The Cost Implications of Less Tight Versus Tight Control of Hypertension in Pregnancy (CHIPS Trial).

The CHIPS randomized controlled trial (Control of Hypertension in Pregnancy Study) found no difference in the primary perinatal or secondary maternal outcomes between planned "less tight" (target diastolic 100 mm Hg) and "tight" (target diastolic 85 mm Hg) blood pressure management strategies among women with chronic or gestational hypertension. This study examined which of these management strategies is more or less costly from a third-party payer perspective. A total of 981 women with singleton pregnancies and nonsevere, nonproteinuric chronic or gestational hypertension were randomized at 14 to 33 weeks to less tight or tight control. Resources used were collected from 94 centers in 15 countries and costed as if the trial took place in each of 3 Canadian provinces as a cost-sensitivity analysis. Eleven hospital ward and 24 health service costs were obtained from a similar trial and provincial government health insurance schedules of medical benefits. The mean total cost per woman-infant dyad was higher in less tight versus tight control, but the difference in mean total cost (DM) was not statistically significant in any province: Ontario ($30 191.62 versus$24 469.06; DM $5723, 95$296 to $12 272; P=0.0725); British Columbia ($30 593.69 versus $24 776.51; DM$5817; 95% confidence interval, -$385 to$12 349; P=0.0725); or Alberta ($31 510.72 versus$25 510.49; DM $6000.23; 95$154 to $12 781; P=0.0637). Tight control may benefit women without increasing risk to neonates (as shown in the main CHIPS trial), without additional (and possibly lower) cost to the healthcare system. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01192412

United States Valuation of EQ-5D-5L Health States Using an International Protocol

Objective To derive a US-based value set for the EQ-5D-5L questionnaire using an international, standardized protocol developed by the EuroQol Group. Methods Respondents from the US adult population were quota-sampled on the basis of age, sex, ethnicity, and race. Trained interviewers guided participants in completing composite time trade-off (cTTO) and discrete choice experiment (DCE) tasks using the EuroQol Valuation Technology software and routine quality control measures. Data were modeled using a Tobit model for cTTO data, a mixed logit model for DCE data, and a hybrid model that combined cTTO and DCE data. Model performance was compared on the basis of logical ordering of coefficients, statistical significance, parsimony, and theoretical considerations. Results Of 1134 respondents, 1062, 1099, and 1102 respondents provided useable cTTO, DCE, and cTTO or DCE responses, respectively, on the basis of quality control criteria and interviewer judgment. Respondent demographic characteristics and health status were similar to the 2015 US Census. The Tobit model was selected as the preferred model to generate the value set. Values ranged from −0.573 (55 555) to 1 (11 111), with 20% of all predicted health states scores less than 0 (ie, worse than dead). Conclusions A societal value set for the EQ-5D-5L was developed that can be used for economic evaluations and decision making in US health systems. The internationally established, standardized protocol used to develop this US-based value set was recommended by the EuroQol Group and can facilitate cross-country comparisons

Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: Patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with health care data, such assumptions unlikely hold. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome

Estimating EQ-5D utilities based on the Short-Form Long Term Conditions Questionnaire (LTCQ-8)

Purpose: The aim of this work was to develop a mapping algorithm for estimating EuroQoL 5 Dimension (EQ-5D) utilities from responses to the Long-Term Conditions Questionnaire (LTCQ), thus increasing LTCQ’s potential as a comprehensive outcome measure for evaluating integrated care initiatives. Methods: We combined data from three studies to give a total sample of 1334 responses. In each of the three datasets, we randomly selected 75% of the sample and combined the selected random samples to generate the estimation dataset, which consisted of 1001 patients. The unselected 25% observations from each dataset were combined to generate an internal validation dataset of 333 patients. We used direct mapping models by regressing responses to the LTCQ-8 directly onto EQ-5D-5L and EQ-5D-3L utilities as well as response (or indirect) mapping to predict the response level that patients selected for each of the five EQ-5D-5L domains. Several models were proposed and compared on mean squared error and mean absolute error. Results: A two-part model with OLS was the best performing based on the mean squared error (0.038) and mean absolute error (0.147) when estimating the EQ-5D-5L utilities. A multinomial response mapping model using LTCQ-8 responses was used to predict EQ-5D-5L responses levels. Conclusions: This study provides a mapping algorithm for estimating EQ-5D utilities from LTCQ responses. The results from this study can help broaden the applicability of the LTCQ by producing utility values for use in economic analyses

Condition-specific or generic preference-based measures in oncology? A comparison of the EORTC-8D and the EQ-5D-3L.

PURPOSE: It has been argued that generic health-related quality of life measures are not sensitive to certain disease-specific improvements; condition-specific preference-based measures may offer a better alternative. This paper assesses the validity, responsiveness and sensitivity of a cancer-specific preference-based measure, the EORTC-8D, relative to the EQ-5D-3L. METHODS: A longitudinal prospective population-based cancer genomic cohort, Cancer 2015, was utilised in the analysis. EQ-5D-3L and the EORTC QLQ-C30 (which gives EORTC-8D values) were asked at baseline (diagnosis) and at various follow-up points (3 months, 6 months, 12 months). Baseline values were assessed for convergent validity, ceiling effects, agreement and sensitivity. Quality-adjusted life-years (QALYs) were estimated and similarly assessed. Multivariate regression analyses were employed to understand the determinants of the difference in QALYs. RESULTS: Complete case analysis of 1678 patients found that the EQ-5D-3L values at baseline were significantly lower than the EORTC-8D values (0.748 vs 0.829, p < 0.001). While the correlation between the instruments was high, agreement between the instruments was poor. The baseline health state values using both instruments were found to be sensitive to a number of patient and disease characteristics, and discrimination between disease states was found to be similar. Mean generic QALYs (estimated using the EQ-5D-3L) were significantly lower than condition-specific QALYs (estimated using the EORTC-8D) (0.860 vs 0.909, p < 0.001). The discriminatory power of both QALYs was similar. CONCLUSIONS: When comparing a generic and condition-specific preference-based instrument, divergences are apparent in both baseline health state values and in the estimated QALYs over time for cancer patients. The variability in sensitivity between the baseline values and the QALY estimations means researchers and decision makers are advised to be cautious if using the instruments interchangeably