Intention-to-treat: methods for dealing with missing values in clinical trials of progressively deteriorating diseases


  • Presented at the International Society for Clinical Biostatistics Twentieth International Meeting, Heidelberg, Germany, September 1999.


Since it came up in the 1960s, the principle of intention-to-treat (ITT) has become widely accepted for the analysis of controlled clinical trials. In this context the question of how to perform such an analysis in the presence of missing information about the main endpoint is of major importance. Uncritical use of several ad hoc strategies for dealing with missing values is common in the practice of clinical trials. On the other hand, little is known about possible dangers and problems of applying these strategies. We therefore performed a detailed investigation of different methods for dealing with missing values in order to develop recommendations for their practical use. A simulation study was performed investigating possible consequences on type I error and power of applying different methods for dealing with missing values. The simulations were based on a clinical trial of osteoporosis, a progressively deteriorating disease. The strategies examined can be roughly classified into numerical imputation strategies (last observation carried forward, mean and regression based methods) and non-parametric strategies (rank and dichotomization based methods). Different drop-out mechanisms and different types of progression of disease are considered. The type I error increases drastically for the different strategies, especially if the courses of disease vary between treatment groups. The loss in power can be substantial. There is no strategy which is adequate for all different combinations of drop-out mechanisms, drop-out rates and courses of disease over time. For drop-out rates less than 20 per cent and similar courses of disease in the treatment groups, missing values might be replaced by the mean of the other group, or counted as treatment failures after dichotomization of the endpoint. For larger drop-out rates or less similar courses of disease, no adequate recommendations can be given. Because of the drastic consequences of increasing drop-out rates, it has to be a primary goal in clinical trials to keep missing values to a minimum. Unobserved information cannot be reliably regained by any methodological resources. As there are no strategies for universal use, reasons for the choice of a certain method have to be provided when designing and analysing clinical trials. Copyright © 2001 John Wiley & Sons, Ltd.