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Articles 1 - 30 of 140
Full-Text Articles in Biostatistics
Marginal Proportional Hazards Models For Clustered Interval-Censored Data With Time-Dependent Covariates, Kaitlyn Cook, Wenbin Lu, Rui Wang
Marginal Proportional Hazards Models For Clustered Interval-Censored Data With Time-Dependent Covariates, Kaitlyn Cook, Wenbin Lu, Rui Wang
Harvard University Biostatistics Working Paper Series
The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana’s national adoption of a universal test-and-treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy (i) modified the observed preventative effects of the study intervention and (ii) was associated with a reduction in the population-level incidence of HIV in Botswana. To address these questions, we propose a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on …
On Assessing Survival Benefit Of Immunotherapy Using Long-Term Restricted Mean Survival Time, Miki Horiguchi, Lu Tian, Hajime Uno
On Assessing Survival Benefit Of Immunotherapy Using Long-Term Restricted Mean Survival Time, Miki Horiguchi, Lu Tian, Hajime Uno
Harvard University Biostatistics Working Paper Series
The pattern of the difference between two survival curves we often observe in randomized clinical trials for evaluating immunotherapy is not proportional hazards; the treatment effect typically appears several months after the initiation of the treatment (i.e., delayed difference pattern). The commonly used logrank test and hazard ratio estimation approach will be suboptimal concerning testing and estimation for those trials. The long-term restricted mean survival time (LT-RMST) approach is a promising alternative for detecting the treatment effect that potentially appears later in the study. A challenge in employing the LT-RMST approach is that it must specify a lower end of …
Nonlinear Mixed-Effects Models For Hiv Viral Load Trajectories Before And After Antiretroviral Therapy Interruption, Incorporating Left Censoring, Sihaoyu Gao, Lang Wu, Tingting Yu, Roger Kouyos, Huldrych F. Gunthard, Rui Wang
Nonlinear Mixed-Effects Models For Hiv Viral Load Trajectories Before And After Antiretroviral Therapy Interruption, Incorporating Left Censoring, Sihaoyu Gao, Lang Wu, Tingting Yu, Roger Kouyos, Huldrych F. Gunthard, Rui Wang
Harvard University Biostatistics Working Paper Series
Characterizing features of the viral rebound trajectories and identifying host, virological, and immunological factors that are predictive of the viral rebound trajectories are central to HIV cure research. In this paper, we investigate if key features of HIV viral decay and CD4 trajectories during antiretroviral therapy (ART) are associated with characteristics of HIV viral rebound following ART interruption. Nonlinear mixed effect (NLME) models are used to model viral load trajectories before and following ART interruption, incorporating left censoring due to lower detection limits of viral load assays. A stochastic approximation EM (SAEM) algorithm is used for parameter estimation and inference. …
Ratio And Difference Of Average Hazard With Survival Weight: New Measures To Quantify Survival Benefit Of New Therapy, Hajime Uno, Miki Horiguchi
Ratio And Difference Of Average Hazard With Survival Weight: New Measures To Quantify Survival Benefit Of New Therapy, Hajime Uno, Miki Horiguchi
Harvard University Biostatistics Working Paper Series
The hazard ratio (HR) has been the most popular measure to quantify the magnitude of treatment effect on time-to-event outcomes in clinical research. However, the HR estimated by Cox's method has several drawbacks. One major issue is that there is no clear interpretation when the proportional hazards (PH) assumption does not hold, because it is affected by study-specific censoring time distribution in non-PH cases. Another major issue is that the lack of a group-specific absolute hazard value in each group obscures the clinical significance of the magnitude of the treatment effect. Given these, we propose average hazard with survival weight …
Causal Mediation Analysis With Multiple Time-Varying Mediators, An-Shun Tai, Sheng-Hsuan Lin, Yu-Cheng Chu, Tsung Yu, Milo A. Puhan, Tyler Vanderweele
Causal Mediation Analysis With Multiple Time-Varying Mediators, An-Shun Tai, Sheng-Hsuan Lin, Yu-Cheng Chu, Tsung Yu, Milo A. Puhan, Tyler Vanderweele
Harvard University Biostatistics Working Paper Series
In longitudinal studies with time-varying exposures and mediators, the mediational g-formula is an important method for the assessment of direct and indirect effects. However, current methodologies based on the mediational g-formula can deal with only one mediator. This limitation makes these methodologies inapplicable to many scenarios. Hence, we develop a novel methodology by extending the mediational g-formula to cover cases with multiple time-varying mediators. We formulate two variants of our approach that are each suited to a distinct set of assumptions and effect definitions and present nonparametric identification results of each variant. We further show how complex causal mechanisms (whose …
Identification And Robust Estimation Of Swapped Direct And Indirect Effects: Mediation Analysis With Unmeasured Mediator–Outcome Confounding And Intermediate Confounding, An-Shun Tai, Sheng-Hsuan Lin
Identification And Robust Estimation Of Swapped Direct And Indirect Effects: Mediation Analysis With Unmeasured Mediator–Outcome Confounding And Intermediate Confounding, An-Shun Tai, Sheng-Hsuan Lin
Harvard University Biostatistics Working Paper Series
Counterfactual-model-based mediation analysis can yield substantial insight into the causal mechanism through the assessment of natural direct effects (NDEs) and natural indirect effects (NIEs). However, the assumptions regarding unmeasured mediator–outcome confounding and intermediate mediator–outcome confounding that are required for the determination of NDEs and NIEs present practical challenges. To address this problem, we introduce an instrumental blocker, a novel quasi-instrumental variable, to relax both of these assumptions, and we define a swapped direct effect (SDE) and a swapped indirect effect (SIE) to assess the mediation. We show that the SDE and SIE are identical to the NDE and NIE, respectively, …
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 …
Robust Inference On Effects Attributable To Mediators: A Controlled-Direct-Effect-Based Approach For Causal Effect Decomposition With Multiple Mediators, An-Shun Tai, Yi-Juan Du, Sheng-Hsuan Lin
Robust Inference On Effects Attributable To Mediators: A Controlled-Direct-Effect-Based Approach For Causal Effect Decomposition With Multiple Mediators, An-Shun Tai, Yi-Juan Du, Sheng-Hsuan Lin
Harvard University Biostatistics Working Paper Series
Effect decomposition is a critical technique for mechanism investigation in settings with multiple causally ordered mediators. Causal mediation analysis is a standard method for effect decomposition, but the assumptions required for the identification process are extremely strong. By extending the framework of controlled direct effects, this study proposes the effect attributable to mediators (EAM) as a novel measure for effect decomposition. For policy making, EAM represents how much an effect can be eliminated by setting mediators to certain values. From the perspective of mechanism investigation, EAM contains information about how much a particular mediator or set of mediators is involved …
Integrated Multiple Mediation Analysis: A Robustness–Specificity Trade-Off In Causal Structure, An-Shun Tai, Sheng-Hsuan Lin
Integrated Multiple Mediation Analysis: A Robustness–Specificity Trade-Off In Causal Structure, An-Shun Tai, Sheng-Hsuan Lin
Harvard University Biostatistics Working Paper Series
Recent methodological developments in causal mediation analysis have addressed several issues regarding multiple mediators. However, these developed methods differ in their definitions of causal parameters, assumptions for identification, and interpretations of causal effects, making it unclear which method ought to be selected when investigating a given causal effect. Thus, in this study, we construct an integrated framework, which unifies all existing methodologies, as a standard for mediation analysis with multiple mediators. To clarify the relationship between existing methods, we propose four strategies for effect decomposition: two-way, partially forward, partially backward, and complete decompositions. This study reveals how the direct and …
Survival Mediation Analysis With The Death-Truncated Mediator: The Completeness Of The Survival Mediation Parameter, An-Shun Tai, Chun-An Tsai, Sheng-Hsuan Lin
Survival Mediation Analysis With The Death-Truncated Mediator: The Completeness Of The Survival Mediation Parameter, An-Shun Tai, Chun-An Tsai, Sheng-Hsuan Lin
Harvard University Biostatistics Working Paper Series
In medical research, the development of mediation analysis with a survival outcome has facilitated investigation into causal mechanisms. However, studies have not discussed the death-truncation problem for mediators, the problem being that conventional mediation parameters cannot be well-defined in the presence of a truncated mediator. In the present study, we systematically defined the completeness of causal effects to uncover the gap, in conventional causal definitions, between the survival and nonsurvival settings. We proposed three approaches to redefining the natural direct and indirect effects, which are generalized forms of the conventional causal effects for survival outcomes. Furthermore, we developed three statistical …
Estimating Marginal Hazard Ratios By Simultaneously Using A Set Of Propensity Score Models: A Multiply Robust Approach, Di Shu, Peisong Han, Rui Wang, Sengwee Toh
Estimating Marginal Hazard Ratios By Simultaneously Using A Set Of Propensity Score Models: A Multiply Robust Approach, Di Shu, Peisong Han, Rui Wang, Sengwee Toh
Harvard University Biostatistics Working Paper Series
The inverse probability weighted Cox model is frequently used to estimate marginal hazard ratios. Its validity requires a crucial condition that the propensity score model is correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in empirical likelihood theory. The proposed estimator is multiply robust in that it is guaranteed to be consistent when a set of postulated propensity score models contains a correctly specified model. Our simulation studies demonstrate satisfactory finite sample performance of the proposed method in terms of consistency and efficiency. We apply the proposed method to …
Estimation Of Conditional Power For Cluster-Randomized Trials With Interval-Censored Endpoints, Kaitlyn Cook, Rui Wang
Estimation Of Conditional Power For Cluster-Randomized Trials With Interval-Censored Endpoints, Kaitlyn Cook, Rui Wang
Harvard University Biostatistics Working Paper Series
Cluster-randomized trials (CRTs) of infectious disease preventions often yield correlated, interval-censored data: dependencies may exist between observations from the same cluster, and event occurrence may be assessed only at intermittent clinic visits. This data structure must be accounted for when conducting interim monitoring and futility assessment for CRTs. In this article, we propose a flexible framework for conditional power estimation when outcomes are correlated and interval-censored. Under the assumption that the survival times follow a shared frailty model, we first characterize the correspondence between the marginal and cluster-conditional survival functions, and then use this relationship to semiparametrically estimate the cluster-specific …
Randomization-Based Confidence Intervals For Cluster Randomized Trials, Dustin J. Rabideau, Rui Wang
Randomization-Based Confidence Intervals For Cluster Randomized Trials, Dustin J. Rabideau, Rui Wang
Harvard University Biostatistics Working Paper Series
In a cluster randomized trial (CRT), groups of people are randomly assigned to different interventions. Existing parametric and semiparametric methods for CRTs rely on distributional assumptions or a large number of clusters to maintain nominal confidence interval (CI) coverage. Randomization-based inference is an alternative approach that is distribution-free and does not require a large number of clusters to be valid. Although it is well-known that a CI can be obtained by inverting a randomization test, this requires randomization testing a non-zero null hypothesis, which is challenging with non-continuous and survival outcomes. In this paper, we propose a general method for …
Power Calculation For Cross-Sectional Stepped-Wedge Cluster Randomized Trials With Binary Outcomes, Linda J. Harrison, Rui Wang
Power Calculation For Cross-Sectional Stepped-Wedge Cluster Randomized Trials With Binary Outcomes, Linda J. Harrison, Rui Wang
Harvard University Biostatistics Working Paper Series
Power calculation for stepped-wedge cluster randomized trials (SW-CRTs) presents unique challenges, beyond those of standard cluster randomized trials (CRTs), due to the need to consider temporal within cluster correlations and background period effects. To date, power calculation methods specific to SW-CRTs have primarily been developed under a linear model. When the outcome is binary, the use of a linear model corresponds to assessing a prevalence difference; yet trial analysis often employs a non-linear link function. We assess power for cross-sectional SW-CRTs under a logistic model fitted by generalized estimating equations. Firstly, under an exchangeable correlation structure, we show the power …
Generalized Interventional Approach For Causal Mediation Analysis With Causally Ordered Multiple Mediators, Sheng-Hsuan Lin
Generalized Interventional Approach For Causal Mediation Analysis With Causally Ordered Multiple Mediators, Sheng-Hsuan Lin
Harvard University Biostatistics Working Paper Series
Causal mediation analysis has demonstrated the advantage of mechanism investigation. In conditions with causally ordered mediators, path-specific effects (PSEs) are introduced for specifying the effect subject to a certain combination of mediators. However, most PSEs are unidentifiable. To address this, an alternative approach termed interventional analogue of PSE (iPSE), is widely applied to effect decomposition. Previous studies that have considered multiple mediators have mainly focused on two-mediator cases due to the complexity of the mediation formula. This study proposes a generalized interventional approach for the settings, with the arbitrary number of ordered multiple mediators to study the causal parameter identification …
Variance Estimation In Inverse Probability Weighted Cox Models, Di Shu, Jessica G. Young, Sengwee Toh, Rui Wang
Variance Estimation In Inverse Probability Weighted Cox Models, Di Shu, Jessica G. Young, Sengwee Toh, Rui Wang
Harvard University Biostatistics Working Paper Series
Inverse probability weighted Cox models can be used to estimate marginal hazard ratios under different treatments interventions in observational studies. To obtain variance estimates, the robust sandwich variance estimator is often recommended to account for the induced correlation among weighted observations. However, this estimator does not incorporate the uncertainty in estimating the weights and tends to overestimate the variance, leading to inefficient inference. Here we propose a new variance estimator that combines the estimation procedures for the hazard ratio and weights using stacked estimating equations, with additional adjustments for the sum of non-independent and identically distributed terms in a Cox …
General Approach Of Causal Mediation Analysis With Causally Ordered Multiple Mediators And Survival Outcome, An-Shun Tai, Pei-Hsuan Lin, Yen-Tsung Huang, Sheng-Hsuan Lin
General Approach Of Causal Mediation Analysis With Causally Ordered Multiple Mediators And Survival Outcome, An-Shun Tai, Pei-Hsuan Lin, Yen-Tsung Huang, Sheng-Hsuan Lin
Harvard University Biostatistics Working Paper Series
Causal mediation analysis with multiple mediators (causal multi-mediation analysis) is critical in understanding why an intervention works, especially in medical research. Deriving the path-specific effects (PSEs) of exposure on the outcome through a certain set of mediators can detail the causal mechanism of interest. However, the existing models of causal multi-mediation analysis are usually restricted to partial decomposition, which can only evaluate the cumulative effect of several paths. Moreover, the general form of PSEs for an arbitrary number of mediators has not been proposed. In this study, we provide a generalized definition of PSE for partial decomposition (partPSE) and for …
Cross-Sectional Hiv Incidence Estimation Accounting For Heterogeneity Across Communities, Yuejia Xu, Oliver B. Laeyendecker, Rui Wang
Cross-Sectional Hiv Incidence Estimation Accounting For Heterogeneity Across Communities, Yuejia Xu, Oliver B. Laeyendecker, Rui Wang
Harvard University Biostatistics Working Paper Series
No abstract provided.
Technical Considerations In The Use Of The E-Value, Tyler J. Vanderweele, Peng Ding, Maya Mathur
Technical Considerations In The Use Of The E-Value, Tyler J. Vanderweele, Peng Ding, Maya Mathur
Harvard University Biostatistics Working Paper Series
The E-value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would have to have with both the exposure and the outcome, conditional on the measured covariates, to explain away the observed exposure-outcome association. We have elsewhere proposed that the reporting of E-values for estimates and for the limit of the confidence interval closest to the null become routine whenever causal effects are of interest. A number of questions have arisen about the use of E-value including questions concerning the interpretation of the relevant confounding association parameters, the nature of the transformation …
Power Calculation For Cross-Sectional Stepped Wedge Cluster-Randomized Trials With Variable Cluster Sizes, Linda J. Harrison, Tom Chen, Rui Wang
Power Calculation For Cross-Sectional Stepped Wedge Cluster-Randomized Trials With Variable Cluster Sizes, Linda J. Harrison, Tom Chen, Rui Wang
Harvard University Biostatistics Working Paper Series
Standard sample size calculation formulas for Stepped Wedge Cluster Randomized Trials (SW-CRTs) assume that cluster sizes are equal. When cluster sizes vary substantially, ignoring this variation may lead to an under-powered study. We investigate the relative efficiency of a SW-CRT with varying cluster sizes to equal cluster sizes, and derive variance estimators for the intervention effect that account for this variation under the assumption of a mixed effects model; a commonly-used approach for analyzing data from cluster randomized trials. When cluster sizes vary, the power of a SW-CRT depends on the order in which clusters receive the intervention, which is …
Quantifying The Totality Of Treatment Effect With Multiple Event-Time Observations In The Presence Of A Terminal Event From A Comparative Clinical Study, Brian Claggett, Lu Tian, Haoda Fu, Scott D. Solomon, L. J. Wei
Quantifying The Totality Of Treatment Effect With Multiple Event-Time Observations In The Presence Of A Terminal Event From A Comparative Clinical Study, Brian Claggett, Lu Tian, Haoda Fu, Scott D. Solomon, L. J. Wei
Harvard University Biostatistics Working Paper Series
To evaluate the totality of one treatment's benefit/risk profile relative to an alternative treatment via a longitudinal comparative clinical study, the timing and occurrence of multiple clinical events are typically collected during the patient's followup. These multiple observations reflect the patient's disease progression/burden over time. The standard practice is to create a composite endpoint from the multiple outcomes, the timing of the occurrence of the first clinical event, to evaluate the treatment via the standard survival analysis techniques. By ignoring all events after the composite outcome, this type of assessment may not be ideal. Various parametric or semi-parametric procedures have …
Mediation Analysis For Censored Survival Data Under An Accelerated Failure Time Model, Isabel Fulcher, Eric J. Tchetgen Tchetgen, Paige Williams
Mediation Analysis For Censored Survival Data Under An Accelerated Failure Time Model, Isabel Fulcher, Eric J. Tchetgen Tchetgen, Paige Williams
Harvard University Biostatistics Working Paper Series
Recent advances in causal mediation analysis have formalized conditions for estimating direct and indirect effects in various contexts. These approaches have been extended to a number of models for survival outcomes including accelerated failure time (AFT) models which are widely used in a broad range of health applications given their intuitive interpretation. In this setting, it has been suggested that under standard assumptions, the “difference” and “product” methods produce equivalent estimates of the indirect effect of exposure on the survival outcome. We formally show that these two methods may produce substantially different estimates in the presence of censoring or truncation, …
Studying The Optimal Scheduling For Controlling Prostate Cancer Under Intermittent Androgen Suppression, Sunil K. Dhar, Hans R. Chaudhry, Bruce G. Bukiet, Zhiming Ji, Nan Gao, Thomas W. Findley
Studying The Optimal Scheduling For Controlling Prostate Cancer Under Intermittent Androgen Suppression, Sunil K. Dhar, Hans R. Chaudhry, Bruce G. Bukiet, Zhiming Ji, Nan Gao, Thomas W. Findley
Harvard University Biostatistics Working Paper Series
This retrospective study shows that the majority of patients’ correlations between PSA and Testosterone during the on-treatment period is at least 0.90. Model-based duration calculations to control PSA levels during off-treatment are provided. There are two pairs of models. In one pair, the Generalized Linear Model and Mixed Model are both used to analyze the variability of PSA at the individual patient level by using the variable “Patient ID” as a repeated measure. In the second pair, Patient ID is not used as a repeated measure but additional baseline variables are included to analyze the variability of PSA.
Efficiency Of Two Sample Tests Via The T-Mean Survival Time For Analyzing Event Time Observations, Lu Tian, Haoda Fu, Stephen J. Ruberg, Hajime Uno, Lj Wei
Efficiency Of Two Sample Tests Via The T-Mean Survival Time For Analyzing Event Time Observations, Lu Tian, Haoda Fu, Stephen J. Ruberg, Hajime Uno, Lj Wei
Harvard University Biostatistics Working Paper Series
In comparing two treatments with the event time observations, the hazard ratio (HR) estimate is routinely used to quantify the treatment difference. However, this model dependent estimate may be difficult to interpret clinically especially when the proportional hazards (PH) assumption is violated. An alternative estimation procedure for treatment efficacy based on the restricted means survival time or t-year mean survival time (t-MST) has been discussed extensively in the statistical and clinical literature. On the other hand, a statistical test 1 via the HR or its asymptotically equivalent counterpart, the logrank test, is asymptotically distribution-free. In this paper, we assess the …
Robust Alternatives To Ancova For Estimating The Treatment Effect Via A Randomized Comparative Study, Fei Jiang, Lu Tian, Haoda Fu, Takahiro Hasegawa, Marc Alan Pfeffer, L. J. Wei
Robust Alternatives To Ancova For Estimating The Treatment Effect Via A Randomized Comparative Study, Fei Jiang, Lu Tian, Haoda Fu, Takahiro Hasegawa, Marc Alan Pfeffer, L. J. Wei
Harvard University Biostatistics Working Paper Series
In comparing two treatments via a randomized clinical trial, the analysis of covari- ance technique is often utilized to estimate an overall treatment effect. The ANCOVA is generally perceived as a more efficient procedure than its simple two sample estima- tion counterpart. Unfortunately when the ANCOVA model is not correctly specified, the resulting estimator is generally not consistent especially when the model is nonlin- ear. Recently various nonparametric alternatives, such as the augmentation methods, to ANCOVA have been proposed to estimate the treatment effect by adjusting the covariates. However, the properties of these alternatives have not been studied in the …
Model Averaged Double Robust Estimation, Matthew Cefalu, Francesca Dominici, Nils D. Arvold Md, Giovanni Parmigiani
Model Averaged Double Robust Estimation, Matthew Cefalu, Francesca Dominici, Nils D. Arvold Md, Giovanni Parmigiani
Harvard University Biostatistics Working Paper Series
Existing methods in causal inference do not account for the uncertainty in the selection of confounders. We propose a new class of estimators for the average causal effect, the model averaged double robust estimators, that formally account for model uncertainty in both the propensity score and outcome model through the use of Bayesian model averaging. These estimators build on the desirable double robustness property by only requiring the true propensity score model or the true outcome model be within a specified class of models to maintain consistency. We provide asymptotic results and conduct a large scale simulation study that indicates …
The Use Of Permutation Tests For The Analysis Of Parallel And Stepped-Wedge Cluster Randomized Trials, Rui Wang, Victor Degruttola
The Use Of Permutation Tests For The Analysis Of Parallel And Stepped-Wedge Cluster Randomized Trials, Rui Wang, Victor Degruttola
Harvard University Biostatistics Working Paper Series
We investigate the use of permutation tests for the analysis of parallel and stepped-wedge cluster randomized trials. Permutation tests for parallel designs with exponential family endpoints have been extensively studied. The optimal permutation tests developed for exponential family alternatives require information on intraclass correlation, a quantity not yet defined for time-to-event endpoints. Therefore, it is unclear how efficient permutation tests can be constructed for cluster-randomized trials with such endpoints. We consider a class of test statistics formed by a weighted average of pair-specific treatment effect estimates and offer practical guidance on the choice of weights to improve efficiency. We apply …
Mediation Analysis For A Survival Outcome With Time-Varying Exposures, Mediators, And Confounders, Sheng-Hsuan Lin, Jessica G. Young, Roger Logan, Tyler J. Vanderweele
Mediation Analysis For A Survival Outcome With Time-Varying Exposures, Mediators, And Confounders, Sheng-Hsuan Lin, Jessica G. Young, Roger Logan, Tyler J. Vanderweele
Harvard University Biostatistics Working Paper Series
We propose an approach to conduct mediation analysis for survival data with time-varying exposures, mediators, and confounders. We identify certain interventional direct and indirect effects through a survival mediational g-formula and describe the required assumptions. We also provide a feasible parametric approach along with an algorithm and software to estimate these effects. We apply this method to analyze the Framingham Heart Study data to investigate the causal mechanism of smoking on mortality through coronary artery disease. The risk ratio of smoking 30 cigarettes per day for ten years compared with no smoking on mortality is 2.34 (95 % CI = …
Crtgeedr: An R Package For Doubly Robust Generalized Estimating Equations Estimations In Cluster Randomized Trials With Missing Data, Melanie Prague, Rui Wang, Victor De Gruttola
Crtgeedr: An R Package For Doubly Robust Generalized Estimating Equations Estimations In Cluster Randomized Trials With Missing Data, Melanie Prague, Rui Wang, Victor De Gruttola
Harvard University Biostatistics Working Paper Series
No abstract provided.
Accounting For Interactions And Complex Inter-Subject Dependency In Estimating Treatment Effect In Cluster Randomized Trials With Missing Outcomes, Melanie Prague, Rui Wang, Alisa Stephens, Eric Tchetgen Tchetgen, Victor Degruttola
Accounting For Interactions And Complex Inter-Subject Dependency In Estimating Treatment Effect In Cluster Randomized Trials With Missing Outcomes, Melanie Prague, Rui Wang, Alisa Stephens, Eric Tchetgen Tchetgen, Victor Degruttola
Harvard University Biostatistics Working Paper Series
No abstract provided.