Objectives: To determine the accuracy of single-reviewer screening in correctly classifying abstracts as relevant or irrelevant for literature reviews. Study Design and Setting: We conducted a crowd-based, parallel-group randomized controlled trial. Using the Cochrane Crowd platform, we randomly assigned eligible participants to 100 abstracts each of a pharmacological or a public health topic. After completing a training exercise, participants screened abstracts online based on predefined inclusion and exclusion criteria. We calculated sensitivities and specificities of single-and dual-reviewer screening using two published systematic reviews as reference standards. Results: Two hundred and eighty participants made 24,942 screening decisions on 2,000 randomly selected abstracts from the reference standard reviews. On average, each abstract was screened 12 times. Overall, single-reviewer abstract screening missed 13% of relevant studies (sensitivity: 86.6%; 95% confidence interval [CI], 80.6%e91.2%). By comparison, dual-reviewer abstract screening missed 3% of relevant studies (sensitivity: 97.5%; 95% CI, 95.1%e98.8%). The corresponding specificities were 79.2% (95% CI, 77.4%e80.9%) and 68.7% (95% CI, 66.4%e71.0%), respectively. Conclusions: Single-reviewer abstract screening does not appear to fulfill the high methodological standards that decisionmakers expect from systematic reviews. It may be a viable option for rapid reviews, which deliberately lower methodological standards to provide decision makers with accelerated evidence synthesis products.

Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. Recent developments in subgroup analysis consider subgroups that are defined in terms of the predicted individual treatment effect, i.e. the difference between the predicted outcome under treatment and the predicted outcome under control for each individual, which in turn may depend on multiple biomarkers. In this work, we study the properties of different modelling strategies to estimate the predicted individual treatment effect. We explore linear models and compare different estimation methods, such as maximum likelihood and the Lasso with and without randomized response. For the latter, we implement confidence intervals based on the selective inference framework to account for the model selection stage. We illustrate the methods in a dataset of a treatment for Alzheimer disease (normal response) and in a dataset of a treatment for prostate cancer (survival outcome). We also evaluate via simulations the performance of using the predicted individual treatment effect to identify subgroups where a novel treatment leads to better outcomes compared to a control treatment.

SUMMARYIn the analysis of survival times, the logrank test and the Cox model have been established as key tools, which do not require specific distributional assumptions. Under the assumption of proportional hazards, they are efficient and their results can be interpreted unambiguously. However, delayed treatment effects, disease progression, treatment switchers or the presence of subgroups with differential treatment effects may challenge the assumption of proportional hazards. In practice, weighted logrank tests emphasizing either early, intermediate or late event times via an appropriate weighting function may be used to accommodate for an expected pattern of non‐proportionality. We model these sources of non‐proportional hazards via a mixture of survival functions with piecewise constant hazard. The model is then applied to study the power of unweighted and weighted log‐rank tests, as well as maximum tests allowing different time dependent weights. Simulation results suggest a robust performance of maximum tests across different scenarios, with little loss in power compared to the most powerful among the considered weighting schemes and huge power gain compared to unfavorable weights. The actual sources of non‐proportional hazards are not obvious from resulting populationwise survival functions, highlighting the importance of detailed simulations in the planning phase of a trial when assuming non‐proportional hazards.We provide the required tools in a software package, allowing to model data generating processes under complex non‐proportional hazard scenarios, to simulate data from these models and to perform the weighted logrank tests.

Background: Several preclinical and epidemiologic studies have indicated tumourpromoting effects of thyroid hormones (THs). However, very limited knowledge exists on the prognostic impact of thyroid function in metastatic cancer. Methods: We compiled a discovery cohort of 1692 patients with newly diagnosed brain metastases (BMs) of solid cancers treated at the Medical University of Vienna and an independent validation cohort of 191 patients with newly diagnosed BMs treated at the University Hospital Zurich. Results: Hypothyroidism before diagnosis of cancer was evident in 133 of 1692 (7.9%) patients of the discovery, and in 18 of 191 (9.4%) patients of the validation cohort. In the discovery cohort, hypothyroidism was statistically significantly associated with favourable survival