Statistical Issues in Assessing Hospital Performance

From the Preface: The Centers for Medicare and Medicaid Services (CMS), through a subcontract with Yale New Haven Health Services Corporation, Center for Outcomes Research and Evaluation (YNHHSC/CORE), is supporting a committee appointed by the Committee of Presidents of Statistical Societies (COPSS) to address statistical issues identified by the CMS and stakeholders about CMS’s approach to modeling hospital quality based on outcomes. In the spring of 2011, with the direct support of YNHHSC/ CORE, COPSS formed a committee comprised of one member from each of its constituent societies, a chair, and a staff member from the American Statistical Association, and held a preliminary meeting in April. In June, YNHHSC/CORE executed a subcontract with COPSS under its CMS contract to support the development of a White Paper on statistical modeling. Specifically, YNHHSC/CORE contracted with COPSS to “provide guidance on statistical approaches . . .when estimating performance metrics,” and “consider and discuss concerns commonly raised by stakeholders (hospitals, consumer, and insurers) about the use of “hierarchical generalized linear models in profiling hospital quality. The committee convened in June and August of 2011, and exchanged a wide variety of materials. To ensure the committee’s independence, YNHHSC/CORE did not comment on the white paper findings, and CMS pre-cleared COPSS’ publication of an academic manuscript based on the White Paper

An In-Class Experiment to Estimate Binomial Probabilities

This hands-on activity is appropriate for a lab or discussion section for an introductory statistics class, with 8 to 40 students. Each student performs a binomial experiment and computes a confidence interval for the true binomial probability. Teams of four students combine their results into one confidence interval, then the entire class combines results into one confidence interval. Results are displayed graphically on an overhead transparency, much like confidence intervals would be displayed in a meta-analysis. Results are discussed and generalized to larger issues about estimating binomial proportions/probabilities