Tilburg University

About: Tilburg University is a education organization based out in Tilburg, Noord-Brabant, Netherlands. It is known for research contribution in the topics: Population & Context (language use). The organization has 5550 authors who have published 22330 publications receiving 791335 citations.

Meta-analysis using effect size distributions of only statistically significant studies.

Abstract: Publication bias threatens the validity of meta-analytic results and leads to overestimation of the effect size in traditional meta-analysis. This particularly applies to meta-analyses that feature small studies, which are ubiquitous in psychology. Here we develop a new method for meta-analysis that deals with publication bias. This method, p-uniform, enables (a) testing of publication bias, (b) effect size estimation, and (c) testing of the null-hypothesis of no effect. No current method for meta-analysis possesses all 3 qualities. Application of p-uniform is straightforward because no additional data on missing studies are needed and no sophisticated assumptions or choices need to be made before applying it. Simulations show that p-uniform generally outperforms the trim-and-fill method and the test of excess significance (TES; Ioannidis & Trikalinos, 2007b) if publication bias exists and population effect size is homogenous or heterogeneity is slight. For illustration, p-uniform and other publication bias analyses are applied to the meta-analysis of McCall and Carriger (1993) examining the association between infants' habituation to a stimulus and their later cognitive ability (IQ). We conclude that p-uniform is a valuable technique for examining publication bias and estimating population effects in fixed-effect meta-analyses, and as sensitivity analysis to draw inferences about publication bias.

Current Guidelines Have Limited Applicability to Patients with Comorbid Conditions: A Systematic Analysis of Evidence-Based Guidelines

Abstract: BACKGROUND: Guidelines traditionally focus on the diagnosis and treatment of single diseases. As almost half of the patients with a chronic disease have more than one disease, the applicability of guidelines may be limited. The aim of this study was to assess the extent that guidelines address comorbidity and to assess the supporting evidence of recommendations related to comorbidity. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a systematic analysis of evidence-based guidelines focusing on four highly prevalent chronic conditions with a high impact on quality of life: chronic obstructive pulmonary disease, depressive disorder, diabetes mellitus type 2, and osteoarthritis. Data were abstracted from each guideline on the extent that comorbidity was addressed (general comments, specific recommendations), the type of comorbidity discussed (concordant, discordant), and the supporting evidence of the comorbidity-related recommendations (level of evidence, translation of evidence). Of the 20 guidelines, 17 (85%) addressed the issue of comorbidity and 14 (70%) provided specific recommendations on comorbidity. In general, the guidelines included few recommendations on patients with comorbidity (mean 3 recommendations per guideline, range 0 to 26). Of the 59 comorbidity-related recommendations provided, 46 (78%) addressed concordant comorbidities, 8 (14%) discordant comorbidities, and for 5 (8%) the type of comorbidity was not specified. The strength of the supporting evidence was moderate for 25% (15/59) and low for 37% (22/59) of the recommendations. In addition, for 73% (43/59) of the recommendations the evidence was not adequately translated into the guidelines. CONCLUSIONS/SIGNIFICANCE: Our study showed that the applicability of current evidence-based guidelines to patients with comorbid conditions is limited. Most guidelines do not provide explicit guidance on treatment of patients with comorbidity, particularly for discordant combinations. Guidelines should be more explicit about the applicability of their recommendations to patients with comorbidity. Future clinical trials should also include patients with the most prevalent combinations of chronic conditions.