Sensitivity Analysis: How Much Impact Does It Have on The Nice Decision Making Process?

Abstract

model selection, health states, hypotheses, survival analysis, clinical data sources and the treatment of uncertainty. Results: Twelve economic evaluations in oncology were submitted to NICE by pharmaceuticals companies (PC) between 2013 and 2015. Seven PC submitted a MCM, two a PSM, two a semi-markov partitioned survival model, and one a semi-markov model (SMM). Differences between modeling techniques were classified into four items: clinical data sources (e.g. published aggregated data for MCM and limited IPD for PSM), structure (e.g calculation of transition probabilities for MCM), hypotheses (e.g. same transition probability of death between two health states for MCM), flexibility of the model (e.g. access to patient level data for comparators required in PSM). ConClusions: Being a more flexible modeling technique, Markov models remain more frequently used compared to PSM. Nevertheless, PSM represent a more straightforward option when patient level data are available but are inappropriate when such data are not accessible for comparators.

DOI: 10.1016/j.jval.2015.09.2638

Cite this paper

@article{Hirst2015SensitivityAH, title={Sensitivity Analysis: How Much Impact Does It Have on The Nice Decision Making Process?}, author={Alison Hirst and Herv{\'e} Guy and Dawn Murphy}, journal={Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research}, year={2015}, volume={18 7}, pages={A704} }