A logistic regression model when some events precede treatment: the effect of thrombolytic therapy for acute myocardial infarction on the risk of cardiac arrest.
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 electrocardiograph for real-time use in an emergency department.
Schmid
C H
CH
Department of Medicine, New England Medical Center, Boston, Massachusetts 02111, USA.
D'Agostino
R B
RB
Griffith
J L
JL
Beshansky
J R
JR
Selker
H P
HP
D000368:Aged; D002986:Clinical Trials as Topic; D004812:Epidemiologic Methods; D006323:Heart Arrest / Q000453:epidemiology* / Q000209:etiology; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D009203:Myocardial Infarction / Q000188:drug therapy*; D011336:Probability; D012044:Regression Analysis; D012307:Risk Factors; D015912:Thrombolytic Therapy