A validation sampling approach for consistent estimation of adverse drug reaction risk with misclassified right‐censored survival data

Gravel, Christopher A.; Dewanji, Anup; Farrell, Patrick J.; Krewski, Daniel

doi:10.1002/sim.7854

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Covariate measurement error, particularly classical measurement error or extensions of it, has been well studied in the literature, and methods to correct the bias resulting from such error have been well developed (Carroll et al., 2006). Although less attention has been given to errors in an outcome of interest, there has been some recent work looking at errors in binary outcomes (Magder & Hughes, 1997; Edwards et al., 2013; Wang et al., 2016), discrete time‐to‐event outcomes (Hunsberger et al., 2010; Magaret, 2008; Meier et al., 2003), and to a lesser extent, continuous time‐to‐event outcomes (Gravel et al., 2018; Oh et al., 2018). There has been even less work to understand the impact of errors in both covariates and a time‐to‐event outcome, but it has recently been shown that ignoring such errors can cause severe bias in estimates of effects of interest (Boe et al., 2020; Giganti et al., 2020; Oh et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Improved generalized raking estimators to address dependent covariate and failure‐time outcome error

Shepherd

Lumley

et al. 2021

Biometrical J

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Biomedical studies that use electronic health records (EHR) data for inference are often subject to bias due to measurement error. The measurement error present in EHR data is typically complex, consisting of errors of unknown functional form in covariates and the outcome, which can be dependent. To address the bias resulting from such errors, generalized raking has recently been proposed as a robust method that yields consistent estimates without the need to model the error structure. We provide rationale for why these previously proposed raking estimators can be expected to be inefficient in failure‐time outcome settings involving misclassification of the event indicator. We propose raking estimators that utilize multiple imputation, to impute either the target variables or auxiliary variables, to improve the efficiency. We also consider outcome‐dependent sampling designs and investigate their impact on the efficiency of the raking estimators, either with or without multiple imputation. We present an extensive numerical study to examine the performance of the proposed estimators across various measurement error settings. We then apply the proposed methods to our motivating setting, in which we seek to analyze HIV outcomes in an observational cohort with EHR data from the Vanderbilt Comprehensive Care Clinic.

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Section: Introductionmentioning

confidence: 99%

Improved generalized raking estimators to address dependent covariate and failure‐time outcome error

Shepherd

Lumley

et al. 2021

Biometrical J

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“…Using a validation data where both the true and error-prone survival times are both available, Braun et al (2018) proposed a nonparametric bias correction procedure for models using 𝒯 𝒯 𝒯 as predictors. Gravel et al (2018) proposed the model-based estimation accounting for temporally induced errors and misclassified event errors in time-to-event outcomes. Recently, Oh et al (2021) developed a raking and regression calibration method for data with measurement errors in both covariates and time-to-event outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…Gravel et al. (2018) proposed the model‐based estimation accounting for temporally induced errors and misclassified event errors in time‐to‐event outcomes. Recently, Oh et al.…”

Section: Introductionmentioning

confidence: 99%

Risk prediction with imperfect survival outcome information from electronic health records

et al. 2021

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Readily available proxies for the time of disease onset such as the time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor proxies. Due to the lack of detailed documentation and labor intensiveness of manual annotation, it is often only feasible to ascertain for a small subset the current status of the disease by a follow-up time rather than the exact time. In this paper, we aim to develop risk prediction models for the onset time efficiently leveraging both a small number of labels on the current status and a large number of unlabeled observations on imperfect proxies. Under a semiparametric transformation model for onset and a highly flexible measurement error model for proxy onset time, we propose the semisupervised risk prediction method by combining information from proxies and limited labels efficiently. From an initially estimator solely based on the labeled subset, we perform a one-step correction with the full data augmenting against a mean zero rank correlation score derived from the proxies. We establish the consistency and asymptotic normality of the proposed semisupervised estimator and provide a resampling procedure for interval estimation. Simulation studies demonstrate that the proposed estimator performs well in a finite sample. We illustrate the proposed estimator by developing a genetic risk prediction model for obesity using data from Mass General Brigham Healthcare Biobank.

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“…Covariate measurement error, particularly classical measurement error or extensions of it, has been well studied in the literature and methods to correct the bias resulting from such error have been well developed (Carroll et al, 2006). Although less attention has been given to errors in an outcome of interest, there has been some recent work looking at errors in binary outcomes (Magder and Hughes, 1997;Edwards et al, 2013;Wang et al, 2016), discrete time-to-event outcomes (Meier et al, 2003;Magaret, 2008;Hunsberger et al, 2010), and to a lesser extent, continuous time-to-event outcomes (Oh et al, 2018;Gravel et al, 2018). There has been even less work to understand the impact of errors in both covariates and a time-to-event outcome, but it has recently been shown that ignoring such errors can cause severe bias in estimates of effects of interest (Oh et al, 2019;Boe et al, 2020;Giganti et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Improved Generalized Raking Estimators to Address Dependent Covariate and Failure-Time Outcome Error

Oh¹,

Shepherd²,

Lumley³

et al. 2020

Preprint

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Biomedical studies that use electronic health records (EHR) data for inference are often subject to bias due to measurement error. The measurement error present in EHR data is typically complex, consisting of errors of unknown functional form in covariates and the outcome, which can be dependent. To address the bias resulting from such errors, generalized raking has recently been proposed as a robust method that yields consistent estimates without the need to model the error structure. We provide rationale for why these previously proposed raking estimators can be expected to be inefficient in failure-time outcome settings involving misclassification of the event indicator. We propose raking estimators that utilize multiple imputation, to impute either the target variables or auxiliary variables, to improve the efficiency. We also consider outcome-dependent sampling designs and investigate their impact on the efficiency of the raking estimators, either with or without multiple imputation.We present an extensive numerical study to examine the performance of the proposed estimators across various measurement error settings. We then apply the proposed methods to our motivating setting, in which we seek to analyze HIV outcomes in an observational cohort with electronic health records data from the Vanderbilt Comprehensive Care Clinic.

show abstract

A validation sampling approach for consistent estimation of adverse drug reaction risk with misclassified right‐censored survival data

Cited by 4 publications

References 12 publications

Improved generalized raking estimators to address dependent covariate and failure‐time outcome error

Improved generalized raking estimators to address dependent covariate and failure‐time outcome error

Risk prediction with imperfect survival outcome information from electronic health records

Improved Generalized Raking Estimators to Address Dependent Covariate and Failure-Time Outcome Error

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