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

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.

What are the risk factors associated with limited musculoskeletal health literacy in shoulder arthroplasty patients?

OBJECTIVES

Low health literacy is both pervasive in the United States and a substantial barrier to satisfactory patient care and the appropriate utilization of healthcare resources. This prospective study aims to evaluate the factors that contribute to limited musculoskeletal literacy in patients who undergo shoulder arthroplasty.

METHODS

Ninety patients undergoing shoulder replacement surgery completed demographics and Literacy in Musculoskeletal Problems (LiMP) surveys. Scores of less than six were considered indicative of limited musculoskeletal literacy.

RESULTS

The overall percentage of participants with limited musculoskeletal literacy was 38.8%. Multivariable logistic regression analysis with multiple imputation modeling demonstrated a significant positive relationship between patient income and adequate LiMP scores (p = 0.009) with an odds ratio of 1.15 (CI: 1.04; 1.28) while level of education (p = 0.173) and patient ethnicity (p = 0.830) among other patient characteristics did not have a significant relationship with LiMP scores.

CONCLUSION

In patients undergoing shoulder replacement surgery, low income was the only variable found to be predictive of limited musculoskeletal health literacy scores. Therefore, when discussing the risks and benefits of shoulder arthroplasty, orthopaedic surgeons should be cognizant of the possibility that any given patient may not meet the threshold of adequate musculoskeletal literacy.

Associations Between Preoperative Hyponatremia and 30-Day Perioperative Complications in Lumbar Interbody Spinal Fusion

STUDY DESIGN

Retrospective population database study.

OBJECTIVE

To investigate the relationship of preoperative hyponatremia to postoperative morbidity and mortality in lumbar interbody fusion patients.

SUMMARY OF BACKGROUND DATA

Optimization of preoperative patient selection and perioperative management can improve patient outcomes in spinal surgery. Hyponatremia, incidentally identified in 1.7% of the US population, has previously been tied to poorer postoperative outcomes in both the general surgery and orthopedic surgery populations.

MATERIALS AND METHODS

Using the National Surgical Quality Improvement Program database, the authors identified all lumbar interbody fusion patients treated between 2012 and 2014. Patients were classified as hyponatremic (Na<135 mEq/L) or as having normal sodium levels (135-145 mEq/L) preoperatively. The primary outcome was major morbidity and secondary endpoints were prolonged hospitalization, 30-day readmission, and reoperation. Multivariable linear regression was used to find independent predictors of these outcomes.

RESULTS

Of 10,654 patients, 45.6% were male individuals, 5.5% were hyponatremic, and 4.2% experienced a major postoperative complication. On multivariable analysis, preoperative hyponatremia was independently associated with major morbidity (odds ratio, 1.22; 95% confidence interval, 1.03-1.44; P<0.05) and prolonged hospitalization (odds ratio, 1.14; 95% confidence interval, 1.02-1.27).

CONCLUSIONS

Here the authors provide the first evidence suggesting preoperative hyponatremia is an independent predictor of major morbidity after lumbar interbody fusion. Hyponatremia may represent a modifiable risk factor for improved patient care and preoperative risk counseling.

Preoperative Hyponatremia and Perioperative Complications in Cervical Spinal Fusion

OBJECTIVE

Preoperative patient optimization is increasingly recognized as key to good surgical outcomes. Preoperative hyponatremia is a modifiable risk factor linked to poorer postoperative outcomes in other surgical fields. We provide the first investigation of the association of preoperative hyponatremia with morbidity and mortality in patients undergoing cervical spine surgery.

METHODS

We queried the National Surgical Quality Improvement Program registry for patients who underwent cervical spine fusion. Preoperative serum sodium levels were classified as normal (135-145 mEq/L) or hyponatremic (<135 mEq/L); hypernatremic patients were excluded from the analysis. Multivariable logistic analyses using a multiple imputations methodology were performed to determine significant predictors of major morbidity and mortality (MMM).

RESULTS

We included 20,817 patients, of whom 5.2% were hyponatremic at presentation. Preoperative hyponatremia was a significant predictor of MMM (odds ratio [OR], 1.23; 95% confidence interval [CI], 1.09-1.39), mortality (OR, 1.36; 95% CI, 1.03-1.77), major morbidity (OR, 1.24; 95% CI, 1.10-1.40), and odds of prolonged hospitalization (OR, 1.13; 95% CI, 1.04-1.23). Other significant predictors of MMM included age, undergoing an emergent versus nonemergent operation, having chronic obstructive pulmonary disease, having disseminated malignancy, being functionally dependent, presenting with sepsis or septic shock, and having an American Society of Anesthesiologists status of 3, 4, or 5. Similar results were seen in analyses using only complete cases and in sensitivity analyses.

CONCLUSIONS

Using the National Surgical Quality Improvement Program database, hyponatremia is observed in approximately 1 in every 20 patients undergoing cervical spine fusion. More importantly, it is a predictor of mortality, major morbidity, and prolonged hospitalization. From a systems-level perspective, preoperative hyponatremia may therefore represent a point of intervention for preoperative patient optimization.

Modelling a preference-based index for EQ-5D-3L and EQ-5D-3L + Sleep using a Bayesian framework

BACKGROUND

Conventionally, frequentist approach has been used to model health state valuation data. Recently, researchers started to explore the use of Bayesian methods in this area.

OBJECTIVES

This paper presents an alternative approach to modelling health state valuation data of the EQ-5D-3L and EQ-5D-3L + Sleep descriptive systems, using a Bayesian framework, and demonstrates its superiority to conventional frequentist methods.

METHODS

The valuation study is composed of 18 EQ-5D-3L health states and 18 EQ-5D-3L + Sleep health states valued by 160 members of the general public in South Yorkshire, UK, using the time tradeo-ff technique. Three different models were developed for EQ-5D-3L and EQ-5D-3L + Sleep accordingly using Bayesian Markov chain Monte Carlo simulation methods. Bayesian methods were applied to models fitted included a linear regression, random effect and random effect with covariates. The models are compared based on their predictive performance using mean predictions, root mean squared error (RMSE) and deviance information criterion (DIC). All analyses were performed using Bayesian Markov chain Monte Carlo simulation methods.

RESULTS

The random effects with covariates model performs best under all criterions for the two preference-based measures, with RMSE (0.037) and DIC (637.5) for EQ-5D-3L and RMSE (0.019), DIC (416.4) for EQ-5D + Sleep. Compared with models previously estimated using frequentist approach, the Bayesian models reported in this paper provided better predictions of observed values.

CONCLUSION

Bayesian methods provide a better way to model EQ-5D-3L valuation data with and without a sleep 'bolt-on' and provide a more flexible in characterizing the full range of uncertainty inherent in these estimates.

Estimation of a Preference-Based Summary Score for the Patient-Reported Outcomes Measurement Information System: The PROMIS®-Preference (PROPr) Scoring System

BACKGROUND

Health-related quality of life (HRQL) preference-based scores are used to assess the health of populations and patients and for cost-effectiveness analyses. The National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS^®) consists of patient-reported outcome measures developed using item response theory. PROMIS is in need of a direct preference-based scoring system for assigning values to health states.

OBJECTIVE

To produce societal preference-based scores for 7 PROMIS domains: Cognitive Function-Abilities, Depression, Fatigue, Pain Interference, Physical Function, Sleep Disturbance, and Ability to Participate in Social Roles and Activities.

SETTING

Online survey of a US nationally representative sample ( n = 983).

METHODS

Preferences for PROMIS health states were elicited with the standard gamble to obtain both single-attribute scoring functions for each of the 7 PROMIS domains and a multiplicative multiattribute utility (scoring) function.

RESULTS

The 7 single-attribute scoring functions were fit using isotonic regression with linear interpolation. The multiplicative multiattribute summary function estimates utilities for PROMIS multiattribute health states on a scale where 0 is the utility of being dead and 1 the utility of "full health." The lowest possible score is -0.022 (for a state viewed as worse than dead), and the highest possible score is 1.

LIMITATIONS

The online survey systematically excludes some subgroups, such as the visually impaired and illiterate.

CONCLUSIONS

A generic societal preference-based scoring system is now available for all studies using these 7 PROMIS health domains.

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.

Predictors of complications and readmission following spinal stereotactic radiosurgery

AIM

to identify preoperative factors associated with morbidity/mortality, hospital length of stay (LOS), 30-day readmission and operation rates following spinal stereotactic radiosurgery (SRS) for spinal tumors.

METHODS

The American College of Surgeons National Quality Improvement Program was queried from 2012 to 2014 to identify patients undergoing SRS for spinal tumors. Logistic regression was performed to identify predictors.

RESULTS

2714 patients were identified; 6.8% had major morbidity or mortality, 6.9% were readmitted within 30 days and 4.3% had a subsequent operation within 30 days. Age, BMI and American Society of Anesthesiologist (ASA) class were predictive of LOS. Major morbidity was predicted by age >80, BMI >35, high ASA, pretreatment functional dependence and baseline comorbidities. Predictors of operation within 30 days included preoperative steroid use, renal failure, BMI >35 and if the treatment was nonelective.

DISCUSSION

4-7% of patients undergoing SRS for spinal tumors have morbidity following the procedure. Factors predictive of morbidity, LOS, and subsequent operation included age, BMI, baseline comorbidities and functional status.

CONCLUSION

Identification of preoperative patient-specific factors that are predictive of post-treatment outcome will aid in patient selection and patient counseling leading to greater patient satisfaction and hospital efficiency.

Cost-effectiveness of pazopanib: an example of improved transparency and accessibility of industry-sponsored economic evaluations through publication in peer-reviewed journals

We congratulate Amdahl et al. on publishing their paper about the cost-effectiveness of pazopanib compared with sunitinib in metastatic renal cell carcinoma (mrcc) in Canada. [...]