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Estimating Risk and Rate Levels, Ratios, and Differences in Case-Control Studies
Gary King,Langche Zeng +1 more
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TL;DR: Methods that allow valid inferences about all relevant quantities of interest from either type of case-control study when completely ignorant of or only partially knowledgeable about relevant auxiliary population information are developed.
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Abstract: Classic (or "cumulative") case-control sampling designs do not admit inferences about quantities of interest other than risk ratios, and then only by making the rare events assumption. Probabilities, risk differences, and other quantities cannot be computed without knowledge of the population incidence fraction. Similarly, density (or "risk set") case-control sampling designs do not allow inferences about quantities other than the rate ratio. Rates, rate differences, cumulative rates, risks, and other quantities cannot be estimated unless auxiliary information about the underlying cohort such as the number of controls in each full risk set is available. Most scholars who have considered the issue recommend reporting more than just the relative risks and rates, but auxiliary population information needed to do this is not usually available. We address this problem by developing methods that allow valid inferences about all relevant quantities of interest from either type of case-control study when completely ignorant of or only partially knowledgeable about relevant auxiliary population information.
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References
Down with odds ratios
TL;DR: This journal reports the results of individual randomised trials in terms of relative risk reductions (RRRs) calculated by dividing the absolute difference in event rates between the control and experimental patients by the event rate for the controls.
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TL;DR: The odds ratio should, in general, give way to the incidence ratio and difference as the measures of choice for exposure effect in epidemiology.
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TL;DR: In this paper, the causes of international conflict are found to be large, stable, and replicable in dyads with large ex ante probability of conflict, and a statistical model that includes these critical features is proposed.