Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Counterfactual (4)
- Causal inference (3)
- Confounding (3)
- Double robust estimation (3)
- G-computation estimation (3)
-
- Cross-validation (2)
- Inverse probability of treatment weighted estimation (2)
- Longitudinal data (2)
- Antiretroviral resistance (1)
- Antiretroviral therapy (1)
- Binary outcome (1)
- Binding site (1)
- Causal treatment effect (1)
- ChIP-Chip data (1)
- Chromatin immunoprecipitation (1)
- Direct causal effect (1)
- Dynamic treatment regime (1)
- False discovery rate (1)
- Family-wise error rate (1)
- Generalized quantile-quantile function (1)
- HIV (1)
- High density oligonucleotide array (1)
- Indirect causal effect (1)
- Instrumental variable (1)
- Inverse probability of treatment (1)
- Loss function (1)
- Model selection (1)
- Multiple testing (1)
- Non-compliance (1)
- Optimal dynamic treatment regime (1)
Articles 1 - 5 of 5
Full-Text Articles in Medicine and Health Sciences
Data Adaptive Estimation Of The Treatment Specific Mean, Yue Wang, Oliver Bembom, Mark J. Van Der Laan
Data Adaptive Estimation Of The Treatment Specific Mean, Yue Wang, Oliver Bembom, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
An important problem in epidemiology and medical research is the estimation of the causal effect of a treatment action at a single point in time on the mean of an outcome, possibly within strata of the target population defined by a subset of the baseline covariates. Current approaches to this problem are based on marginal structural models, i.e., parametric models for the marginal distribution of counterfactural outcomes as a function of treatment and effect modifiers. The various estimators developed in this context furthermore each depend on a high-dimensional nuisance parameter whose estimation currently also relies on parametric models. Since misspecification …
History-Adjusted Marginal Structural Models And Statically-Optimal Dynamic Treatment Regimes, Mark J. Van Der Laan, Maya L. Petersen
History-Adjusted Marginal Structural Models And Statically-Optimal Dynamic Treatment Regimes, Mark J. Van Der Laan, Maya L. Petersen
U.C. Berkeley Division of Biostatistics Working Paper Series
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a treatment. These models, introduced by Robins, model the marginal distributions of treatment-specific counterfactual outcomes, possibly conditional on a subset of the baseline covariates. Marginal structural models are particularly useful in the context of longitudinal data structures, in which each subject's treatment and covariate history are measured over time, and an outcome is recorded at a final time point. However, the utility of these models for some applications has been limited by their inability to incorporate modification of the causal effect of treatment by time-varying covariates. …
Estimation Of Treatment Effects In Randomized Trials With Noncompliance And A Dichotomous Outcome , Mark J. Van Der Laan, Alan E. Hubbard, Nicholas P. Jewell
Estimation Of Treatment Effects In Randomized Trials With Noncompliance And A Dichotomous Outcome , Mark J. Van Der Laan, Alan E. Hubbard, Nicholas P. Jewell
U.C. Berkeley Division of Biostatistics Working Paper Series
We propose a class of estimators of the treatment effect on a dichotomous outcome among the treated subjects within covariate and treatment arm strata in randomized trials with non-compliance. Recent articles by Vansteelandt and Goethebeur (2003) and Robins and Rotnitzky (2004) have presented consistent and asymptotically linear estimators of a causal odds ratio, which rely, beyond correct specification of a model for the causal odds ratio, on a correctly specified model for a potentially high dimensional nuisance parameter. In this article we propose consistent, asymptotically linear and locally efficient estimators of a causal relative risk and a new parameter -- …
Estimation Of Direct And Indirect Causal Effects In Longitudinal Studies, Mark J. Van Der Laan, Maya L. Petersen
Estimation Of Direct And Indirect Causal Effects In Longitudinal Studies, Mark J. Van Der Laan, Maya L. Petersen
U.C. Berkeley Division of Biostatistics Working Paper Series
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 mediated by a given intermediate variable (the indirect effect of the treatment), and the component that is not mediated by that intermediate variable (the direct effect of the treatment) is often relevant to mechanistic understanding and to the design of clinical and public health interventions. Under the assumption of no-unmeasured confounders, Robins & Greenland (1992) and Pearl (2000), develop two identifiability results for direct and indirect causal effects. They define an …
Multiple Testing Methods For Chip-Chip High Density Oligonucleotide Array Data, Sunduz Keles, Mark J. Van Der Laan, Sandrine Dudoit, Simon E. Cawley
Multiple Testing Methods For Chip-Chip High Density Oligonucleotide Array Data, Sunduz Keles, Mark J. Van Der Laan, Sandrine Dudoit, Simon E. Cawley
U.C. Berkeley Division of Biostatistics Working Paper Series
Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription factors along human chromosomes 21 and 22 using ChIP-Chip experiments. ChIP-Chip experiments are a new approach to the genome-wide identification of transcription factor binding sites and consist of chromatin (Ch) immunoprecipitation (IP) of transcription factor-bound genomic DNA followed by high density oligonucleotide hybridization (Chip) of the IP-enriched DNA. We investigate the ChIP-Chip data structure and propose methods for inferring the location of transcription factor binding sites from these data. The proposed methods involve testing for each probe whether it is part of a bound sequence …