Information‐based sample size re‐estimation in group sequential design for longitudinal trials

Group sequential design has become more popular in clinical trials because it allows for trials to stop early for futility or efficacy to save time and resources. However, this approach is less well‐known for longitudinal analysis. We have observed repeated cases of studies with longitudinal data where there is an interest in early stopping for a lack of treatment effect or in adapting sample size to correct for inappropriate variance assumptions. We propose an information‐based group sequential design as a method to deal with both of these issues. Updating the sample size at each interim analysis makes it possible to maintain the target power while controlling the type I error rate. We will illustrate our strategy with examples and simulations and compare the results with those obtained using fixed design and group sequential design without sample size re‐estimation. Copyright © 2014 John Wiley & Sons, Ltd.     

Extending cluster lot quality assurance sampling designs for surveillance programs

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two‐stage cluster designs for surveillance sampling is often more cost‐effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate. Copyright © 2014 John Wiley & Sons, Ltd.     

Sample size calculation in cost‐effectiveness cluster randomized trials: optimal and maximin approaches

In this paper, the optimal sample sizes at the cluster and person levels for each of two treatment arms are obtained for cluster randomized trials where the cost‐effectiveness of treatments on a continuous scale is studied. The optimal sample sizes maximize the efficiency or power for a given budget or minimize the budget for a given efficiency or power. Optimal sample sizes require information on the intra‐cluster correlations (ICCs) for effects and costs, the correlations between costs and effects at individual and cluster levels, the ratio of the variance of effects translated into costs to the variance of the costs (the variance ratio), sampling and measuring costs, and the budget. When planning, a study information on the model parameters usually is not available. To overcome this local optimality problem, the current paper also presents maximin sample sizes. The maximin sample sizes turn out to be rather robust against misspecifying the correlation between costs and effects at the cluster and individual levels but may lose much efficiency when misspecifying the variance ratio. The robustness of the maximin sample sizes against misspecifying the ICCs depends on the variance ratio. The maximin sample sizes are robust under misspecification of the ICC for costs for realistic values of the variance ratio greater than one but not robust under misspecification of the ICC for effects. Finally, we show how to calculate optimal or maximin sample sizes that yield sufficient power for a test on the cost‐effectiveness of an intervention. Copyright © 2014 John Wiley & Sons, Ltd.     

A highly efficient design strategy for regression with outcome pooling

The potential for research involving biospecimens can be hindered by the prohibitive cost of performing laboratory assays on individual samples. To mitigate this cost, strategies such as randomly selecting a portion of specimens for analysis or randomly pooling specimens prior to performing laboratory assays may be employed. These techniques, while effective in reducing cost, are often accompanied by a considerable loss of statistical efficiency. We propose a novel pooling strategy based on the k‐means clustering algorithm to reduce laboratory costs while maintaining a high level of statistical efficiency when predictor variables are measured on all subjects, but the outcome of interest is assessed in pools. We perform simulations motivated by the BioCycle study to compare this k‐means pooling strategy with current pooling and selection techniques under simple and multiple linear regression models. While all of the methods considered produce unbiased estimates and confidence intervals with appropriate coverage, pooling under k‐means clustering provides the most precise estimates, closely approximating results from the full data and losing minimal precision as the total number of pools decreases. The benefits of k‐means clustering evident in the simulation study are then applied to an analysis of the BioCycle dataset. In conclusion, when the number of lab tests is limited by budget, pooling specimens based on k‐means clustering prior to performing lab assays can be an effective way to save money with minimal information loss in a regression setting. Copyright © 2014 John Wiley & Sons, Ltd.     

Review of mixed treatment comparisons in published systematic reviews shows marked increase since 2009

ObjectivesTo identify and summarize published systematic reviews that report results of meta-analyses that combined direct and indirect comparisons.Study Design and SettingNarrative review of mixed treatment comparisons (MTCs) reported in systematic reviews of health interventions. MEDLINE, MEDLINE In-Process, Embase, CINAHL, DARE, the Cochrane Database of Systematic Reviews, and SIGLE were searched for reviews published up to June 2012 in which a meta-analysis had been conducted that combined direct and indirect comparisons among more than two interventions.ResultsReviews reporting MTCs are difficult to identify when searching major databases. These databases offer no way to identify MTCs, and authors use various names when reporting them. Of the 201 eligible reviews identified, more than three-quarters had been published in full. MTC methods have been used to study a wide range of clinical topics. The reported use of these methods has increased rapidly since 2009, and results from MTCs are commonly used in health policy decisions, through the evidence considered in health technology assessments.ConclusionIn view of the increasing use of MTCs, indexing of this study type in databases and a consensus on terminology and standards for conduct and reporting would be timely.     

Dual computer monitors to increase efficiency of conducting systematic reviews

ObjectiveSystematic reviews (SRs) are the cornerstone of evidence-based medicine. In this study, we evaluated the effectiveness of using two computer screens on the efficiency of conducting SRs.Study Design and SettingA cohort of reviewers before and after using dual monitors were compared with a control group that did not use dual monitors. The outcomes were time spent for abstract screening, full-text screening and data extraction, and inter-rater agreement. We adopted multivariate difference-in-differences linear regression models.ResultsA total of 60 SRs conducted by 54 reviewers were included in this analysis. We found a significant reduction of 23.81 minutes per article in data extraction in the intervention group relative to the control group (95% confidence interval: −46.03, −1.58, P = 0.04), which was a 36.85% reduction in time. There was no significant difference in time spent on abstract screening, full-text screening, or inter-rater agreement between the two groups.ConclusionUsing dual monitors when conducting SRs is associated with significant reduction of time spent on data extraction. No significant difference was observed on time spent on abstract screening or full-text screening. Using dual monitors is one strategy that may improve the efficiency of conducting SRs.     

医院战略薪酬体系设计的研究概述

以医院医院战略薪酬体系设计为研究对象,探讨构建基于医院战略的薪酬体系及其对医院战略实施的促进作用。在对医院薪酬现状调查及薪酬激励因素实证分析的基础上,构建医院战略薪酬管理模型,设计医院战略薪酬框架体系。     

Identification of Type II Inhibitors Targeting BRAF Using Privileged Pharmacophores

V‐RAF murine sarcoma viral oncogene homologue B1 (BRAF) is the most frequently mutated protein kinase in human cancers. The most common mutant BRAF V600E constitutively activates the RAS/RAF/MEK/ERK signaling pathway. BRAF has been validated as an important therapeutic target in human cancers. Phenylaminopyrimidine and unsymmetrical diaryl urea are two privileged pharmacophores in kinase inhibitor drug discovery. Herein, we describe the design of a novel hybrid pharmacophore, 4‐phenylaminopyrimidine urea, using the above two pharmacophores. A new series of compounds were in turn synthesized and evaluated to successfully identify selective inhibitors of BRAF and oncogenic BRAF V600E. Once daily oral dosing of lead compound 3 demonstrated sustained antitumor efficacy in A549 human non‐small‐cell lung cancer xenograft model. Molecular docking suggested that compound 3 might be a type II kinase inhibitor binding to the DFG‐out conformation of BRAF.     

Optimization of Compound Ranking for Structure‐Based Virtual Ligand Screening Using an Established FRED–Surflex Consensus Approach

The use of multiple target conformers has been applied successfully in virtual screening campaigns; however, a study on how to best combine scores for multiple targets in a hierarchic method that combines rigid and flexible docking is not available. In this study, we used a data set of 59 479 compounds to screen multiple conformers of four distinct protein targets to obtain an adapted and optimized combination of an established hierarchic method that employs the programs FRED and Surflex. Our study was extended and verified by application of our protocol to ten different data sets from the directory of useful decoys (DUD). We quantitated overall method performance in ensemble docking and compared several consensus scoring methods to improve the enrichment during virtual ligand screening. We conclude that one of the methods used, which employs a consensus weighted scoring of multiple target conformers, performs consistently better than methods that do not include such consensus scoring. For optimal overall performance in ensemble docking, it is advisable to first calculate a consensus of FRED results and use this consensus as a sub‐data set for Surflex screening. Furthermore, we identified an optimal method for each of the chosen targets and propose how to optimize the enrichment for any target.