By Prof Richard Emsley
Centre for Biostatistics, The University of Manchester, Manchester Academic Health Science Centre
Abstract: Stratified or personalised medicine is an attempt to move beyond a `one size fits all’ approach based on comparing group-level average outcomes to improve patient-level outcomes by identifying personalised treatment recommendations (PTR). A PTR maps a set of predictive markers to a decision of whether or not to treat an individual patient. A PTR can be estimated from a weighted sum of predictive markers and the treatment effect using either regression models, inverse probability weighting (IPW), augmented IPW, or classification methods.
Once estimated, PTRs can be evaluated by testing if the expected outcome under the PTR improves on the expected outcome under an alternative policy – such as one where either every patient receives the treatment or every patient receives the control condition. Evaluating a PTR differs from the evaluation of prognostic or diagnostic models because the object of inference (whether a subject benefited from treatment) remains unobserved.
In this talk, we will describe the statistical methods for estimating a PTR. Monte-Carlo simulations are used to compare the statistical properties of the estimation methods under a range of data generating scenarios. These methods will be demonstrated with application to data from a randomised controlled trial in Chronic Fatigue Syndrome, using our new user-written Stata command -ptr-.