239 research outputs found
Marginal Structural Illness-Death Models for Semi-Competing Risks Data
The three-state illness death model has been established as a general
approach for regression analysis of semi-competing risks data. For
observational data the marginal structural models (MSM) are a useful tool,
under the potential outcomes framework to define and estimate parameters with
causal interpretations. In this paper we introduce a class of marginal
structural illness death models for the analysis of observational semi
competing risks data. We consider two specific such models, the usual Markov
illness death MSM and the general Markov illness death MSM where the latter
incorporates a frailty term. For interpretation purposes, risk contrasts under
the MSMs are defined. Inference under the usual Markov MSM can be carried out
using estimating equations with inverse probability weighting, while inference
under the general Markov MSM requires a weighted EM algorithm. We study the
inference procedures under both MSMs using extensive simulations, and apply
them to the analysis of mid-life alcohol exposure on late life cognitive
impairment as well as mortality using the Honolulu-Asia Aging Study data set.
The R codes developed in this work have been implemented in the R package
semicmprskcoxmsm that is publicly available on CRAN
On Defense of the Hazard Ratio
There has been debate on whether the hazard function should be used for
causal inference in time-to-event studies. The main criticism is that there is
selection bias because the risk sets beyond the first event time are comprised
of subsets of survivors who are no longer balanced in the risk factors, even in
the absence of unmeasured confounding, measurement error, and model
misspecification. In this short communication we use the potential outcomes
framework and the single-world intervention graph to show that there is indeed
no selection bias when estimating the average treatment effect, and that the
hazard ratio over time can provide a useful interpretation in practical
settings
Doubly Robust Estimation under Covariate-Induced Dependent Left Truncation
In prevalent cohort studies with follow-up, the time-to-event outcome is
subject to left truncation leading to selection bias. For estimation of the
distribution of time-to-event, conventional methods adjusting for left
truncation tend to rely on the (quasi-)independence assumption that the
truncation time and the event time are "independent" on the observed region.
This assumption is violated when there is dependence between the truncation
time and the event time possibly induced by measured covariates. Inverse
probability of truncation weighting leveraging covariate information can be
used in this case, but it is sensitive to misspecification of the truncation
model. In this work, we apply the semiparametric theory to find the efficient
influence curve of an expected (arbitrarily transformed) survival time in the
presence of covariate-induced dependent left truncation. We then use it to
construct estimators that are shown to enjoy double-robustness properties. Our
work represents the first attempt to construct doubly robust estimators in the
presence of left truncation, which does not fall under the established
framework of coarsened data where doubly robust approaches are developed. We
provide technical conditions for the asymptotic properties that appear to not
have been carefully examined in the literature for time-to-event data, and
study the estimators via extensive simulation. We apply the estimators to two
data sets from practice, with different right-censoring patterns
Semiparametrically Efficient Score for the Survival Odds Ratio
We consider a general proportional odds model for survival data under binary
treatment, where the functional form of the covariates is left unspecified. We
derive the efficient score for the conditional survival odds ratio given the
covariates using modern semiparametric theory. The efficient score may be
useful in the development of doubly robust estimators, although computational
challenges remain
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