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At least 559 records · Page 31Linked to original sources
PubMed1987

Determination of radiology staffing needs by regression analysis.

In the past, analysis of staffing needs has caused friction between radiology departments and hospital administration. To overcome this difficulty, the department of radiology at the University of New Mexico developed a regression-based analysis which ties departmental staffing needs to hospital statistics. Patient days and number of facility outpatient visits are used to project procedure statistics and staffing needs.

Decision Making↗
PubMed1992

Multiple regression analysis of twin data: a model-fitting approach.

The multiple regression methodology proposed by DeFries and Fulker (DF; 1985, 1988) for the analysis of twin data is compared with maximum-likelihood estimation of genetic and environmental parameters from covariance structure. Expectations for the regression coefficients from submodels omitting the h2 and c2 terms are derived. Model comparisons similar to those conducted using maximum-likelihood estimation procedures are illustrated using multiple regression. Submodels of the augmented DF model are shown to yield parameter estimates highly similar to those obtained from the traditional latent variable model. While maximum-likelihood estimation of covariance structure may be the optimal statistical method of estimating genetic and environmental parameters, the model-fitting approach we propose is a useful extension to the highly flexible and conceptually simple DF methodology.

Aptitude↗
PubMed1992

The effect of influential outliers on parameter estimation in regression analysis.

A comment is made on an article recently published in this journal by Borgeat, Elie, and Castonguay (1991). It is noted that the raw data contained an outlier which is shown to have had a large influence on their estimation of the regression coefficient. Analysis of the data using a nonparametric statistic that is optimal for long-tailed distributions showed that the true regression coefficient is probably smaller than that reported. Other approaches to the analysis of data containing influential outliers are discussed.

Biofeedback, Psychology↗
PubMed1989

Improvement of Durie & Salmon staging for multiple myeloma by adding platelet count as a stratifying variable: a multivariate regression analysis of 163 untreated patients.

The presenting clinical features of 163 previously untreated patients with multiple myeloma were correlated with survival duration using univariate and multivariate regression analyses. The univariate proportional hazard analysis ranked the parameters in the following order of importance: platelet count, haemoglobin level (Hb), tumour cell mass stage, lytic bone lesions, creatinine and age. When the individual contribution of each variable was assessed by multivariate regression analysis, platelet count was confirmed to be the dominant feature for prognosis, while clinical stage provided additional information. The introduction of platelet count could then be used to improve the discriminating power of Durie & Salmon staging, by allowing separation of the high-risk group (stages II and III) into a smaller subgroup (22%) of thrombocytopenic patients (less than 150 x 10(9) platelets/l) whose risk of death was actually very high (median survival: 9 months) and a larger subgroup (46%) of patients with normal platelet count and intermediate or standard risk (median survival: 48 months).

Humans↗
PubMed1991

[Multi-center evaluation of Showa disk susceptibility to presumptively determine minimum inhibitory concentrations through linear regression analysis].

To confirm the reliability of minimum inhibitory concentrations (MICs) determined by use of predefined linear regressions to bacterial growth inhibitory zone diameter on Showa disk susceptibility test, the multi-center evaluation along daily routines was performed in comparison with the standard agar dilution method. In total, 4,107 (89.0%) of 4,613 testings gave comparable MICs with 4-fold or less differences to those determined by the standard agar dilutions. The agreement of MICs (less than or equal to 4-fold differences) for gram-negative rods, excluding Pseudomonas aeruginosa, against 10 antimicrobial agents was estimated to 92.1%, and those for gram-positive cocci against 9 agents and for the strains of Pseudomonas aeruginosa against 8 agents were 84.5% and 81.7%, respectively. With these results, we can conclude that, under the well-controlled test procedures, the MIC correlates determined by Showa disk susceptibility test are enough comparable to those determined by the standard agar dilution method.

Anti-Bacterial Agents↗
PubMedDate not supplied

An application of ridge regression analysis in the study of syphilis data.

We provide a brief summary of the theory of ridge regression, regress reported rates of congenital syphilis on three stages of adult syphilis using both ridge regression and least squares techniques, and discuss the results. We also develop a principal components regression model for the same example and compare the results. The example demonstrates the usefulness of ridge regression to handle multicollinearity in data. Problems of overestimation and instability associated with ordinary least squares estimates of these non-orthogonal data diminish with use of the ridge regression techniques.

Adult↗
PubMed2006

Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis.

OBJECTIVE: To use routine data to identify patients at high risk of future emergency hospital admissions. DESIGN: Descriptive analysis of inpatient hospital episode statistics. Predictive model developed using multiple logistic regression. SETTING: National Health Service hospital trusts in England. PARTICIPANTS: All patients with an emergency admission to an NHS hospital between 1 April 2000 and 31 March 2001. MAIN OUTCOME MEASURES: 'High-impact users' were defined as patients who had at least one emergency inpatient admission and who then went on to have at least two further emergency hospital admissions in the 12 months following the start date of that index admission. RESULTS: 2,895,234 patients were admitted as emergencies in 2000/2001, of whom 147,725 (5.1%) did not survive their first spell. Of the 2,747,509 surviving patients, 269,686 (9.8%) subsequently had at least two or more emergency admissions within 365 days of the index date of admission. A further 236,779 (8.6%) died during this period. Risk factors for becoming a high-impact user included the number of emergencies in the 36 months before index spell, comorbidity, age, an admission for an ambulatory care sensitive condition, ethnicity, area-level socioeconomic data, local admission rates, the number of episodes in the index spell, sex and the source of admission. The predictive model based on all emergency admissions produced a receiver operating characteristic curve score of 0.72. CONCLUSIONS: Routine hospital episode statistics can be used to identify patients who are at high risk of suffering future multiple emergency hospital admissions. The potential cost savings in preventing a proportion of these subsequent admissions need to be compared with the costs of case management of these patients.

Adolescent↗