Sample Design for Audit Populations
Michelle Norris

TL;DR
This paper introduces new statistical tools and models for designing audit samples, specifically for healthcare claim audits, addressing gaps in existing literature with methods for variance estimation, sample size calculation, and optimal stratification.
Contribution
It develops novel estimators and criteria for sample design in audit populations, including a hypergeometric process model and variance estimators under partial error assumptions.
Findings
Tools demonstrated on simulated populations
Improved variance estimation methods
Guidelines for choosing estimation techniques
Abstract
We develop several tools for the determination of sample size and design for MediCal audits. This audit setting involves a population of claims for reimbursement by a healthcare provider which need to be reviewed by an auditor to determine the correct amount for each claim. The existing literature regarding sample planning for audits is incomplete and often includes restrictive assumptions. To fill these gaps, we exploit the special relationship between the known claim amounts and the unknown post-audit amounts. We propose a hypergeometric generative process for audit populations which we use to derive estimators of variances needed for sample size determination. We further develop a criterion for choosing between simple expansion and ratio estimation and an efficient method for determining exact optimal strata breakpoints in populations with repeated values. We also derive a variance…
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Statistical Methods and Bayesian Inference · Clinical Laboratory Practices and Quality Control
