Application of two-part models and Cholesky decomposition to incorporate covariate-adjusted utilities in probabilistic cost-effectiveness models

Arrospide, Arantzazu; Ramos-Goñi, Juan Manuel; Pechlivanoglou, Petros; Mar, Javier

doi:10.1016/j.gaceta.2018.09.003

Cited by 3 publications

(8 citation statements)

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“…Utility measures of HRQL are preference values that patients attach to their overall health status. In clinical trials, utility measures summarize both positive and negative effects of an intervention into one value between 0 (equal to death) and 1 (equal to perfect health) [17]. When quality of life scores are close to 1, and hence, the data are not normally distributed, ordinary least square regression models are not recommended [17].…”

Section: Discussionmentioning

confidence: 99%

“…In clinical trials, utility measures summarize both positive and negative effects of an intervention into one value between 0 (equal to death) and 1 (equal to perfect health) [17]. When quality of life scores are close to 1, and hence, the data are not normally distributed, ordinary least square regression models are not recommended [17]. For this reason, we carried out a two-part regression analysis to compare quality of life measured using the EQ-5D-5L in the two treatment groups under study [17,18].…”

Section: Discussionmentioning

confidence: 99%

“…When quality of life scores are close to 1, and hence, the data are not normally distributed, ordinary least square regression models are not recommended [17]. For this reason, we carried out a two-part regression analysis to compare quality of life measured using the EQ-5D-5L in the two treatment groups under study [17,18]. These models take into account the high percentage of cases in which patients indicated that they were in perfect health, in their responses to the EQ-5D-5L (utility of 1 or level 1 in all five dimensions), a pattern which hinders the use of conventional multivariate models, and they allow us to calculate utilities for each group, u(x).…”

Section: Discussionmentioning

confidence: 99%

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Quality of life in patients with nonvalvular atrial fibrillation treated with oral anticoagulants

et al. 2021

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Objectives: Health-related quality of life (HRQL) is a key factor in making anticoagulant treatment decisions. The objective of this study was to assess the HRQL of patients with nonvalvular atrial fibrillation by treatment type: direct oral anticoagulants (DOACs) or vitamin K antagonists (VKAs). Methods: We carried out a cross-sectional observational study with clinical practice data, gathering demographic and clinical variables. HRQL was measured using the 5-level 5dimension EuroQol questionnaire (EQ-5D-5L). Differences between the study groups in HRQL as measured by the EQ-5D-5L were analyzed using two-part multivariate regression models. First, using logistic regression, the adjusted probability, p(x), of having perfect health was estimated in each subgroup. Secondly, generalized linear models were used to estimate mean disutility values, w(x), in a population that does not have perfect health, i.e. utility less than 1 or 1-w(x). Results: We recruited 333 patients, of whom 126 were on DOACs and 207 on VKAs. A significant difference was observed in the EQ-5D-5L anxiety/depression dimension, with a higher percentage of patients classified in the 'no problems' category in the DOAC group. The same type of analysis did not identify significant differences in any of the other dimensions (mobility, self-care, usual activities or pain/discomfort). Discussion: In the multivariate model, utility was significantly higher in the DOAC group than in the VKA group, although the difference was small (0.0121). This difference is attributable to patients on DOACs having less anxiety/depression. Conclusion: Patients treated with DOACs report a slightly better quality of life than those treated with VKAs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Quality of life in patients with nonvalvular atrial fibrillation treated with oral anticoagulants

et al. 2021

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“…In addition, to vary the regression coefficients in each simulation for the probabilistic sensitivity analysis (PSA), to represent parameter (second-order) uncertainty, the model was run using Cholesky decomposition of the variance-covariance matrix formed from the estimated coefficients (Supplementary File, Tables SM1 and SM2). 25,33 Third, the statistical analysis phase also allowed heterogeneity analysis. As Briggs et al 25 point out “its relevance lies in the identification of subgroups for whom separate cost-effectiveness analyses should be undertaken.” In our case, alternative decisions for obesity interventions could be made regarding the service provision to people living in deprived areas or with a low socioeconomic status.…”

Section: Methods (Model Development)mentioning

confidence: 99%

“…In addition, as regression models were used for parameter estimation, this allowed correlated parameter uncertainty to be easily included in the model using Cholesky decomposition. 33,39 Other model inputs obtained from the literature, such as relative risk values, were also considered acceptable by the experts consulted.…”

Section: Input Data Validationmentioning

confidence: 99%

Development and Validation of a Discrete Event Simulation Model to Evaluate the Cardiovascular Impact of Population Policies for Obesity

et al. 2021

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Introduction Our aim was to describe the development and validation of an obesity model representing the cardiovascular risks associated with different body mass index (BMI) categories, through simulation, designed to evaluate the epidemiological and economic impact of population policies for obesity. Methods A discrete event simulation model was built in R considering the risk of cardiovascular events (heart failure, stroke, coronary heart disease, and diabetes) associated with BMI categories in the Spanish population. The main parameters included in the model were estimated from Spanish hospital discharge records and the Spanish Health Survey and allowed both first-order and second-order (probabilistic sensitivity analysis) uncertainty to be programmed into the model. The simulation yielded the incidence and prevalence of cardiovascular events as validation outputs. To illustrate the capacity of the model, we estimated the reduction in cardiovascular events and cost-utility (incremental cost/incremental quality-adjusted life-years [QALYs]) of a hypothetical intervention that fully eliminated the cardiovascular risks associated with obesity and overweight. Results The Validation Status of Health-Economic decision models (AdViSHE) tool was applied. Internal validation plots showed adequate goodness of fit for the Spanish population. External validation was achieved by comparing the simulated and real incidence by age group for stroke, acute myocardial infarction, and heart failure. The intervention reduced the population hazard ratios of stroke, acute myocardial infarction, and heart failure to 0.81, 0.74, and 0.78, respectively, and added 0.74 QALYs to the whole population. Conclusions This obesity simulation model evidenced good properties for estimating the long-term epidemiological and economic impact of policies to tackle obesity in Spain. The conceptual model could be implemented for other counties using country-specific input data.

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Using Age-Specific Rates for Parametric Survival Function Estimation in Simulation Models

Arrospide,

Ibarrondo,

Blasco-Aguado

et al. 2024

Med Decis Making

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Purpose To describe a procedure for incorporating parametric functions into individual-level simulation models to sample time to event when age-specific rates are available but not the individual data. Methods Using age-specific event rates, regression analysis was used to parametrize parametric survival distributions (Weibull, Gompertz, log-normal, and log-logistic), select the best fit using the R2 statistic, and apply the corresponding formula to assign random times to events in simulation models. We used stroke rates in the Spanish population to illustrate our procedure. Results The 3 selected survival functions (Gompertz, Weibull, and log-normal) had a good fit to the data up to 85 y of age. We selected Gompertz distribution as the best-fitting distribution due to its goodness of fit. Conclusions Our work provides a simple procedure for incorporating parametric risk functions into simulation models without individual-level data. Highlights We describe the procedure for sampling times to event for individual-level simulation models as a function of age from parametric survival functions when age-specific rates are available but not the individual data We used linear regression to estimate age-specific hazard functions, obtaining estimates of parameter uncertainty. Our approach allows incorporating parameter (second-order) uncertainty in individual-level simulation models needed for probabilistic sensitivity analysis in the absence of individual-level survival data.

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Application of two-part models and Cholesky decomposition to incorporate covariate-adjusted utilities in probabilistic cost-effectiveness models

Cited by 3 publications

References 8 publications

Quality of life in patients with nonvalvular atrial fibrillation treated with oral anticoagulants

Quality of life in patients with nonvalvular atrial fibrillation treated with oral anticoagulants

Development and Validation of a Discrete Event Simulation Model to Evaluate the Cardiovascular Impact of Population Policies for Obesity

Using Age-Specific Rates for Parametric Survival Function Estimation in Simulation Models

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