Confidence intervals for the risk ratio under inverse sampling.
In this paper, we investigate various confidence intervals for the risk ratio under inverse sampling (also known as negative binomial sampling). Three existing confidence intervals (namely, the confidence intervals that are based on Fieller's theorem, the delta method and the F-statistic) are reviewed and three new confidence intervals (namely, the score, likelihood ratio and saddlepoint approximation (SA)-based confidence intervals) are developed. Comparative studies among these confidence intervals through Monte Carlo simulations are evaluated in terms of their coverage probabilities and expected interval widths under different settings. Our simulation results suggest that the SA-based confidence interval is generally more appealing. We illustrate these confidence interval construction methods with real data sets from a drug comparison study and a congenital heart disease study.
Tian
M
M
The School of Statistics, Renmin University of China, Beijing 100872, China.
Tang
M L
ML
Ng
H K T
HK
Chan
P S
PS
D016010:Binomial Distribution; D002317:Cardiovascular Agents / Q000009:adverse effects; D056486:Chemical and Drug Induced Liver Injury; D003198:Computer Simulation; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D005260:Female; D006330:Heart Defects, Congenital / Q000150:complications; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D016013:Likelihood Functions; D009010:Monte Carlo Method; D009203:Myocardial Infarction / Q000188:drug therapy; D016017:Odds Ratio; D011247:Pregnancy; D011249:Pregnancy Complications, Cardiovascular / Q000503:physiopathology; D018401:Sample Size
D002317:Cardiovascular Agents