IDENTIFICATION AND PREDICTION OF RESPONDERS TO A THERAPY - A MODEL AND ITS PRELIMINARY APPLICATION TO HYPERTENSION

The effect of a given treatment for a given disease may be estimated f rom randomized controlled clinical trials, expressed as a single treat ment effect averaged over the trial population. However, in recent yea rs there has been an increasing willingness to individualize therapeut ic decisions. The method we report here Identifies responders by asses sing the individual probability of an event, according to the treatmen t group. We used a treatment-stratified Cox regression model including interaction between treatment and patient's covariates, with common r egression coefficients for treated and untreated, except for the speci al case of a prognostic variable which has an interaction with treatme nt. Further we used a discriminate function based on the final model, representing the absolute individual therapeutic effect, to identify t he patients to be treated according to a given threshold of clinical e fficacy. The model was explored on the INDANA database (which pools in dividual patient data from clinical trials of anti-hypertensive drug i ntervention). Data on 36 444 patients, from five randomized controlled trials were included. The results show the relationship between the p roportion of avoided events among the avoidable ones, and the proporti on of patients treated who were responders, as a function of the thres hold of absolute benefit defining responders. The confidence intervals of the absolute therapeutic benefit for each individual were calculat ed, by using the Monte Carlo simulation method. A comparison of the su rvival of treated and controlled individuals, in both subgroups of res ponders and non responders, illustrated the relevance of the model. We conclude that the tools for predicting individual therapeutic benefit do exist. It will be important to assess the reproducibility of these results in other models or in other populations before widespread app lication. It will be necessary to have a properly computerized environ ment and to train doctors to use these tools.