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Sharp bounds on causal effects in case-control and cohort studies
MANABU KUROKI, ZHIHONG CAI and ZHI GENG
Vol. 97, No. 1 (MARCH 2010), pp. 123-132
Stable URL: http://www.jstor.org/stable/27798901
Page Count: 10
You can always find the topics here!Topics: Disease risks, Case control studies, Linear programming, Cohort studies, Mathematical monotonicity, Hormone replacement therapy, Ratios, Missing data, Prior learning, Inference
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Evaluating the causal effect of an exposure on a response from case-control and cohort studies is a major concern in epidemiological and medical research. Since causal effects are in general nonidentifiable from such studies, this paper derives bounds on two causal measures: the causal risk difference and the causal risk ratio. We use the potential response approach and a linear programming method to derive sharp bounds on the causal risk difference, and a novel fractional programming method to derive bounds on the causal risk ratio. In addition, in the presence of missing data, we consider three different missingness mechanisms and propose sharp bounds under these situations. The results provide new guidance on causal inference in case-control and cohort studies.
Biometrika © 2010 Biometrika Trust