Pretreatment BQ chewing habit worsens the prognosis of BMSCC in Taiwan. BQ chewing is a prognostic indicator that can be used in conjunction with clinical staging to help plan the treatment for the patients.

Case-deletion diagnostics are a routine component of regression analysis since they identify unusual observations that substantially affect parameter estimates. The exact approach is to compute the change in each regression parameter by dropping that individual and refitting the model. Repeating a Cox regression for the removal of each individual is very time consuming and therefore not done in practice. The two methods commonly used to approximate the exact case-deletion change for Cox regression are the empirical influence function approach and the covariate-vector augmentation approach. This paper reports the results of a simulation study on how well these methods estimate the exact change in a parameter estimate when deleting a known outlier or a known non-outlier. Additionally, we investigate how well these methods correctly identify outliers and non-outliers. The covariate augmentation approach clearly outperformed the influence function approach in these simulations.

Regression diagnostic methods are developed and investigated under the Class A regressive model proposed by Bonney [(1984) Am J Med Genet 18:731–749]. We call a family whose phenotypic distribution does not conform to the same genetic model as the majority of the families an etiotic family. The exact case‐deletion approach for identifying etiotic families, based on examining the changes in each model parameter estimate by excluding one family at a time, is very time‐consuming. We proposed three alternative diagnostic methods: the empirical influence function (EIF), the one‐step approximation, and the approximated one‐step approach. These methods can be computed efficiently and were incorporated into the existing software package S.A.G.E. A thorough Monte‐Carlo investigation of the performance of the diagnostic methods was conducted and generally supports the EIF approach as the recommended alternative. The phenotypic variance is the parameter whose associated regression diagnostic most frequently and correctly identified etiotic families in the models that were examined. An analysis of body mass index data from 402 individuals in 122 Muscatine, Iowa families is used to illustrate the methods. A Class A regressive model with a recessive major locus and equal mother‐offspring and father‐offspring correlations provided the best‐fitting model. The proposed regression diagnostics identified up to 7.4% of the 122 families as etiotic. As a result of this investigation, case‐deletion diagnostic assessment is now a practical component in the analysis of quantitative family data. Genet. Epidemiol. 17:174–187, 1999. © 1999 Wiley‐Liss, Inc.