Download or read book Can Variation in Subgroups Average Treatment Effects Explain Treatment Effect Heterogeneity written by Marianne P. Bitler and published by . This book was released on 2014 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we assess whether welfare reform affects earnings only through mean impacts that are constant within but vary across subgroups. This is important because researchers interested in treatment effect heterogeneity typically restrict their attention to estimating mean impacts that are only allowed to vary across subgroups. Using a novel approach to simulating treatment group earnings under the constant mean-impacts within subgroup model, we find that this model does a poor job of capturing the treatment effect heterogeneity for Connecticut's Jobs First welfare reform experiment using quantile treatment effects. Notably, ignoring within-group heterogeneity would lead one to miss evidence that the Jobs First experiment's effects are consistent with central predictions of basic labor supply theory.
Download or read book Can Variation in Subgroups Average Treatment Effects Explain Treatment Effect Heterogeneity written by Marianne P. Bitler and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we assess whether welfare reform affects earnings only through mean impacts that are constant within but vary across subgroups. This is important because researchers interested in treatment effect heterogeneity typically restrict their attention to estimating mean impacts that are only allowed to vary across subgroups. Using a novel approach to simulating treatment group earnings under the constant mean-impacts within subgroup model, we find that this model does a poor job of capturing the treatment effect heterogeneity for Connecticut's Jobs First welfare reform experiment using quantile treatment effects. Notably, ignoring within-group heterogeneity would lead one to miss evidence that the Jobs First experiment's effects are consistent with central predictions of basic labor supply theory.
Download or read book Can Variation in Subgroups s Average Treatment Effects Explain Treatment Effect Heterogeneity written by Marianne P. Bitler and published by . This book was released on 2014 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Developing a Protocol for Observational Comparative Effectiveness Research A User s Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)
Download or read book Doing Meta Analysis with R written by Mathias Harrer and published by CRC Press. This book was released on 2021-09-15 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book
Download or read book Individual Treatment Effect Heterogeneity in Multiple Time Points Trials written by and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In biomedical studies, the treatment main effect is often expressed in terms of an "average difference." A treatment that appears superior based on the average effect may not be superior for all subjects in a population if there is substantial "subject-treatment interaction." A parameter quantifying subject-treatment interaction is inestimable in two sample completely randomized designs. Crossover designs have been suggested as a way to estimate the variability in individual treatment effects since an "individual treatment effect" can be measured. However, variability in these observed individual effects may include variability due to the treatment plus inherent variability of a response over time. We use the "Neyman - Rubin Model of Causal Inference" (Neyman, 1923; Rubin, 1974) for analyses. This dissertation consists of two parts: The quantitative and qualitative response analyses. The quantitative part focuses on disentangling the variability due to treatment effects from variability due to time effects using suitable crossover designs. Next, we propose a variable that defines the variance of a true individual treatment effect in a two crossover designs and show that they are not directly estimable but the mean effect is estimable. Furthermore, we show the variance of individual treatment effects is biased under both designs. The bias depends on time effects. Under certain design considerations, linear combinations of time effects can be estimated, making it possible to separate the variability due to time from that due to treatment. The qualitative section involves a binary response and is centered on estimating the average treatment effect and bounding a probability of a negative effect, a parameter which relates to the individual treatment effect variability. Using a stated joint probability distribution of potential outcomes, we express the probability of the observed outcomes under a two treatment, two periods crossover design. Maximum likelihood estimates of these probabilities are found using an iterative numerical method. From these, we propose bounds for an inestimable probability of negative effect. Tighter bounds are obtained with information from subjects that receive the same treatments over the two periods. Finally, we simulate an example of observed count data to illustrate estimation of the bounds.
Download or read book Cochrane Handbook for Systematic Reviews of Interventions written by Julian P. T. Higgins and published by Wiley. This book was released on 2008-11-24 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves.
Download or read book Theory of U Statistics written by Vladimir S. Korolyuk and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theory of U-statistics goes back to the fundamental work of Hoeffding [1], in which he proved the central limit theorem. During last forty years the interest to this class of random variables has been permanently increasing, and thus, the new intensively developing branch of probability theory has been formed. The U-statistics are one of the universal objects of the modem probability theory of summation. On the one hand, they are more complicated "algebraically" than sums of independent random variables and vectors, and on the other hand, they contain essential elements of dependence which display themselves in the martingale properties. In addition, the U -statistics as an object of mathematical statistics occupy one of the central places in statistical problems. The development of the theory of U-statistics is stipulated by the influence of the classical theory of summation of independent random variables: The law of large num bers, central limit theorem, invariance principle, and the law of the iterated logarithm we re proved, the estimates of convergence rate were obtained, etc.
Download or read book Comparative Effectiveness Review Methods written by U. S. Department of Health and Human Services and published by Createspace Independent Pub. This book was released on 2013-05-17 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Agency for Healthcare Research and Quality (AHRQ) commissioned the RTI International–University of North Carolina at Chapel Hill (RTI-UNC) Evidence-based Practice Center (EPC) to explore how systematic review groups have dealt with clinical heterogeneity and to seek out best practices for addressing clinical heterogeneity in systematic reviews (SRs) and comparative effectiveness reviews (CERs). Such best practices, to the extent they exist, may enable AHRQ's EPCs to address critiques from patients, clinicians, policymakers, and other proponents of health care about the extent to which “average” estimates of the benefits and harms of health care interventions apply to individual patients or to small groups of patients sharing similar characteristics. Such users of reviews often assert that EPC reviews typically focus on broad populations and, as a result, often lack information relevant to patient subgroups that are of particular concern to them. More important, even when EPCs evaluate literature on homogeneous groups, there may be varying individual treatment for no apparent reason, indicating that average treatment effect does not point to the best treatment for any given individual. Thus, the health care community is looking for better ways to develop information that may foster better medical care at a “personal” or “individual” level. To address our charge for this methods project, the EPC set out to answer six key questions (KQ). Key questions for methods report on clinical heterogeneity include: 1. What is clinical heterogeneity? a. How has it been defined by various groups? b. How is it distinct from statistical heterogeneity? c. How does it fit with other issues that have been addressed by the AHRQ Methods Manual for CERs? 2. How have systematic reviews dealt with clinical heterogeneity in the key questions? a. What questions have been asked? b. How have they pre-identified population subgroups with common clinical characteristics that modify their intervention-outcome association? c. What are best practices in key questions and how these subgroups have been identified? 3. How have systematic reviews dealt with clinical heterogeneity in the review process? a. What do guidance documents of various systematic review groups recommend? b. How have EPCs handled clinical heterogeneity in their reviews? c. What are best practices in searching for and interpreting results for particular subgroups with common clinical characteristics that may modify their intervention-outcome association? 4. What are critiques in how systematic reviews handle clinical heterogeneity? a. What are critiques from specific reviews (peer and public) on how EPCs handled clinical heterogeneity? b. What general critiques (in the literature) have been made against how systematic reviews handle clinical heterogeneity? 5. What evidence is there to support how to best address clinical heterogeneity in a systematic review? 6. What questions should an EPC work group on clinical heterogeneity address? Heterogeneity (of any type) in EPC reviews is important because its appearance suggests that included studies differed on one or more dimensions such as patient demographics, study designs, coexisting conditions, or other factors. EPCs then need to clarify for clinical and other audiences, collectively referred to as stakeholders, what are the potential causes of the heterogeneity in their results. This will allow the stakeholders to understand whether and to what degree they can apply this information to their own patients or constituents. Of greatest importance for this project was clinical heterogeneity, which we define as the variation in study population characteristics, coexisting conditions, cointerventions, and outcomes evaluated across studies included in an SR or CER that may influence or modify the magnitude of the intervention measure of effect (e.g., odds ratio, risk ratio, risk difference).
Download or read book Advanced Medical Statistics 2nd Edition written by Ying Lu and published by World Scientific. This book was released on 2015-06-29 with total page 1471 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book aims to provide both comprehensive reviews of the classical methods and an introduction to new developments in medical statistics. The topics range from meta analysis, clinical trial design, causal inference, personalized medicine to machine learning and next generation sequence analysis. Since the publication of the first edition, there have been tremendous advances in biostatistics and bioinformatics. The new edition tries to cover as many important emerging areas and reflect as much progress as possible. Many distinguished scholars, who greatly advanced their research areas in statistical methodology as well as practical applications, also have revised several chapters with relevant updates and written new ones from scratch.The new edition has been divided into four sections, including, Statistical Methods in Medicine and Epidemiology, Statistical Methods in Clinical Trials, Statistical Genetics, and General Methods. To reflect the rise of modern statistical genetics as one of the most fertile research areas since the publication of the first edition, the brand new section on Statistical Genetics includes entirely new chapters reflecting the state of the art in the field.Although tightly related, all the book chapters are self-contained and can be read independently. The book chapters intend to provide a convenient launch pad for readers interested in learning a specific topic, applying the related statistical methods in their scientific research and seeking the newest references for in-depth research.
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 Estimation of Average Treatment Effects Using Panel Data when Treatment Effect Heterogeneity Depends on Unobserved Fixed Effects written by Shosei Sakaguchi and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a new panel data approach to identify and estimate the time-varying average treatment effect (ATE). The approach allows for treatment effect heterogeneity that depends on unobserved fixed effects. In the presence of this type of heterogeneity, existing panel data approaches identify the ATE for limited subpopulations only. In contrast, the proposed approach identifies and estimates the ATE for the entire population. The approach relies on the linear fixed effects specification of potential outcome equations and uses exogenous variables that are correlated with the fixed effects. I apply the approach to study the impact of a mother's smoking during pregnancy on her child's birth weight.
Download or read book Evaluating the Performance of Continuous Analysis of Symmetrically Predicted Endogenous Subgroups written by Anthony J. Gambino and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recognizing and measuring treatment effect heterogeneity is essential to improving our understanding of the experimental "black box" and our expectations for how interventions' impacts may vary across diverse settings and contexts. However, this becomes difficult to accomplish when researchers are interested in how an intermediate variable (one measured after random assignment, such as a fidelity of implementation measure) may have been related to variation in the average treatment effect. An additional complexity commonly present in these scenarios is that the intermediate variable may have only been measured in one experimental group. Analysis of symmetrically predicted endogenous subgroups (ASPES) is an understudied statistical method that can allow researchers to assess how intermediate variables in their randomized experiments may have been related to heterogeneity in their average treatment effect. Two benefits of this method are that it can accommodate discrete or continuous intermediate variables, and it is designed to be applied in studies where the intermediate variable was only measured in one experimental group. ASPES has been studied in the setting of a discrete intermediate variable, but its performance in the setting of a continuous one has not received similar attention. Thus far, insufficient research has been done on the bias mechanisms present in the continuous ASPES estimator or its general performance across reasonable conditions researchers could expect to experience in practice. This dissertation research was an attempt to help fill these gaps in the literature and pave the way for future research on continuous ASPES. A Monte Carlo simulation study was conducted to evaluate the performance of continuous ASPES across several settings, including ones where the relationship between the intermediate variable and the average treatment effect was nonlinear, and ones where other intermediate variables related to the causal process were omitted. The simulation results showed promise for its application in large samples.
Download or read book Treatment Heterogeneity and Potential Outcomes in Linear Mixed Effects Models written by Troy E. Richardson and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Studies commonly focus on estimating a mean treatment effect in a population. However, in some applications the variability of treatment effects across individual units may help to characterize the overall effect of a treatment across the population. Consider a set of treatments, {T, C}, where T denotes some treatment that might be applied to an experimental unit and C denotes a control. For each of N experimental units, the duplet {gamma[subscript]Ti, gamma[subscript]Ci}, i=1,2 ..., N, represents the potential response of the i[superscript]th experimental unit if treatment were applied and the response of the experimental unit if control were applied, respectively. The causal effect of T compared to C is the difference between the two potential responses, gamma[subscript]Ti- gamma[subscript]Ci. Much work has been done to elucidate the statistical properties of a causal effect, given a set of particular assumptions. Gadbury and others have reported on this for some simple designs and primarily focused on finite population randomization based inference. When designs become more complicated, the randomization based approach becomes increasingly difficult. Since linear mixed effects models are particularly useful for modeling data from complex designs, their role in modeling treatment heterogeneity is investigated. It is shown that an individual treatment effect can be conceptualized as a linear combination of fixed treatment effects and random effects. The random effects are assumed to have variance components specified in a mixed effects "potential outcomes" model when both potential outcomes, gamma[subscript]T, gamma[subscript]C, are variables in the model. The variance of the individual causal effect is used to quantify treatment heterogeneity. Post treatment assignment, however, only one of the two potential outcomes is observable for a unit. It is then shown that the variance component for treatment heterogeneity becomes non-estimable in an analysis of observed data. Furthermore, estimable variance components in the observed data model are demonstrated to arise from linear combinations of the non-estimable variance components in the potential outcomes model. Mixed effects models are considered in context of a particular design in an effort to illuminate the loss of information incurred when moving from a potential outcomes framework to an observed data analysis.
Download or read book The Theory of Probability written by Hans Reichenbach and published by Univ of California Press. This book was released on 1971 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Heterogeneity written by Sofia Dias and published by . This book was released on 2012 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Technical Support Document focuses on heterogeneity in relative treatment effects. Heterogeneity indicates the presence of effect-modifiers. A distinction is usually made between true variability in treatment effects due to variation between patient populations or settings, and biases related to the way in which trials were conducted. Variability in relative treatment effects threatens the external validity of trial evidence, and limits the ability to generalise from the results, imperfections in trial conduct represent threats to internal validity. In either case it is emphasised that, although we continue to focus attention on evidence from trials, the study of effect-modifying covariates is in every way a form of observational study, because patients cannot be randomised to covariate values. This document provides guidance on methods for outlier detection, meta-regression and bias adjustment, in pair-wise meta-analysis, indirect comparisons and network meta-analysis, using illustrative examples. Guidance is given on the implications of heterogeneity in cost-effectiveness analysis. We argue that the predictive distribution of a treatment effect in a "new" trial may, in many cases, be more relevant to decision making than the distribution of the mean effect. Investigators should consider the relative contribution of true variability and random variation due to biases, when considering their response to heterogeneity. Where subgroup effects are suspected, it is suggested that a single analysis including an interaction term is superior to running separate analyses for each subgroup. Three types of meta-regression models are discussed for use in network meta-analysis where trial-level effect-modifying covariates are present or suspected: (1) Separate unrelated interaction terms for each treatment; (2) Exchangeable and related interaction terms; (3) A single common interaction term. We argue that the single interaction term is the one most likely to be useful in a decision making context. Illustrative examples of Bayesian metaregression against a continuous covariate and meta-regression against "baseline" risk are provided and the results are interpreted. Annotated WinBUGS code is set out in an Appendix. Meta-regression with individual patient data is capable of estimating effect modifiers with far greater precision, because of the much greater spread of covariate values. Methods for combining IPD in some trials with aggregate data from other trials are explained. Finally, four methods for bias adjustment are discussed: meta-regression; use of external priors to adjust for bias associated with markers of lower study quality; use of network synthesis to estimate and adjust for quality-related bias internally; and use of expert elicitation of priors for bias.
Download or read book The Economics of Artificial Intelligence written by Ajay Agrawal and published by University of Chicago Press. This book was released on 2024-03-05 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
