American journal of epidemiology
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Review Meta Analysis
Bias in observational studies of prevalent users: lessons for comparative effectiveness research from a meta-analysis of statins.
Randomized clinical trials (RCTs) are usually the preferred strategy with which to generate evidence of comparative effectiveness, but conducting an RCT is not always feasible. Though observational studies and RCTs often provide comparable estimates, the questioning of observational analyses has recently intensified because of randomized-observational discrepancies regarding the effect of postmenopausal hormone replacement therapy on coronary heart disease. Reanalyses of observational data that excluded prevalent users of hormone replacement therapy led to attenuated discrepancies, which begs the question of whether exclusion of prevalent users should be generally recommended. ⋯ The pooled, multivariate-adjusted mortality hazard ratio for statin use was 0.77 (95% confidence interval (CI): 0.65, 0.91) in 4 studies that compared incident users with nonusers, 0.70 (95% CI: 0.64, 0.78) in 13 studies that compared a combination of prevalent and incident users with nonusers, and 0.54 (95% CI: 0.45, 0.66) in 13 studies that compared prevalent users with nonusers. The corresponding hazard ratio from 18 RCTs was 0.84 (95% CI: 0.77, 0.91). It appears that the greater the proportion of prevalent statin users in observational studies, the larger the discrepancy between observational and randomized estimates.
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The Icelandic study of melanoma trends by Héry et al. in this issue of the Journal (Am J Epidemiol. 2010;172(7):762-767) is a fascinating analysis of an ecologic association. The authors noted a sharp increase in melanoma incidence that appeared to lag a few years behind the increased prevalence of sunbeds in Iceland. Caution, however, must be exercised in interpreting the data because of the lack of understanding of emissions of ultraviolet radiation from sunbeds and the ecologic nature of the data.
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Review Meta Analysis
Birth weight, early weight gain, and subsequent risk of type 1 diabetes: systematic review and meta-analysis.
Previous studies suggest that birth weight and weight gain during the first year of life are related to later risk of type 1 diabetes. The authors performed a systematic review and meta-analysis on these associations. Twelve studies involving 2,398,150 persons of whom 7,491 had type 1 diabetes provided odds ratios and 95% confidence intervals of type 1 diabetes associated with birth weight. ⋯ Each 1,000-g increase in birth weight was associated with a 7% increase in type 1 diabetes risk. In all studies, patients with type 1 diabetes showed increased weight gain during the first year of life, compared with controls. This meta-analysis indicates that high birth weight and increased early weight gain are risk factors for type 1 diabetes.
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The author evaluated the implications of nominal statistical significance for changing the credibility of null versus alternative hypotheses across a large number of observational associations for which formal statistical significance (p < 0.05) was claimed. Calculation of the Bayes factor (B) under different assumptions was performed on 272 observational associations published in 2004-2005 and a data set of 50 meta-analyses on gene-disease associations (752 studies) for which statistically significant associations had been claimed (p < 0.05). Depending on the formulation of the prior, statistically significant results offered less than strong support to the credibility (B > 0.10) for 54-77% of the 272 epidemiologic associations for diverse risk factors and 44-70% of the 50 associations from genetic meta-analyses. ⋯ Five of six meta-analyses with less than substantial support (B > 0.032) lost their nominal statistical significance in a subsequent (more recent) meta-analysis, while this did not occur in any of seven meta-analyses with decisive support (B < 0.01). In these large data sets of observational associations, formal statistical significance alone failed to increase much the credibility of many postulated associations. Bayes factors may be used routinely to interpret "significant" associations.
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Immortal time is a span of cohort follow-up during which, because of exposure definition, the outcome under study could not occur. Bias from immortal time was first identified in the 1970s in epidemiology in the context of cohort studies of the survival benefit of heart transplantation. It recently resurfaced in pharmaco-epidemiology, with several observational studies reporting that various medications can be extremely effective at reducing morbidity and mortality. ⋯ The author shows that for time-based, event-based, and exposure-based cohort definitions, the bias in the rate ratio resulting from misclassified or excluded immortal time increases proportionately to the duration of immortal time. The bias is more pronounced with a decreasing hazard function for the outcome event, as illustrated with the Weibull distribution compared with a constant hazard from the exponential distribution. In conclusion, observational studies of drug benefit in which computerized databases are used must be designed and analyzed properly to avoid immortal time bias.