal stratification method to adjust for noncompliance in two treatment arms trial originally developed for binary data for survival analysis in terms of causal risk ratio. In a Bayesian framework, we apply the method to Esprit data to account for noncompliance in both treatment arms and estimate principal effects. We apply statistically designed simulation studies to evaluate the performance of the method in terms of bias in the causal effect estimates for each stratum. ITT analysis of the Esprit data showed the effects of taking HRT tablets was not statistically significantly different from placebo for both all cause mortality and myocardial reinfarction outcomes. Average compliance rate for HRT treatment was 43% and compliance rate decreased as the study progressed. CHARM and C-Prophet methods produced similar results but CALM performed best for Esprit: suggesting HRT would reduce risk of death by 50%. Simulation studies comparing the methods suggested that while both C-Prophet and CHARM methods performed equally well in terms of bias, the CALM method performed best in terms of both bias and 95% confidence interval coverage albeit with the largest RMSE. The principal stratification method failed for the Esprit study possibly due to the strong distribution assumption implicit in the method and lack of adequate compliance information in the data which produced large 95% credible intervals for the principal effect estimates. For moderate value of sensitivity parameter, principal stratification results suggested compliance with HRT tablets relative to placebo would reduce risk of mortality by 43% among the most compliant. Simulation studies on performance of this method showed narrower corresponding mean 95% credible intervals corresponding to the the causal risk ratio estimates for this subgroup compared to other strata. However, the results were sensitive to the unknown sensitivity parameter.