ECONOMIC EVALUATIONS OF PERSONALIZED MEDICINE IN ONCOLOGY: WHY ARE THEY SO IMPERSONAL?

Purpose: To determine the overall quality of economic evaluations (EEs) of pharmacogenomic (PGx) oncology treatments, identify methodological factors specific to the evaluation of personalized medicine (PM) and the extent to which these have been incorporated adequately.

Method: A systematic review of published EEs considering 14 oncology treatments listed on the FDA “Table of Pharmacogenomic Biomarkers in Drug Labels” was conducted. Electronic citations indexed in Cochrane, EMBASE, MEDLINE, EconLit, EMB Reviews, Health Economic Evaluation Database and PubMed up to January 2012 in English were considered. Included articles were reviewed for 1) type of EE (trial- (TB) or model-based (MB)) and characterization of uncertainty (univariate and probabilistic sensitivity analyses (SA)) 2) overall quality measured using the Quality of Health Economic Studies (QHES) scale and 3) methodological characteristics specific to PGx therapies (modelling the genetic test and its sensitivity/specificity, indirect costs specific to PGx therapies and consideration of racial differences).

Result: 3,672 citations were identified; ten (7 MB, 3 TB) for cetuximab (one study also evaluated panitumumab), three (3 MB) for dasatinib/nilotinib, two (2 MB) for fulvestrant, thirteen (12 MB, 1 TB) for imatinib, one for lapatinib (1 MB), six for tamoxifen (4 MB, 2 TB), thirty (27 MB, 3 TB) for trastuzumab were included. Five of the 14 included therapies had no published EEs. Univariate (n=58, 89%) was more common than probabilistic SA (n=37, 57%). Model-based EEs had an average quality score of 70 (95%CI 67 to 73). Forty-nine studies (75%) evaluated the therapy of interest in a genetically selected population, with 16 studies (25%) incorporating the genetic test in the model. Sensitivity and specificity was modelled in only 8 studies (12%). None of the included studies assessed indirect costs specific to PGx therapies or considered racial differences.    

Conclusion: Although estimates of overall quality are acceptable, characteristics specific to PM have not been thoroughly assessed. By neglecting to model the genetic test or include racial differences the value in avoiding severe adverse events and expensive treatment in non-responders has been lost, resulting in bias against personalized therapies. In contrast, exclusion of indirect costs specific to PGx treatments may understate the incremental costs of these therapies. The net effect of ignoring the personal nature of these therapies is uncertain, but could potentially cause bias against personalized cancer therapies.