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- Longitudinal Data Analysis and Time Series (3)
- General Biostatistics (2)
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Articles 1 - 12 of 12
Full-Text Articles in Physical Sciences and Mathematics
Causal Models And Learning From Data: Integrating Causal Modeling And Statistical Estimation, Maya Petersen, M J. Van Der Laan
Causal Models And Learning From Data: Integrating Causal Modeling And Statistical Estimation, Maya Petersen, M J. Van Der Laan
Maya Petersen
No abstract provided.
Targeted Maximum Likelihood Estimation For Dynamic And Static Longitudinal Marginal Structural Working Models, Maya Petersen, J Schwab, S Gruber, N Blaser, M Schomaker, M J. Van Der Laan
Targeted Maximum Likelihood Estimation For Dynamic And Static Longitudinal Marginal Structural Working Models, Maya Petersen, J Schwab, S Gruber, N Blaser, M Schomaker, M J. Van Der Laan
Maya Petersen
No abstract provided.
Super Learning, Maya Petersen, M J. Van Der Laan
Diagnosing And Responding To Violations In The Positivity Assumption., Maya Petersen, K E. Porter, S Gruber, Y Wang, M J. Van Der Laan
Diagnosing And Responding To Violations In The Positivity Assumption., Maya Petersen, K E. Porter, S Gruber, Y Wang, M J. Van Der Laan
Maya Petersen
No abstract provided.
Adaptive Matching In Randomized Trials And Observational Studies., M J. Van Der Laan, L Balzer, Maya Petersen
Adaptive Matching In Randomized Trials And Observational Studies., M J. Van Der Laan, L Balzer, Maya Petersen
Maya Petersen
No abstract provided.
Pre-Specified Analysis Plans And Learning From Data. Where Are We And Where Are We Going?”, Maya Petersen
Pre-Specified Analysis Plans And Learning From Data. Where Are We And Where Are We Going?”, Maya Petersen
Maya Petersen
No abstract provided.
“'Loss-To-Follow-Up' In Observational Clinical Cohorts: Longitudinal Effect Estimation In The Presence Of Informative Monitoring.”, Maya Petersen
“'Loss-To-Follow-Up' In Observational Clinical Cohorts: Longitudinal Effect Estimation In The Presence Of Informative Monitoring.”, Maya Petersen
Maya Petersen
No abstract provided.
Compound Treatments, Transportability, And The Structural Causal Model: The Power And Simplicity Of Causal Graphs., Maya Petersen
Compound Treatments, Transportability, And The Structural Causal Model: The Power And Simplicity Of Causal Graphs., Maya Petersen
Maya Petersen
No abstract provided.
Application Of Causal Inference Methods To Improve The Treatment Of Hiv In Resource-Limited Settings., Maya Petersen
Application Of Causal Inference Methods To Improve The Treatment Of Hiv In Resource-Limited Settings., Maya Petersen
Maya Petersen
No abstract provided.
Direct Effect Models, Mark J. Van Der Laan, Maya L. Petersen
Direct Effect Models, Mark J. Van Der Laan, Maya L. Petersen
Maya Petersen
The causal effect of a treatment on an outcome is generally mediated by several intermediate variables. Estimation of the component of the causal effect of a treatment that is not mediated by an intermediate variable (the direct effect of the treatment) is often relevant to mechanistic understanding and to the design of clinical and public health interventions. Robins, Greenland and Pearl develop counterfactual definitions for two types of direct effects, natural and controlled, and discuss assumptions, beyond those of sequential randomization, required for the identifiability of natural direct effects. Building on their earlier work and that of others, this article …
Long-Term Consequences Of The Delay Between Virologic Failure Of Highly Active Antiretroviral Therapy And Regimen Modification, Maya L. Petersen, Mark J. Van Der Laan, Napravnik Sonia, Joseph J. Eron, Richard G. Moore, Steven G. Deeks
Long-Term Consequences Of The Delay Between Virologic Failure Of Highly Active Antiretroviral Therapy And Regimen Modification, Maya L. Petersen, Mark J. Van Der Laan, Napravnik Sonia, Joseph J. Eron, Richard G. Moore, Steven G. Deeks
Maya Petersen
Objectives: Current treatment guidelines recommend immediate modification of antiretroviral therapy in HIV-infected individuals with incomplete viral suppression. These recommendations have not been tested in observational studies or large randomized trials. We evaluated the consequences of delayed modification following virologic failure. Design/methods: We used prospective data from two clinical cohorts to estimate the effect of time until regimen modification following first regimen failure on all-cause mortality. The impact of regimen type was also assessed. As the effect of delayed switching can be confounded if patients with a poor prognosis modify therapy earlier than those with a good prognosis, we used a …
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 …