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- Causal inference (3)
- Clinical Epidemiology (3)
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- Longitudinal Data Analysis and Time Series (3)
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- Inverse probability of treatment/censoring weighted estimation (1)
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Articles 1 - 13 of 13
Full-Text Articles in Medicine and Health Sciences
Super Learner Analysis Of Electronic Adherence Data Improves Viral Prediction And May Provide Strategies For Selective Hiv Rna Monitoring., Maya Petersen, E Ledell, J Schwab, V Sarovar, R Gross, N Reynolds, J Haberer, K Goggin, C Golin, J Arnsten, M Rosen, R Remien, D Etoori, I Wilson, J Simoni, J Erlen, M Van Der Laan, H Liu, D Bangsberg
Super Learner Analysis Of Electronic Adherence Data Improves Viral Prediction And May Provide Strategies For Selective Hiv Rna Monitoring., Maya Petersen, E Ledell, J Schwab, V Sarovar, R Gross, N Reynolds, J Haberer, K Goggin, C Golin, J Arnsten, M Rosen, R Remien, D Etoori, I Wilson, J Simoni, J Erlen, M Van Der Laan, H Liu, D Bangsberg
Maya Petersen
No abstract provided.
Applying A Causal Road Map In Settings With Time-Dependent Confounding: Commentary On “The Parametric G-Formula For Time-To-Event Data: Toward Intuition With A Worked Example.", Maya Petersen
Maya Petersen
No abstract provided.
Delayed Switch Of Antiretroviral Therapy Following Confirmed Virological Failure Is Associated With Elevated Mortality Among Hiv-Infected Adults In Africa., Maya Petersen, L Tran, E Geng, S Deeks, S Reynolds, A Kamugu, R Wood, M Bwanag, D Bangsberg, C Yiannoutsos, J Martin
Delayed Switch Of Antiretroviral Therapy Following Confirmed Virological Failure Is Associated With Elevated Mortality Among Hiv-Infected Adults In Africa., Maya Petersen, L Tran, E Geng, S Deeks, S Reynolds, A Kamugu, R Wood, M Bwanag, D Bangsberg, C Yiannoutsos, J Martin
Maya Petersen
No abstract provided.
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.
Estimating The Causal Effects Of Longitudinal Exposures. Novel Statistical Methods And Software, Maya Petersen
Estimating The Causal Effects Of Longitudinal Exposures. Novel Statistical Methods And Software, Maya Petersen
Maya Petersen
No abstract provided.
Casual Methods In The Service Of Good Epidemiological Practice: A Roadmap, Maya Petersen
Casual Methods In The Service Of Good Epidemiological Practice: A Roadmap, 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 …
History-Adjusted Marginal Structural Models To Estimate Time-Varying Effect Modification, Maya Petersen, Steven Deeks, Jefferey Martin, Mark Van Der Laan
History-Adjusted Marginal Structural Models To Estimate Time-Varying Effect Modification, Maya Petersen, Steven Deeks, Jefferey Martin, Mark Van Der Laan
Maya Petersen
Much of epidemiology and clinical medicine is focused on the estimation of treatments or interventions administered over time. In such settings of longitudinal treatment, time-dependent confounding is often an important source of bias. Marginal structural models are a powerful tool for estimating the causal effect of a treatment using observational data, particularly when time-dependent confounding is present. Recent statistical work presented a generalization of marginal structural models, called history-adjusted marginal structural models. Unlike standard marginal structural models, history-adjusted marginal structural models can be used to estimate modification of treatment effects by time-varying covariates. Estimation of time-dependent causal effect modification is …
Virologic Efficacy Of Boosted Double Vs. Boosted Single Protease Inhibitor Therapy., Maya Petersen, Yue Wang, Mark Van Der Laan, Soo-Yon Rhee, Robert Shafer, W. Jefferey Fessel
Virologic Efficacy Of Boosted Double Vs. Boosted Single Protease Inhibitor Therapy., Maya Petersen, Yue Wang, Mark Van Der Laan, Soo-Yon Rhee, Robert Shafer, W. Jefferey Fessel
Maya Petersen
Objective: Although regimens containing two protease inhibitor (PI) together with ritonavir boosting are used with the aim of improving virologic response to salvage therapy, there is little evidence to support or reject this approach. We compared the probability of attaining an undetectable HIV RNA level after using either boosted double or boosted single PI regimens. Design: Retrospective clinical cohort. Methods: PI-experienced subjects in a Northern California-based database who initiated either a boosted single or boosted double PI salvage therapy regimen were analysed. Traditional multivariable regression and marginal structural model analyses were used to compare the effects of the two regimens …
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 …
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, …