Estimation and hypothesis testing with error‐contaminated survival data under possibly misspecified measurement error models

Yi, Grace Y.; Ying, Yan

doi:10.1002/cjs.11594

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…One may generalize the proposed method to accommodate other parametric models for treatment assignment probabilities. When the classical measurement error model () is in doubt, sensitivity analysis can be conducted 33 …”

Section: Discussion and Extensionmentioning

confidence: 99%

“…When the classical measurement error model ( 8) is in doubt, sensitivity analysis can be conducted. 33 One may extend the proposed method to other causal estimands rather than the average treatment effect. One may generalize the proposed method to the situation that time-varying mediators exist in causal mediation analysis with longitudinal data.…”

Section: Discussion and Extensionmentioning

confidence: 99%

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Consistent inverse probability of treatment weighted estimation of the average treatment effect with mismeasured time‐dependent confounders

Ying

Ren

2022

Statistics in Medicine

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In longitudinal studies, the inverse probability of treatment weighted (IPTW) method is commonly employed to estimate the effect of time‐dependent treatments on an outcome of interest. However, it has been documented that when the confounders are subject to measurement error, the naive IPTW method which simply ignores measurement error leads to biased treatment effect estimation. In the existing literature, there is a lack of measurement error correction methods that fully remove measurement error effect and produce consistent treatment effect estimation. In this article, we develop a novel consistent IPTW estimation procedure for longitudinal studies. The key step of the proposed method is to use the observed data to construct a corrected function that is unbiased of the unknown IPTW function. Simulation studies reveal that the proposed method outperforms the existing consistent and approximate measurement error correction methods for IPTW estimation of the average treatment effect. Finally, we apply the proposed method to analyze a real dataset.

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Section: Discussion and Extensionmentioning

confidence: 99%

Section: Discussion and Extensionmentioning

confidence: 99%

Consistent inverse probability of treatment weighted estimation of the average treatment effect with mismeasured time‐dependent confounders

Ying

Ren

2022

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

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Nonparametric estimation of a survival function in the presence of measurement errors on the failure time of interest

Jin,

Liu,

Mao

et al. 2023

Can J Statistics

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This article discusses nonparametric estimation of a survival function in the presence of measurement errors on the observation of the failure time of interest. One situation where such issues arise would be clinical studies of chronic diseases where the observation on the time to the failure event of interest such as the onset of the disease relies on patient recall or chart review of electronic medical records. It is easy to see that both situations can be subject to measurement errors. To resolve this problem, we propose a simulation extrapolation approach to correct the bias induced by the measurement error. To overcome potential computational difficulties, we use spline regression to approximate the unspecified extrapolated coefficient function of time, and establish the asymptotic properties of our proposed estimator. The proposed method is applied to nonparametric estimation based on interval‐censored data. Extensive numerical experiments involving both simulated and actual study datasets demonstrate the feasibility of this proposed estimation procedure.

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Estimation and hypothesis testing with error‐contaminated survival data under possibly misspecified measurement error models

Cited by 2 publications

References 33 publications

Consistent inverse probability of treatment weighted estimation of the average treatment effect with mismeasured time‐dependent confounders

Consistent inverse probability of treatment weighted estimation of the average treatment effect with mismeasured time‐dependent confounders

Nonparametric estimation of a survival function in the presence of measurement errors on the failure time of interest

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