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Int J Biostat. 2011 Mar 30;7(1):19. doi: 10.2202/1557-4679.1307.

Targeted maximum likelihood estimation of effect modification parameters in survival analysis.

Author information

1
University of California, Berkeley, USA.

Abstract

The Cox proportional hazards model or its discrete time analogue, the logistic failure time model, posit highly restrictive parametric models and attempt to estimate parameters which are specific to the model proposed. These methods are typically implemented when assessing effect modification in survival analyses despite their flaws. The targeted maximum likelihood estimation (TMLE) methodology is more robust than the methods typically implemented and allows practitioners to estimate parameters that directly answer the question of interest. TMLE will be used in this paper to estimate two newly proposed parameters of interest that quantify effect modification in the time to event setting. These methods are then applied to the Tshepo study to assess if either gender or baseline CD4 level modify the effect of two cART therapies of interest, efavirenz (EFV) and nevirapine (NVP), on the progression of HIV. The results show that women tend to have more favorable outcomes using EFV while males tend to have more favorable outcomes with NVP. Furthermore, EFV tends to be favorable compared to NVP for individuals at high CD4 levels.

KEYWORDS:

Cox-proportional hazards; G-computation; Targeted Maximum Likelihood Estimation; causal effect; censored longitudinal data; double robust; efficient influence curve; influence curve; semi-parametric; survival analysis

PMID:
21556287
PMCID:
PMC3083138
DOI:
10.2202/1557-4679.1307
[Indexed for MEDLINE]
Free PMC Article

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