Weighted comparison of two cumulative incidence functions with R-CIFsmry package

Risk Factors and Incidence of Epilepsy after Severe Traumatic Brain Injury

We determined the incidence of post-traumatic epilepsy after severe traumatic brain injury. Of 392 patients surviving to discharge, cumulative incidence of post-traumatic epilepsy was 25% at 5 years and 32% at 15 years, an increase compared with historical reports. Among patients with one late seizure (>7 days post-trauma), the risk of seizure recurrence was 62% after 1 year and 82% at 10 years. Competing hazards regression identified age, decompressive hemicraniectomy, and intracranial infection as independent predictors of post-traumatic epilepsy. Patients with severe traumatic brain injury and a single late post-traumatic seizure will likely require long-term antiseizure medicines. ANN NEUROL 2022;92:663-669.

Implementation of an Alternative Method for Assessing Competing Risks: Restricted Mean Time Lost

In clinical and epidemiologic studies, hazard ratios are often applied to compare treatment effects between 2 groups for survival data. For competing-risks data, the corresponding quantities of interest are cause-specific hazard ratios and subdistribution hazard ratios. However, they both have some limitations related to model assumptions and clinical interpretation. Therefore, we recommend restricted mean time lost (RMTL) as an alternative measure that is easy to interpret in a competing-risks framework. Based on the difference in RMTL (RMTLd), we propose a new estimator, hypothetical test, and sample-size formula. Simulation results show that estimation of the RMTLd is accurate and that the RMTLd test has robust statistical performance (both type I error and statistical power). The results of 3 example analyses also verify the performance of the RMTLd test. From the perspectives of clinical interpretation, application conditions, and statistical performance, we recommend that the RMTLd be reported along with the hazard ratio in analyses of competing-risks data and that the RMTLd even be regarded as the primary outcome when the proportional hazards assumption fails.

Comparison of two treatments in the presence of competing risks

Competing risks data arise frequently in clinical trials, and a common problem encountered is the overall homogeneity between two groups. In competing risks analysis, when the proportional subdistribution hazard assumption is violated or two cumulative incidence function (CIF) curves cross; currently, the most commonly used testing methods, for example, the Gray test and the Pepe and Mori test, may lead to a significant loss of statistical testing power. In this article, we propose a testing method based on the area between the CIF curves (ABC). The ABC test captures the difference over the whole time interval for which survival information is available for both groups and is not based on any special assumptions regarding the underlying distributions. The ABC test was also extended to test short-term and long-term effects. We also consider a combined test and a two-stage procedure based on this new method, and a bootstrap resampling procedure is suggested in practice to approximate the limiting distribution of the combined test and two-stage test. An extensive series of Monte Carlo simulations is conducted to investigate the power and the type I error rate of the methods. In addition, based on our simulations, our proposed TS, Comb, and ABC tests have a relatively high power in most situations. In addition, the methods are illustrated using two different datasets with different CIF situations.