The Robustness of Tilton's Measure of Overlap

Elster, Richard S.; Dunnette, Marvin D.

doi:10.1177/001316447103100309

Cited by 11 publications

(8 citation statements)

References 6 publications

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“…The large population sizes make even small differences (Ͻ0.1%) statistically significant, so Tilton's overlap procedure (17,18) was used to calculate the percentage overlap in the distributions of the three entitlement subpopulations and to assure that the overlaps were sufficient to allow the three subpopulations, to be treated as one. Because the data covered slightly different periods of time, as required for use by the QIOs (periods staggered by 3 months), temporal differences were examined and found to be noncontributory to differences in rates of the three measures among states.…”

Section: Statistical Adjustment Methodsmentioning

confidence: 99%

Variation in Diabetes Care Among States

Arday¹,

Fleming

Keller

et al. 2002

Diabetes Care

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OBJECTIVE -To examine state variability in diabetes care for Medicare beneficiaries and the impact of certain beneficiary characteristics on those variations. RESEARCH DESIGN AND METHODS -Medicare beneficiaries with diabetes, aged18 -75 years, were identified from 1997 to 1999 claims data. Claims data were used to construct rates for three quality of care measures (HbA 1c tests, eye examinations, and lipid profiles). Person-level variables (e.g., age, sex, race, and socioeconomic status) were used to adjust state rates using logistic regression.RESULTS -A third of 2 million beneficiaries with diabetes aged 18 -75 years did not have annual HbA 1c tests, biennial eye examinations, or biennial lipid profiles. There was wide variability in the measures among states (e.g., receipt of HbA 1c tests ranged from 52 to 83%). Adjustment using person-level variables reduced the variance in HbA 1c tests, eye examinations, and lipid profiles by 30, 23, and 27%, respectively, but considerable variability remained. The impact of the adjustment variables was also inconsistent across measures.CONCLUSIONS -Opportunities remain for improvement in diabetes care. Large variations in care among states were reduced significantly by adjustment for characteristics of state residents. However, much variability remained unexplained. Variability of measures within states and variable impact of the adjustment variables argues against systems effects operating with uniformity on the three measures. These findings suggest that a single approach to quality improvement is unlikely to be effective. Further understanding variability will be important to improving quality. Diabetes Care 25:2230 -2237, 2002D iabetes is a major cause of morbidity and mortality for millions of Medicare recipients with diagnosed and undiagnosed diabetes (1-3). Over $27 billion was spent on diabetes care for individuals aged Ն65 years in the U.S. in 1997 (4). For these reasons, the Centers for Medicare and Medicaid Services (CMS), the nation's largest purchaser of health care services, has chosen diabetes as a national priority for quality monitoring and improvement in the Medicare population (5,6). While providing national direction, CMS implements and evaluates its Medicare quality improvement program at the state level, using individual state quality improvement organizations (QIOs), formerly known as peer review organizations. This makes knowledge and understanding of statespecific differences in care important to the Medicare program and to CMS in particular.Three diabetes quality of care measures (HbA 1c tests, eye examinations, and lipid profiles) were selected as the focus of CMS quality improvement efforts in the late 1990s. Initial analysis of the 1997-1999 claims data, from which these measures were generated, demonstrated both a significant opportunity for improvement and wide variability among states (7). The median rate (and extremes) among the states for annual HbA 1c testing was 71% (low 52%, high 85%). Similarly, it was 69% (low 56%, high 80%) for bienn...

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Section: Statistical Adjustment Methodsmentioning

confidence: 99%

Variation in Diabetes Care Among States

Arday¹,

Fleming

Keller

et al. 2002

Diabetes Care

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“…in 1937, John W. Tilton (1891Tilton ( -1980 suggested that the amount of group overlap be considered-in two-group univariate mean comparisons-in determining whether two means are significantly different" (p. 232). Tilton's idea of area overlap was revisited 30 years later by Dunnette (1966), Alf and Abrahams (1968), Elster and Dunnette (1971), and later in EPM by Huberty and Holmes (1983).…”

Section: Indices Of Distribution Overlap As Effect Sizesmentioning

confidence: 99%

Extending Improvement-Over-Chance I-Index Effect Size Simulation Studies to Cover Some Small-Sample Cases

Natesan

Thompson

2007

Educational and Psychological Measurement

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All effect sizes are sensitive to design flaws and the failure to meet analytic assumptions. But some effect sizes appear to be more robust to assumption violations (e.g., homogeneity of variance). The present study extended prior Monte Carlo research by exploring the robustness of group overlap I indices at the relatively small sample sizes used in some research. I effects are statistically appealing because these indices can be applied across (a) both univariate and multivariate analyses and (b) conditions of either variance homogeneity or variance heterogeneity.

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“…661-662). Tilton's notion of group overlap as related to two-group statistical testing sat dormant for about 30 years, until it was revisited by Dunnette (1966) and Alf and Abrahams (1968) and a few years later by Elster and Dunnette (1971). Dunnette (1966) restated Tilton's idea: "The greater the amount of overlap, the less effective is the predictor in separating the two distributions" (p. 142).…”

Section: Group Overlap Indicesmentioning

confidence: 99%

“…Alf and Abrahams (1968) presented a fair bit of detail of calculating the percent of group overlap assuming two normal distributions for the outcome variable. Elster and Dunnette (1971) studied the robustness of Tilton's (1937) measure of overlap when the two distributions of outcome variable scores are nonnormal. Oakes (1986, p. 54) mentioned that the misclassified proportion was considered by Eysenck (1971, p. 34) to distinguish the theoretical interest in differences in IQ scores between races.…”

Section: Group Overlap Indicesmentioning

confidence: 99%

A History of Effect Size Indices

Huberty¹

2002

Educational and Psychological Measurement

199

119

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Depending on how one interprets what an effect size index is, it may be claimed that its history started around 1940, or about 100 years prior to that. An attempt is made in this article to trace histories of a variety of effect size indices. Effect size bases discussed pertain to (a) relationship, (b) group differences, and (c) group overlap. Multivariable as well as univariate indices are considered in reviewing the histories.

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The Robustness of Tilton's Measure of Overlap

Cited by 11 publications

References 6 publications

Variation in Diabetes Care Among States

Variation in Diabetes Care Among States

Extending Improvement-Over-Chance I-Index Effect Size Simulation Studies to Cover Some Small-Sample Cases

A History of Effect Size Indices

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