Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks
Volume 20, Issue 1 (2022), pp. 51–78
Pub. online: 9 December 2021
Type: Data Science In Action
Open Access
Received
8 June 2021
8 June 2021
Accepted
2 October 2021
2 October 2021
Published
9 December 2021
9 December 2021
Abstract
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.
Supplementary material
Supplementary MaterialIn the Supplementary Materials, we provide additional simulation results for scenarios when the hazard models are misspecified. We also compare our classifiers with those proposed in Lin et al. (2020) for binary covariates. In addition, we provide results for parameter estimation performance under low-dimensional settings. Additional details of the P. vivax malaria study, including the data and codes are provided as well.
References
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