Indirect Utility Elicitation in Rare Diseases with Myelofibrosis as a Case Study

Cost-utility analyses (CUAs) are a type of health economic evaluation in the service of providing funders the information required to determine if a new health technology (e.g. a drug, device, public health measure or others) is economically attractive relative to current standard of practice and therefore worthy of funding. A key requirement for CUAs is the availability of the preference weights (also known as utilities) associated with health states. Utilities require elicitation studies which may be lacking for rare diseases such as myelofibrosis (MF). A solution to this problem is to map non-preference based patient reported outcomes (PROs) measuring constructs such as symptom burden and quality of life to utilities via statistical techniques. A successful map may obviate the need for dual collection of PRO data and utility elicitation in current randomized clinical trials (RCTs) and may allow estimation of utilities from prior RCTs, which had only collected PRO data. Mapping studies require a sufficient number of participants. Therefore, in this thesis, I employed MF as a case study of mapping PROs to utilities for a rare disease. I undertook a scoping review of the literature to seek guidance on the best way to achieve adequate enrolment in rare diseases using social media notification and found few studies on the topic. From the available literature, I concluded that a combination of conventional and social media-based notification of the existence of a study could yield a sufficient sample size. I used the combination to recruit 105 MF sufferers who then completed two MF-specific patient reported outcome measures (PROMs) – the myelofibrosis symptom assessment form (MF-SAF) and the myeloproliferative neoplasm symptom assessment form (MPN-SAF) – as well as the EuroQol five-dimension, three level (EQ-5D-3L) instrument which has an existing method to convert responses to utility values. I then assessed various statistical regression techniques to map MF PROM scores to utilities: Ordinary least squares regression, generalized linear models, Tobit regression and the least absolute shrinkage and selection operator model (LASSO). I developed two LASSO models, one for MF-SAF and another for MPN-SAF that were parsimonious yet had acceptable model diagnostics, fit and calibration. I then illustrated how MF-SAF average PROM scores from a hypothetical randomized controlled trial could employ my LASSO mapping model to estimate the utilities required for a subsequent CUA.

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