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Abstract:
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We present statistical definitions of the individual benefit of a medical or behavioral treatment and of the severity of a chronic illness. These definitions are used to develop methods that can be used by statisticians and clinicians in the data analysis of clinical trials from the perspective of personalized medicine, and in the measurement of the benefits achieved by new patients. The methods focus on assessing and comparing individual effects of treatments rather than average effects, and can be used with continuous and discrete responses, including dichotomous and count responses. Our approach allows investigating the probability distribution of individual benefits in the patient population and the variables that influence it. The methods are based on recently published developments in regression models with random coefficients. We present an application to the prediction of the individual benefits of the anti-depressant imipramine in severely ill patients.
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