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Articles 1 - 30 of 60
Full-Text Articles in Statistics and Probability
A Causal Inference Approach For Spike Train Interactions, Zach Saccomano
A Causal Inference Approach For Spike Train Interactions, Zach Saccomano
Dissertations, Theses, and Capstone Projects
Since the 1960s, neuroscientists have worked on the problem of estimating synaptic properties, such as connectivity and strength, from simultaneously recorded spike trains. Recent years have seen renewed interest in the problem coinciding with rapid advances in experimental technologies, including an approximate exponential increase in the number of neurons that can be recorded in parallel and perturbation techniques such as optogenetics that can be used to calibrate and validate causal hypotheses about functional connectivity. This thesis presents a mathematical examination of synaptic inference from two perspectives: (1) using in vivo data and biophysical models, we ask in what cases the …
Bayesian Strategies For Propensity Score Estimation In Causal Inference., Uthpala I. Wanigasekara
Bayesian Strategies For Propensity Score Estimation In Causal Inference., Uthpala I. Wanigasekara
Electronic Theses and Dissertations
Causal inference is a method used in various fields to draw causal conclusions based on data. It involves using assumptions, study designs, and estimation strategies to minimize the impact of confounding variables. Propensity scores are used to estimate outcome effects, through matching methods, stratification, weighting methods, and the Covariate Balancing Propensity Score method. However, they can be sensitive to estimation techniques and can lead to unstable findings. Researchers have proposed integrating weighing with regression adjustment in parametric models to improve causal inference validity. The first project focuses on Bayesian joint and two-stage methods for propensity score analysis. Propensity score modeling …
Estimation Of Causal Effects In Complex Clustered Data, Joshua R. Nugent
Estimation Of Causal Effects In Complex Clustered Data, Joshua R. Nugent
Doctoral Dissertations
Analysis of clustered data from randomized trials or observational data often poses theoretical and practical statistical challenges, including but not limited to small numbers of independent units, many adjustment variables, continuous exposures, and/or differential clustering across trial arms. Further, commonly-used parametric methods rely on assumptions that may be violated in practice. Motivated by three scientific questions in public health, methods are developed and/or demonstrated for non-parametric estimation of causal effects. In Chapter 1, methods are elaborated for a cluster randomized trial (CRT) with missing individual-level data at baseline and follow-up, a complex sampling strategy, and limited number of clusters. Chapter …
Bayesian Hierarchical Temporal Modeling And Targeted Learning With Application To Reproductive Health, Herbert P. Susmann
Bayesian Hierarchical Temporal Modeling And Targeted Learning With Application To Reproductive Health, Herbert P. Susmann
Doctoral Dissertations
The international community via the United Nations Sustainable Development Goals has set the target of universal access to reproductive health-care services, including family planning, by 2030. Progress towards reaching this goal is assessed by tracking appropriate demographic and health indicators at national and subnational levels. This task is challenging, however, in populations where relevant data are limited or of low quality. Statistical models are then needed to estimate and project demographic and health indicators in populations based on the available data. Our first contribution, in Chapter 1, is to unify many existing demographic and health indicator models by proposing an …
Statistical Methods For Assessing Drug Interactions And Identifying Effect Modifiers Using Observational Data., Qian Xu
Electronic Theses and Dissertations
This dissertation consists of three projects related to causal inference based on observational data. In the first project, we propose a double robust to identify the effect modifiers and estimate optimal treatment. Observational studies differ from experimental studies in that assignment of subjects to treatments is not randomized but rather occurs due to natural mechanisms, which are usually hidden from the researchers. Many statistical methods to identify the treatment effect and select the optimal personalized treatment for experimental studies may not be suitable for observational studies any more. In this project, we propose a exible outcome model to select the …
Impact Of Loss To Follow-Up And Time Parameterization In Multiple-Period Cluster Randomized Trials And Assessing The Association Between Institution Affiliation And Journal Publication, Jonathan Moyer
Doctoral Dissertations
Difference-in-difference cluster randomized trials (CRTs) use baseline and post-test measurements. Standard power equations for these trials assume no loss to follow-up. We present a general equation for calculating treatment effect variance in difference-in-difference CRTs, with special cases assuming loss to follow-up with replacement of lost participants and loss to follow-up with no replacement but retaining the baseline measurements of all participants. Multiple-period CRTs can represent time as continuous using random coefficients (RC) or categorical using repeated measures ANOVA (RM-ANOVA) analytic models. Previous work recommends the use of RC over RM-ANOVA for CRTs with more than two periods because RC exhibited …
Estimating Treatment Effect On Medical Cost And Examining Medical Cost Trajectory Using Splines And Change Point Techniques., Indranil Ghosh
Estimating Treatment Effect On Medical Cost And Examining Medical Cost Trajectory Using Splines And Change Point Techniques., Indranil Ghosh
Electronic Theses and Dissertations
In the world of growing medical needs, other than the clinical outcomes, the cost of healthcare is one of the important aspects to evaluate. The cost of treatment could act as a decisive factor on which one to choose from two equally likely effective treatment options. In literature, the most used quantity for the cost of treatment is cumulative lifetime cost since the diagnosis of a disease. While it provides a bird' eye view of the treatment cost, it fails to capture the underlying pattern of the treatment cost trajectory. We developed a marginal structural functional model (MSFM) using an …
High-Dimensional Feature Selection And Multi-Level Causal Mediation Analysis With Applications To Human Aging And Cluster-Based Intervention Studies, Hachem Saddiki
Doctoral Dissertations
Many questions in public health and medicine are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome of interest. As a result, causal inference frameworks and methodologies have gained interest as a promising tool to reliably answer scientific questions. However, the tasks of identifying and efficiently estimating causal effects from observed data still pose significant challenges under complex data generating scenarios. We focus on (1) high-dimensional settings where the number of variables is orders of magnitude higher than the number of observations; and (2) multi-level settings, where study participants …
How To Apply Multiple Imputation In Propensity Score Matching With Partially Observed Confounders: A Simulation Study And Practical Recommendations, Albee Ling, Maria Montez-Rath, Maya Mathur, Kris Kapphahn, Manisha Desai
How To Apply Multiple Imputation In Propensity Score Matching With Partially Observed Confounders: A Simulation Study And Practical Recommendations, Albee Ling, Maria Montez-Rath, Maya Mathur, Kris Kapphahn, Manisha Desai
Journal of Modern Applied Statistical Methods
Propensity score matching (PSM) has been widely used to mitigate confounding in observational studies, although complications arise when the covariates used to estimate the PS are only partially observed. Multiple imputation (MI) is a potential solution for handling missing covariates in the estimation of the PS. However, it is not clear how to best apply MI strategies in the context of PSM. We conducted a simulation study to compare the performances of popular non-MI missing data methods and various MI-based strategies under different missing data mechanisms. We found that commonly applied missing data methods resulted in biased and inefficient estimates, …
Observational Studies In Group Testing And Potential Applications., Alexander Christopher Noll
Observational Studies In Group Testing And Potential Applications., Alexander Christopher Noll
Electronic Theses and Dissertations
The use of group testing to identify individuals with targeted outcomes in a population can greatly improve the efficiency, speed, and cost effectiveness of testing a population for an outcome, or at least for identifying the prevalence of an outcome in a population. The implementation of causal inference techniques can provide the basis for an observational study that would allow an investigator to gather estimates for treatment effectiveness if group testing was conducted on the population in a certain way. This thesis examines a simulation of the above outlined principles in order to demonstrate a potential application for determining treatment …
Causal Mediation Analysis For Difference-In-Difference Design And Panel Data, Pei-Hsuan Hsia, An-Shun Tai, Chu-Lan Michael Kao, Yu-Hsuan Lin, Sheng-Hsuan Lin
Causal Mediation Analysis For Difference-In-Difference Design And Panel Data, Pei-Hsuan Hsia, An-Shun Tai, Chu-Lan Michael Kao, Yu-Hsuan Lin, Sheng-Hsuan Lin
Harvard University Biostatistics Working Paper Series
Advantages of panel data, i.e., difference in difference (DID) design data, are a large sample size and easy availability. Therefore, panel data are widely used in epidemiology and in all social science fields. The literatures on causal inferences of panel data setting or DID design are growing, but no theory or mediation analysis method has been proposed for such settings. In this study, we propose a methodology for conducting causal mediation analysis in DID design and panel data setting. We provide formal counterfactual definitions for controlled direct effect and natural direct and indirect effect in panel data setting and DID …
Modified-Half-Normal Distribution And Different Methods To Estimate Average Treatment Effect., Jingchao Sun
Modified-Half-Normal Distribution And Different Methods To Estimate Average Treatment Effect., Jingchao Sun
Electronic Theses and Dissertations
This dissertation consists of three projects related to Modified-Half-Normal distribution and causal inference. In my first project, a new distribution called Modified-Half-Normal distribution was introduced. I explored a few of its distributional properties, the procedures for generating random samples based on Bayesian approaches, and the parameter estimation based on the method of moments. The second project deals with the problem of selection bias of average treatment effect (ATE) if we use the observational data. I combined the propensity score based inverse probability of treatment weighting (IPTW) method and the directed acyclic graph (DAG) to solve this problem. The third project …
Statistical Methods For Estimating And Testing Treatment Effect For Multiple Treatment Groups In Observational Studies., Xiaofang Yan
Statistical Methods For Estimating And Testing Treatment Effect For Multiple Treatment Groups In Observational Studies., Xiaofang Yan
Electronic Theses and Dissertations
Note: Abstract would not save due to an issue with some of the characters.
Methods For Making Policy-Relevant Forecasts Of Infectious Disease Incidence, Stephen A. Lauer
Methods For Making Policy-Relevant Forecasts Of Infectious Disease Incidence, Stephen A. Lauer
Doctoral Dissertations
Infectious diseases place an enormous burden on the people of the developing world and their governments. When, where, and how to allocate resources in order to slow the spread of a virus or deal with the aftermath of an outbreak is often the responsibility of local public health officials. In this thesis, we develop statistical methods for forecasting future incidence of infectious diseases and estimating the effects of interventions designed to reduce future incidence, bearing in mind the needs and concerns of those public health officials. While most infectious disease forecasting models focus on short-term horizons (i.e. weeks or …
Bayesian Approach On Short Time-Course Data Of Protein Phosphorylation, Casual Inference For Ordinal Outcome And Causal Analysis Of Dietary And Physical Activity In T2dm Using Nhanes Data., You Wu
Electronic Theses and Dissertations
This dissertation contains three different projects in proteomics and causal inferences. In the first project, I apply a Bayesian hierarchical model to assess the stability of phosphorylated proteins under short-time cold ischemia. This study provides inference on the stability of these phosphorylated proteins, which is valuable when using these proteins as biomarkers for a disease. in the second project, I perform a comparative study of different confounding-adjusted to estimate the treatment effect when the outcome variable is ordinal using observational data. The adjusted U-statistics method is compared with other methods such as ordinal logistic regression, propensity score based stratification and …
Tmle For Marginal Structural Models Based On An Instrument, Boriska Toth, Mark J. Van Der Laan
Tmle For Marginal Structural Models Based On An Instrument, Boriska Toth, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
We consider estimation of a causal effect of a possibly continuous treatment when treatment assignment is potentially subject to unmeasured confounding, but an instrumental variable is available. Our focus is on estimating heterogeneous treatment effects, so that the treatment effect can be a function of an arbitrary subset of the observed covariates. One setting where this framework is especially useful is with clinical outcomes. Allowing the causal dose-response curve to depend on a subset of the covariates, we define our parameter of interest to be the projection of the true dose-response curve onto a user-supplied working marginal structural model. We …
Propensity Score Methods : A Simulation And Case Study Involving Breast Cancer Patients., John Craycroft
Propensity Score Methods : A Simulation And Case Study Involving Breast Cancer Patients., John Craycroft
Electronic Theses and Dissertations
Observational data presents unique challenges for analysis that are not encountered with experimental data resulting from carefully designed randomized controlled trials. Selection bias and unbalanced treatment assignments can obscure estimations of treatment effects, making the process of causal inference from observational data highly problematic. In 1983, Paul Rosenbaum and Donald Rubin formalized an approach for analyzing observational data that adjusts treatment effect estimates for the set of non-treatment variables that are measured at baseline. The propensity score is the conditional probability of assignment to a treatment group given the covariates. Using this score, one may balance the covariates across treatment …
A Weighted Instrumental Variable Estimator To Control For Instrument-Outcome Confounders, Douglas Lehmann, Yun Li, Rajiv Saran, Yi Li
A Weighted Instrumental Variable Estimator To Control For Instrument-Outcome Confounders, Douglas Lehmann, Yun Li, Rajiv Saran, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
No abstract provided.
Semi-Parametric Estimation And Inference For The Mean Outcome Of The Single Time-Point Intervention In A Causally Connected Population, Oleg Sofrygin, Mark J. Van Der Laan
Semi-Parametric Estimation And Inference For The Mean Outcome Of The Single Time-Point Intervention In A Causally Connected Population, Oleg Sofrygin, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
We study the framework for semi-parametric estimation and statistical inference for the sample average treatment-specific mean effects in observational settings where data are collected on a single network of connected units (e.g., in the presence of interference or spillover). Despite recent advances, many of the current statistical methods rely on estimation techniques that assume a particular parametric model for the outcome, even though some of the most important statistical assumptions required by these models are most likely violated in the observational network settings, often resulting in invalid and anti-conservative statistical inference. In this manuscript, we rely on the recent methodological …
Contrails: Causal Inference Using Propensity Scores, Dean S. Barron
Contrails: Causal Inference Using Propensity Scores, Dean S. Barron
Journal of Modern Applied Statistical Methods
Contrails are clouds caused by airplane exhausts, which geologists contend decrease daily temperature ranges on Earth. Following the 2001 World Trade Center attack, cancelled domestic flights triggered the first absence of contrails in decades. Resultant exceptional data capacitated causal inference analysis by propensity score matching. Estimated contrail effect was 6.8981°F.
A General Framework For Diagnosing Confounding Of Time-Varying And Other Joint Exposures, John W. Jackson
A General Framework For Diagnosing Confounding Of Time-Varying And Other Joint Exposures, John W. Jackson
Harvard University Biostatistics Working Paper Series
No abstract provided.
Adaptive Pre-Specification In Randomized Trials With And Without Pair-Matching, Laura B. Balzer, Mark J. Van Der Laan, Maya L. Petersen
Adaptive Pre-Specification In Randomized Trials With And Without Pair-Matching, Laura B. Balzer, Mark J. Van Der Laan, Maya L. Petersen
Laura B. Balzer
In randomized trials, adjustment for measured covariates during the analysis can reduce variance and increase power. To avoid misleading inference, the analysis plan must be pre-specified. However, it is unclear a priori which baseline covariates (if any) should be included in the analysis. Consider, for example, the Sustainable East Africa Research in Community Health (SEARCH) trial for HIV prevention and treatment. There are 16 matched pairs of communities and many potential adjustment variables, including region, HIV prevalence, male circumcision coverage and measures of community-level viral load. In this paper, we propose a rigorous procedure to data-adaptively select the adjustment set …
Adaptive Pre-Specification In Randomized Trials With And Without Pair-Matching, Laura B. Balzer, Mark J. Van Der Laan, Maya L. Petersen
Adaptive Pre-Specification In Randomized Trials With And Without Pair-Matching, Laura B. Balzer, Mark J. Van Der Laan, Maya L. Petersen
U.C. Berkeley Division of Biostatistics Working Paper Series
In randomized trials, adjustment for measured covariates during the analysis can reduce variance and increase power. To avoid misleading inference, the analysis plan must be pre-specified. However, it is unclear a priori which baseline covariates (if any) should be included in the analysis. Consider, for example, the Sustainable East Africa Research in Community Health (SEARCH) trial for HIV prevention and treatment. There are 16 matched pairs of communities and many potential adjustment variables, including region, HIV prevalence, male circumcision coverage and measures of community-level viral load. In this paper, we propose a rigorous procedure to data-adaptively select the adjustment set …
Applying Multiple Imputation For External Calibration To Propensty Score Analysis, Yenny Webb-Vargas, Kara E. Rudolph, D. Lenis, Peter Murakami, Elizabeth A. Stuart
Applying Multiple Imputation For External Calibration To Propensty Score Analysis, Yenny Webb-Vargas, Kara E. Rudolph, D. Lenis, Peter Murakami, Elizabeth A. Stuart
Johns Hopkins University, Dept. of Biostatistics Working Papers
Although covariate measurement error is likely the norm rather than the exception, methods for handling covariate measurement error in propensity score methods have not been widely investigated. We consider a multiple imputation-based approach that uses an external calibration sample with information on the true and mismeasured covariates, Multiple Imputation for External Calibration (MI-EC), to correct for the measurement error, and investigate its performance using simulation studies. As expected, using the covariate measured with error leads to bias in the treatment effect estimate. In contrast, the MI-EC method can eliminate almost all the bias. We confirm that the outcome must be …
Estimating Controlled Direct Effects Of Restrictive Feeding Practices In The `Early Dieting In Girls' Study, Yeying Zhu, Debashis Ghosh, Donna L. Coffman, Jennifer S. Williams
Estimating Controlled Direct Effects Of Restrictive Feeding Practices In The `Early Dieting In Girls' Study, Yeying Zhu, Debashis Ghosh, Donna L. Coffman, Jennifer S. Williams
Debashis Ghosh
In this article, we examine the causal effect of parental restrictive feeding practices on children’s weight status. An important mediator we are interested in is children’s self-regulation status. Traditional mediation analysis (Baron and Kenny, 1986) applies a structural equation modelling (SEM) approach and decomposes the intent-to-treat (ITT) effect into direct and indirect effects. More recent approaches interpret the mediation effects based on the potential outcomes framework. In practice, there often exist confounders that jointly influence the mediator and the outcome. Inverse probability weighting based on propensity scores are used to adjust for confounding and reduce the dimensionality of confounders simultaneously. …
Moving Towards Best Practice When Using Inverse Probability Of Treatment Weighting (Iptw) Using The Propensity Score To Estimate Causal Treatment Effects In Observational Studies, Peter Austin, Elizabeth Stuart
Moving Towards Best Practice When Using Inverse Probability Of Treatment Weighting (Iptw) Using The Propensity Score To Estimate Causal Treatment Effects In Observational Studies, Peter Austin, Elizabeth Stuart
Peter Austin
The propensity score is defined as a subject’s probability of treatment selection, conditional on observed baseline covariates.Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in …
Super-Learning Of An Optimal Dynamic Treatment Rule, Alexander R. Luedtke, Mark J. Van Der Laan
Super-Learning Of An Optimal Dynamic Treatment Rule, Alexander R. Luedtke, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
We consider the estimation of an optimal dynamic two time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, where the candidate rules are restricted to depend only on a user-supplied subset of the baseline and intermediate covariates. This estimation problem is addressed in a statistical model for the data distribution that is nonparametric, beyond possible knowledge about the treatment and censoring mechanisms. We propose data adaptive estimators of this optimal dynamic regime which are defined by sequential loss-based learning under both the blip function and weighted classification frameworks. Rather than \textit{a priori} selecting …
Targeted Learning Of The Mean Outcome Under An Optimal Dynamic Treatment Rule, Mark J. Van Der Laan, Alexander R. Luedtke
Targeted Learning Of The Mean Outcome Under An Optimal Dynamic Treatment Rule, Mark J. Van Der Laan, Alexander R. Luedtke
U.C. Berkeley Division of Biostatistics Working Paper Series
We consider estimation of and inference for the mean outcome under the optimal dynamic two time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, where the candidate rules are restricted to depend only on a user-supplied subset of the baseline and intermediate covariates. This estimation problem is addressed in a statistical model for the data distribution that is nonparametric beyond possible knowledge about the treatment and censoring mechanism. This contrasts from the current literature that relies on parametric assumptions. We establish that the mean of the counterfactual outcome under the optimal dynamic treatment …
A Unification Of Mediation And Interaction: A Four-Way Decomposition, Tyler J. Vanderweele
A Unification Of Mediation And Interaction: A Four-Way Decomposition, Tyler J. Vanderweele
Harvard University Biostatistics Working Paper Series
It is shown that the overall effect of an exposure on an outcome, in the presence of a mediator with which the exposure may interact, can be decomposed into four components: (i) the effect of the exposure in the absence of the mediator, (ii) the interactive effect when the mediator is left to what it would be in the absence of exposure, (iii) a mediated interaction, and (iv) a pure mediated effect. These four components, respectively, correspond to the portion of the effect that is due to neither mediation nor interaction, to just interaction (but not mediation), to both mediation …
Estimating Population Treatment Effects From A Survey Sub-Sample, Kara E. Rudolph, Ivan Diaz, Michael Rosenblum, Elizabeth A. Stuart
Estimating Population Treatment Effects From A Survey Sub-Sample, Kara E. Rudolph, Ivan Diaz, Michael Rosenblum, Elizabeth A. Stuart
Johns Hopkins University, Dept. of Biostatistics Working Papers
We consider the problem of estimating an average treatment effect for a target population from a survey sub-sample. Our motivating example is generalizing a treatment effect estimated in a sub-sample of the National Comorbidity Survey Replication Adolescent Supplement to the population of U.S. adolescents. To address this problem, we evaluate easy-to-implement methods that account for both non-random treatment assignment and a non-random two-stage selection mechanism. We compare the performance of a Horvitz-Thompson estimator using inverse probability weighting (IPW) and two double robust estimators in a variety of scenarios. We demonstrate that the two double robust estimators generally outperform IPW in …