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.

“Screening for Breast Cancer with Mammography” (2013), by Peter

Abstract: “Screening for Breast Cancer with Mammography” is a Cochrane systematic review originally published by

Response to Meissner: our preference of patient-reported to observer-reported outcomes has trivial influence on our updated systematic review of the effect of placebo

Abstract: Dear Sir, Meissner claims that our preference for patientreported outcomes substantially influences the result of our updated review of the effect of placebo [1]. This is not correct. Our main analysis of observer-reported continuous outcomes was based on 43 trials with 2254 patients and gave a pooled standardized mean difference of )0.10 (95% confidence interval )0.20 to 0.01) [2]. The lower confidence interval is close to zero, implying that the observed difference between placebo and no treatment is close to being statistically significant (P 1⁄4 0.06). We also identified 14 trials with corresponding patientand observer-reported outcomes, of which 13 had been included in the main analyses of patient-reported outcomes, according to our prepublished protocol [3]. When Meissner reversed this decision post-hoc, and pooled the additional trials with the trials from the primary analysis of observerreported outcomes, the overall standardized mean difference unsurprisingly reached statistically significance, )0.16 ()0.26 to )0.06). But more important than statistical significance is the size of the effect measure, which was quite similar )0.10 vs. )0.16. Thus, the overall point estimate is low, and fairly insensitive to whether or not the additional trials are included. However,Meissner’s inclusion of the 13 trials in the main analysis of observer-reported outcomes is inappropriate because it is a post-hoc analysis conducted after having observed the somewhat larger effect in the subgroup of trials with corresponding patientreported outcomes. We decided a priori to prefer patient-reported outcomes in the main analyses as such outcomes are often more relevant to patients, and this analysis should therefore be the most important when interpreting the result. Furthermore, Meissner claims that we have suggested that effects of placebo interventions on objective outcomes is a myth ; and refer to our main result as there is no objective placebo effect . We have never made such suggestions. To find no evidence of an effect is not the same as evidence of no effect, and we cautiously concluded that we did not find a statistically significant pooled effect of placebo on observer-reported outcomes. Finally, observing a small tendency for improvement in the placebo group, even assuming no effect of placebo on observer-reported outcomes, is to be expected. The comparison between placebo and no-treatment is not blinded, and thus susceptible to various forms of bias, for example observation bias [4]. It is possible that further updates will produce enough statistical power to rule out the observed minor improvement as a random event. However, a possible true minor effect of placebo on observer-reported outcomes has to be distinguished from the probable bias induced by lack of blinding.

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