Structural Nested Failure Time Models
- 15 February 2005
- book chapter
- Published by Wiley
Abstract
Structural nested failure time models are causal models for the effect of a time‐dependent treatment or exposure on a survival time outcome in the presence of time‐dependent confounders. A time‐dependent confounder is a repeatedly measured covariate, which acts as a confounder for future exposure measurements but is on the causal path between earlier exposure measurements and ultimate response (i.e. here it acts as intermediate variable). Standard epidemiologic methodology fails if time‐dependent confounders are present.Under the essential condition ofno unmeasured confounders(the mathematical definition of which is a central issue) these models allow for unbiased causal inference, generalizing Robins'sg‐computation algorithm. The models use as building blocks time‐dependent accelerated failure time models.Important applications are intricate endogenous (feedback) selection effects in occupational epidemiology (healthy worker effect) and clinical epidemiology (AIDS treatment).Keywords
This publication has 24 references indexed in Scilit:
- Adjusting for Differential Rates of Prophylaxis Therapy for PCP in High-Versus Low-Dose AZT Treatment Arms in an AIDS Randomized TrialJournal of the American Statistical Association, 1994
- Correcting for non-compliance in randomized trials using structural nested mean modelsCommunications in Statistics - Theory and Methods, 1994
- Plotting summary predictions in multistate survival models: Probabilities of relapse and death in remission for bone marrow transplantation patientsStatistics in Medicine, 1993
- Estimating the causal effect of smoking cessation in the presence of confounding factors using a rank preserving structural failure time modelStatistics in Medicine, 1993
- The Robust Inference for the Cox Proportional Hazards ModelJournal of the American Statistical Association, 1989
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effectMathematical Modelling, 1986
- Conditional Permutation Tests and the Propensity Score in Observational StudiesJournal of the American Statistical Association, 1984
- The Consquences of Adjustment for a Concomitant Variable That Has Been Affected by the TreatmentJournal of the Royal Statistical Society. Series A (General), 1984
- Bayesian Inference for Causal Effects: The Role of RandomizationThe Annals of Statistics, 1978
- Nonparametric Estimation from Incomplete ObservationsJournal of the American Statistical Association, 1958