BACKGROUND AND OBJECTIVES:Traumatic brain injury (TBI) is a major public health challenge in India but there is a lack of high-quality data on its clinical characteristics and outcomes. We aimed to describe the TBI population of a tertiary care center in India, identify predictors of inpatient mortality, and assess the performance of existing prognostic tools.METHODS:We conducted a prospective observational cohort study of patients admitted to a high-volume tertiary care center in Vellore, India, after a TBI between 2013 and 2019.RESULTS:

Meta-analysis of time-to-event data using non-parametric measures

In survival analysis, Kaplan–Meier is by far the most popular non-parametric method of estimating survival probabilities. However, in meta-analysis of time-to-event data, various proposed non-parametric methods of pooling the estimates from multiple studies co-exist, yet still lack universal acceptance. For this purpose, methodology for the meta-analysis of individual patient data with survival end-points is being evaluated, using non-parametric measures. We tackle the problem of combining information from independent two-armed trials in order to compare survival distributions, taking censoring into account. We do not rely on the proportionality, since such a simultaneous assumption across studies may seem arbitrary. To this end, three approaches based on the median ratio, the restricted mean survival time (RMST) and the use of the log(-log) survival function difference, are considered. A detailed guideline on how to implement these measures in a meta-analytic framework is presented, with the aim being to gain an appropriate non-parametric estimator and its corresponding weighting factor. Concerning the latter, we utilize traditional, asymptotic techniques and we also propose an alternative procedure via bootstrapping. We illustrate our methodology via simulation experiments, under various distributional schemes and censoring levels. Finally, the performance of these measures is tested under the assumption of small treatment effects. Simulations show that all three meta-analytic approaches produce similar results with mild levels of bias. The RMST produces the most robust results, consistently across the different scenarios studied. Nevertheless, all three measures share the same qualitative behavior and can offer insights as a useful preliminary analysis.

Most statistical methods for censored survival data assume there is no dependence between the lifetime and censoring mechanisms, an assumption which is often doubtful in practice. In this paper we study a parametric model which allows for dependence in terms of a parameter δ and a bias function B(t, θ). We propose a sensitivity analysis on the estimate of the parameter of interest for small values of δ. This parameter measures the dependence between the lifetime and the censoring mechanisms. Its size can be interpreted in terms of a correlation coefficient between the two mechanisms. A medical example suggests that even a small degree of dependence between the failure and censoring processes can have a noticeableeffect on the analysis.

Summary. In this article, we explore the use of a parametric model (for analyzing survival data)which is defined to allow sensitivity analysis for the presence of informative censoring. The dependence between the failure and the censoring processes is expressed through a parameter δ and a general bias function B(t, θ). We calculate the expectation of the potential bias due to informative censoring, which is an overall measure of how misleading our results might be if censoring is actually nonignorable. Bounds are also calculated for quantities of interest, e.g., parameter of the distribution of the failure process, which do not depend on the choice of the bias function for fixed δ. An application that relates to systematic lupus erythematosus data illustrates how additional information can result in reducing the uncertainty on estimates of the location parameter. Sensitivity analysis on a relative risk parameter is also explored.