Download or read book Causal Inference Approaches for Dealing with Time dependent Confounding in Longitudinal Studies with Applications to Multiple Sclerosis Research written by Mohammad Ehsanul Karim and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applications of Causal Inference to Problems of Occupational Epidemiology written by Daniel Martin Brown and published by . This book was released on 2014 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation concerns the application of the techniques of causal inference to problems of occupational health. The abstracts of the three works which comprise the primary substance of this disseration are reproduced below. The healthy worker survivor effect (HWSE) is a feature of occupational cohort studies which can lead to biased estimates of the etiologic effects of exposures if the estimation procedure does not account for its sources. The HWSE arises from underlying temporal processes characteristic of working populations in which time-varying health status is a criteria for entry into follow-up as well as both a predictor and a consequence of exposure. We distinguish two sources of HWSE: left-truncation in the presence of heterogeneous susceptibility as well as time-varying confounding on the causal pathway. We apply longitudinal minimum-loss-based estimation to simulated data in order to illustrate the effect of each process on estimates of exposure response, and clarify the extent to which methodological solutions can properly adjust for the bias. We consider the problem of the estimation of parameters of the full-data distribution from data structures in which some confounding variables are unmeasured in a portion of the population. Our focus is on evaluating approaches to implementation of an augmented inverse probability of censoring weighted targeted minimum-loss based estmator (A-IPCW TMLE) first proposed by Rose and Van der Laan. This is an inverse probability weighted estimator in which estimation proceeds using a reweighted set of fully observed data points. The weights used are the inverses the estimated probability of being fully observed which is then augmented by an estimate of the expectation of the full data influence function, given the always observed variables. The estimator's performance is compared to standard weighting approaches and multiple imputation in both a simulation study and an applied data example. We investigate the effect of cumulative exposure to particulate matter with an aerodynamic diameter
Download or read book Longitudinal Research written by Scott W. Menard and published by SAGE. This book was released on 2002-07-19 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Since ... writing the first edition of this monograph in 1990, ... the 1990s have seen an increasing focus on more sophisticated approaches to dealing with missing data in both cross-sectional and longitudinal research. Software applicable to longitudinal research has also improved, and more evidence for the rapid pace of change in longitudinal analysis can be found in the dozen or so books written and edited about longitudinal research design and data analysis published in the 1990s and early in the present millennium. The organization of this monograph remains the same as in the first edition. ... There is much less said about the application of traditional methods of analysis to longitudinal data, and more focus on analytical methods specifically designed for longitudinal data, including time series analysis, linear panel analyis, multilevel and latent growth curve modeling, and event history analysis."--Preface.
Download or read book Causal Inference with Mendelian Randomization for Longitudinal Data written by Jialin Qu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mendelian Randomization (MR) uses genetic variants as instrumental variables (IVs) to examine the causal relationship between an exposure and an outcome in observational studies. When confounding factors exist, the correlation between a predictor variable and an outcome variable does not imply causation. IV regression has been a popular method to control the confounding effect for causal inference. According to Mendel's first and second laws of inheritance, genetic variants can be considered as valid IVs. Popular MR methods include the ratio estimator, the inverse-variance weighted estimator and the two stage estimator. However, all these methods are based on cross-sectional data. In practice, data in the observational studies can be collected overtime, the so-called longitudinal data. Longitudinal data makes it possible to capture changes within subjects over time and thus offers advantages to causal modeling to establish causal relationships. However, causal inference method that can control the time-varying confounding effect is largely lacking in literature. In this dissertation, we explore MR analysis for longitudinal data by proposing different causal models and assuming different casual mechanisms. The proposed methods are strongly motivated by a real study to examine the causal relationship between hormone secretion and emotional eating disorder in teen girls.We start with a concurrent model which assumes current outcome is only affected by current exposure. Coefficients of both genetic variants (i.e., IVs) and exposure are considered as time-varying effects. We apply the quadratic inference function approach in a two-step IV regression framework and focus on statistical testing to infer causality. Through extensive simulation studies, we show that the proposed method can well protect type I error and has reasonable testing power.In Chapter 3, we generalize the concurrent model to a more complex case and propose a time lag model to investigate time delayed causal effects. In the time lag model, we assume current outcome at time t is affected by previous exposures measured up to t 8́2 s time points, where the time lag 6́đt can be determined by a rigorous model selection procedure based on data. Similar to the concurrent model, we assume the effects of genetic variants on exposure and the effects of exposure on outcome both are time-varying. We propose different tests for point-wise and simultaneous testing to assess the causal relationship.In Chapter 4, We further generalize the time lag model to the case where the cumulative effect of previous t exposures contributes to the outcome at time t, under a sparse functional data analysis framework. The causal relationship is examined under the functional principal component regression framework with sparse functional data. Simulation results show that the type I error is well controlled.We apply our models to the emotional eating disorder data to examine if hormone secretion during the menstrual cycle in teen girls has a causal effect on emotional eating behavior and identify interesting results. This thesis work represents the very first exploration in MR analysis with longitudinal data.
Download or read book Longitudinal Data Analysis written by Jason Newsom and published by Routledge. This book was released on 2013-06-19 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes. The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.
Download or read book Probabilistic Causality in Longitudinal Studies written by Mervi Eerola and published by . This book was released on 1994-10-01 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Causality in a Social World written by Guanglei Hong and published by John Wiley & Sons. This book was released on 2015-08-17 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory. Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.
Download or read book Analysis of Longitudinal Data written by Peter Diggle and published by Oxford University Press, USA. This book was released on 2013-03-14 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.
Download or read book Causal Inference Using Variation in Treatment Over Time written by Xinyao Ji and published by . This book was released on 2017 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis and related research is motivated by my interest in understanding the use of time-varying treatments in causal inference from complex longitudinal data, which play a prominent role in public health, economics, and epidemiology, as well as in biological and medical sciences. Longitudinal data allow the direct study of temporal changes within individuals and across populations, therefore give us the edge to utilize time this important factor to explore causal relationships than static data. There are also a variety challenges that arise in analyzing longitudinal data. By the very nature of repeated measurements, longitudinal data are multivariate in various dimensions and have completed random-error structures, which make many conventional causal assumptions and related statistical methods are not directly applicable. Therefore, new methodologies, most likely data-driven, are always encouraged and sometimes necessary in longitudinal causal inference, as will be seen throughout this thesis.As a result of the various topics explored, this thesis is split into four parts corresponding to three dierent patterns of variation in treatment. The rst pattern is the one-directional change of a binary treatment assignment, meaning that each study participant is only allowed to experience the change from untreated to treated at the staggered time. Such pattern is observed in a novel cluster-randomized design called the stepped-wedge. The second pattern is the arbitrary switching of a binary treatment caused by changes in person-specic characteristics and general time trend. The patterns is the most common thing one would observe in longitudinal data and we develop a method utilizing trends in treatment to account for unmeasured confounding. The third pattern is that the underlying treatment, outcome, covariates are time-continuous, yet are only observed at discrete time points. Instead of modeling cross-sectional and pooled longitudinal data, we take a mechanistic view by modeling reactions among variables using stochastic dierential equations and investigate whether it is possible to draw sensible causal conclusions from discrete measurements.
Download or read book Causal Inference with Longitudinal Data Moving Beyond Difference in Difference written by Landon Manzano Gibson and published by . This book was released on 2020 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Difference-in-Difference is a widely used method in health policy and health services research for estimating a causal effect. Unfortunately, the validity of difference-in-difference is difficult to evaluate without a tool to directly assess the parallel trends assumption. For example, existing tools indirectly examine the parallel trends assumption using pre-treatment observations. Developments in the methodological literature have given rise to an alternative class of estimators -- Synthetic Controls -- that do not make the parallel trends assumption and to sensitivity analysis tools that provide a novel approach for directly evaluating the parallel trends assumption The first chapter of this dissertation develops guidelines for the use of synthetic control methods alongside difference-in-difference. Synthetic control methods are a valuable tool because they don't assume parallel trends; however, they are not without assumptions of their own. This chapter provides guidance for the utilization of synthetic controls and difference-in-difference and proposes several post-estimation validity analyses to further evaluate the assumptions made by each method. The second chapter examines the effect of Medicaid Expansion on State Medicaid spending. The analysis is done using a subset of states among which the parallel trends assumptions is tenuous. Using a kernel-balanced synthetic control, and the post-estimation analyses introduced in the first chapter, this paper shows no evidence for Medicaid Expansion increasing or decreasing State Medicaid spending over a three-year period. The third chapter extends a suite of sensitivity tools for estimating the sensitivity of difference-in-difference to unobserved time-varying confounders -- parallel trends violations. The tools utilize the explanatory power of observed covariates to estimate how strong unobserved confounders must be to change the conclusions. They not only relax the strict binary nature of classic indirect parallel trends tests but also utilize the post-period outcome data to directly examine the parallel trends assumption.
Download or read book Causal Inference on the Marginal Effect of an Exposure written by Janie Coulombe and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Causal inference focuses on the estimation of effects due to specific, well-defined causes (such as exposures on which we can intervene). With the advent of powerful computers and smart electronic devices, data are now collected more rapidly than ever. That abundance of data provides a rich landscape for research on causal inference. However, the collection of these data does not always rely on a study design made expressly for answering the question of interest. For instance, contrary to some randomized controlled studies where exposure is randomized and observation times are set in advance, longitudinal observational data from medical health records are filled with biasing associations that, should they not be taken into account, could adversely affect the inference. In my doctoral thesis, I focus on two such challenges, the confounding bias, and the bias due to covariate-driven monitoring times, in the inference on the causal marginal effect of an exposure on a longitudinal outcome. While there is a vast statistical literature on how to model covariate-driven monitoring times, it has not been studied in a causal framework, nor considered simultaneously with confounding. This thesis proposes ways to consistently estimate the marginal effect of exposure in settings subject to those biases.In a first manuscript, I propose two novel estimators for the marginal effect of a binary exposure on a continuous, longitudinal outcome. These estimators allow for the outcome to be observed irregularly across individuals. They consider confounding factors and covariate-driven monitoring times that may affect inference via a monitoring and an exposure models, and the corresponding inverse weights. In extensive simulation studies, they are compared along with other common estimators. The asymptotic properties of the best estimator are developed.The second manuscript is motivated by the estimation of the marginal effects of two antidepressants, citalopram and fluoxetine, on body mass index, in data from the Clinical Practice Research Datalink (CPRD) in the United Kingdom. It is assumed that the longitudinal characteristics of the patients change with physician visits, and therefore, interact with the monitoring process. Different causal diagrams are used to describe how bias due to covariate-driven monitoring times can arise in different situations, including the complex setting where the endogenous covariate process can be modified by the monitoring process. A new stabilized and cumulated inverse weight is proposed for the latter setting. The weight serves to break the association between the full history of covariates and the monitoring process. In the third manuscript, I aim to evaluate the marginal (causal) effect of the time spent on video games weekly, on suicide attempts. To investigate that effect, I use longitudinal data from the Add Health Study, in the United States; these data are subject to confounding and monitoring times that may be associated with patients' characteristics. I first extend one of the estimators proposed in the first manuscript of this thesis to allow consideration of a continuous exposure via a generalized inverse probability of treatment weight, along with a categorical ordinal outcome via a proportional odds model. Simulation studies are used to demonstrate the consistency of the approach, which is further used to estimate the marginal odds ratio for a 2-fold or an 8-fold increases in the time spent playing video games on the number of suicide attempts (categorized as 0, 1, or 2 or more).Using causal diagrams, I provided in this thesis a thorough demonstration of the bias due to covariate-driven monitoring times. I proposed a sound methodology for evaluating causal effects in observational studies subject to confounding and covariate-driven monitoring times. The proposed methods were further used to answer mental health-related research questions"--
Download or read book Longitudinal Research with Latent Variables written by Kees van Montfort and published by Springer. This book was released on 2014-11-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since Charles Spearman published his seminal paper on factor analysis in 1904 and Karl Joresk ̈ og replaced the observed variables in an econometric structural equation model by latent factors in 1970, causal modelling by means of latent variables has become the standard in the social and behavioural sciences. Indeed, the central va- ables that social and behavioural theories deal with, can hardly ever be identi?ed as observed variables. Statistical modelling has to take account of measurement - rors and invalidities in the observed variables and so address the underlying latent variables. Moreover, during the past decades it has been widely agreed on that serious causal modelling should be based on longitudinal data. It is especially in the ?eld of longitudinal research and analysis, including panel research, that progress has been made in recent years. Many comprehensive panel data sets as, for example, on human development and voting behaviour have become available for analysis. The number of publications based on longitudinal data has increased immensely. Papers with causal claims based on cross-sectional data only experience rejection just for that reason.
Download or read book Optic written by Beth Ann Griffin and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This tool can help researchers assess how co-occurring policies and confounding can affect the performance of statistical models commonly used in state policy evaluations. Specifically, the tool helps users compare the performance of various causal inference models using their own longitudinal data. Users can select from a variety of simulation options to explore how different state policy evaluation methods perform. Although the tool was initially created to examine data related to opioids, its framework can be used with longitudinal data on any topic. Recent research on difference-in-differences (DID) models revealed issues with traditional DID models, and there has been an explosion of new methods in this area for researchers to consider. Researchers found it difficult to evaluate the relative performance of different causal inference methods using longitudinal outcome data on opioid mortality and opioid prescribing rates; thus, they designed a series of simulations to study the performance of various methods under different scenarios for any type of repeated measures outcome data. The tool's introductory vignette provides a working example of how to use the package. This example uses the sample overdoses dataset provided with the package. Users need the R software environment (version 4.1.0 or above) to use the package.
Download or read book A Finite Population Approach for Causal Inference in Nested Case control Studies written by Katarina Majetic and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "The nested case-control design is employed by researchers when it is too difficult or expensive to collect and/or analyze data prospectively on rare outcomes. The sampling design is retrospective in nature but the conclusions are prospective in nature, which can lead to bias when analyzed inappropriately. Most nested case-control approaches employ logistic regression, however, in this retrospective analysis, a difficulty arises when one wants to employ causal inference methods to adjust to time-varying confounding. In this thesis, we introduce methods that allow us to use prospective causal inference methods with time-varying confounding, under a retrospective nested case-control sub-sampling scheme which requires a different approach to the classic nested case-control design. We interpret the entire cohort data set as a fixed finite population, thus, when we take our nested case-control sample, it will be viewed as a draw from the finite population. In order to account for causal effects, we use inverse probability (IP) treatment weighting on top of the sampling weights. Thus, we introduce methods to solve a nested case-control problem using finite population methods in a causal setting"--
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 Registries for Evaluating Patient Outcomes written by Agency for Healthcare Research and Quality/AHRQ and published by Government Printing Office. This book was released on 2014-04-01 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. 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.
Download or read book Semiparametric Theory and Missing Data written by Anastasios Tsiatis and published by Springer Science & Business Media. This book was released on 2007-01-15 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.
