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Our objective was to develop a comprehensive algorithm to convert all types of effect measures to one standard metric, relative risk reduction (RRR).
When necessary, we converted other effect measures to odds ratios by using available data.
According to the CONSORT statement [ 21] confidence intervals should be reported for estimated effect measures to indicate the precision of the estimates.
Other methodological developments to address data deficiencies have drawn on meta-analysis approaches where the results of selected studies that fulfill a set criterion are summarized into a few effect measures to address specific research questions.
We believe these trials should also be included if RR or OR are the effect measures to provide a more conservative estimate of effect size (even if this change in effect size is very small for RR and OR), and to provide analytic consistency and include the same number of trials in the meta-analysis, regardless of the summary effect measure used.
However, we present odds ratios as our effect measures to estimate the associations between respiratory symptoms and residential exposures (coal burning and environmental tobacco smoke); using the log-binomial model (Proc Genmod in SAS System for Windows, Version 8.01) [ 20] to estimate prevalence proportion ratios for the outcomes did not alter any of our conclusions.
Similar(54)
Because the outcome was onset of cardiovascular disease subsequent to the measurement of MBPS, the suitable effect measure to quantify the association was a hazard ratio (HR).
Meta-analytic techniques are used to combine each study's effect measure to generate a pooled effect measure.
In modeling, one main decision concerns what effect measure to apply to compute intervention outcomes.
The risk difference (b i ) is the natural effect measure to use in this situation.
In one study, adjustment for possible confounding factors attenuated the effect measure to a value above 1 [ 11].
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