Approximate and Asymptotic Confidence Intervals for Epdemiologic Rate under Inverse Sampling
逆抽样下流行病发病率的逼近与渐近置信区间

One of the important tasks in epidemiological investigations is to estimate the prevalence of the disease. As confidence interval estimator is one way to represent how ``good" an estimate is, it is an important reminder of the limitations of the estimates. In this paper, we'll explore seven approximate and asymptotic confidence interval estimators for epidemiologic rate underinverse sampling. Extensive comparisons of their performance is completedby Monte Carlo simulation. To facilitate further the application of the results given in this paper, we present lots of tables which clearly indicate the minimum required number of cases for the ratio of the expected size of a confidence interval. Simulation results show that these approximate and asymptotic methods are better than exact ones for interval estimation of the epidemic rate $p$ in the case of the stability of coverage probability and size. Applications to real data are also presented.