Most of the included CDSS studies were associated with positive patient outcomes effects but with substantial differences regarding the clinical impact. A subset of 6 disease entities could be filtered in which CDSS should be given special consideration at sites where computer-assisted decision-making is deemed to be underutilized. Registration number on PROSPERO: CRD42016049946.

Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.

Background: The World Health Organisation stresses the need to collect high quality longitudinal data on rehabilitation and to improve the comparability between studies. This implies using all the information available and transparent reporting. We therefore investigated the quality of reported or planned randomised controlled trials on rehabilitation post-stroke with a repeated measure of physical functioning, provided recommendations on the presentation of results using regression parameters, and focused on the difficulties of adjustment for baseline outcome measures.

We present the package distdicho, which contains a range of commands covering the present development of the distributional method for the dichotomization of continuous outcomes. The method provides estimates with standard error of a comparison of proportions (difference, odds ratio, and risk ratio) derived, with similar precision, from a comparison of means.

BackgroundGiven the prevalence of untreated pain among cancer patients, there have been calls for more and better research in the domain. Increasingly, calls for less waste and more optimal use of trial data collected are being made. Waste of data includes non-optimal statistical analysis and non-presentation of interpretable effect size as a measure of effectiveness of an intervention which also enable comparisons across studies.MethodsWe reviewed the recent literature on randomised trials on longitudinal cancer pain to identify sources of loss of data information by collecting material on the nature of outcomes collected, analysed, the method of analysis and what was presented as a result of the trial. Illustrated with real data, we propose some guidelines on how to adequately analyse longitudinal data and report the results using mixed models.ResultsWe identified some major source of data information loss, one of which is the transformation of a continuous pain outcome. Not adjusting for the collected outcome baseline value is moreover a source of bias. Multiple testing by analysing the data cross-sectionnally at each time-point leads to loss of information and power. Finally, effect sizes reflecting the effectiveness of the intervention were never reported.ConclusionsWe identified several sources of information loss in the way longitudinal trials on pain were analysed and reported. However these problems could be easily solved by using regression methods like mixed models and presenting regression parameters to provide a concrete quantitative effect of the intervention.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-016-2818-8) contains supplementary material, which is available to authorized users.