Description: The study of genetic influences on drug response and efficacy (‘pharmacogenetics’) has existed for over 50 years. Yet, we still lack a complete picture of how genetic variation, both common and rare, affects each individual's responses to medications. Exome sequencing is a promising alternative method for pharmacogenetic discovery as it provides information on both common and rare variation in large numbers of individuals. Using exome data from 2203 AA and 4300 Caucasian individuals through the NHLBI Exome Sequencing Project, we conducted a survey of coding variation within 12 Cytochrome P450 ( CYP ) genes that are collectively responsible for catalyzing nearly 75% of all known Phase I drug oxidation reactions. In addition to identifying many polymorphisms with known pharmacogenetic effects, we discovered over 730 novel nonsynonymous alleles across the 12 CYP genes of interest. These alleles include many with diverse functional effects such as premature stop codons, aberrant splicesites and mutations at conserved active site residues. Our analysis considering both novel, predicted functional alleles as well as known, actionable CYP alleles reveals that rare, deleterious variation contributes markedly to the overall burden of pharmacogenetic alleles within the populations considered, and that the contribution of rare variation to this burden is over three times greater in AA individuals as compared with Caucasians. While most of these impactful alleles are individually rare, 7.6–11.7% of individuals interrogated in the study carry at least one newly described potentially deleterious alleles in a major drug-metabolizing CYP .

Description: Objective To assess adjustment practices for primary outcomes of randomized controlled trials and their impact on the results.Design Meta-epidemiologic study.Data sources 25 biomedical journals with...

Description: Purpose Many studies have reported on the use of narrow band imaging (NBI) colonoscopy to differentiate neoplastic from non-neoplastic colorectal polyps. It has potential to replace pathological diagnosis of diminutive polyps. We aimed to perform a systematic review and meta-analysis on the real-time diagnostic operating characteristics of NBI colonoscopy. Methods We searched PubMed, SCOPUS and Cochrane databases and abstracts. We used a two-level bivariate meta-analysis following a random effects model to summarise the data and fit hierarchical summary receiver-operating characteristic (HSROC) curves. The area under the HSROC curve serves as an indicator of the diagnostic test strength. We calculated summary sensitivity, specificity and negative predictive value (NPV). We assessed agreement of surveillance interval recommendations based on endoscopic diagnosis compared to pathology. Results For NBI diagnosis of colorectal polyps, the area under the HSROC curve was 0.92 (95% CI 0.90 to 0.94), based on 28 studies involving 6280 polyps in 4053 patients. The overall sensitivity was 91.0% (95% CI 87.6% to 93.5%) and specificity was 82.6% (95% CI 79.0% to 85.7%). In eight studies (n=2146 polyps) that used high-confidence diagnostic predictions, sensitivity was 93.8% and specificity was 83.3%. The NPVs exceeded 90% when 60% or less of all polyps were neoplastic. Surveillance intervals based on endoscopic diagnosis agreed with those based on pathology in 92.6% of patients (95% CI 87.9% to 96.3%). Conclusions NBI diagnosis of colorectal polyps is highly accurate—the area under the HSROC curve exceeds 0.90. High-confidence predictions provide 〉90% sensitivity and NPV. It shows high potential for real-time endoscopic diagnosis.

Description: Many biases affect scientific research, causing a waste of resources, posing a threat to human health, and hampering scientific progress. These problems are hypothesized to be worsened by lack of consensus on theories and methods, by selective publication processes, and by career systems too heavily oriented toward productivity, such as...

Description: Several commonly used medications have been associated with increased cancer risk in the literature. Here, we evaluated the strength and consistency of these claims in published meta-analyses. We carried out an umbrella review of 74 meta-analysis articles addressing the association of commonly used medications (antidiabetics, antihyperlipidemics, antihypertensives, antirheumatics, drugs for osteoporosis, and others) with cancer risk where at least one meta-analysis in the medication class included some data from randomized trials. Overall, 51 articles found no statistically significant differences, 13 found some decreased cancer risk, and 11 found some increased risk (one reported both increased and decreased risks). The 11 meta-analyses that found some increased risks reported 16 increased risk estimates, of which 5 pertained to overall cancer and 11 to site-specific cancer. Six of the 16 estimates were derived from randomized trials and 10 from observational data. Estimates of increased risk were strongly inversely correlated with the amount of evidence (number of cancer cases) (Spearman's correlation coefficient = –0.77, P 〈 0.001). In 4 of the 16 topics, another meta-analysis existed that was larger ( n = 2) or included better controlled data ( n = 2) and in all 4 cases there was no statistically significantly increased risk of malignancy. No medication or class had substantial and consistent evidence for increased risk of malignancy. However, for most medications we cannot exclude small risks or risks in population subsets. Such risks are unlikely to be possible to document robustly unless very large, collaborative studies with standardized analyses and no selective reporting are carried out.

Description: Introduction Studies of the diagnostic accuracy of depression screening tools often used data-driven methods to select optimal cut-offs. Typically, these studies report results from a small range of cut-off points around whatever cut-off score is identified as most accurate. When published data are combined in meta-analyses, estimates of accuracy for different cut-off points may be based on data from different studies, rather than data from all studies for each cut-off point. Thus, traditional meta-analyses may exaggerate accuracy estimates. Individual patient data (IPD) meta-analyses synthesise data from all studies for each cut-off score to obtain accuracy estimates. The 10-item Edinburgh Postnatal Depression Scale (EPDS) is commonly recommended for depression screening in the perinatal period. The primary objective of this IPD meta-analysis is to determine the diagnostic accuracy of the EPDS to detect major depression among women during pregnancy and in the postpartum period across all potentially relevant cut-off scores, accounting for patient factors that may influence accuracy (age, pregnancy vs postpartum). Methods and analysis Data sources will include Medline, Medline In-Process & Other Non-Indexed Citations, PsycINFO, and Web of Science. Studies that include a diagnosis of major depression based on a validated structured or semistructured clinical interview administered within 2 weeks of (before or after) the administration of the EPDS will be included. Risk of bias will be assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Bivariate random-effects meta-analysis will be conducted for the full range of plausible cut-off values. Analyses will evaluate data from pregnancy and the postpartum period separately, as well as combining data from all women in a single model. Ethics and dissemination This study does not require ethics approval. Dissemination will include journal articles and presentations to policymakers, healthcare providers and researchers. Systematic review registration PROSPERO 2015:CRD42015024785.

Description: Medical and scientific advances are predicated on new knowledge that is robust and reliable and that serves as a solid foundation on which further advances can be built. In biomedical research, we are in the midst of a revolution with the generation of new data and scientific publications at a previously unprecedented rate. However, unfortunately, there is compelling evidence that the majority of these discoveries will not stand the test of time. To a large extent, this reproducibility crisis in basic and preclinical research may be as a result of failure to adhere to good scientific practice and the desperation to publish or perish. This is a multifaceted, multistakeholder problem. No single party is solely responsible, and no single solution will suffice. Here we review the reproducibility problems in basic and preclinical biomedical research, highlight some of the complexities, and discuss potential solutions that may help improve research quality and reproducibility.

Description: Objectives Evaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures like 2 or I 2 , but their clinical interpretation is not straightforward. A prediction interval is less complicated: it presents the expected range of true effects in similar studies. We aimed to show the advantages of having the prediction interval routinely reported in meta-analyses. Design We show how the prediction interval can help understand the uncertainty about whether an intervention works or not. To evaluate the implications of using this interval to interpret the results, we selected the first meta-analysis per intervention review of the Cochrane Database of Systematic Reviews Issues 2009–2013 with a dichotomous (n=2009) or continuous (n=1254) outcome, and generated 95% prediction intervals for them. Results In 72.4% of 479 statistically significant (random-effects p〈0.05) meta-analyses in the Cochrane Database 2009–2013 with heterogeneity (I 2 〉0), the 95% prediction interval suggested that the intervention effect could be null or even be in the opposite direction. In 20.3% of those 479 meta-analyses, the prediction interval showed that the effect could be completely opposite to the point estimate of the meta-analysis. We demonstrate also how the prediction interval can be used to calculate the probability that a new trial will show a negative effect and to improve the calculations of the power of a new trial. Conclusions The prediction interval reflects the variation in treatment effects over different settings, including what effect is to be expected in future patients, such as the patients that a clinician is interested to treat. Prediction intervals should be routinely reported to allow more informative inferences in meta-analyses.

Description: Objectives Meta-analyses are considered generally as the highest level of evidence, but concerns have been voiced about their massive, low-quality production. This paper aimed to evaluate the landscape of meta-analyses in the field of occupational and environmental health and medicine. Methods Using relevant search terms, all meta-analyses were searched for, but those published in 2015 were assessed for their origin, whether they included randomised trials and individual-level data and whether they had authors from the industry or consultancy firms. Results PubMed searches (last update February 2017) identified 1251 eligible meta-analyses in this field. There was a rapid increase over time (n=16 published in 1995 vs n=163 published in 2015). Of the 163 eligible meta-analyses published in 2015, 49 were from China, followed at a distance by the USA (n=19). Only 16 considered randomised (intervention) trials and 13 included individual-level data. Only 1 of the 150 meta-analyses had industry authors and none had consultancy firm authors. As an example of conflicting findings, 12 overlapping meta-analyses addressed mobile phones and brain cancer risk and they differed substantially in number of studies included, eligibility criteria and conclusions. Conclusions There has been a major increase in the publication of meta-analyses in occupational and environmental health over time, with the majority of these studies focusing on observational data, while a commendable fraction used individual-level data. Authorship is still limited largely to academic and non-profit authors. With massive production of meta-analyses, redundancy needs to be anticipated and efforts should be made to safeguard quality and protect from bias.

Description: Objective To assess the quantity and distribution of evidence from randomised controlled trials for the treatment of the major neglected tropical diseases and to identify gaps in the evidence with...