Where you live matters: Area deprivation predicts poor survival and liver transplant waitlisting.

Social determinants of health (SDOH) are important predictors of poor clinical outcomes in chronic diseases, but their associations among the general cirrhosis population and liver transplantation (LT) are limited. We conducted a retrospective, multiinstitutional analysis of adult (≥18-years-old) patients with cirrhosis in metropolitan Chicago to determine the associations of poor neighborhood-level SDOH on decompensation complications, mortality, and LT waitlisting. Area deprivation index and covariates extracted from the American Census Survey were aspects of SDOH that were investigated. Among 15 101 patients with cirrhosis, the mean age was 57.2 years; 6414 (42.5%) were women, 6589 (43.6%) were non-Hispanic White, 3652 (24.2%) were non-Hispanic Black, and 2662 (17.6%) were Hispanic. Each quintile increase in area deprivation was associated with poor outcomes in decompensation (sHR [subdistribution hazard ratio] 1.07; 95% CI 1.05-1.10; P < .001), waitlisting (sHR 0.72; 95% CI 0.67-0.76; P < .001), and all-cause mortality (sHR 1.09; 95% CI 1.06-1.12; P < .001). Domains of SDOH associated with a lower likelihood of waitlisting and survival included low income, low education, poor household conditions, and social support (P < .001). Overall, patients with cirrhosis residing in poor neighborhood-level SDOH had higher decompensation, and mortality, and were less likely to be waitlisted for LT. Further exploration of structural barriers toward LT or optimizing health outcomes is warranted.

Designing superiority trials with window mean survival time as a primary endpoint.

Window mean survival time (WMST), a simple extension of restricted mean survival time (RMST), allows for clinicians to evaluate the mean survival difference between treatment groups in specific windows of time during the follow-up period of a trial. The advantages of WMST are numerous. Not only does it produce estimates of treatment effect that can be meaningfully interpreted, but also has power advantages over competing methods when hazards are non-proportional (NPH). WMST, like RMST, is currently underutilized due to clinicians' lack of familiarity with tests comparing mean survival times and the lack of tools to facilitate trial design with this endpoint. The aim of this article is to provide investigators with insights and software to design trials with WMST as the primary endpoint. Functions for performing power and sample size calculations are provided in the survWMST package in R available on GitHub.

Versatile tests for window mean survival time.

Window mean survival time (WMST) evaluates the mean survival between a lower time horizon, τ 0 $$ {\tau}_0 $$ , and an upper time horizon, τ 1 $$ {\tau}_1 $$ . As a flexible extension of restricted mean survival time, specific clinically relevant windows of time can be assessed for survival difference accompanied by a communicable interpretation of estimates and tests. In its original application, WMST required the pre-specification of a window through the selection of appropriate window bounds, τ 0 $$ {\tau}_0 $$ and τ 1 $$ {\tau}_1 $$ . In the instance of severe window misspecification of τ 0 $$ {\tau}_0 $$ and τ 1 $$ {\tau}_1 $$ , the analysis may suffer from low power and a less meaningful interpretation. In this article, we introduce versatile tests whose procedures are based on the simultaneous use of multiple WMST test statistics that are asymptotically normal under the null hypothesis of no difference between two groups. Simulations are performed to examine the power of the tests in moderate sample sizes when the data are uncensored to heavily censored with a ramp-up enrollment period. The survival scenarios chosen for simulation are intended to imitate those which are commonly encountered in oncology, especially in trials involving immunotherapies. Implementation of the procedures is discussed in two real data examples for illustration. Functions for performing versatile WMST tests are provided in the survWMST package in R.

Window mean survival time.

We propose a class of alternative estimates and tests to restricted mean survival time (RMST) which improves power in numerous survival scenarios while maintaining a level of interpretability. The industry standards for interpretable hypothesis tests in survival analysis, RMST and logrank tests (LRTs), can suffer from low power in cases where the proportional hazards assumption fails. In particular, when late differences occur between survival curves, our proposed estimate and class of tests, window mean survival time (WMST), outperforms both RMST and LRT without sacrificing interpretability, unlike weighted rank tests (WRTs). WMST has the added advantage of maintaining high power when the proportional hazards assumption is met, while WRTs do not. With testing methods often being chosen in advance of data collection, WMST can ensure adequate power without distributional assumptions and is robust to the choice of its restriction parameters. Functions for performing WMST analysis are provided in the survWM2 package in R.

A Comparison Between Chemo-Radiotherapy Combined With Immunotherapy and Chemo-Radiotherapy Alone for the Treatment of Newly Diagnosed Glioblastoma: A Systematic Review and Meta-Analysis.

BACKGROUND: Immunotherapy for GBM is an emerging field which is increasingly being investigated in combination with standard of care treatment options with variable reported success rates. OBJECTIVE: To perform a systematic review of the available data to evaluate the safety and efficacy of combining immunotherapy with standard of care chemo-radiotherapy following surgical resection for the treatment of newly diagnosed GBM. METHODS: A literature search was performed for published clinical trials evaluating immunotherapy for GBM from January 1, 2000, to October 1, 2020, in PubMed and Cochrane using PICOS/PRISMA/MOOSE guidelines. Only clinical trials with two arms (combined therapy vs. control therapy) were included. Outcomes were then pooled using weighted random effects model for meta-analysis and compared using the Wald-type test. Primary outcomes included 1-year overall survival (OS) and progression-free survival (PFS), secondary outcomes included severe adverse events (SAE) grade 3 or higher. RESULTS: Nine randomized phase II and/or III clinical trials were included in the analysis, totaling 1,239 patients. The meta-analysis revealed no statistically significant differences in group's 1-year OS [80.6% (95% CI: 68.6%-90.2%) vs. 72.6% (95% CI: 65.7%-78.9%), p = 0.15] or in 1-year PFS [37% (95% CI: 26.4%-48.2%) vs. 30.4% (95% CI: 25.4%-35.6%) p = 0.17] when the immunotherapy in combination with the standard of care group (combined therapy) was compared to the standard of care group alone (control). Severe adverse events grade 3 to 5 were more common in the immunotherapy and standard of care group than in the standard of care group (47.3%, 95% CI: 20.8-74.6%, vs 43.8%, 95% CI: 8.7-83.1, p = 0.81), but this effect also failed to reach statistical significance. CONCLUSION: Our results suggests that immunotherapy can be safely combined with standard of care chemo-radiotherapy without significant increase in grade 3 to 5 SAE; however, there is no statistically significant increase in overall survival or progression free survival with the combination therapy.