G-Estimation of the Effect of Prophylaxis Therapy for Pneumocystis carinii Pneumonia on the Survival of AIDS Patients

By James M. Robins, D. Blevins, G. Ritter, M. Wulfsohn. 1992

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Citations in the corpus

Learning When-to-Treat Policies (2019, methodology): “There are a few predominant approaches: the G-estimation procedure (Robins, 1989; Robins et al., 1992) learns the Structural Nested Mean Models (SNMM) (Robins, 1994) which model the difference in the marginal outcome functions directly.”
Structural nested models and G-estimation: the partially realized promise (2014, methodology): “Random censoring or loss to follow-up can be dealt with through inverse probability of censoring weighting (Robins et al. (1992)).”
Effect of Physical Activity on Functional Performance and Knee Pain in Patients With Osteoarthritis: Analysis With Marginal Structural Models (2012, unknown): “Depression 76 (12) 64 (10) 49 (8) 72 (11)”
A design for testing clinical strategies: biased adaptive within‐subject randomization (2000, unknown): “G-estimation (Robins et al., 1992) alsoworks in this context.”
The Chronic Renal Insufficiency Cohort (CRIC) Study: Design and Methods. (2003, methodology): “Proportional hazards analysis also will be used to explore the joint effects of treatments or exposures received at different times on the rate of failure using weighted estimation of marginal structural models (13) and G-estimation of structural nested models ( 14 ).”
Standardization and Control for Confounding in Observational Studies: A Historical Perspective (2015, methodology): “On the other hand, the approach via structural nested models [e.g., Robins and Tsiatis (1992), Robins et al. (1992)] focusses on the effect of a “blip” of exposure at time t conditional on treatments and covariate values before t.”
Marginal Structural Models and Causal Inference in Epidemiology (2000, unknown): “log pr@Y 5 1uA0 5 a0# 5 u90 1 u91 a0 (5)”
G-estimation of Causal Effects, Allowing for Time-varying Confounding (2002, methodology): “The method of G-estimation of causal effects in the presence of time-varying confounding was introduced by Robins; see, for example, Robins et al. (1992), Witteman et al. (1998), or Tilling et al. (2002). We briefly outline the method here.”
Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments (2001, background): “Given the covariates recorded in L4t5, following Robins et al. (1992), we say that there are no unmeasured confounders for the effect of A4t5 on Y if, for each Na, Y Na a A4t5 — SA4t ƒ 151SL4t50 (5) We also refer to the assumption of no unmeasured confounders (5) as the assumption that treatment…”
Pattern–mixture and selection models for analysing longitudinal data with monotone missing patterns (2003, background): “Therefore, this choice specifies that, with regard to inference about Y , assignment to censoring can be viewed as sequentially randomized, with randomization probabilities depending on the observed past (Robins et al., 1992).”