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.

Impaired reproduction after exposure to ADHD drugs: Systematic review of animal studies.

Abstract: Background Few studies have reported on long-term harms caused by ADHD drugs but they are known to impair growth. Objective To assess whether ADHD drugs impair reproduction in mammals. Methods Systematic review of reproduction in studies of animals treated with ADHD drugs. Data sources PubMed, Biosis and EMBASE. Results We included 17 studies. The studies were generally of poor quality or poorly reported. Two studies reported the use of one of three advised randomisation methods. Fifteen studies used placebo which suggested blinding. On clonidine, the ability to produce offspring was reduced for male rats, which approached two females each. In one study, 10 treated rats produced no offspring while all four controls did. In another study, 10 treated rats impregnated nine females while 10 controls impregnated 16. On methylphenidate, vaginal opening was delayed in two studies (in one, the mean difference was 4.0 days, 95% CI 2.5 to 5.6, and number of estrous cycles was halved; in the other, the minimum delay was 6 days), while in two other studies no difference occurred. Generally, the impairments improved after a drug-free period and were less pronounced when treatment started later in life. Conclusion ADHD drugs impair the reproduction in animals.

Trend towards decreased survival in patients infected with HIV resistant to zidovudine.

Abstract: The survival of 35 patients with AIDS or advanced HIV infection on treatment with zidovu-dine was related to the viral sensitivity to the drug and to the CD4+ cell count. 14 patients died, the survivors were followed up for an average of 804 days. In a univariate Cox model, survival was strongly related to Iog IC90 (p = 0.0003) and to the CD4+ count (p = 0.0002). In a bivariate model, Iog IC90 and the CD4+ count contributed to the prediction of survival (p = 0.12 and 0.06, respectively). Large studies of combination or alternation therapy with several anti-HIV drugs should be given high priority.

Wampold et al.'s reiterate spin in the conclusion of a re‐analysis of placebo versus no‐treatment trials despite similar results as in original review

Abstract: In an earlier comment (Hrobjartsson & Gotzsche, this issue) we pointed out that Wampold et al.'s conclusion “the placebo effect was robust” (2005) was not substantiated by their analysis, which came to essentially the same result as our original analysis (2001). Wampold et al. replied (this issue) that their conclusion was “… not made based on the magnitude of effect … but on a pattern of results interpreted in the context of the theory of placebo effects, the nature of the studies reviewed, other literature, and a pattern of results that corroborate predictions” (Wampold et al., this issue). In this follow-up commentary we argue that an estimation of how robust the effect of placebo is should primarily rest on the magnitude and reproducibility of the effect. We also comment on other aspects of Wampold et al.'s reply, for example that Wampold et al.'s critique of our review is not persuasive as their analysis came to essentially the same result, indicating that the difference in methodological and theoretical approaches had little importance. © 2007 Wiley Periodicals, Inc. J Clin Psychol 63: 405–408, 2007.

A totally new system is needed for drug research and development.

Abstract: In response to my paper, where I call for abolishment of patents and for drug research and development as a public enterprise, Raj asserts that there is an increase in novel drug discoveries. The fact is that even the drug industry laments that drug innovation has dried out, which is why they buy start-up companies. Big Pharma doesn’t invest in innovative research, as it is far more profitable to have creative marketing and legal departments than creative research divisions and to develop an endless array of “me too” products. I referred to France because Prescrire is one of the very few journals where none of the editors or authors are allowed to have conflicts of interest in relation to the drug industry. The French results have global value, as the drug market is global, and I believe it is clear that I have tried to address the global problems we are facing with development, pricing and usage of drugs and not only those in high-income countries. Since 1981, Prescrire’s Pilule d’Or (Golden Pill) has been granted to drugs that constitute a major therapeutic advance in a field in which no treatment was previously available. There was no worthy candidate for 1982, 1984, 1985, 1990, 1991, from 1993 to 1995, for 1997, from 1999 to 2005, from 2008 to 2013, and for 2015. The statement by Raj that novel drugs for infectious diseases come second after cancer drugs among new FDA approvals is not reassuring. Everywhere, people complain about antibiotic resistance and the lack of innovative antibiotics, and no matter how many “novel” drugs FDA has approved, the clinical situation has not improved during the last 20 years. Patients die in huge numbers because we do not have effective antibiotics against tuberculosis, methicillinresistant staphylococci, beta-lactamase-producing E. coli, carbapenemase-producing organisms and so forth. The paper Raj refers to only says that 14% of the novel drugs were for infections, not whether these drugs were antibiotics, immunoglobulins, vaccines, or something else, or whether they were simply “me too” drugs with no added value. It is similarly misleading when Raj praises progress against cancer because extremely few cancer drugs add anything of value. Raj asserts that around half of the newer drugs were approved for acute and intermediate disease conditions and argues that this contradicts my statement that the industry tends to focus on drugs to treat chronic conditions that affect many people. Raj seems to have a naive view about what the drug industry does and why. Illegal marketing is one of the most common and profitable crimes the drug industry commits, and many drugs that should only have been used short-term end up being used for life, for example psychiatric drugs, although the long-term use of these drugs causes vastly more harm than good. Raj believes I underestimate the importance of adaptive trial designs. These designs allow drugs to be approved based on observational data only, which is a disaster for public health. Our drugs are already the third leading cause of death after heart disease and cancer, and the lowering of regulatory standards for new cancer drugs and other drugs has increased the rate of drug withdrawals because of safety issues and has undoubtedly increased the death toll further. Considering all the unnecessary deaths we cause with drugs most people don’t really need, we must require that trials submitted for obtaining marketing authorization are large enough and have run for sufficient lengths of time to capture rare but lethal harms and we should avoid approving drugs based on surrogate outcomes. We do not approve cars based on the fact that we can start the engine. We require more than this, in particular safety studies. But with drugs, which kill about 10 times more people in my country than cars do, we do not care that we have not requested adequate safety data. This is insane. Raj argues for a holistic approach where nondrug and drug therapies complement each other rather than considering them as mutually exclusive components. Fine, but first we must stop the lethal drug epidemic, which means using drugs far less than we do today. Whenever we can, we should prefer nondrug therapies, as they don’t kill people. [The copyright line for this article was changed on 25 July 2018 after original online publication.]

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