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At least 739 records · Page 41Linked to original sources
PubMed1997

Prognostic determinants in supraglottic carcinoma: univariate and Cox regression analysis.

BACKGROUND: A series of 281 consecutive patients affected by supraglottic cancer and treated with surgery alone or with surgery followed by radiotherapy between 1983 and 1989 was reviewed to identify significant prognostic determinants. METHODS: Fifty-one variables (related to host, tumor, and treatment) were tested by univariate and multivariate analysis performed on absolute and determinate survival. RESULTS: The final model of the multivariate analysis for absolute survival included the following covariates listed in order of higher relative risk of death: extracapsular spread, involvement of the medial wall of the pyriform sinus, thyroid cartilage invasion, metachronous tumor, anesthesiologic risk according to the American Society of Anesthesiologists classification (chi 2 = 71.28 with 6 d.f., p < .00001). The definitive model for determinate survival included: extracapsular spread, involvement of the medial wall of the pyriform sinus, extralaryngeal soft tissue invasion, and thyroid cartilage invasion (chi 2 = 82.74 with 5 d.f., p < .0001). CONCLUSIONS: Extracapsular spread was the most important factor affecting the prognosis of patients with supraglottic carcinoma. A second important finding was that T and N category did not emerge as a significant independent prognostic predictor at multivariate analysis. The negative impact on absolute survival of physical status and metachronous tumor could be the expression of the influence of concomitant diseases on survival. These observations concur to reinforce the concept that the current TNM classification is rather inadequate in predicting the prognosis of patients with supraglottic carcinoma when the aforementioned variables are considered.

Adult↗
PubMedDate not supplied

Regression analysis applied to PVC histories: a statistical procedure for evaluating antiarrhythmic drug efficacy.

Suppression of premature ventricular contractions (PVCs) is one of the goals of antiarrhythmic therapy. In a clinical trial, however, it may be difficult to distinguish antiarrhythmic drug effect from spontaneous variation in PVCs. We propose the application of linear regression to PVC histories to ascertain drug effect in individual patients. The model determines which variables are important in explaining a patient's PVCs. One such variable indicates the presence or absence of the drug; the model determines whether the drug has an effect on the patient's PVCs, while compensating for the other explanatory variables. In addition to determining the statistical significance of any drug effect, the model estimates the strength of the effect for each patient. We demonstrate the method with data from a three-day clinical trial which used 24-hour Holter monitoring. The method is flexible and can be modified to apply to any clinical study design. It allows for inferences concerning populations and subpopulations of patients.

Anti-Arrhythmia Agents↗
PubMed1999

Neuropsychometric tests in cross sectional and longitudinal studies - a regression analysis of ADAS - cog, SKT and MMSE.

INTRODUCTION: In clinical and drug studies, different neuropsychometric tests are used. So far, no empirical data have been published to compare studies using different tests. The purpose of this study was to calculate a regression formula allowing a comparison of cross-sectional and longitudinal data from three neuropsychometric tests that are frequently used in drug studies (Alzheimer's Disease Assessment Scale, ADAS-cog; Syndrom Kurz Test, SKT; Mini Mental State Examination, MMSE). METHOD: 177 patients with dementia according to ICD10 criteria were studied for the cross sectional and 61 for the longitudinal analysis. Correlations and linear regressions were calculated between tests. Significance was proven with ANOVA and t-tests using the SPSS statistical package. RESULTS: Significant Spearman correlations and slopes in the regression occurred in the cross sectional analysis (ADAS-cog-SKT r(s) = 0.77, slope = 0.45, SKT-ADAS-cog slope = 1.3, r2 = 0.59; ADAS-cog-MMSE r2 = 0.76, slope = -0.42, MMSE-ADAS-cog slope = -1.5, r2 = 0.64; MMSE-SKT r(s) = -0.79, slope = -0.87, SKT-MMSE slope = -0.71, r2 = 0.62; p<0.001 after Bonferroni correction; N = 177) and in the longitudinal analysis (SKT-ADAS-cog, r(s) = 0.48, slope = 0.69, ADAS-cog-SKT slope = 0.69, p<0.001, r2 = 0.32, MMSE-SKT, r(s) = 0.44, slope = -0.41, SKT-MMSE, slope = -0.55, p<0.001, r2 = 0.21). CONCLUSIONS: The results allow calculation of ADAS-scores when SKT scores are given, and vice versa. In longitudinal studies or in the course of the disease, scores assessed with the ADAS-cog and the SKT may now be statistically compared. In all comparisons, bottom and ceiling effects of the tests have to be taken into account.

Aged↗
PubMed2000

Non-linear regression analysis with errors in both variables: estimation of co-operative binding parameters.

Four different parameter estimation criteria, the geometric mean functional relationship (GMFR), the maximum likelihood (ML), the perpendicular least-squares (PLS) and the non-linear weighted least squares (WLS), were used to fit a model to the observed data when both regression variables were subject to error. Performances of these criteria were evaluated by fitting the co-operative drug-protein binding Hill model on simulated data containing errors in both variables. Six types of data were simulated with known variances. Comparison of the criteria was done by evaluating the bias, the relative standard deviation (S.D.) and the root-mean-squared error (RMSE), between estimated and true parameter values. Results show that (1) for data with correlated errors, all criteria perform poorly; in particular, the GMFR and ML criteria. For data with uncorrelated errors, all criteria perform equally well with regard to the RMSE. (2) Use of GMFR and ML lead to lower values for S.D. but higher biases compared with WLS and PLS. (3) WLS performs less well when equal dispersion is applied to the two observed variables.

Binding Sites↗