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We analysed the relation between tumour sizes and stages and the reported effects on breast cancer mortality with and without screening in trials and observational studies. The average tumour sizes in all the trials suggest only a 12% reduction in breast cancer mortality, which agrees with the 10% reported in the most reliable trials. Recent studies of tumour sizes and tumour stages show that screening has not lowered the rate of advanced cancers. In agreement with this, recent observational studies of breast cancer mortality have failed to find an effect of screening. In contrast, screening leads to serious harms in healthy women through overdiagnosis with subsequent overtreatment and false-positive mammograms. We suggest that the rationale for breast screening be urgently reassessed by policy-makers. The observed decline in breast cancer mortality in many countries seems to be caused by improved adjuvant therapy and breast cancer awareness, not screening. We also believe it is more important to reduce the incidence of cancer than to detect it 'early.' Avoiding getting screening mammograms reduces the risk of becoming a breast cancer patient by one-third. [ABSTRACT FROM AUTHOR]

Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover 3 main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors, to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. Eighteen items are common to all 3 study designs and 4 are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available at http://www.annals.org and on the Web sites of PLoS Medicine and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies. [ABSTRACT FROM AUTHOR]

Context: Meta-analysis of trials that have used different continuous or rating scales to record outcomes of a similar nature requires sophisticated data handling and data transformation to a uniform scale, the standardized mean difference (SMD). It is not known how reliable such meta-analyses are.Objective: To study whether SMDs in meta-analyses are accurate.Data Sources: Systematic review of meta-analyses published in 2004 that reported a result as an SMD, with no language restrictions. Two trials were randomly selected from each meta-analysis. We attempted to replicate the results in each meta-analysis by independently calculating SMD using Hedges adjusted g.Data Extraction: Our primary outcome was the proportion of meta-analyses for which our result differed from that of the authors by 0.1 or more, either for the point estimate or for its confidence interval, for at least 1 of the 2 selected trials. We chose 0.1 as cut point because many commonly used treatments have an effect of 0.1 to 0.5, compared with placebo.Results: Of the 27 meta-analyses included in this study, we could not replicate the result for at least 1 of the 2 trials within 0.1 in 10 of the meta-analyses (37%), and in 4 cases, the discrepancy was 0.6 or more for the point estimate. Common problems were erroneous number of patients, means, standard deviations, and sign for the effect estimate. In total, 17 meta-analyses (63%) had errors for at least 1 of the 2 trials examined. For the 10 meta-analyses with errors of at least 0.1, we checked the data from all the trials and conducted our own meta-analysis, using the authors' methods. Seven of these 10 meta-analyses were erroneous (70%); 1 was subsequently retracted, and in 2 a significant difference disappeared or appeared.Conclusions: The high proportion of meta-analyses based on SMDs that show errors indicates that although the statistical process is ostensibly simple, data extraction is particularly liable to errors that can negate or even reverse the findings of the study. This has implications for researchers and implies that all readers, including journal reviewers and policy makers, should approach such meta-analyses with caution. [ABSTRACT FROM AUTHOR]

Background: Placebo treatments have been reported to help patients with many diseases, but the quality of the evidence supporting this finding has not been rigorously evaluated.Methods: We conducted a systematic review of clinical trials in which patients were randomly assigned to either placebo or no treatment. A placebo could be pharmacologic (e.g., a tablet), physical (e.g., a manipulation), or psychological (e.g., a conversation).Results: We identified 130 trials that met our inclusion criteria. After the exclusion of 16 trials without relevant data on outcomes, there were 32 with binary outcomes (involving 3795 patients, with a median of 51 patients per trial) and 82 with continuous outcomes (involving 4730 patients, with a median of 27 patients per trial). As compared with no treatment, placebo had no significant effect on binary outcomes (pooled relative risk of an unwanted outcome with placebo, 0.95; 95 percent confidence interval, 0.88 to 1.02), regardless of whether these outcomes were subjective or objective. For the trials with continuous outcomes, placebo had a beneficial effect (pooled standardized mean difference in the value for an unwanted outcome between the placebo and untreated groups, -0.28; 95 percent confidence interval, -0.38 to -0.19), but the effect decreased with increasing sample size, indicating a possible bias related to the effects of small trials. The pooled standardized mean difference was significant for the trials with subjective outcomes (-0.36; 95 percent confidence interval, -0.47 to -0.25) but not for those with objective outcomes. In 27 trials involving the treatment of pain, placebo had a beneficial effect (-0.27; 95 percent confidence interval, -0.40 to -0.15). This corresponded to a reduction in the intensity of pain of 6.5 mm on a 100-mm visual-analogue scale.Conclusions: We found little evidence in general that placebos had powerful clinical effects. Although placebos had no significant effects on objective or binary outcomes, they had possible small benefits in studies with continuous subjective outcomes and for the treatment of pain. Outside the setting of clinical trials, there is no justification for the use of placebos. [ABSTRACT FROM AUTHOR]