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RT or S + RT results in significantly better local control than S. Even after dividing the groups into cases with free and positive margins and cases with primary and recurrent tumors, the best local control is achieved with RT or S + RT.

PLANKEY M I C H A E L W, JUNE STEVENS, KATHERINE M FLEGAL, PHILIP F RUST.Prediction equations do not eliminate systematic error in selfreported body mass index. Obes Res. 1997;5:30&314. Epidemiological studies of the risks of obesity often use body mass index (BMI) calculated from self-reported height and weight. The purpose of this study was to examine the pattern of reporting error associated with self-reported values of BMI and to evaluate the extent to which linear regression models predict measured BMI from self-reported data and whether these models could compensate for this reporting error. We examined measured and self-reported weight and height on 5079 adults aged 30 years to 64 years from the second National Health and Nutrition Examination Survey. Measured and self-reported BMI (kg/m2) was calculated, and multiple linear regression techniques were used to predict measured BMI from self-reported BMI. The error in self-reported BMI (self-reported BMI minus measured BMI) was not constant but varied systematically with BMI. The correlation between measured BMI and the error in self-reported BMI was -0.37 for men and -0.38 for women. The pattern of reporting error was only weakly associated with self-reported BMI, with the correlation being 0.05 for men and -0.001 for women. Error in predicted BMI (predicted BMI minus measured BMI) also varied systematically with measured BMI, but less consistently with self-reported BMI. More complex models only slightly improved the ability to predict measured BMI compared with self-reported BMI alone. None of the equations were able to eliminate the systematic reporting error in determining measured BMI values from self-reported data. The characteristic pattern of error associated with self-reported BMI is difficult or impossible to correct by the use of linear regression models.

Antibody titres to whole ovary, theca cells, granulosa cells and endometrium were determined by passive haemagglutination and immunofluorescence assays in sera and in cervical and vaginal secretions from 13 patients with endometriosis. Antibody titres to endometrium (mean log2 ±s.e.m., 7-08+0-80; P<0-0001), ovary (3-58+0-87; P= 0-0092), theca cells (4-42+0-73; P<0-0001) and granulosa cells (3-33+0-63; P=0-0024) were significantly higher in the patients' sera than in sera from 15 normal non-pregnant females. Antibody titres to granulosa cells were elevated (7-97 + 1-46; P= 0-0424) in their cervical secretions. Antibody titres to all tissues tested were similar in vaginal secretions of patients and controls. Immunofluorescent antibody assay of biopsied endometrical tissue and sera from the patients revealed the antibodies to be primarily IgG and IgA. The results suggest that autoantibodies to endometrium and ovary are present in patients with endometriosis.

OBJECTIVE -To evaluate the impact of diabetes status and race, in addition to other covariables, on the estimation of resting energy expenditure (REE).RESEARCH DESIGN AND METHODS -Demographic, anthropometric, and clinical parameters were assessed in 166 adults of varying weights. Subjects were categorized by race (white versus black) and into three subgroups based on glucose tolerance (normoglycemia, impaired glucose tolerance, and type 2 diabetes), termed the diabetes status index (DSI). REE was measured by indirect calorimetry. A multiple regression model was established for optimal prediction of REE based on covariables.RESULTS -An average decrease in REE of 135 kcal/day independent of all other variables was observed in blacks (P Ͻ 0.001). DSI was found to be a significant covariable (P ϭ 0.002) in predicting REE, which was observed to be higher in diabetic women. Therefore, race and DSI entered the multiple regression equation to predict REE as significant independent variables, together with lean body mass (LBM) and age ϫ BMI interaction (P Ͻ 0.001). Overall, REE prediction resulted in an R 2 of 0.79 and a root mean square error of 136 kcal/day. These values indicate that the resultant equations could offer advantages over other key published prediction equations. The equations are: 1) REE female ϭ 803.8 ϩ 0.3505 ϫ age ϫ (BMI -34.524) -135.0 ϫ race ϩ 15.866 ϫ LBM ϩ 50.90 ϫ DSI; and 2) REE male ϭ 909.4 ϩ 0.3505 ϫ age ϫ (BMI -34.524) -135.0 ϫ race ϩ 15.866 ϫ LBM -9.10 ϫ DSI. The predictive value of the equations did not diminish substantially when fat-free mass estimated by skinfold calipers was substituted for dual-energy X-ray absorptiometry scan measurements of LBM.CONCLUSIONS -Race and diabetes status are important when predicting REE, coupled with LBM, age, BMI, and sex. Race and DSI have not been considered in equations commonly used to predict REE. Their inclusion could improve individualization of dietary prescriptions for type 2 diabetic subjects and heterogeneous populations. Diabetes Care 27:1405-1411, 2004T ype 2 diabetes and obesity have emerged as leading public health challenges in western societies and constitute an increasing health burden in developing countries. Obesity increases risk for diabetes in the context of the insulin resistance syndrome, a trait cluster consisting of insulin resistance, obesity, glucose intolerance, upper body fat distribution, hypertension, dyslipidemia, and dysfibrinolysis (1-4). The insulin resistance syndrome is a major factor conferring increased risk for cardiovascular disease (1-4). Therefore, type 2 diabetes, obesity, and the insulin resistance syndrome are important targets for dietetic intervention. Often, nutrition professionals must estimate energy expenditure when measurements of metabolic rate are unavailable and must consider the potential influence of obesity, glucose intolerance, and diabetes, which could alter energy metabolism. As current dietary recommendations in the treatment of diabetes include care plans based on lifestyle factors and diab...

Evaluation of the performance of a new diagnostic procedure with respect to a standard procedure arises frequently in practice. The response of interest, often in a dichotomous form, is measured twice, once with each procedure. The two procedures are administered to either two matched individuals, or when practical, to the same individual. A large sample test for matched-pair data is the McNemar test. The main assumption of this test is independent paired responses; however, when more than one outcome from an individual is measured by each procedure, the data are clustered. Examples of such cases can be seen in dental and ophthalmology studies. Variance adjustment methods for the analysis of clustered matched-pair data have been proposed; however, because of unequal cluster sizes, variability of correlation structures within a cluster (within paired responses in a cluster as well as between paired responses in a cluster), and unequal success probabilities among the clusters, the performances of some available methods are not consistent. This research proposes a simple adjustment to the McNemar test for the analysis of clustered matched-pair data. Method of moments is used to calculate a consistent variance estimator. Using Monte Carlo simulation, the size and power of the proposed test are compared to those of two currently available methods. To illustrate practical application, clustered matched-pair data from two clinical studies are analysed.

The failure of BMI and fat patterning to predict mortality in black women challenges previously held assumptions regarding the role of overweight in the higher mortality experienced by black women.

STEYENS, JUNE, MICHAEL W. PLANKEY, DAYID F. WILLIAMSON, MICHAEL J. THUN, PHILIP F. RUST, YUKO PALESCH. The body mass index-mortality relationship in white and African American women. Obes Res. 1998;6:268-277. Objective: To examine the association of body mass index to all-cause and cardiovascular disease (CYD) mortality in white and African American women. Research methods and procedures:Women who were members of the American Cancer Society Prevention Study I were examined in 1959 to 1960 and then followed 12 years for vital status. Data for this analysis were from 8,142 black and 100,000 white women. Body mass index (BMI) was calculated from reported height and weight. Associations were examined using Cox proportional hazards modeling with some analyses stratified by smoking (current or never) and educational status (less than complete high school or high school graduate).Results: There was a significant interaction between ethnicity and BMI for both all-cause (p<0.05) and CYD mortality (p<0.00l). BMI (as a continuous variable) was associated with all-cause mortality in white women in all four groups defined by smoking and education. In black women with less than a high school education, there were no significant associations between BMI mortality. For high schooleducated black women, there was a significant association between BMI and all-cause mortality. Among never smoking women with at least a high school education, models using the lowest BMI as the reference indicated a 40% Submitted for publication April 2, 1997. Accepted for publication February 9, 1998. From the 'Departments of Nutrition and Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, NC; tDepartments of Biometry and Psychiatry, Medical University of South Carolina, Charleston, SC; :j:National Center for Chronic Disease Prevention and Health Promotion, Atlanta, GA; and §American Cancer Society, Atlanta, GA. Reprint requests to Dr. June Stevens, Departments of Nutrition and Epidemiology, CB 7400, University of North Carolina, Chapel Hill, NC 27599. Copyright © 1998 NAASO.268 OBESITY RESEARCH Vol. 6 NO.4 July 1998 higher risk of all-cause mortality at a BMI of 35.9 in black women vs. 27.3 in white women. Discussion:The impact of BMI on mortality was modified by educational level in black women; however, BMI was a less potent risk factor in black women than in white women in the same category of educational status.