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- Causal inference (9)
- Counterfactual (6)
- Confounding (3)
- Double robust estimation (3)
- G-computation estimation (3)
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- Inverse probability of treatment weighted estimation (3)
- Case-control sampling (2)
- Cross-validation (2)
- Current status data (2)
- Dynamic treatment regime (2)
- HIV (2)
- Longitudinal data (2)
- Marginal structural model (2)
- Marginal structural models (2)
- Nonparametric maximum likelihood estimation (2)
- Observational studies (2)
- 0causal inference; semi-parametric models; environmental exposure; limit of detection; population intervention model (1)
- Adaptive designs; Average treatment effect; Cluster randomized trials; Pair-matching; Randomized trials; Targeted minimum loss-based estimation (TMLE) (1)
- Antiretroviral (1)
- Antiretroviral resistance (1)
- Antiretroviral therapy (1)
- Binary outcome (1)
- Binding site (1)
- Biomarkers; Variable Importance; Targeted Maximum Likelihood; Standard Method (1)
- Case Fatality Rate (1)
- Case-control study; cross-validation; threshold regression model (1)
- Causal Inference; Marginal Structural Models; Function Approximation; Statistical Learning; Local Learning; Penalized Learning (1)
- Causal inference; confounding; counterfactual; direct causal effect (1)
- Causal inference; confounding; counterfactual; direct causal effect; double robust estimation; G-computation estimation; indirect causal effect; inverse probability of treatment/censoring weighted estimation; longitudinal data (1)
- Causal inference; confounding; counterfactual; double robust estimation; G-computation estimation; inverse probability of treatment weighted estimation (1)
Articles 31 - 34 of 34
Full-Text Articles in Medicine and Health Sciences
An Empirical Study Of Marginal Structural Models For Time-Independent Treatment, Tanya A. Henneman, Mark J. Van Der Laan
An Empirical Study Of Marginal Structural Models For Time-Independent Treatment, Tanya A. Henneman, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
In non-randomized treatment studies a significant problem for statisticians is determining how best to adjust for confounders. Marginal structural models (MSMs) and inverse probability of treatment weighted (IPTW) estimators are useful in analyzing the causal effect of treatment in observational studies. Given an IPTW estimator a doubly robust augmented IPTW (AIPTW) estimator orthogonalizes it resulting in a more e±cient estimator than the IPTW estimator. One purpose of this paper is to make a practical comparison between the IPTW estimator and the doubly robust AIPTW estimator via a series of Monte- Carlo simulations. We also consider the selection of the optimal …
Case-Control Current Status Data, Nicholas P. Jewell, Mark J. Van Der Laan
Case-Control Current Status Data, Nicholas P. Jewell, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Current status observation on survival times has recently been widely studied. An extreme form of interval censoring, this data structure refers to situations where the only available information on a survival random variable, T, is whether or not T exceeds a random independent monitoring time C, a binary random variable, Y. To date, nonparametric analyses of current status data have assumed the availability of i.i.d. random samples of the random variable (Y, C), or a similar random sample at each of a set of fixed monitoring times. In many situations, it is useful to consider a case-control sampling scheme. Here, …
Current Status Data: Review, Recent Developments And Open Problems, Nicholas P. Jewell, Mark J. Van Der Laan
Current Status Data: Review, Recent Developments And Open Problems, Nicholas P. Jewell, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Researchers working with survival data are by now adept at handling issues associated with incomplete data, particular those associated with various forms of censoring. An extreme form of interval censoring, known as current status observation, refers to situations where the only available information on a survival random variable T is whether or not T exceeds a random independent monitoring time C. This article contains a brief review of the extensive literature on the analysis of current status data, discussing the implications of response-based sampling on these methods. The majority of the paper introduces some recent extensions of these ideas to …
Estimating Causal Parameters In Marginal Structural Models With Unmeasured Confounders Using Instrumental Variables, Tanya A. Henneman, Mark Johannes Van Der Laan, Alan E. Hubbard
Estimating Causal Parameters In Marginal Structural Models With Unmeasured Confounders Using Instrumental Variables, Tanya A. Henneman, Mark Johannes Van Der Laan, Alan E. Hubbard
U.C. Berkeley Division of Biostatistics Working Paper Series
For statisticians analyzing medical data, a significant problem in determining the causal effect of a treatment on a particular outcome of interest, is how to control for unmeasured confounders. Techniques using instrumental variables (IV) have been developed to estimate causal parameters in the presence of unmeasured confounders. In this paper we apply IV methods to both linear and non-linear marginal structural models. We study a specific class of generalized estimating equations that is appropriate to these data, and compare the performance of the resulting estimator to the standard IV method, a two-stage least squares procedure. Our results are applied to …