Nonparametric Analysis of Competing Risks Data with Event Category Missing at Random

Gouskova, Natalia; Lin, Feng; Fine, Jason P.

doi:10.1111/biom.12547

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…In our analysis, we adopt the MAR assumption (4) with Z i including covariates, Sex , MI , NewScreen , and FamilyHis , which means, these observed covariates can fully account for the missingness of the PA infection type. This is a reasonable assumption for the CFFPR dataset because, according to the investigation of Gouskova et al (2017), the two major causes of missing PA infection types are (a) lack of technology to classify the type of PA infection as mucoid or nonmucoid; (b) data recording negligence. Since the MAR assumption is not statistically verifiable (Little and Rubin, 2002), we perform a sensitivity analysis by considering different specifications of Z i .…”

Section: A Real Data Examplementioning

confidence: 99%

Generalized Accelerated Recurrence Time Model for Multivariate Recurrent Event Data with Missing Event Type

Peng

Zhang

et al. 2018

Biometrics

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Summary Recurrent events data are frequently encountered in biomedical follow-up studies. The generalized accelerated recurrence time (GART) model (Sun et al., 2016), which formulates covariate effects on the time scale of the mean function of recurrent events (i.e. time to expected frequency), has arisen as a useful secondary analysis tool to provide meaningful physical interpretations. In this paper, we investigate the GART model in a multivariate recurrent events setting, where subjects may experience multiple types of recurrent events and some event types may be missing. We propose methods for the GART model that utilize the inverse probability weighting technique or the estimating equation projection strategy to handle event types that are missing at random. The new methods do not require imposing any parametric model for the missing mechanism, and thus are robust; moreover they enjoy easy and stable implementation. We establish the uniform consistency and weak convergence of the resulting estimators and develop appropriate inferential procedures. Extensive simulation studies and an application to a dataset from Cystic Fibrosis Foundation Patient Registry (CFFPR) illustrate the validity and practical utility of the proposed methods.

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Section: A Real Data Examplementioning

confidence: 99%

Generalized Accelerated Recurrence Time Model for Multivariate Recurrent Event Data with Missing Event Type

Peng

Zhang

et al. 2018

Biometrics

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Section: Conclusion and Discussionmentioning

confidence: 99%

“…As discussed in the introduction, a parametric estimator for p ij (t) proposed by Schaubel and Cai 18 may be subject to model misspecification. One may consider using a nonparametric estimator for p ij (t), such as the local likelihood estimator in Lin et al 20 or Nadaraya-Watson estimator in Gouskova et al 28 A general inverse probability weighted estimator based on model ( 9) assumes constant regression coefficients, independent of time t. However, while X i (t) in the model ( 9) is naturally time-varying, the regression coefficient 0 may also be assumed time-varying and is interpreted as a time-dependent association between X i (t) and the likelihood of missingness. To estimate the time-varying coefficient 0 (t) = ( 00 (t), 01 (t), … , 0J (t)) ′ , one can use a spline function to approximate 0j (t), for example, a cubic B-spline function that is written as…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Inverse probability weighted estimation for recurrent events data with missing category

Lin

Cai

Deng

et al. 2021

Statistics in Medicine

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Modeling recurrent event data with multiple event types has drawn interest in recent biomedical studies due to its flexibility for understanding different risk factors for multiple recurrent event processes. However, in such data type, missing event type appears frequently because of various reasons such as recording ignorance or resource limitation. In this study, we aim to propose an inverse probability weighted estimation that is commonly used in the missing data literature to correct possibly biased estimation by a complete‐case analysis. This approach is not limited to a specific form of the recurrent event model. We derive the large sample theory in a general form. We demonstrate that our approach can be applied to either multiplicative or additive rates model with practical sample size via comprehensive simulations. Nonmucoid and mucoid Pseudomonas aeruginosa infections of 14 888 patients in 2016 Cystic Fibrosis Foundation Patient Registry data are analyzed to show that, without including 12% events with missing event type in the analysis, several factors may be misidentified as risk factors for the nonmucoid type of infections.

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“…There are two possible approaches for estimating competing risks data with missing cause of failure when the cause is missing at random ( Rubin, 1976 ): (1) complete-case analysis, utilizing only complete observations, e.g., Effraimidis and Dahl (2014) , or, (2) construct a regression model for the missing cause using all observations, including those with missing cause of failure. In the second approach, one can use a global parametric model ( Lu and Tsiatis, 2001 ), a semi-parametric framework ( Goetghebeur and Ryan, 1995 ) or a nonparametric regression method ( Gouskova et al, 2017 ) to estimate the cause-specific hazard functions. A similar problem is also considered in Sun and Gilbert (2012) and Juraska and Gilbert (2016) when considering the competing cause as a mark for the mark-specific hazard function.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks

Liu¹,

Lin²,

Lin³

et al. 2021

Journal of Data Science

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A standard competing risks set-up requires both time to event and cause of failure to be fully observable for all subjects. However, in application, the cause of failure may not always be observable, thus impeding the risk assessment. In some extreme cases, none of the causes of failure is observable. In the case of a recurrent episode of Plasmodium vivax malaria following treatment, the patient may have suffered a relapse from a previous infection or acquired a new infection from a mosquito bite. In this case, the time to relapse cannot be modeled when a competing risk, a new infection, is present. The efficacy of a treatment for preventing relapse from a previous infection may be underestimated when the true cause of infection cannot be classified. In this paper, we developed a novel method for classifying the latent cause of failure under a competing risks set-up, which uses not only time to event information but also transition likelihoods between covariates at the baseline and at the time of event occurrence. Our classifier shows superior performance under various scenarios in simulation experiments. The method was applied to Plasmodium vivax infection data to classify recurrent infections of malaria.

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Nonparametric Analysis of Competing Risks Data with Event Category Missing at Random

Cited by 6 publications

References 17 publications

Generalized Accelerated Recurrence Time Model for Multivariate Recurrent Event Data with Missing Event Type

Generalized Accelerated Recurrence Time Model for Multivariate Recurrent Event Data with Missing Event Type

Inverse probability weighted estimation for recurrent events data with missing category

Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks

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