Instrumental variable estimation of complier causal treatment effect with interval‐censored data

Li, Shuwei; Peng, Limin

doi:10.1111/biom.13565

Cited by 6 publications

(3 citation statements)

References 28 publications

(53 reference statements)

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“…Sun et al [33] considered variable selection in a semiparametric nonmixture cure model with intervalcensored data. Li and Peng [34] investigated instrumental variable estimation of complier causal treatment effect with interval-censored data. Withana Gamage et al [35] considered the estimation of the PH model with left-truncated and arbitrarily interval-censored data.…”

Section: Introductionmentioning

confidence: 99%

Variable Selection for Length-Biased and Interval-Censored Failure Time Data

Feng,

Cheng,

Sun

2023

Mathematics

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Length-biased failure time data occur often in various biomedical fields, including clinical trials, epidemiological cohort studies and genome-wide association studies, and their analyses have been attracting a surge of interest. In practical applications, because one may collect a large number of candidate covariates for the failure event of interest, variable selection becomes a useful tool to identify the important risk factors and enhance the estimation accuracy. In this paper, we consider Cox’s proportional hazards model and develop a penalized variable selection technique with various popular penalty functions for length-biased data, in which the failure event of interest suffers from interval censoring. Specifically, a computationally stable and reliable penalized expectation-maximization algorithm via two-stage data augmentation is developed to overcome the challenge in maximizing the intractable penalized likelihood. We establish the oracle property of the proposed method and present some simulation results, suggesting that the proposed method outperforms the traditional variable selection method based on the conditional likelihood. The proposed method is then applied to a set of real data arising from the Prostate, Lung, Colorectal and Ovarian cancer screening trial. The analysis results show that African Americans and having immediate family members with prostate cancer significantly increase the risk of developing prostate cancer, while having diabetes exhibited a significantly lower risk of developing prostate cancer.

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Section: Introductionmentioning

confidence: 99%

Variable Selection for Length-Biased and Interval-Censored Failure Time Data

Feng,

Cheng,

Sun

2023

Mathematics

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“…Only a few methods have been proposed for causal inference on interval-censored time-to-event outcomes. Li and Peng [2021] proposed an instrumental variable estimation procedure for compiler causal treatment effect with interval-censored data. To the best of our knowledge, there is no research that has been done on causal inference with interval-censored data when we are interested in the commonly used causal estimands for all the subjects in the target population, not only for compilers.…”

Section: Discussionmentioning

confidence: 99%

“…However, time-to-event outcomes are often subject to interval censoring rather than the simpler right censoring due to the realistic intermittent follow-up allowed by various scientific studies [Sun, 2006].Only few methods have been proposed for causal inference on interval censored time-to event outcomes. Li and Peng [2021] proposed an instrumental variable estimation procedure for compiler causal treatment effect with interval censored data.…”

Section: The Cause Effect On Survival Outcomementioning

confidence: 99%

Variable selection and causal treatment effect estimation based on interval-censored failure time data

Chen¹

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[EMBARGOED UNTIL 6/1/2023] Variable selection has been discussed under many contexts and especially a great deal of literature has been established in the failure time context with constant coefficients. However, the time-varying effect sometimes could show more insight of how the influence changing through time. For example, the treatment effect may vanish because of mutation of virus. In addition, to identify important variables or covariates, a desired feature of a variable selection method is to distinguish time-varying coefficients from time-independent ones, which also presents an additional challenge. Nevertheless, only limited research exists on the variable selection for time-varying effect. Existing methods focused on right-censored data or generalized linear model. In Chapter 2 and Chapter 3, we discuss simultaneous parameter estimation and variable selection for interval censored data with time-varying effects, which can simultaneously select between time-dependent and time-independent covariate effects. To implement the proposed procedure, an EM algorithm is developed, and a simulation study is conducted and suggests that the proposed method works well in practical situations. What's more, the augmented Lagrangian method is used in implementation [Bertsekas, 1996] to deal with the compositional covariates in microbiome data. Finally, its usefulness is illustrated by the real data that motivated these studies. Another focus of this dissertation is the treatment effect estimation. In the presence of censoring, standard methods of summarizing the treatment effect estimates, Kaplan-Meier curves (survival function), the logrank test, etc., are not proper in observational studies as they all based on randomized experimental designs. For causal inference on survival outcomes, the commonly used causal estimands are: restricted average survival time, survival probability, survival quantile, and the marginal hazard ratio. But for the commonly used marginal hazard ratio in survival data analysis, it does not fit into Rubin's causal model framework because the observed baseline covariate balance is not guaranteed after the first failure happened in the sample. The susceptible subjects tend to experience failure events earlier, which will introduce selection bias problem to the analysis. For this reason, our target estimand is restricted average survival time, which is the difference between restricted mean survival time (RMST) defined on the potential survival time in treated and control groups. In Chapter 4, we propose a method for causal inference on interval-censored data in observational studies utilizing the pseudo observation approach. The pseudo observation for the interval-censored data is based on two methods. One is jack-knife method where the pseudo restricted mean survival time is calculated with the method proposed in Zhang et al. [2020]. Another approach uses the fast approximation of the jack-knife pseudo observations proposed by Bouaziz [2021]. With the calculated pseudo-observations, we propose to use IPW method to adjust the confounding effect arose in observation studies, where it tends to have different distributions of treatment assignment in treated and control group.

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Overview of Recent Advances on the Analysis of Interval-Censored Failure Time Data

2022

Emerging Topics in Modeling Interval-Censored Survival Data

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Instrumental variable estimation of complier causal treatment effect with interval‐censored data

Cited by 6 publications

References 28 publications

Variable Selection for Length-Biased and Interval-Censored Failure Time Data

Variable Selection for Length-Biased and Interval-Censored Failure Time Data

Variable selection and causal treatment effect estimation based on interval-censored failure time data

Overview of Recent Advances on the Analysis of Interval-Censored Failure Time Data

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