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.
Highlights
1.1 Plasmodium vivax Malaria InfectionPlasmodium vivax, in short, P. vivax, is the most widespread human malaria (Howes et al, 2016)
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
We developed a novel method for classifying the latent cause of failure under a competing risks set-up, which uses time to event information and transition likelihoods between covariates at the baseline and at the time of event occurrence
Summary
Plasmodium vivax, in short, P. vivax, is the most widespread human malaria (Howes et al, 2016). Previous studies have concluded that even when the level of transmission is relatively low, there is a high genetic diversity in P. vivax parasites within patient populations in Cambodia (Lin et al, 2013, Friedrich et al, 2016) Such genetic diversity, often resulting in multiple parasites haplotypes present in a single infection, provides an opportunity for researchers to distinguish relapse from a recurrent infection by examining the overlap of haplotypes between infections and the appearance of haplotypes associated with relapse. After extracting DNA from filter paper blood spots, Lin et al (2015) applied deep sequencing to this region and used a bioinformatics pipeline called SeekDeep (Hathaway et al, 2018) to determine different haplotypes of pvmsp defined by at least a single nucleotide difference between haplotypes They identified 67 unique pvmsp haplotypes across 108 isolates from either initial infection or recurrent infections, with each patient isolate harboring, on average, three different haplotypes. Two subjects with the shortest time to recurrent infection did not have any shared haplotypes
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