Testing the non-unity of rate ratio under inverse sampling.
Inverse sampling is considered to be a more appropriate sampling scheme than the usual binomial sampling scheme when subjects arrive sequentially, when the underlying response of interest is acute, and when maximum likelihood estimators of some epidemiologic indices are undefined. In this article, we study various statistics for testing non-unity rate ratios in case-control studies under inverse sampling. These include the Wald, unconditional score, likelihood ratio and conditional score statistics. Three methods (the asymptotic, conditional exact, and Mid-P methods) are adopted for P-value calculation. We evaluate the performance of different combinations of test statistics and P-value calculation methods in terms of their empirical sizes and powers via Monte Carlo simulation. In general, asymptotic score and conditional score tests are preferable for their actual type I error rates are well controlled around the pre-chosen nominal level, and their powers are comparatively the largest. The exact version of Wald test is recommended if one wants to control the actual type I error rate at or below the pre-chosen nominal level. If larger power is expected and fluctuation of sizes around the pre-chosen nominal level are allowed, then the Mid-P version of Wald test is a desirable alternative. We illustrate the methodologies with a real example from a heart disease study.
Tang
Man-Lai
ML
Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong. mltang@math.hkbu.edu.hk
Liao
Yi Jie
YJ
Ng
Hong Keung Tony
HK
Chan
Ping Shing
PS
D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D006330:Heart Defects, Congenital / Q000453:epidemiology*; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D016013:Likelihood Functions; D016017:Odds Ratio; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D018401:Sample Size; D013223:Statistics as Topic