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Full-Text Articles in Biostatistics
Small Area Variation Analysis. Methods For Comparing Several Diagnosis-Related Groups., Paula Diehr
Small Area Variation Analysis. Methods For Comparing Several Diagnosis-Related Groups., Paula Diehr
Paula Diehr
In small-area variation analysis, the variation of health care utilization rates, e.g., admission rates, among small areas is calculated. Frequently, the variation of one diagnosis, diagnosis-related group (DRG), or procedure is compared with the variation of another. Unfortunately, the methods generally used to make these comparisons are not consistent. They differ on whether they 1) adjust for the prevalence of the DRGs, 2) distinguish between variation among areas and variation within areas, 3) weight all areas equally, and 4) adjust for multiple admissions per person. None has an associated confidence interval. These discrepancies occur in part because there is no …
Can Small-Area Analysis Detect Variation In Surgery Rates? The Power Of Small-Area Variation Analysis., Paula Diehr
Can Small-Area Analysis Detect Variation In Surgery Rates? The Power Of Small-Area Variation Analysis., Paula Diehr
Paula Diehr
A variety of statistical methods can be used in small-area analysis to test whether there is more variation than would be expected by chance alone. However, the power of these methods to detect existing variation has never been studied. The authors used data regarding back surgery in Washington State to suggest several types of variation that might exist (alternative hypotheses), and then used computer simulation to determine the power, or the probability of detecting this variation. The chi-square test had the highest power of all methods considered against most alternative hypotheses. Power is higher if there are no multiple admissions, …
A Small Area Simulation Approach To Determining Variation In Dental Procedure Rates, Paula Diehr
A Small Area Simulation Approach To Determining Variation In Dental Procedure Rates, Paula Diehr
Paula Diehr
All small area analyses need to compare the observed variability in rates to that expected by chance alone, but the expected variability is usually not known. This paper uses patient-level data for five dental procedures to simulate the distributions of the summary statistics that are usually generated in such studies. These statistics are found to vary greatly even under the "null hypothesis" that all dentists are using procedures at the same rates. The simulated dentist rates are compared to observed rates obtained in a different study. These findings illustrate problems that can occur in small area analysis studies, and emphasize …
What Is Too Much Variation? The Null Hypothesis In Small-Area Analysis, Paula Diehr
What Is Too Much Variation? The Null Hypothesis In Small-Area Analysis, Paula Diehr
Paula Diehr
A small-area analysis (SAA) in health services research often calculates surgery rates for several small areas, compares the largest rate to the smallest, notes that the difference is large, and attempts to explain this discrepancy as a function of service availability, physician practice styles, or other factors. SAAs are often difficult to interpret because there is little theoretical basis for determining how much variation would be expected under the null hypothesis that all of the small areas have similar underlying surgery rates and that the observed variation is due to chance. We developed a computer program to simulate the distribution …
Small Area Statistics: Large Statistical Problems, Paula Diehr
Small Area Statistics: Large Statistical Problems, Paula Diehr
Paula Diehr
No abstract provided.