The rapid growth of human genetics creates countless opportunities for studies of disease association. Given the number of potentially identifiable genetic markers and the multitude of clinical outcomes to which these may be linked, the testing and validation of statistical hypotheses in genetic epidemiology is a task of unprecedented scale. Meta-analysis provides a quantitative approach for combining the results of various studies on the same topic, and for estimating and explaining their diversity. Here, we have evaluated by meta-analysis 370 studies addressing 36 genetic associations for various outcomes of disease. We show that significant between-study heterogeneity (diversity) is frequent, and that the results of the first study correlate only modestly with subsequent research on the same association. The first study often suggests a stronger genetic effect than is found by subsequent studies. Both bias and genuine population diversity might explain why early association studies tend to overestimate the disease protection or predisposition conferred by a genetic polymorphism. We conclude that a systematic meta-analytic approach may assist in estimating population-wide effects of genetic risk factors in human disease.

Despite good correlation between randomized trials and nonrandomized studies-in particular, prospective studies-discrepancies beyond chance do occur and differences in estimated magnitude of treatment effect are very common.

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DILEMMAIt has long been an axiom in clinical pediatrics that "children are not just little adults." It has also been recognized that there are many changes from birth through childhood and the adolescent years. However, the full implications of pediatric age groupings for health care and research are still not adequately understood. There is still much to be discovered about children' s biological and psychological development and how these processes affect the effectiveness and efficacy of interventions. Trial design that accounts for age differences and promotes consistency in reporting of age-related data is essential to ensure the validity and clinical usefulness of pediatric trial data.A recent study highlighted variable treatment efficacy in children versus adults. In this study, 128 meta-analyses from Cochrane reviews, containing data on at least 1 adult and 1 pediatric randomized controlled trial (RCT) with a binary primary efficacy outcome, were reviewed. 1 The authors found that in all except 1 case, the 95% confidence intervals could not exclude a relative difference in treatment efficacy between adults and children of .20%; in two-thirds of these cases, the relative difference in observed point estimates was .50%. The study also highlighted the paucity of RCTs in pediatrics; the median number of children per metaanalysis was 2.5 times smaller than the number of adults.Children and adults seem to have distinctive responses to treatments. For example, administration of phenobarbitones is useful for adults with cerebral malaria and is associated with decreased convulsions. However, in children, this drug is associated with increased 6-month mortality. Similarly, corticosteroids may offer survival benefit for adults with bacterial meningitis but not for children with the same condition. In acute traumatic brain injury, corticosteroids did not decrease mortality in adults, but there was a trend for increased mortality in children. 1 In asthma, long-acting b2-agonists decreased exacerbations in adults but tended to increase exacerbations in children. 1 Intravenous lorazepam, when compared with diazepam in status epilepticus, led to significantly more discontinuations of status in adults but not in children. It is not surprising then that although using an algorithm for extrapolation of adult data for use in pediatric drug licensing and development was found to be useful for streamlining drug development and approvals for pediatric use, complete extrapolation from adult data were only appropriate for 6% of drugs reviewed. 2 Beyond the stark contrast in the efficacy of pharmacologic interventions between children and adults, considerable variation of adverse events and morbidity can be anticipated across the pediatric age range. Authors of a recent study of pediatric drug surveillance and adverse event reporting concluded that "suspect drugs and adverse events should be evaluated in the context of age groups" because both drug utilization and the ability to report adverse events vary by age. 3 For example, t...

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Objective To determine how often health surveys and quality of life evaluations reach different conclusions from those of primary efficacy outcomes and whether discordant results make a difference in the interpretation of trial findings. Design Systematic review. Data sources PubMed, contact with authors for missing information, and author survey for unpublished SF-36 data. Study selection Randomised trials with SF-36 outcomes (the most extensively validated and used health survey instrument for appraising quality of life) that were published in 2005 in 22 journals with a high impact factor. Data extraction Analyses on the two composite and eight subdomain SF-36 scores that corresponded to the time and mode of analysis of the primary efficacy outcome. Results Of 1057 screened trials, 52 were identified as randomised trials with SF-36 results (66 separate comparisons). Only eight trials reported all 10 SF-36 scores in the published articles. For 21 of the 66 comparisons, SF-36 results were discordant for statistical significance compared with the results for primary efficacy outcomes. Of 17 statistically significant SF-36 scores where primary outcomes were not also statistically significant in the same direction, the magnitude of effect was small in six, moderate in six, large in three, and not reported in two. Authors modified the interpretation of study findings based on SF-36 results in only two of the 21 discordant cases. Among 100 additional randomly selected trials not reporting any SF-36 information, at least five had collected SF-36 data but only one had analysed it. Conclusions SF-36 measurements sometimes produce different results from those of the primary efficacy outcomes but rarely modify the overall interpretation of randomised trials. Quality of life and health related survey information should be utilised more systematically in randomised trials.

Recent concerns about the reproducibility of science have led to several calls for more open and transparent research practices and for the monitoring of potential improvements over time. However, with tens of thousands of new biomedical articles published per week, manually mapping and monitoring changes in transparency is unrealistic. We present an open-source, automated approach to identify 5 indicators of transparency (data sharing, code sharing, conflicts of interest disclosures, funding disclosures, and protocol registration) and apply it across the entire open access biomedical literature of 2.75 million articles on PubMed Central (PMC). Our results indicate remarkable improvements in some (e.g., conflict of interest [COI] disclosures and funding disclosures), but not other (e.g., protocol registration and code sharing) areas of transparency over time, and map transparency across fields of science, countries, journals, and publishers. This work has enabled the creation of a large, integrated, and openly available database to expedite further efforts to monitor, understand, and promote transparency and reproducibility in science.