When outcomes occur in clinical trials before treatment can be given, neither intent-to-treat nor according-to-protocol analyses give optimal estimates of the treatment effect.
A better approach employs a time-dependent variable for treatment.
Intent-to-treat analyses are conservative, biasing against treatment ; according-to-protocol analyses bias in favor of treatment.
We show how to measure the effect of a time-dependent variable in a logistic regression using person-time intervals as units of measurement and describe appropriate methods for reporting model performance.
The method is applied to develop a model to predict the probability that a patient with a myocardial infarction will have a sudden cardiac arrest within 48 hours of presentation to emergency medical services both when treated with thrombolysis and when not treated.
We use a time-dependent treatment variable because many patients went into cardiac arrest while awaiting treatment.
This technique has been programmed into an electricardiograph for real-time use in an emergency department.
Mots-clés Pascal : Infarctus, Myocarde, Arrêt cardiocirculatoire, Fibrinolytique, Chimiothérapie, Traitement, Prédiction, Modèle, Régression logistique, Méthode statistique, Homme, Dépendance du temps, Appareil circulatoire pathologie, Cardiopathie coronaire, Myocarde pathologie
Mots-clés Pascal anglais : Infarct, Myocardium, Cardiocirculatory arrest, Fibrinolytic, Chemotherapy, Treatment, Prediction, Models, Logistic regression, Statistical method, Human, Time dependence, Cardiovascular disease, Coronary heart disease, Myocardial disease
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0044463
Code Inist : 002B30A01A1. Création : 14/05/1998.