International journal of epidemiology
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Disparities in health status between Aboriginal and Torres Strait Islander peoples and the total Australian population have been documented in a fragmentary manner using disparate health outcome measures. ⋯ Comprehensive information on the burden of disease for Indigenous Australians is essential for informed health priority setting. This assessment has identified large health gaps which translate into opportunities for large health gains. It provides the empirical base to determine a more equitable and efficient funding of Indigenous health in Australia. The methods are replicable and would benefit priority setting in other countries with great disparities in health experienced by Indigenous peoples or other disadvantaged population groups.
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Random error may cause misleading evidence in meta-analyses. The required number of participants in a meta-analysis (i.e. information size) should be at least as large as an adequately powered single trial. Trial sequential analysis (TSA) may reduce risk of random errors due to repetitive testing of accumulating data by evaluating meta-analyses not reaching the information size with monitoring boundaries. This is analogous to sequential monitoring boundaries in a single trial. ⋯ Many apparently conclusive Cochrane neonatal meta-analyses may become inconclusive when the statistical analyses take into account the risk of random error due to repetitive testing.
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Results from apparently conclusive meta-analyses may be false. A limited number of events from a few small trials and the associated random error may be under-recognized sources of spurious findings. The information size (IS, i.e. number of participants) required for a reliable and conclusive meta-analysis should be no less rigorous than the sample size of a single, optimally powered randomized clinical trial. If a meta-analysis is conducted before a sufficient IS is reached, it should be evaluated in a manner that accounts for the increased risk that the result might represent a chance finding (i.e. applying trial sequential monitoring boundaries). ⋯ Evaluating statistical inference with trial sequential monitoring boundaries when meta-analyses fall short of a required IS may reduce the risk of false positive results and important inaccurate effect estimates.
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Several approaches are available for evaluating heterogeneity in meta-analysis. Sensitivity analyses are often used, but these are often implemented in various non-standardized ways. ⋯ The proposed algorithms can be routinely applied in meta-analyses as standardized sensitivity analyses for heterogeneity. Caution is needed evaluating post hoc which specific studies are responsible for the heterogeneity.