期刊
LIFETIME DATA ANALYSIS
卷 16, 期 1, 页码 71-84出版社
SPRINGER
DOI: 10.1007/s10985-009-9135-3
关键词
Causal inference; Survival analysis; Simulation; Marginal structural models; Structural nested models
资金
- NHLBI NIH HHS [R01 HL080644] Funding Source: Medline
- NIAID NIH HHS [R37 AI032475] Funding Source: Medline
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL080644] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES [R37AI032475] Funding Source: NIH RePORTER
Standard methods for estimating the effect of a time-varying exposure on survival may be biased in the presence of time-dependent confounders themselves affected by prior exposure. This problem can be overcome by inverse probability weighted estimation of Marginal Structural Cox Models (Cox MSM), g-estimation of Structural Nested Accelerated Failure Time Models (SNAFTM) and g-estimation of Structural Nested Cumulative Failure Time Models (SNCFTM). In this paper, we describe a data generation mechanism that approximately satisfies a Cox MSM, an SNAFTM and an SNCFTM. Besides providing a procedure for data simulation, our formal description of a data generation mechanism that satisfies all three models allows one to assess the relative advantages and disadvantages of each modeling approach. A simulation study is also presented to compare effect estimates across the three models.
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