OBJECTIVES: The case-control study is commonly used to examine adverse drug events, in which prevalence of exposure in the source population is frequently very low. The objective of the current study was to examine the bias inherent in the odds ratio assessing the association between exposure and an adverse outcome when prevalence of exposure in the source population is extremely low. DESIGN: Monte Carlo simulations examined the effect of sample size, exposure prevalence, and magnitude of the underlying odds ratio on the bias of the estimated risk ratio, and the power to detect a non-zero risk ratio. RESULTS: Once the underlying odds ratio was at least four, the adverse effects of low prevalence of exposure was minimal. Studies with small sample sizes and low prevalence of exposure, coupled with small to moderate effect sizes, can result in biased estimates of association between exposure and disease status. With a sample size of 200 and an exposure prevalence of 0.5% in the control population, the bias in the estimated odds ratio can be as large as 115%. However, bias becomes negligible as sample size becomes large (n > or = 2000), even when prevalence of exposure is very low. Once the expected number of exposed controls is at least eight, the bias in the estimated odds ratio was no more than 5%. CONCLUSIONS: Studies with small sample sizes and low prevalence of exposure, coupled with small to moderate effect sizes can result in biased estimates of association between exposure status and adverse drug effects. However, bias becomes negligible as sample size becomes large.
OBJECTIVES: The case-control study is commonly used to examine adverse drug events, in which prevalence of exposure in the source population is frequently very low. The objective of the current study was to examine the bias inherent in the odds ratio assessing the association between exposure and an adverse outcome when prevalence of exposure in the source population is extremely low. DESIGN: Monte Carlo simulations examined the effect of sample size, exposure prevalence, and magnitude of the underlying odds ratio on the bias of the estimated risk ratio, and the power to detect a non-zero risk ratio. RESULTS: Once the underlying odds ratio was at least four, the adverse effects of low prevalence of exposure was minimal. Studies with small sample sizes and low prevalence of exposure, coupled with small to moderate effect sizes, can result in biased estimates of association between exposure and disease status. With a sample size of 200 and an exposure prevalence of 0.5% in the control population, the bias in the estimated odds ratio can be as large as 115%. However, bias becomes negligible as sample size becomes large (n > or = 2000), even when prevalence of exposure is very low. Once the expected number of exposed controls is at least eight, the bias in the estimated odds ratio was no more than 5%. CONCLUSIONS: Studies with small sample sizes and low prevalence of exposure, coupled with small to moderate effect sizes can result in biased estimates of association between exposure status and adverse drug effects. However, bias becomes negligible as sample size becomes large.
Authors: J C Roujeau; J P Kelly; L Naldi; B Rzany; R S Stern; T Anderson; A Auquier; S Bastuji-Garin; O Correia; F Locati Journal: N Engl J Med Date: 1995-12-14 Impact factor: 91.245
Authors: Mustafa A M Salih; Waleed R Murshid; Ashry Gad Mohamed; Lena C Ignacio; Julie E de Jesus; Rubana Baabbad; Hassan M El Bushra Journal: Sudan J Paediatr Date: 2014