140s Abstracts

P48 SUMMARY ROC CURVE APPROACH TO ASSESS THE

ROLE OF PROGNOSTIC FACTORS IN LIVER CIRRHOSIS

Albert0 Morabito Universitci de@ Studi di Milano

Mihno, Italy

The meta-analytic approach SROC (Summary Receiver Operating Characteristics) allows to enlighten the performance of diagnostic tests and is applied to evaluate the role of continuous variables that, according to the medical literature have a prognostic effect on survival. Thirty six studies on the detection and the assessment of predictors of death or bleeding from varices among patients suffering from liver cirrhosis were selected on the basis of specific inclusion criteria. A test of correlation between the estimates of the true-positive rate (TPR: high level of bed prognostic factors among dead patients) and the false-positive rate (FPR: high level of bed prognostic factors among survivors) was performed. The presence of high correlation allows to summarize the results from each study with a single ROC curve. The absence of such correlation indicates a certain extent of heterogeneity of the estimates of sensitive and specificity. Differences among studies can be due to chance or to selection bias. Subgroup analyses on the stratification criteria of the studies was performed to explore alternative explanations of the variability in TPR and FPR. The difference in TPR between randomized studies and natural history studies was striking. Age of recruited patients in different studies seemed to have a significant role in the explanation of variability.

P49 AGE AT ANY BIRTH AND BREAST CANCER RISK

Chris Robertson and Peter Boyle European Institute of Oncology

Mihno, Italy

Recently a number of models of the effect of parity and age at any birth on breast cancer risk have been published. In essence these are linear logistic regression models. It is demonstrated that these models are conceptually similar and a general model which incorporates the previous models is derived. Various restrictions to the parameters of the general model yield the specific models. This permits the assessment of the important characteristics of the models and facilitates their comparison.

These models are applicable in Case Control studies and are illustrated using data from a Case Control study of Breast Cancer in Italy. The results indicate that the first and second births both have an important contribution to the risk of breast cancer but that subsequent births do not. Also the effect of a second birth, as well as that of the first, are modified by the age at diagnosis. Thii model permits the separate identification of the effect of age at the birth from the effect of the number of births.

Diagnostic methods for investigating the linearity assumption are presented. When the linear assumption is not valid a more appropriate model is obtained by extending the model within a generalized additive model using spline regressions. The non-parametric regression model is recommended in view of the deviations from non linearity in the risk which occur immediately after a birth. This model is illustrated and compared to the linear model.

The models are effectively change-point additive logistic regression models which include discrete changes in risk. Consequently they have a wider applicability than solely breast cancer and the methodology may be considered in case control studies whenever there is a risk factor which has an instantaneous impact.