Methodology for the meta‐analysis of individual patient data with survival end‐points is proposed. Motivated by questions about the reliance on hazard ratios as summary measures of treatment effects, a parametric approach is considered and percentile ratios are introduced as an alternative to hazard ratios. The generalized log‐gamma model, which includes many common time‐to‐event distributions as special cases, is discussed in detail. Likelihood inference for percentile ratios is outlined. The proposed methodology is used for a meta‐analysis of glioma data that was one of the studies which motivated this work. A simulation study exploring the validity of the proposed methodology is available electronically. Copyright © 2010 John Wiley & Sons, Ltd.

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: We identified 3172 patients (2667 males, 84%) admitted after a TBI (median age = 34 years [IQR 23-48]). Two-wheeler road traffic accidents caused 2259 (71%) injuries, in which 13 (0.6%) patients were wearing a helmet. There were 174 (5%) inpatient deaths (median length of stay = 6 days [IQR 4-10]) and overall mortality (median follow-up = 6 months [IQR 3-9]) was 17% (n = 540). Age, Glasgow Coma Scale motor score, systolic blood pressure ≤90 mm Hg, and key computed tomography imaging features were independently associated with inpatient mortality. Existing prognostic models predicted inpatient mortality with good performance (International Mission for Prognosis and Analysis of Clinical Trials in TBI: Brier = 0.0876, area under the curve (AUC) = 83% [95% CI 79%-87%]; Rotterdam CT: Brier = 0.0890, AUC 79% [95% CI 75%-83%]), but showed poorer performance for post-discharge mortality (International Mission for Prognosis and Analysis of Clinical Trials in TBI: Brier = 0.134, AUC = 75% [95% CI 72%-78%]; Rotterdam CT: Brier = 0.145, AUC 66% [95% CI 63%-69%]). CONCLUSION: In a tertiary care center in India, we described a predominantly young male TBI population with a high contribution of 2-wheeler road traffic accidents and significant post-discharge mortality. Existing prognostic models showed poor performance when predicting which patients died after discharge. These findings should inform public health interventions to reduce the significant burden of TBI in India.

This study explored the patterns of adjustment to the quarantine restrictions imposed during the first Covid-19 wave in Greece. The sample comprised 1377 respondents from all geographical districts, mostly women (78.9%), with university degrees (80.7%) and an age range between 18-81. The Quarantine Adjustment Questionnaire was designed to assess the reactions of the general population to the lockdown restrictions and three diverse patterns. emerged from the analyses: positive adjustment (the Resilient), confinement stress (the Rebels) and introspection (the Internalizers), which served as the dependent variables in this study. Multiple linear regressions revealed that each profile consisted of characteristic childhood backgrounds, personal attributes, fears and concerns regarding the Covid-19 pandemic. Economic distress and negative childhood experiences differentiated the psychological effects of quarantine restrictions and determined to a large extent the adjustment pattern employed by each group. Women presented the most resilient profile but also the most vulnerable one, depending on the social and personal resources at their disposal. Young people were the most reactive group to the quarantine restrictions, but also the most unprotected to current and future adversity. Implications regarding the impact of sociocultural factors on patterns of adjustment and the need for new policies to support the vulnerable population are discussed.

Background/Objectives: Prostate cancer is the most frequent male malignancy. The incidence of disease varies among different ethnic groups. CYP3A polymorphisms are candidates for prostate cancer susceptibility studies. The aim of the present study is to investigate the ethnicity-related clinical impact of CYP3A4 variants on prostate cancer risk. Methods: A systematic literature search and meta-analysis were conducted according to PRISMA guidelines. A total of 10 eligible studies, including 3116 prostate cancer cases and 3008 healthy controls, were analyzed. We evaluated the association between the CYP3A4*1B (rs2740574, −392 A > G) variant and prostate cancer risk in European Caucasians. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using six genetic models. Data were analyzed using fixed and random-effects models based on the I2 value of heterogeneity magnitude. Funnel plots and Egger’s linear regression tests were used to assess publication bias. Results: CYP3A4*1B was associated with prostate cancer susceptibility in the allele (G vs. A: OR = 1.32, CI = 0.91–1.93), dominant (AG + GG vs. AA OR = 1.41, CI = 0.95–2.09), recessive (GG vs. AA + AG, OR = 1.82, CI = 1.26–2.63), homozygous (GG vs. AA, OR = 1.92, CI = 1.32–2.77), heterozygous model (AG vs. AA, OR = 1.31, CI = 0.89–1.93) and co-dominant model (AG vs. AA + GG; OR = 1.27, CI = 0.88–1.85). Significant heterogeneity characterized the allele, as well as the dominant model (I2 = 84.1%, I2 = 80.0%). Egger’s tests (p < 0.05) and funnel plots did not identify publication bias. Conclusions: The present meta-analysis indicates that the G allele and GG genotype might affect prostate cancer susceptibility in European Caucasians; however, the validity and reliability of the results need to be examined in future research.