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.

Long-term use of antipsychotics and antidepressants is not evidence-based.

Abstract: Background The widespread use of psychiatric drugs does not appear to be evidence-based but seems to be driven mainly by commercial pressures. I studied whether two widely differing drug classes, antipsychotics and antidepressants, showed similar patterns in long-term usage. Methods I constructed usage curves over a ten-year period, from 2006 to 2016, based on data from Statistics Denmark. Results In 2006, a total of 110,235 patients deemed a prescription for an antipsychotic and 395,018 for an antidepressant, corresponding to 2.0% and 7.3% of the Danish population. Only 21,846 vs. 79,030 of these were first-time users (19.8% vs. 20.0%). The percentage of current users who redeemed a prescription for the same or a similar drug in each of the following years was remarkably similar for the two classes of drugs, and after ten years, it was 35% vs. 33%.Using the requirement that the patients identified in 2006 needed to redeem the prescription only once during the next ten years, 42% vs. 43% were taking a drug in 2016. This suggests that most patients identified at any given point in time as drug users continue taking such drugs for many years, with little or no interruption in drug intake.For first-time users, the drop in usage was much quicker. The percentage of first-time users who redeemed a prescription for the same or a similar drug in each of the following years fell to about one-third (29% vs. 36%) already after two years.Using 2011 as the starting year yielded similar results. Conclusions If we accept the evidence-based premises that antipsychotics and antidepressants do not have clinically relevant effects and that the patients dislike them, the data suggest massive overuse of the drugs, to a remarkably similar degree. We need to focus on helping patients withdraw slowly and safely from the drugs they are on instead of telling them that they need to stay on them.

Office of NHS cancer screening programme misrepresents Nordic work in breast screening row

Abstract: EDITOR—In Mayor's news story in the issue of 27 October the office of the NHS cancer screening programme in the United Kingdom misrepresents our research entirely.1 The office says that our findings of more aggressive treatment of breast cancer among screened women are based on only two studies, classified as poor quality. They are not. Numbers of mastectomies as well as numbers of tumourectomies increase when women are screened. This finding is consistent and is based on all four of the seven screening trials that have published data on this, including the two medium quality trials from Canada and Malmo.2 The office …

[Are relative risks and odds ratios in abstracts believable? Secondary publication].

Abstract: The first result in 520 abstracts that reported relative risks or odds ratios was statistically significant in 70% of the randomized trials, 84% of cohort studies and 84% of case-control studies. Many results were derived from subgroup or secondary analyses or biased selection of results. The distribution of p values in the interval 0.04-0.06 was extremely skewed for trials, but most of the significant results were wrong, very doubtful, or could be discussed. The skewness was even larger in observational studies. This study shows that significant results reported in abstracts should be interpreted with great caution.

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