April 24, 2024
The motivation was assessing continuous risk scores withbiomarker distributions. The authors defined that the precision-recall curve is a plot of the true positive rate (which is also known as recall or sensitivity) against the positive predictive value, also known as precision, which is the
conditional probability that a subject with a positive test result actually has
the disease, for all possible cutoff values. This curve can be summarized by
the area under this curve. This has been put forth as a competitor to the ROC
and AUC, which are thought to overestimate performances. The method has two
issues. One is that the event status is assumed unknown for all subjects in the
study. And the second is that it assumes independent censoring. Therefore,
their paper aimed to address these limitations and propose a novel
nonparametric estimation method for time-dependent precision- recall curve and
its area under curve.
In their methods they proposed a time-dependentprecision-recall curve which was defined as a plot of the time-dependent true positive rate vs the time-dependent positive predictive values for all possible cutoff values. They defined empiricalpoint estimates for their TPR and their PPV by calculating weights which are related to the conditional survival, calculated by the Beran estimator, for the biomarker. They also calculated variance for their AUC under the TD precision
curve by bootstrapping, in order also to create confidence intervals around the
estimate. In simulations they compared their method to the Yuan method. Overall, their method performed well onseveral metrics as compared to the Yuan method in their simulations.
In a real dataset analysis, they used a Mayo clinic PBCdataset that is freely available. Thedifferences between their method and the Yuan methods are not as distinct in
the real data analysis, but their AUC estimator came out lower than the Yuan
one, however, as they state, this difference was not statistically significant
for either method, but then again, it is hard to tell the differences when
somehow their method did so well on several simulation parameters. Nevertheless, they came up with an R package tdPRC. Their method was developed under right censoringonly so they proposed to incorporate other censoring types.
Written by,
Usha Govindarajulu, PhD
Keywords: survival analysis, nonparametric, right-censoring, time-dependent, precision-recall
References
Beyene KM, Chen D-G, and Kifle YG (2024). “A novel nonparametrictime-dependent precision-recall curve estimator for right-censored survival
data” Biometrical Journal.
