Causal Inference on the Difference of the Restricted Mean Lifetime Between Two Groups

Chen, Pei‐Yun; Tsiatis, Anastasios A.

doi:10.1111/j.0006-341x.2001.01030.x

Cited by 132 publications

(181 citation statements)

References 10 publications

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“…When survival time is the primary endpoint of interest, Chen and Tsiatis (2001) proposed using a Cox model with treatment-covariate interaction terms to estimate the optimal individualized treatment regime. Tian et al (2014) proposed a similar approach by fitting a Cox model with modified covariates.…”

Section: Introductionmentioning

confidence: 99%

Doubly robust estimation of optimal treatment regimes for survival data—with application to an HIV/AIDS study

Jiang¹,

Song²,

Hudgens³

et al. 2017

Ann. Appl. Stat.

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In many biomedical settings, assigning every patient the same treatment may not be optimal due to patient heterogeneity. Individualized treatment regimes have the potential to dramatically improve clinical outcomes. When the primary outcome is censored survival time, a main interest is to find optimal treatment regimes that maximize the survival probability of patients. Since the survival curve is a function of time, it is important to balance short-term and long-term benefit when assigning treatments. In this paper, we propose a doubly robust approach to estimate optimal treatment regimes that optimize a user specified function of the survival curve, including the restricted mean survival time and the median survival time. The empirical and asymptotic properties of the proposed method are investigated. The proposed method is applied to a data set from an ongoing HIV/AIDS clinical observational study conducted by the University of North Carolina (UNC) Center of AIDS Research (CFAR), and shows the proposed methods significantly improve the restricted mean time of the initial treatment duration. Finally, the proposed methods are extended to multi-stage studies.

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Section: Introductionmentioning

confidence: 99%

Doubly robust estimation of optimal treatment regimes for survival data—with application to an HIV/AIDS study

Jiang¹,

Song²,

Hudgens³

et al. 2017

Ann. Appl. Stat.

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“…Although the methods allow for treatment to be time varying, proportional hazards are assumed under the marginal structural model employed. Chen and Tsiatis [5], Schaubel and Wei [18], and Zhang and Schaubel [23] used restricted mean survival time as an effect measure, such that subgroup contrasts are cumulative in nature. However, contrasts between restricted mean lifetime do not describe how the essential effect changes over time.…”

Section: Discussionmentioning

confidence: 99%

“…In Sect. 6, we contrast the proposed methods with many existing methods which accommodate dependent censoring through IPCW, such as Robins et al [17]; Hernan et al [10,11]; Chen and Tsiatis [5]; Schaubel and Wei [18]; and Zhang and Schaubel [23]. …”

Section: Introductionmentioning

confidence: 99%

Semiparametric Contrasts of Cumulative Pre-Treatment Mortality in the Presence of Dependent Censoring

Gong

Schaubel

2014

Stat Biosci

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In clinical settings, the necessity of treatment is often measured in terms of the patient’s prognosis in the absence of treatment. Along these lines, it is often of interest to compare subgroups of patients (e.g., based on underlying diagnosis) with respect to pre-treatment survival. Such comparisons may be complicated by at least two important issues. First, mortality contrasts by subgroup may differ over follow-up time, as opposed to being constant, and may follow a form that is difficult to model parametrically. Moreover, in settings where the proportional hazards assumption fails, investigators tend to be more interested in cumulative (as opposed to instantaneous) effects on mortality. Second, pre-treatment death is censored by the receipt of treatment and in settings where treatment assignment depends on time-dependent factors that also affect mortality, such censoring is likely to be informative. We propose semiparametric methods for contrasting subgroup-specific cumulative mortality in the presence of dependent censoring. The proposed estimators are based on the cumulative hazard function, with pre-treatment mortality assumed to follow a stratified Cox model. No functional form is assumed for the nature of the non-proportionality. Asymptotic properties of the proposed estimators are derived, and simulation studies show that the proposed methods are applicable to practical sample sizes. The methods are then applied to contrast pre-transplant mortality for acute versus chronic End-Stage Liver Disease patients.

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“…Other methods based on weighting and stratification include Nieto and Coresh (1996) and Amato (1988) where the general approach is to stratify individuals by the observed confounders, estimate survival in each strata, and appropriately combine the resulting survival estimates. Alternatively, survival estimates can be adjusted for observed confounders and compared using a specified regression model such as the Cox model, but as in the case where one aims to gain efficiency by using a Cox model, when the model is not correctly specified the resulting estimates may not be valid (Thomsen et al, 1991; Therneau, 2000; Chen and Tsiatis, 2001). A number of doubly robust estimators that combine IPT weights (IPTW) and a model for survival, often a Cox regression model, have been proposed and lead to consistent estimates when either the model used to obtain the IPTW or the regression model is correct (Zhang and Schaubel, 2012b, a; Bai et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Landmark estimation of survival and treatment effects in observational studies

Parast

Griffin

2016

Lifetime Data Anal

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Clinical studies aimed at identifying effective treatments to reduce the risk of disease or death often require long term follow-up of participants in order to observe a Sufficient number of events to precisely estimate the treatment effect. In such studies, observing the outcome of interest during follow-up may be difficult and high rates of censoring may be observed which often leads to reduced power when applying straightforward statistical methods developed for time-to-event data. Alternative methods have been proposed to take advantage of auxiliary information that may potentially improve efficiency when estimating marginal survival and improve power when testing for a treatment effect. Recently, Parast et al (2014) proposed a landmark estimation procedure for the estimation of survival and treatment effects in a randomized clinical trial setting and demonstrated that significant gains in efficiency and power could be obtained by incorporating intermediate event information as well as baseline covariates. However, the procedure requires the assumption that the potential outcomes for each individual under treatment and control are independent of treatment group assignment which is unlikely to hold in an observational study setting. In this paper we develop the landmark estimation procedure for use in an observational setting. In particular, we incorporate inverse probability of treatment weights (IPTW) in the landmark estimation procedure to account for selection bias on observed baseline (pretreatment) covariates. We demonstrate that consistent estimates of survival and treatment effects can be obtained by using IPTW and that there is improved efficiency by using auxiliary intermediate event and baseline information. We compare our proposed estimates to those obtained using the Kaplan-Meier estimator, the original landmark estimation procedure, and the IPTW Kaplan Meier estimator. We illustrate our resulting reduction in bias and gains in efficiency through a simulation study and apply our procedure to an AIDS dataset to examine the effect of previous antiretroviral therapy on survival.

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Causal Inference on the Difference of the Restricted Mean Lifetime Between Two Groups

Cited by 132 publications

References 10 publications

Doubly robust estimation of optimal treatment regimes for survival data—with application to an HIV/AIDS study

Doubly robust estimation of optimal treatment regimes for survival data—with application to an HIV/AIDS study

Semiparametric Contrasts of Cumulative Pre-Treatment Mortality in the Presence of Dependent Censoring

Landmark estimation of survival and treatment effects in observational studies

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