Longitudinal assessment of utilities in patients with migraine: an analysis of erenumab randomized controlled trials

Abstract

BackgroundCost-effectiveness analyses in patients with migraine require estimates of patients’ utility values and how these relate to monthly migraine days (MMDs). This analysis examined four different modelling approaches to assess utility values as a function of MMDs.MethodsDisease-specific patient-reported outcomes from three erenumab clinical studies (two in episodic migraine [NCT02456740 and NCT02483585] and one in chronic migraine [NCT02066415]) were mapped to the 5-dimension EuroQol questionnaire (EQ-5D) as a function of the Migraine-Specific Quality of Life Questionnaire (MSQ) and the Headache Impact Test (HIT-6™) using published algorithms. The mapped utility values were used to estimate generic, preference-based utility values suitable for use in economic models. Four models were assessed to explain utility values as a function of MMDs: a linear mixed effects model with restricted maximum likelihood (REML), a fractional response model with logit link, a fractional response model with probit link and a beta regression model.ResultsAll models tested showed very similar fittings. Root mean squared errors were similar in the four models assessed (0.115, 0.114, 0.114 and 0.114, for the linear mixed effect model with REML, fractional response model with logit link, fractional response model with probit link and beta regression model respectively), when mapped from MSQ. Mean absolute errors for the four models tested were also similar when mapped from MSQ (0.085, 0.086, 0.085 and 0.085) and HIT-6 and (0.087, 0.088, 0.088 and 0.089) for the linear mixed effect model with REML, fractional response model with logit link, fractional response model with probit link and beta regression model, respectively.ConclusionsThis analysis describes the assessment of longitudinal approaches in modelling utility values and the four models proposed fitted the observed data well. Mapped utility values for patients treated with erenumab were generally higher than those for individuals treated with placebo with equivalent number of MMDs. Linking patient utility values to MMDs allows utility estimates for different levels of MMD to be predicted, for use in economic evaluations of preventive therapies.Trial registrationClinicalTrials.gov numbers of the trials used in this study: STRIVE, NCT02456740 (registered May 14, 2015), ARISE, NCT02483585 (registered June 12, 2015) and NCT02066415 (registered Feb 17, 2014).

Response to erenumab assessed by Headache Impact Test‐6 is modulated by genetic factors and arterial hypertension: An explorative cohort study

“…Registration trials showed a benefit of ERE on various migraine‐specific PRO measures including HIT‐6 [6, 7, 9, 29]. Interestingly, a recent paper found that in these trials, PRO measures indicated better migraine‐related quality of life in individuals treated with ERE compared to those receiving placebo and having the same number of MMDs [30]. This strongly supports the existence of treatment benefits beyond MMD reduction that translate into improvements in health‐related quality of life.…”

Health State Utility Mapping of Rimegepant for the Preventive Treatment of Migraine: Double-Blind Treatment Phase and Open Label Extension (BHV3000-305)

“…MSQv2 data from several other migraine prevention trials have been mapped to EQ-5D values using similar methods as the current post hoc analysis. For example, Di Tanna mapped MSQv2 to EQ-5D using data from three erenumab studies for migraine prevention [ 32 ]. In these studies, MMDs ranged from 8.2 ± 2.5 to 18.2 ± 4.7 at baseline, and mapped EQ-5D values from MSQv2 improved from 0.62 (0.18) at baseline to 0.74 (0.14) at week 12 with erenumab 140 mg treatment [ 32 ].…”

“…For example, Di Tanna mapped MSQv2 to EQ-5D using data from three erenumab studies for migraine prevention [ 32 ]. In these studies, MMDs ranged from 8.2 ± 2.5 to 18.2 ± 4.7 at baseline, and mapped EQ-5D values from MSQv2 improved from 0.62 (0.18) at baseline to 0.74 (0.14) at week 12 with erenumab 140 mg treatment [ 32 ]. This is comparable to the improvement in mapped EQ-5D values with rimegepant in the current study.…”

Modeling Monthly Migraine-Day Distributions Using Longitudinal Regression Models and Linking Quality of Life to Inform Cost-Effectiveness Analysis: A Case Study of Fremanezumab in Japanese-Korean Migraine Patients

“…Conventional methods that have been used for modeling the effectiveness of migraine drugs in a CEM consist of decision tree and Markov model-based approaches that use health states with predefined ranges of MMD or headache day frequencies [ 12 – 14 ]. A downside of these approaches is that they compartmentalize patients that seem to have a similar response to the treatment or predefined categories of MMD, which can lead to a loss of information and introduce bias when the relation between model parameters and the number of MMD is non-linear in a heterogeneous population such as migraine patients [ 1 , 2 , 15 – 17 ].…”

“…In this way, marginal differences (i.e., the difference between a patient with 25 MMD compared with 26 MMD) can be identified, and therefore the model is more sensitive to QALY changes and can potentially capture smaller QALY gains than if the patients were grouped together and assumed to have the same utility. This has a benefit on its own as a non-linear association was shown between decreasing utility values and increasing number of MMD with a potential ceiling effect [ 2 ]. However, linking utility values to individual MMD frequencies requires migraine-specific HRQOL that is sensitive enough to the MMD distributions.…”