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- Genetics (5)
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- Causal inference; instrumental variables; measurement error; non-compliance; prior information; structural models; weak identifability (1)
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Articles 1 - 30 of 35
Full-Text Articles in Physical Sciences and Mathematics
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 …
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 …
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.
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 …
Attributing Effects To Interactions, Tyler J. Vanderweele, Eric J. Tchetgen Tchetgen
Attributing Effects To Interactions, Tyler J. Vanderweele, Eric J. Tchetgen Tchetgen
Harvard University Biostatistics Working Paper Series
A framework is presented which allows an investigator to estimate the portion of the effect of one exposure that is attributable to an interaction with a second exposure. We show that when the two exposures are independent, the total effect of one exposure can be decomposed into a conditional effect of that exposure and a component due to interaction. The decomposition applies on difference or ratio scales. We discuss how the components can be estimated using standard regression models, and how these components can be used to evaluate the proportion of the total effect of the primary exposure attributable to …
A Regularization Corrected Score Method For Nonlinear Regression Models With Covariate Error, David M. Zucker, Malka Gorfine, Yi Li, Donna Spiegelman
A Regularization Corrected Score Method For Nonlinear Regression Models With Covariate Error, David M. Zucker, Malka Gorfine, Yi Li, Donna Spiegelman
Harvard University Biostatistics Working Paper Series
No abstract provided.
Landmark Prediction Of Survival, Layla Parast, Tianxi Cai
Landmark Prediction Of Survival, Layla Parast, Tianxi Cai
Harvard University Biostatistics Working Paper Series
No abstract provided.
Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations, Lu Wang, Andrea Rotnitzky, Xihong Lin
Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations, Lu Wang, Andrea Rotnitzky, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Survival Analysis With Error-Prone Time-Varying Covariates: A Risk Set Calibration Approach, Xiaomei Liao, David M. Zucker, Yi Li, Donna Spiegelman
Survival Analysis With Error-Prone Time-Varying Covariates: A Risk Set Calibration Approach, Xiaomei Liao, David M. Zucker, Yi Li, Donna Spiegelman
Harvard University Biostatistics Working Paper Series
No abstract provided.
Lot Quality Assurance Sampling (Lqas) And The Mozambique Malaria Indicator Surveys, Caitlin Biedron, Marcello Pagano, Bethany L. Hedt, Albert Kilian, Amy Ratcliffe, Samuel Mabunda, Joseph J. Valadez
Lot Quality Assurance Sampling (Lqas) And The Mozambique Malaria Indicator Surveys, Caitlin Biedron, Marcello Pagano, Bethany L. Hedt, Albert Kilian, Amy Ratcliffe, Samuel Mabunda, Joseph J. Valadez
Harvard University Biostatistics Working Paper Series
No abstract provided.
Spatial Cluster Detection For Repeatedly Measured Outcomes While Accounting For Residential History, Andrea J. Cook, Diane Gold, Yi Li
Spatial Cluster Detection For Repeatedly Measured Outcomes While Accounting For Residential History, Andrea J. Cook, Diane Gold, Yi Li
Harvard University Biostatistics Working Paper Series
No abstract provided.
Spatial Cluster Detection For Weighted Outcomes Using Cumulative Geographic Residuals, Andrea J. Cook, Yi Li, David Arterburn, Ram C. Tiwari
Spatial Cluster Detection For Weighted Outcomes Using Cumulative Geographic Residuals, Andrea J. Cook, Yi Li, David Arterburn, Ram C. Tiwari
Harvard University Biostatistics Working Paper Series
No abstract provided.
The Importance Of Scale For Spatial-Confounding Bias And Precision Of Spatial Regression Estimators, Christopher J. Paciorek
The Importance Of Scale For Spatial-Confounding Bias And Precision Of Spatial Regression Estimators, Christopher J. Paciorek
Harvard University Biostatistics Working Paper Series
Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias …
Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei
Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Evaluating Subject-Level Incremental Values Of New Markers For Risk Classification Rule, Tianxi Cai, Lu Tian, Donald M. Lloyd-Jones, L. J. Wei
Evaluating Subject-Level Incremental Values Of New Markers For Risk Classification Rule, Tianxi Cai, Lu Tian, Donald M. Lloyd-Jones, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin
A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Semiparametric Maximum Likelihood Estimation In Normal Transformation Models For Bivariate Survival Data, Yi Li, Ross L. Prentice, Xihong Lin
Semiparametric Maximum Likelihood Estimation In Normal Transformation Models For Bivariate Survival Data, Yi Li, Ross L. Prentice, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Marginal Structural Models For Partial Exposure Regimes, Stijn Vansteelandt, Karl Mertens, Carl Suetens, Els Goetghebeur
Marginal Structural Models For Partial Exposure Regimes, Stijn Vansteelandt, Karl Mertens, Carl Suetens, Els Goetghebeur
Harvard University Biostatistics Working Paper Series
Intensive care unit (ICU) patients are ell known to be highly susceptible for nosocomial (i.e. hospital-acquired) infections due to their poor health and many invasive therapeutic treatments. The effects of acquiring such infections in ICU on mortality are however ill understood. Our goal is to quantify these effects using data from the National Surveillance Study of Nosocomial Infections in Intensive Care
Units (Belgium). This is a challenging problem because of the presence of time-dependent confounders (such as exposure to mechanical ventilation)which lie on the causal path from infection to mortality. Standard statistical analyses may be severely misleading in such settings …
Estimation Of Controlled Direct Effects, Sylvie Goetgeluk, Stijn Vansteelandt, Els Goetghebeur
Estimation Of Controlled Direct Effects, Sylvie Goetgeluk, Stijn Vansteelandt, Els Goetghebeur
Harvard University Biostatistics Working Paper Series
No abstract provided.
Correcting Instrumental Variables Estimators For Systematic Measurement Error, Stijn Vansteelandt, Manoochehr Babanezhad, Els Goetghebeur
Correcting Instrumental Variables Estimators For Systematic Measurement Error, Stijn Vansteelandt, Manoochehr Babanezhad, Els Goetghebeur
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Likelihood Based Method For Real Time Estimation Of The Serial Interval And Reproductive Number Of An Epidemic, Laura Forsberg White, Marcello Pagano
A Likelihood Based Method For Real Time Estimation Of The Serial Interval And Reproductive Number Of An Epidemic, Laura Forsberg White, Marcello Pagano
Harvard University Biostatistics Working Paper Series
No abstract provided.
Spatio-Temporal Analysis Of Areal Data And Discovery Of Neighborhood Relationships In Conditionally Autoregressive Models, Subharup Guha, Louise Ryan
Spatio-Temporal Analysis Of Areal Data And Discovery Of Neighborhood Relationships In Conditionally Autoregressive Models, Subharup Guha, Louise Ryan
Harvard University Biostatistics Working Paper Series
No abstract provided.
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Harvard University Biostatistics Working Paper Series
No abstract provided.
Spatial Cluster Detection For Censored Outcome Data, Andrea J. Cook, Diane Gold, Yi Li
Spatial Cluster Detection For Censored Outcome Data, Andrea J. Cook, Diane Gold, Yi Li
Harvard University Biostatistics Working Paper Series
No abstract provided.
Structural Inference In Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Xihong Lin, Donglin Zeng
Structural Inference In Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Xihong Lin, Donglin Zeng
Harvard University Biostatistics Working Paper Series
No abstract provided.
Estimation In Semiparametric Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Donglin Zeng, Xihong Lin
Estimation In Semiparametric Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Donglin Zeng, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin
Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.