Peter C Gøtzsche

Bio: Peter C Gøtzsche is an academic researcher from Cochrane Collaboration. The author has contributed to research in topics: Systematic review & Placebo. The author has an hindex of 90, co-authored 413 publications receiving 147009 citations. Previous affiliations of Peter C Gøtzsche include University of Copenhagen & Copenhagen University Hospital.

What have antidepressants been tested for? A systematic review

Abstract: Background Antidepressants are much used and have been tested for many conditions. Objective To investigate the type of diagnoses in placebo-controlled trials apart from depression and anxiety. Methods This was a systematic review. Results We downloaded 5471 records from PubMed and excluded 3017 that contained depression or anxiety. After exclusion of non-eligible studies, meta-analyses and reviews, and records that were unclear, 1273 records remained. We counted 214 unique diagnoses, of which the most common were abuse of drugs or substances (227 records), pain or neuropathy (170), obesity (125), other eating disorders (45), obsessive compulsive disorder (66), sexual dysfunction (41), gastrointestinal disorders (40), menopausal symptoms/hot flashes (36), premenstrual dysphoric disorder (27), urinary incontinence (21), post-traumatic stress disorder (38), schizophrenia (31), dementia or cognition problems (25), insomnia (19), ADHD (17), autism spectrum disorders (14), and stroke or traumatic brain injury (15). Conclusions Trials of antidepressants may be driven mainly by commercial interests, focusing on prevalent diseases and everyday problems. No one can live a full life without experiencing several of the problems for which these drugs were tested. Antidepressants, sometimes called happy pills, could be seen as the modern version of Aldous Huxley's soma pill intended to keep everyone happy in the "Brave New World".

Elementary flaws in the FH01 study.

Abstract: 1basic premises of cancer screening research are disregarded. They selected a group of women screened between 2003 and 2007, who were at increased risk of dying from breast cancer because of their family history, and compared them with another group at increased risk, selected from the unscreened group in the UK Age Trial during 1991–99. They used tumour characteristics to predict 10-year survival (which they converted to 10-year mortality). The compared groups diff er greatly, and how Duff y and co-workers tried to make them comparable is unclear. Some of the detailed inclusion and exclusion criteria that applied to the screened group, such as family history, were largely unavailable for the UK Age Trial, the age-groups studied diff ered, and the women were screened for only 4 years, whereas the cancers in the UK Age Trial were identifi ed over 8 years. Duff y and co-workers conceal

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

Abstract: David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.

Abstract: Systematic reviews and meta-analyses have become increasingly important in health care. Clinicians read them to keep up to date with their field,1,2 and they are often used as a starting point for developing clinical practice guidelines. Granting agencies may require a systematic review to ensure there is justification for further research,3 and some health care journals are moving in this direction.4 As with all research, the value of a systematic review depends on what was done, what was found, and the clarity of reporting. As with other publications, the reporting quality of systematic reviews varies, limiting readers' ability to assess the strengths and weaknesses of those reviews. Several early studies evaluated the quality of review reports. In 1987, Mulrow examined 50 review articles published in 4 leading medical journals in 1985 and 1986 and found that none met all 8 explicit scientific criteria, such as a quality assessment of included studies.5 In 1987, Sacks and colleagues6 evaluated the adequacy of reporting of 83 meta-analyses on 23 characteristics in 6 domains. Reporting was generally poor; between 1 and 14 characteristics were adequately reported (mean = 7.7; standard deviation = 2.7). A 1996 update of this study found little improvement.7 In 1996, to address the suboptimal reporting of meta-analyses, an international group developed a guidance called the QUOROM Statement (QUality Of Reporting Of Meta-analyses), which focused on the reporting of meta-analyses of randomized controlled trials.8 In this article, we summarize a revision of these guidelines, renamed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses), which have been updated to address several conceptual and practical advances in the science of systematic reviews (Box 1). Box 1 Conceptual issues in the evolution from QUOROM to PRISMA

Bias in meta-analysis detected by a simple, graphical test

Abstract: Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews . Main outcome measure: Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. Results: In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. Conclusions: A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution. Key messages Systematic reviews of randomised trials are the best strategy for appraising evidence; however, the findings of some meta-analyses were later contradicted by large trials Funnel plots, plots of the trials9 effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials Funnel plot asymmetry was found in 38% of meta-analyses published in leading general medicine journals and in 13% of reviews from the Cochrane Database of Systematic Reviews Critical examination of systematic reviews for publication and related biases should be considered a routine procedure