THE BIVARIATE PROBIT MODEL OF UNCOMPLICATED CONTROL OF TUMOR - A HEURISTIC EXPOSITION OF THE METHODOLOGY

Purpose: To describe the concept, models, and methods for the construc tion of estimates of joint probability of uncomplicated control of tum ors in radiation oncology. Interpolations using this model can lead to the identification of more efficient treatment regimens for an indivi dual patient. The requirement to find the treatment regimen that will maximize the joint probability of uncomplicated control of tumors sugg ests a new class of evolutionary experimental designs-Response Surface Methods-for clinical trials in radiation oncology, Methods and Materi als: The software developed by Lesaffre and Molenberghs is used to con struct bivariate probit models of the joint probability of uncomplicat ed control of cancer of the oropharynx from a set of 45 patients for e ach of whom the presence/absence of recurrent tumor (the binary event (E) over bar(1)/E-1) and the presence/absence of necrosis (the binary event E-2/(E) over bar(2)) of the normal tissues of the target volume is recorded, together with the treatment variables dose, time, and fra ctionation, Results: The bivariate probit model can be used to select a treatment regime that will give a specified probability, say P(S) = 0.60, of uncomplicated control of tumor by interpolation within a set of treatment regimens with known outcomes of recurrence and necrosis, The bivariate probit model can be used to guide a sequence of clinical trials to find the maximum probability of uncomplicated control of tu mor for patients in a given prognostic stratum using Response Surface Methods by extrapolation from an initial set of treatment regimens, Co nclusions: The design of treatments for individual patients and the de sign of clinical trials might be improved by use of a bivariate probit model and Response Surface Methods. (C) 1997 Elsevier Science Inc.