TY - JOUR
T1 - Asymptotic confidence interval construction for risk difference under inverse sampling
AU - Tang, Man Lai
AU - Tian, Maozai
N1 - Funding Information:
The authors gratefully acknowledge the editor, the associate editor and the three referees for their constructive comments, suggestions and encouragement. This work was fully supported by a grant from the Research Grant Council of the Hong Kong Special Administration Region (project number HKBU261007) and the second author is partially supported by the National Philosophy and Social Science Foundation Grant, No. 07BTJ002 (2007–2009).
PY - 2009/1/15
Y1 - 2009/1/15
N2 - Risk difference (RD) has played an important role in a lot of biological and epidemiological investigations to compare the risks of developing certain disease or tumor for two drugs or treatments. When the disease is rare and acute, inverse sampling (rather than binomial sampling) is usually recommended to collect the binary outcomes. In this paper, we derive an asymptotic confidence interval estimator for RD based on the score statistic. To compare its performance with three existing confidence interval estimators, we employ Monte Carlo simulation to evaluate their coverage probabilities, expected confidence interval widths, and the mean difference of the coverage probabilities from the nominal confidence level. Our simulation results suggest that the score-test-based confidence interval estimator is generally more appealing than the Wald, uniformly minimum variance unbiased estimator and likelihood ratio confidence interval estimators for it maintains the coverage probability close to the desired confidence level and yields the shortest expected width in most cases. We illustrate these confidence interval construction methods with real data sets from a drug comparison study and a congenital heart disease study.
AB - Risk difference (RD) has played an important role in a lot of biological and epidemiological investigations to compare the risks of developing certain disease or tumor for two drugs or treatments. When the disease is rare and acute, inverse sampling (rather than binomial sampling) is usually recommended to collect the binary outcomes. In this paper, we derive an asymptotic confidence interval estimator for RD based on the score statistic. To compare its performance with three existing confidence interval estimators, we employ Monte Carlo simulation to evaluate their coverage probabilities, expected confidence interval widths, and the mean difference of the coverage probabilities from the nominal confidence level. Our simulation results suggest that the score-test-based confidence interval estimator is generally more appealing than the Wald, uniformly minimum variance unbiased estimator and likelihood ratio confidence interval estimators for it maintains the coverage probability close to the desired confidence level and yields the shortest expected width in most cases. We illustrate these confidence interval construction methods with real data sets from a drug comparison study and a congenital heart disease study.
UR - http://www.scopus.com/inward/record.url?scp=56349165091&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2008.07.020
DO - 10.1016/j.csda.2008.07.020
M3 - Journal article
AN - SCOPUS:56349165091
SN - 0167-9473
VL - 53
SP - 621
EP - 631
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 3
ER -