科研之友
微信
新浪微博
Facebook
分享链接
摘要
Consider a population of N payments by Medicare to a health care provider, each payment for $4000. Suppose that an unknown number M of the payments are not justified and the other N M payments are justified. A random sample of n payments is chosen and audited to determine if money should be recouped from the provider for the unjustified payments. Medicare guidelines state that in most situations the recoupment figure be determined by the lower end of a one-sided 90% confidence interval estimate of M based on a finding of X sample payments not to be justified. An exact lower estimate L = L(X) with the property P(M) (L <= M)>= 0.90 for all M = 0, 1, ... , N - 1 is an integral component of the minimum sum method that has been used in setting recoupment figures in Medicare audits (Edwards et al. 2003).
In this article, we show the simple construction of a randomized lower estimate L R that improves upon L in the sense that L(R) >= L with probability 1 with P*(M) (L(R) <= M) = 0.90 for all M = 0, 1, ... , N - 1. In addition, we report on coverage probability and expectation from a Monte Carlo study that compares L(R) to lower estimates that are based on normal approximation.
Whereas randomized confidence intervals for the parameter pi of the Binomial distribution are well-studied and discussed in the literature, this seems not to be the case with the estimation of the parameter M of the Hypergeometric distribution.
- 出版日期2011-8
