Relation between three classes of structural models for the effect of a time-varying exposure on survival.
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.
Young
Jessica G
JG
Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Kresge Bldg Suite 820, Boston, MA 02115, USA. jyoung@hsph.harvard.edu
Hernán
Miguel A
MA
Picciotto
Sally
S
Robins
James M
JM
D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D016019:Survival Analysis