Creating a Multiply Imputed Value Set for the EQ-5D-5L in Canada: State-Level Misspecification Terms Are Needed to Characterize Parameter Uncertainty Correctly.

BACKGROUND: Parameter uncertainty in EQ-5D-5L value sets often exceeds the instrument's minimum important difference, yet this is routinely ignored. Multiple imputation (MI) accounts for parameter uncertainty in the value set; however, no valuation study has implemented this methodology. Our objective was to create a Canadian MI value set for the EQ-5D-5L, thus enabling users to account for parameter uncertainty in the value set. METHODS: Using the Canadian EQ-5D-5L valuation study (N = 1,073), we first refit the original model followed by models with state-level misspecification. Models were compared based on the adequacy of 95% credible interval (CrI) coverage for out-of-sample predictions. Using the best-fitting model, we took 100 draws from the posterior distribution to create 100 imputed value sets. We examined how much the standard error of the estimated mean health utilities increased after accounting for parameter uncertainty in the value set by using the MI and original value sets to score 2 data sets: 1) a sample of 1,208 individuals from the Canadian general public and 2) a sample of 401 women with breast cancer. RESULTS: The selected model with state-level misspecification outperformed the original model (95% CrI coverage: 94.2% v. 11.6%). We observed wider standard errors for the estimated mean utilities on using the MI value set for both the Canadian general public (MI: 0.0091; original: 0.0035) and patients with breast cancer (MI: 0.0169; original: 0.0066). DISCUSSION AND CONCLUSIONS: We provide 1) the first MI value sets for the EQ-5D-5L and 2) code to construct MI value sets while accounting for state-level model misspecification. Our study suggests that ignoring parameter uncertainty in value sets leads to falsely narrow SEs. HIGHLIGHTS: Value sets for health state utility instruments are estimated subject to parameter uncertainty; this parameter uncertainty may exceed the minimum important difference of the instrument, yet it is not fully captured using current methods.This study creates the first multiply imputed value set for a multiattribute utility instrument, the EQ-5D-5L, to fully capture this parameter uncertainty.We apply the multiply imputed value set to 2 data sets from 1) the Canadian general public and 2) women with invasive breast cancer.Scoring the EQ-5D-5L using a multiply imputed value set led to wider standard error estimates, suggesting that the current practice of ignoring parameter uncertainty in the value set leads to falsely low standard errors.Our work will be of interest to methodologists and developers of the EQ-5D-5L and users of the EQ-5D-5L, such as health economists, researchers, and policy makers.

Disease activity trajectories in juvenile dermatomyositis from childhood to adulthood.

OBJECTIVES: To assess whether there are identifiable subgroups of disease activity trajectory in a population of juvenile dermatomyositis (JDM) patients-followed throughout childhood and into adulthood-and determine factors that predict those trajectory groupings. METHODS: This is a retrospective, longitudinal inception cohort of patients with idiopathic inflammatory myopathies, largely JDM. We sought to identify baseline factors that predict membership into different groups (latent classes) of disease activity trajectory. RESULTS: A total of 172 patients (64% females), with median age at diagnosis of 7.7 years, were analyzed. We studied 4,725 visits (1,471 patient-years). We identified 3 latent classes of longitudinal disease activity, as measured by the modified disease activity score (DASm), with distinct class trajectories predicted by DASm at baseline, and by the changes of DASm from either baseline to 3 months or baseline to 6 months (early response to therapy). In the analysis in which DASm at baseline and the changes of DASm from baseline to 6 months are included as predictors, Class 1 (10%) has persistently high disease activity, Class 2 (34%) is characterized by moderate disease activity, and Class 3 (56%) is characterized by individuals with a high early disease activity but an apparently good response to treatment and long-term low disease activity. CONCLUSION: High early disease activity, and treatment resistance in the first few months, predict a more chronic longitudinal course of JDM.

Causal inference with longitudinal data subject to irregular assessment times.

Data collected in the context of usual care present a rich source of longitudinal data for research, but often require analyses that simultaneously enable causal inferences from observational data while handling irregular and informative assessment times. An inverse-weighting approach to this was recently proposed, and handles the case where the assessment times are at random (ie, conditionally independent of the outcome process given the observed history). In this paper, we extend the inverse-weighting approach to handle a special case of assessment not at random, where assessment and outcome processes are conditionally independent given past observed covariates and random effects. We use multiple outputation to accomplish the same purpose as inverse-weighting, and apply it to the Liang semi-parametric joint model. Moreover, we develop an alternative joint model that does not require covariates for the outcome model to be known at times where there is no assessment of the outcome. We examine the performance of these methods through simulation and illustrate them through a study of the causal effect of wheezing on time spent playing outdoors among children aged 2-9 years and enrolled in the TargetKids! study.

Evaluating the Impact of Ex Vivo Lung Perfusion on Organ Transplantation: A Retrospective Cohort Study.

BACKGROUND: Ex vivo lung perfusion (EVLP) sustains and allows advanced assessment of potentially useable donor lungs before transplantation, potentially relieving resource constraints. OBJECTIVE: We sought to characterize the effect of EVLP on organ utilization and patient outcomes. METHODS: We performed a retrospective, before-after cohort study using linked institutional data sources of adults wait-listed for lung transplant and donor organs transplanted in Ontario, Canada between 2005 and 2019. We regressed the annual number of transplants against year, EVLP use, and organ characteristics. Time-to-transplant, waitlist mortality, primary graft dysfunction, tracheostomy insertion, in-hospital mortality, and chronic lung allograft dysfunction were evaluated using propensity score-weighted regression. RESULTS: EVLP availability ( P =0.01 for interaction) and EVLP use ( P <0.001 for interaction) were both associated with steeper increases in transplantation than expected by historical trends. EVLP was associated with more donation after circulatory death and extended-criteria donors transplanted, while the numbers of standard-criteria donors remained relatively stable. Significantly faster time-to-transplant was observed after EVLP was available (hazard ratio=1.64 [1.41-1.92]; P <0.001). Fewer patients died on the waitlist after EVLP was available, but no difference in the hazard of waitlist mortality was observed (HR=1.19 [0.81-1.74]; P =0.176). We observed no difference in the likelihood of chronic lung allograft dysfunction before versus after EVLP was available. CONCLUSIONS: We observed a significant increase in organ transplantation since EVLP was introduced into practice, predominantly from increased acceptance of donation after circulatory death and extended-criteria lungs. Our findings suggest that EVLP-associated increases in organ availability meaningfully alleviated some barriers to transplant.

Genetics of longitudinal kidney function in children and adults with systemic lupus erythematosus.

OBJECTIVES: Genome-wide association studies (GWAS) have identified loci associated with estimated glomerular filtration rate (eGFR). Few LN risk loci have been identified to date. We tested the association of SLE and eGFR polygenic risk scores (PRS) with repeated eGFR measures from children and adults with SLE. METHODS: Patients from two tertiary care lupus clinics that met ≥4 ACR and/or SLICC criteria for SLE were genotyped on the Illumina MEGA or Omni1-Quad arrays. PRSs were calculated for SLE and eGFR, using published weighted GWA-significant alleles. eGFR was calculated using the CKD-EPI and Schwartz equations. We tested the effect of eGFR- and SLE-PRSs on eGFR mean and variance, adjusting for age at diagnosis, sex, ancestry, follow-up time, and clinical event flags. RESULTS: We included 1158 SLE patients (37% biopsy-confirmed LN) with 36 733 eGFR measures over a median of 7.6 years (IQR: 3.9-15.3). LN was associated with lower within-person mean eGFR [LN: 93.8 (s.d. 26.4) vs non-LN: 101.6 (s.d. 17.7) mL/min per 1.73 m2; P < 0.0001] and higher variance [LN median: 157.0 (IQR: 89.5, 268.9) vs non-LN median: 84.9 (IQR: 46.9, 138.2) (mL/min per 1.73 m2)2; P < 0.0001]. Increasing SLE-PRSs were associated with lower mean eGFR and greater variance, while increasing eGFR-PRS was associated with increased eGFR mean and variance. CONCLUSION: We observed significant associations between SLE and eGFR PRSs and repeated eGFR measurements, in a large cohort of children and adults with SLE. Longitudinal eGFR may serve as a powerful alternative outcome to LN categories for discovery of LN risk loci.

Determining the impact of ex-vivo lung perfusion on hospital costs for lung transplantation: A retrospective cohort study.

INTRODUCTION: Ex-vivo lung perfusion (EVLP) has improved organ utilization for lung transplantation, but it is not yet known whether the benefits of this technology offset its additional costs. We compared the institutional costs of lung transplantation before vs after EVLP was available to identify predictors of costs and determine the health-economic impact of EVLP. METHODS: We performed a retrospective, before-after, propensity-score weighted cohort study of patients wait-listed for lung transplant at University Health Network (UHN) in Ontario, Canada, between January 2005 and December 2019 using institutional administrative data. We compared costs, in 2019 Canadian Dollars ($), between patients referred for transplant before EVLP was available (Pre-EVLP) to after (Modern EVLP). Cumulative costs were estimated using a novel application of multistate survival models. Predictors of costs were identified using weighted log-gamma generalized linear regression. RESULTS: A total of 1,199 patients met inclusion criteria (352 Pre-EVLP; 847 Modern EVLP). Mean total costs for the transplant hospitalization were $111,878 ($94,123-$130,767) in the Pre-EVLP era and $110,969 ($87,714-$136,000) in the Modern EVLP era. Cumulative five-year costs since referral were $278,777 ($82,575-$298,135) in the Pre-EVLP era and $293,680 ($252,832-$317,599) in the Modern EVLP era. We observed faster progression to transplantation when EVLP was available. EVLP availability was not a predictor of waitlist (cost ratio [CR] 1.04 [0.81-1.37]; p = 0.354) or transplant costs (CR 1.02 [0.80-1.29]; p = 0.425) but was associated with lower costs during posttransplant years 1&2 (CR 0.75 [0.58-1.06]; p = 0.05) and posttransplant years 3+ (CR 0.43 [0.26-0.74]; p = 0.001). CONCLUSIONS: At our center, EVLP availability was associated with faster progression to transplantation at no significant marginal cost.

Randomized Trials With Repeatedly Measured Outcomes: Handling Irregular and Potentially Informative Assessment Times.

Randomized trials are often designed to collect outcomes at fixed points in time after randomization. In practice, the number and timing of outcome assessments can vary among participants (i.e., irregular assessment). In fact, the timing of assessments may be associated with the outcome of interest (i.e., informative assessment). For example, in a trial evaluating the effectiveness of treatments for major depressive disorder, not only did the timings of outcome assessments vary among participants but symptom scores were associated with assessment frequency. This type of informative observation requires appropriate statistical analysis. Although analytic methods have been developed, they are rarely used. In this article, we review the literature on irregular assessments with a view toward developing recommendations for analyzing trials with irregular and potentially informative assessment times. We show how the choice of analytic approach hinges on assumptions about the relationship between the assessment and outcome processes. We argue that irregular assessment should be treated with the same care as missing data, and we propose that trialists adopt strategies to minimize the extent of irregularity; describe the extent of irregularity in assessment times; make their assumptions about the relationships between assessment times and outcomes explicit; adopt analytic techniques that are appropriate to their assumptions; and assess the sensitivity of trial results to their assumptions.

Quantifying the extent of visit irregularity in longitudinal data.

The timings of visits in observational longitudinal data may depend on the study outcome, and this can result in bias if ignored. Assessing the extent of visit irregularity is important because it can help determine whether visits can be treated as repeated measures or as irregular data. We propose plotting the mean proportions of individuals with 0 visits per bin against the mean proportions of individuals with >1 visit per bin as bin width is varied and using the area under the curve (AUC) to assess the extent of irregularity. The AUC is a single score which can be used to quantify the extent of irregularity and assess how closely visits resemble repeated measures. Simulation results confirm that the AUC increases with increasing irregularity while being invariant to sample size and the number of scheduled measurement occasions. A demonstration of the AUC was performed on the TARGet Kids! study which enrolls healthy children aged 0-5 years with the aim of investigating the relationship between early life exposures and later health problems. The quality of statistical analyses can be improved by using the AUC as a guide to select the appropriate analytic outcome approach and minimize the potential for biased results.

Estimating the population health burden of Lyme disease in Ontario, Canada: a microsimulation modelling approach.

BACKGROUND: If untreated, Lyme disease can lead to long-term sequelae and post-treatment Lyme disease syndrome (PTLDS), resulting in reduced health-related quality of life. The objective of this study was to develop a microsimulation model to estimate the population-level health burden of Lyme disease in Ontario, Canada. METHODS: We developed a Lyme disease history model using microsimulation, simulating 100 000 people (mean age 37.6 yr, 51% female) from 2017 in Ontario over a lifetime risk of infection and time horizon. We extracted the sensitivity and specificity of the 2-tier testing recommended by the Canadian Public Health Laboratory Network, probabilities and health state utility values from the published literature and health administrative data. Our reported outcomes from our stochastic analysis include diagnosed cases of Lyme disease (stratified by stage), undiagnosed infections, sequelae, individuals with PTLDS and quality-adjusted life-years (QALYs) lost. RESULTS: Our model estimated 333 (95% confidence interval [CI] 329-337) infections over the lifetime of 100 000 simulated people (mean age 37.6 yr, 51% female), with 92% (95% CI 91%-93%) of infections diagnosed. Of those 308 people with Lyme Disease diagnoses, 67 (95% CI 65-69) developed sequelae (e.g., arthritic, cardiac, neurologic sequelae), and 34 (95% CI 33-35) developed PTLDS. Lyme disease resulted in a loss of 84.5 QALYs (95% CI 82.9-86.2) over the lifetime of the simulated cohort. Sensitivity and scenario analysis showed that increasing incidence rates of Lyme disease, potential underreporting, duration of PTLDS and quality of life (health state utility) associated with PTLDS had the greatest impact on health burden. INTERPRETATION: Lyme disease contributes considerable health burden in terms of QALYs lost. Our analysis provides evidence to understand the disease burden and lays the foundation to assess the cost-effectiveness of pharmaceutical and nonpharmaceutical interventions.

Clustered longitudinal data subject to irregular observation.

Data collected longitudinally as part of usual health care is becoming increasingly available for research, and is often available across several centres. Because the frequency of follow-up is typically determined by the patient's health, the timing of measurements may be related to the outcome of interest. Failure to account for the informative nature of the observation process can result in biased inferences. While methods for accounting for the association between observation frequency and outcome are available, they do not currently account for clustering within centres. We formulate a semi-parametric joint model to include random effects for centres as well as subjects. We also show how inverse-intensity weighted GEEs can be adapted to account for clustering, comparing stratification, frailty models, and covariate adjustment to account for clustering in the observation process. The finite-sample performance of the proposed methods is evaluated through simulation and the methods illustrated using a study of the relationship between outdoor play and air quality in children aged 2-9 living in the Greater Toronto Area.

Using Both Time Tradeoff and Discrete Choice Experiments in Valuing the EQ-5D: Impact of Model Misspecification on Value Sets.

Introduction. The EQ-5D-5L valuation protocol contains both time tradeoff (TTO) tasks and discrete choice experiments (DCE), raising the question of how to best use these in creating a value set. The hybrid model, which combines TTO and DCE data, has emerged as a commonly used approach. However, this model assumes independence among responses from the same individual, a linear relationship between TTO and DCE utilities, and, in many implementations, homoscedastic residuals. The aims of this study are to examine alternatives to these assumptions and determine the impact of misspecification on value sets. Methods. We performed a simulation study, parameterized using the US EQ-5D-5L valuation study, to assess the impact of model misspecification. We simulated TTO and DCE data with nonlinear relationships between TTO and DCE utilities, heteroscedastic errors, and correlated responses. Simulated data were analyzed using hybrid models with and without heteroscedasticity, Tobit models with and without heteroscedasticity, a latent class model, and a mixed model. Results. Mean absolute errors (MAEs) for correctly specified models were <0.05, whereas models that incorrectly assumed a linear relationship between TTO and DCE utilities or homoscedasticity of TTO responses featured states with an MAE >0.1. When a linear relationship between TTO and DCE utilities held, using both TTO and DCE data under correct specification yielded smaller MAEs compared with using TTO data alone but yielded larger MAEs when a linear relationship did not hold. Mistakenly assuming homoscedasticity led to increased MAEs, whereas ignoring dependence did not. Conclusions. Because heteroscedasticity in TTO utilities and nonlinear associations between DCE and TTO utilities have been noted, we recommend careful assessment of scedasticity and linearity to ascertain the suitability of a hybrid model.

The Theoretical Relationship between Sample Size and Expected Predictive Precision for EQ-5D Valuation Studies: A Mathematical Exploration and Simulation Study.

Background. Scoring algorithms of multi-attribute utility instruments (MAUI) are developed in valuation studies and are hence estimated subject to uncertainty. Valuation studies need to be designed to achieve reasonable accuracy. We aim to provide the first closed-form mathematical formula for the mean square error (MSE) of an additive MAUI as a function of sample size that acknowledges that the MAUI model for the mean utility is not a perfect fit. Methods. Based on the design of the EQ-5D valuation study, we derived our closed-form formula in terms of sample size and number of directly valued health states overall and per subject. We validated our formula by conducting a simulation study using the US EQ-5D-3L valuation data set and examined the effect of using a random-effects versus an ordinary least-squares model and the effect of heteroscedasticity. We explored the effect of sample size and number of valued health states. Results. The simulation study validated our MSE-based closed-form formula regardless of whether assuming a random-effects model versus an ordinary least squares model or heteroscedasticity versus homoscedasticity. As the sample size approaches infinity, the MSE does not approach zero but levels off asymptotically. The improvement based on increasing sample is more prominent when the sample is small. When the sample size is greater than 300 to 500, further increases do not meaningfully improve the MSE, while increasing the number of health states can further improve the MSE. Conclusion. We have derived a closed-form formula to calculate the MSE of an additive MAUI scoring algorithm based on sample size and number of health states, which will enable the developers of MAUI valuation studies to calculate the required sample size for their desired predictive precision.

Summarizing the extent of visit irregularity in longitudinal data.

BACKGROUND: Observational longitudinal data often feature irregular, informative visit times. We propose descriptive measures to quantify the extent of irregularity to select an appropriate analytic outcome approach. METHODS: We divided the study period into bins and calculated the mean proportions of individuals with 0, 1, and > 1 visits per bin. Perfect repeated measures features everyone with 1 visit per bin. Missingness leads to individuals with 0 visits per bin while irregularity leads to individuals with > 1 visit per bin. We applied these methods to: 1) the TARGet Kids! study, which invites participation at ages 2, 4, 6, 9, 12, 15, 18, 24 months, and 2) the childhood-onset Systemic Lupus Erythematosus (cSLE) study which recommended at least 1 visit every 6 months. RESULTS: The mean proportions of 0 and > 1 visits per bin were above 0.67 and below 0.03 respectively in the TARGet Kids! study, suggesting repeated measures with missingness. For the cSLE study, bin widths of 6 months yielded mean proportions of 1 and > 1 visits per bin of 0.39, suggesting irregular visits. CONCLUSIONS: Our methods describe the extent of irregularity and help distinguish between protocol-driven visits and irregular visits. This is an important step in choosing an analytic strategy for the outcome.

Long-Term Sequelae and Health-Related Quality of Life Associated With Lyme Disease: A Systematic Review.

Lyme disease (LD) is the most commonly reported vector-borne disease, but its clinical consequences remain uncertain. We conducted a systematic review of the long-term sequelae and health-related quality of life (HRQoL) associated with LD in North America and Europe. We performed searches in 6 electronic databases up to December 2018 following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, including observational studies reporting long-term sequelae, HRQoL, and prognostic factors. We included 46 studies, published between 1994 and 2019. Based on 21 studies reporting attributable outcomes, higher proportions of sequelae reported from exposed patients were: neck pain, myalgia, arthralgia, paresthesia, sleep disorder, poor appetite, and concentration difficulties. Patients with PTLDS reported impaired HRQoL compared to the general US population. Included studies were highly heterogeneous in terms of study design, settings, patient characteristics, and quality. Patients with LD are more likely to report nonspecific long-term sequelae, especially those experiencing persistent symptoms posttreatment. Opportunities exist for prospective longitudinal studies to better understand LD outcomes.

Care and outcomes of Canadian children hospitalised with periorbital and orbital cellulitis: protocol for a multicentre, retrospective cohort study.

INTRODUCTION: Skin and soft tissue infections of the eye can be classified based on anatomic location as either anterior to the orbital septum (ie, periorbital cellulitis) or posterior to the orbital septum (ie, orbital cellulitis). These two conditions are often considered together in hospitalised children as clinical differentiation is difficult, especially in young children. Prior studies have identified variation in management of hospitalised children with orbital cellulitis; however, they have been limited either as single centre studies or by the use of administrative data which lacks clinical details important for interpreting variation in care. We aim to describe the care and outcomes of Canadian children hospitalised with periorbital and orbital cellulitis. METHOD AND ANALYSIS: This is a multisite retrospective cohort study including previously healthy children aged 2 months to 18 years admitted to hospital with periorbital or orbital cellulitis from 2009 to 2018. Clinical data from medical records from multiple Canadian hospitals will be collected, including community and academic centres. Demographic characteristics and study outcomes will be summarised using descriptive statistics, including diagnostic testing, antibiotic therapy, adjunctive therapy, surgical intervention and clinical outcomes. Variation will be described and evaluated using χ² test or Kruskal-Wallis test. Generalised linear mixed models will be used to identify predictors of surgical intervention and longer length of stay. ETHICS AND DISSEMINATION: Approval of the study by the Research Ethics Board at each participating site has been obtained prior to data extraction. Study results will be disseminated by presentations at national and international meetings and by publications in high impact open access journals. By identifying important differences in management and outcomes by each hospital, the results will identify areas where care can be improved, practice standardised, unnecessary diagnostic imaging reduced, pharmacotherapy rationalised and where trials are needed.

Latent Class Models Reveal Poor Agreement between Discrete-Choice and Time Tradeoff Preferences.

Background. In health economics, there has been interest in using discrete-choice experiments (DCEs) to derive preferences for health states in lieu of previously established approaches like time tradeoff (TTO). We examined whether preferences elicited through DCEs are associated and agree with preferences elicited through TTO tasks. Methods. We used data from 1073 respondents to the Canadian EQ-5D-5L valuation study. Multivariate mixed-effects models specified a common likelihood for the TTO and discrete-choice data, with separate but correlated random effects for the TTO and DCE data, for each of the 5 EQ-5D-5L dimensions. Multivariate latent class models allowed separate but associated latent classes for the DCE and TTO data. Results. Correlation between the random effects for the 2 tasks ranged from -0.12 to 0.75, with only pain/discomfort and anxiety/depression having at least a 50% posterior probability of strong (>0.6) correlation. Latent classes for the TTO and DCE data both featured 1 latent class capturing participants attaching large disutilities to pain/discomfort, another capturing participants attaching large disutility to anxiety/depression, and the third class capturing the remainder. Agreement in class membership was poor (κ coefficient: 0.081; 95% credible interval, 0.033-0.13). Fewer respondents expressed strong disutilities for problems with anxiety/depression or pain/discomfort in the TTO than the DCE data (17% v. 55%, respectively). Conclusions. Stated preferences using TTO and DCEs show association across dimensions but poor agreement at the level of individual health states within respondents. Joint models that assume agreement between DCE and TTO have been used to develop national value sets for the EQ-5D-5L. This work indicates that when combining data from both techniques, methods requiring association but not agreement are needed.

Conducting EQ-5D Valuation Studies in Resource-Constrained Countries: The Potential Use of Shrinkage Estimators to Reduce Sample Size.

BACKGROUND: Resource-constrained countries have difficulty conducting large EQ-5D valuation studies, which limits their ability to conduct cost-utility analyses using a value set specific to their own population. When estimates of similar but related parameters are available, shrinkage estimators reduce uncertainty and yield estimators with smaller mean square error (MSE). We hypothesized that health utilities based on shrinkage estimators can reduce MSE and mean absolute error (MAE) when compared to country-specific health utilities. METHODS: We conducted a simulation study (1,000 iterations) based on the observed means and standard deviations (or standard errors) of the EQ-5D-3L valuation studies from 14 counties. In each iteration, the simulated data were fitted with the model based on the country-specific functional form of the scoring algorithm to create country-specific health utilities ("naïve" estimators). Shrinkage estimators were calculated based on the empirical Bayes estimation methods. The performance of shrinkage estimators was compared with those of the naïve estimators over a range of different sample sizes based on MSE, MAE, mean bias, standard errors and the width of confidence intervals. RESULTS: The MSE of the shrinkage estimators was smaller than the MSE of the naïve estimators on average, as theoretically predicted. Importantly, the MAE of the shrinkage estimators was also smaller than the MAE of the naïve estimators on average. In addition, the reduction in MSE with the use of shrinkage estimators did not substantially increase bias. The degree of reduction in uncertainty by shrinkage estimators is most apparent in valuation studies with small sample size. CONCLUSION: Health utilities derived from shrinkage estimation allow valuation studies with small sample size to "borrow strength" from other valuation studies to reduce uncertainty.

Underestimation of Variance of Predicted Health Utilities Derived from Multiattribute Utility Instruments.

BACKGROUND: Parameter uncertainty in value sets of multiattribute utility-based instruments (MAUIs) has received little attention previously. This false precision leads to underestimation of the uncertainty of the results of cost-effectiveness analyses. The aim of this study is to examine the use of multiple imputation as a method to account for this uncertainty of MAUI scoring algorithms. METHOD: We fitted a Bayesian model with random effects for respondents and health states to the data from the original US EQ-5D-3L valuation study, thereby estimating the uncertainty in the EQ-5D-3L scoring algorithm. We applied these results to EQ-5D-3L data from the Commonwealth Fund (CWF) Survey for Sick Adults ( n = 3958), comparing the standard error of the estimated mean utility in the CWF population using the predictive distribution from the Bayesian mixed-effect model (i.e., incorporating parameter uncertainty in the value set) with the standard error of the estimated mean utilities based on multiple imputation and the standard error using the conventional approach of using MAUI (i.e., ignoring uncertainty in the value set). RESULT: The mean utility in the CWF population based on the predictive distribution of the Bayesian model was 0.827 with a standard error (SE) of 0.011. When utilities were derived using the conventional approach, the estimated mean utility was 0.827 with an SE of 0.003, which is only 25% of the SE based on the full predictive distribution of the mixed-effect model. Using multiple imputation with 20 imputed sets, the mean utility was 0.828 with an SE of 0.011, which is similar to the SE based on the full predictive distribution. CONCLUSION: Ignoring uncertainty of the predicted health utilities derived from MAUIs could lead to substantial underestimation of the variance of mean utilities. Multiple imputation corrects for this underestimation so that the results of cost-effectiveness analyses using MAUIs can report the correct degree of uncertainty.

Handling Regional Variation in Health State Preferences within a Country.

BACKGROUND: Health state preferences vary among countries, and country-specific value sets are important in health care reimbursement decisions. When decisions are made at the regional level, regional variation in health state preferences may be important. We propose that shrinkage analysis and Bland-Altman plots can be a helpful way to investigate regional variation. METHODS: The presence of regional variation can be investigated by introducing interactions between regions and the regression coefficients in the scoring algorithm. When variation is present, regional scoring algorithms can be derived through shrinkage analysis. The impact of using regional algorithms in place of the national algorithm can be investigated using simulation and illustrated using Bland-Altman plots. We applied this methodological approach to the Canadian EQ-5D-5L valuation study, which used time-tradeoff (TTO) tasks to elicit health state preferences from 1073 participants from 4 regions (Alberta, British Columbia, Ontario, and Quebec). RESULTS: There were statistically significant interactions between the fixed effects of the scoring algorithm and region. On computing regional scoring algorithms and applying them to the EQ-5D-5L health states reported by our population, the mean utility using the Canada-wide scoring algorithm was 0.87 (standard error, 0.0013), compared to 0.85 (0.0013) on using the algorithm for Alberta, 0.80 (0.0013) on using the algorithm for British Columbia, 0.91 (0.0013) for Ontario, and 0.89 (0.0014) for Quebec. CONCLUSIONS: When health care falls under regional jurisdiction, shrinkage estimators can be used to generate regional scoring algorithms for the EQ-5D-5L and Bland-Altman plots used to assess the importance of regional variation in health state preferences. Our results suggest that mean health state preferences vary among Canada's regions and make a sizable impact on estimates of population mean utility.

Bayesian approach to the assessment of the population-specific risk of inhibitors in hemophilia A patients: a case study.

BACKGROUND: Developing inhibitors is a rare event during the treatment of hemophilia A. The multifacets and uncertainty surrounding the development of inhibitors further complicate the process of estimating inhibitor rate from the limited data. Bayesian statistical modeling provides a useful tool in generating, enhancing, and exploring the evidence through incorporating all the available information. METHODS: We built our Bayesian analysis using three study cases to estimate the inhibitor rates of patients with hemophilia A in three different scenarios: Case 1, a single cohort of previously treated patients (PTPs) or previously untreated patients; Case 2, a meta-analysis of PTP cohorts; and Case 3, a previously unexplored patient population - patients with baseline low-titer inhibitor or history of inhibitor development. The data used in this study were extracted from three published ADVATE (antihemophilic factor [recombinant] is a product of Baxter for treating hemophilia A) post-authorization surveillance studies. Noninformative and informative priors were applied to Bayesian standard (Case 1) or random-effects (Case 2 and Case 3) logistic models. Bayesian probabilities of satisfying three meaningful thresholds of the risk of developing a clinical significant inhibitor (10/100, 5/100 [high rates], and 1/86 [the Food and Drug Administration mandated cutoff rate in PTPs]) were calculated. The effect of discounting prior information or scaling up the study data was evaluated. RESULTS: Results based on noninformative priors were similar to the classical approach. Using priors from PTPs lowered the point estimate and narrowed the 95% credible intervals (Case 1: from 1.3 [0.5, 2.7] to 0.8 [0.5, 1.1]; Case 2: from 1.9 [0.6, 6.0] to 0.8 [0.5, 1.1]; Case 3: 2.3 [0.5, 6.8] to 0.7 [0.5, 1.1]). All probabilities of satisfying a threshold of 1/86 were above 0.65. Increasing the number of patients by two and ten times substantially narrowed the credible intervals for the single cohort study (1.4 [0.7, 2.3] and 1.4 [1.1, 1.8], respectively). Increasing the number of studies by two and ten times for the multiple study scenarios (Case 2: 1.9 [0.6, 4.0] and 1.9 [1.5, 2.6]; Case 3: 2.4 [0.9, 5.0] and 2.6 [1.9, 3.5], respectively) had a similar effect. CONCLUSION: Bayesian approach as a robust, transparent, and reproducible analytic method can be efficiently used to estimate the inhibitor rate of hemophilia A in complex clinical settings.