Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Entire DC Network
Identifying Important Explanatory Variables For Time-Varying Outcomes., Oliver Bembom, Maya L. Petersen, Mark J. Van Der Laan
Identifying Important Explanatory Variables For Time-Varying Outcomes., Oliver Bembom, Maya L. Petersen, Mark J. Van Der Laan
Maya Petersen
This chapter describes a systematic and targeted approach for estimating the impact of each of a large number of baseline covariates on an outcome that is measured repeatedly over time. These variable importance estimates can be adjusted for a user-specified set of confounders and lend themselves in a straightforward way to obtaining confidence intervals and p-values. Hence, they can in particular be used to identify a subset of baseline covariates that are the most important explanatory variables for the time-varying outcome of interest. We illustrate the methodology in a data analysis aimed at finding mutations of the human immunodeficiency virus …
Assessing The Effectiveness Of Antiretroviral Adherence Interventions: Using Marginal Structural Models To Replicate The Findings Of Randomized Controlled Trials., Maya L. Petersen, Yue Wang, Mark J. Van Der Laan, David R. Bangsberg
Assessing The Effectiveness Of Antiretroviral Adherence Interventions: Using Marginal Structural Models To Replicate The Findings Of Randomized Controlled Trials., Maya L. Petersen, Yue Wang, Mark J. Van Der Laan, David R. Bangsberg
Maya Petersen
Randomized controlled trials of interventions to improve adherence to antiretroviral medications are not always feasible. Marginal Structural Models (MSM) are a statistical methodology that aims to replicate the findings of randomized controlled trials using observational data. Under the assumption of no unmeasured confounders, three MSM estimators are available to estimate the causal effect of an intervention. Two of these estimators, G-computation and Inverse Probability of Treatment Weighted, can be implemented using standard software. G-computation relies on fitting a multivariable regression of adherence on the intervention and confounders. Thus, it is related to the standard multivariable regression approach to estimating causal …
Estimation Of Direct Causal Effects., Maya L. Petersen, Sandra E. Sinisi, Mark J. Van Der Laan
Estimation Of Direct Causal Effects., Maya L. Petersen, Sandra E. Sinisi, Mark J. Van Der Laan
Maya Petersen
Many common problems in epidemiologic and clinical research involve estimating the effect of an exposure on an outcome while blocking the exposure's effect on an intermediate variable. Effects of this kind are termed direct effects. Estimation of direct effects arises frequently in research aimed at understanding mechanistic pathways by which an exposure acts to cause or prevent disease, as well as in many other settings. Although multivariable regression is commonly used to estimate direct effects, this approach requires assumptions beyond those required for the estimation of total causal effects. In addition, when the exposure and intermediate interact to cause disease, …