Magnesium hydroxide versus macrogol/electrolytes in the prevention of opioid-induced constipation in incurable cancer patients: study protocol for an open-label, randomized controlled trial (the OMAMA study)

BACKGROUND

Opioid-induced constipation (OIC) is a common symptom in cancer patients treated with opioids with a prevalence of up to 59%. International guidelines recommend standard laxatives such as macrogol/electrolytes and magnesium hydroxide to prevent OIC, although evidence from randomized controlled trials is largely lacking. The aim of our study is to compare magnesium hydroxide with macrogol /electrolytes in the prevention of OIC in patients with incurable cancer and to compare side-effects, tolerability and cost-effectiveness.

METHODS

Our study is an open-label, randomized, multicenter study to examine if magnesium hydroxide is non-inferior to macrogol/electrolytes in the prevention of OIC. In total, 330 patients with incurable cancer, starting with opioids for pain management, will be randomized to treatment with either macrogol/electrolytes or magnesium hydroxide. The primary outcome measure is the proportion of patients with a score of < 30 on the Bowel Function Index (BFI), measured on day 14. The Rome IV criteria for constipation, side effects of and satisfaction with laxatives, pain scores, quality of life (using the EQ-5D-5L), daily use of laxatives and escape medication, and cost-effectiveness will also be assessed.

DISCUSSION

In this study we aim to examine if magnesium hydroxide is non-inferior to macrogol/electrolytes in the prevention of OIC. The outcome of our study will contribute to prevention of OIC and scientific evidence of guidelines on (opioid-induced) constipation.

TRIAL REGISTRATION

This trial is registered at clinicaltrials.gov: NCT05216328 and in the Dutch trial register: NTR80508. EudraCT number 2022-000408-36.

Mapping the EQ-5D-5L from the Spanish national health survey functional disability scale through Bayesian networks

PURPOSE

Preference-based measures are valuable tools for evaluating therapeutic interventions and for cost-effectiveness studies. Mapping procedures are useful when it is not possible to collect these kind of measures. The objective of this study was to evaluate which mapping method is the most appropriate to estimate the EQ-5D-5L index from the Spanish National Health Survey functional disability scale.

METHODS

The sample, formed by 5708 older adults (aged 65 years or older), was drawn from the Spanish National Health Survey ("Encuesta Nacional de Salud en España," ENSE in Spanish 2011-2012). The predictions of EQ-5D-5L index were performed with response mapping using Bayesian network (BN), ordered logit (Ologit), and multinomial logistic (ML). The following direct methods were used: ordinary least squares (OLS) and Tobit regression. The intraclass correlation coefficient (ICC), absolute error (MAE), mean squared error (MSE), and root-mean squared error (RMSE) were calculated to compare all models. The predictions of response models were obtained through the expected value method.

RESULTS

BN model showed the highest ICC (0.756, 95% confidence interval, CI 0.733-0.777) and lowest MAE (0.110, 95% CI 0.104-0.115). OLS was the model with worse accuracy results with lowest ICC (0.621, 95% CI 0.553-0.681) and highest MAE (0.159, 95%CI: 0.145-0.173).

CONCLUSION

Indirect mapping methods (BN, Ologit, and ML) had a better accuracy than the direct methods. The response mapping approach provides a robust method to estimate EQ-5D-5L scores from the functional disability scale.

Flexible Bayesian longitudinal models for cost-effectiveness analyses with informative missing data

Cost-effectiveness analyses (CEA) are recommended to include sensitivity analyses which make a range of contextually plausible assumptions about missing data. However, with longitudinal data on, for example, patients' health-related quality of life (HRQoL), the missingness patterns can be complicated because data are often missing both at specific timepoints (interim missingness) and following loss to follow-up. Methods to handle these complex missing data patterns have not been developed for CEA, and must recognize that data may be missing not at random, while accommodating both the correlation between costs and health outcomes and the non-normal distribution of these endpoints. We develop flexible Bayesian longitudinal models that allow the impact of interim missingness and loss to follow-up to be disentangled. This modeling framework enables studies to undertake sensitivity analyses according to various contextually plausible missing data mechanisms, jointly model costs and outcomes using appropriate distributions, and recognize the correlation among these endpoints over time. We exemplify these models in the REFLUX study in which 52% of participants had HRQoL data missing for at least one timepoint over the 5-year follow-up period. We provide guidance for sensitivity analyses and accompanying code to help future studies handle these complex forms of missing data.

Response Mapping Methods to Estimate the EQ-5D-5L From the Western Ontario McMaster Universities Osteoarthritis in Patients With Hip or Knee Osteoarthritis

OBJECTIVES

The mapping technique can estimate generic preference-based measure scores through a specific measure that cannot be used in economic evaluations. This study compared 2 response mapping methods to estimate EQ-5D-5L scores using the Western Ontario McMaster Universities Osteoarthritis (WOMAC).

METHODS

The sample consisted of 758 patients with the hip or knee osteoarthritis recruited in baseline. Bayesian networks (BN) and multinomial logistic regression (ML) were used as response mapping models. Predictions were obtained using the 6-month follow-up as a validation sample. The mean absolute error, mean squared error, deviation from the root mean squared error and intraclass correlation coefficient were calculated as precision measures.

RESULTS

There was 5.5% of missing data, which was removed. The mean age was 69.6 years (standard deviation = 10.5), with 61.6% of women. The BN model presented lower mean absolute error, mean squared error, root mean squared error and higher intraclass correlation coefficient than the ML model. Only the WOMAC items pain and physical function items were related with the EQ-5D-5L dimensions.

CONCLUSION

BN response mapping models are more robust methods, with better prediction results, than ML models. The BN model also provided a graphic representation of the dependency relationships between the EQ-5D-5L dimensions and the different WOMAC items that could be useful in the clinical investigation of patients with hip or knee osteoarthritis.

EQ-5D-Derived Health State Utility Values in Hematologic Malignancies: A Catalog of 796 Utilities Based on a Systematic Review

OBJECTIVES

We performed a systematic review of health state utility values (HSUVs) obtained using the EQ-5D questionnaire for patients with hematologic malignancies.

METHODS

The following databases were searched up to September 2018: MEDLINE, EMBASE, The Cochrane Library, and the EQ-5D publications database on the EuroQol website. Additional references were extracted from reviewed articles. Only studies presenting EQ-Index results were incorporated. In view of the heterogeneity across the included publications, we limited ourselves to a narrative synthesis of original HSUVs found.

RESULTS

Fifty-nine studies (described in 63 articles) met the inclusion criteria. Data from 21 635 respondents provided 796 HSUV estimates for hematologic malignancy patients. EQ-Index scores ranged from -0.025 to 0.980. The most represented area was multiple myeloma (4 studies, 11 112 patients, and 249 HSUVs). In clinical areas such as chronic myeloid leukemia, acute myeloid leukemia, chronic lymphocytic leukemia, non-Hodgkin lymphoma, and mantle cell lymphoma, we described over 50 health utilities in each. In contrast, we identified only 13 HSUVs (based on 4 studies and the data of 166 patients) for Hodgkin lymphoma. Areas without EQ-5D-based health utilities comprised: polycythemia vera, primary myelofibrosis, essential thrombocythemia, mastocytosis, myeloid sarcoma, chronic myelomonocytic, eosinophilic leukemia, and neutrophilic leukemia.

CONCLUSIONS

There is a wide range of HSUVs available for hematologic cancer patients with different indications. The review provides a catalog of utility values for use in cost-effectiveness models for hematologic malignancies.

'Mapping' Health State Utility Values from Non-preference-Based Measures: A Systematic Literature Review in Rare Diseases

BACKGROUND

The use of patient-reported outcome measures (PROMs) to monitor the effects of disease and treatment on patient symptomatology and daily life is increasing in rare diseases (RDs) (i.e. those affecting less than one in 2000 people); however, these instruments seldom yield health state utility values (HSUVs) for cost-utility analyses. In such a context, 'mapping' allows HSUVs to be obtained by establishing a statistical relationship between a 'source' (e.g. a disease-specific PROM) and a 'target' preference-based measure [e.g. the EuroQol-5 Dimension (EQ-5D) tool].

OBJECTIVE

This study aimed to systematically review all published studies using 'mapping' to derive HSUVs from non-preference-based measures in RDs, and identify any critical issues related to the main features of RDs, which are characterised by small, heterogeneous, and geographically dispersed patient populations.

METHODS

The following databases were searched during the first half of 2019 without time, study design, or language restrictions: MEDLINE (via PubMed), the School of Health and Related Research Health Utility Database (ScHARRHUD), and the Health Economics Research Centre (HERC) database of mapping studies (version 7.0). The keywords combined terms related to 'mapping' with Orphanet's list of RD indications (e.g. 'acromegaly') in addition to 'rare' and 'orphan'. 'Very rare' diseases (i.e. those with fewer than 1000 cases or families documented in the medical literature) were excluded from the searches. A predefined, pilot-tested extraction template (in Excel^®) was used to collect structured information from the studies.

RESULTS

Two groups of studies were identified in the review. The first group (n = 19) developed novel mapping algorithms in 13 different RDs. As a target measure, the majority used EQ-5D, and the others used the Short-Form Six-Dimension (SF-6D) and 15D; most studies adopted ordinary least squares (OLS) regression. The second group of studies (n = 9) applied previously published algorithms in non-RDs to comparable RDs, mainly in the field of cancer. The critical issues relating to 'mapping' in RDs included the availability of very few studies, the relatively high number of cancer studies, and the absence of research in paediatric RDs. Moreover, the reviewed studies recruited small samples, showed a limited overlap between RD-specific and generic PROMs, and highlighted the presence of cultural and linguistic factors influencing results in multi-country studies. Lastly, the application of existing algorithms developed in non-RDs tended to produce inaccuracies at the bottom of the EQ-5D scale, due to the greater severity of RDs.

CONCLUSIONS

More research is encouraged to develop algorithms for a broader spectrum of RDs (including those affecting young children), improve mapping study quality, test the generalisability of algorithms developed in non-RDs (e.g. HIV) to rare variants or evolutions of the same condition (e.g. AIDS wasting syndrome), and verify the robustness of results when mapped HSUVs are used in cost-utility models.

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.

An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values

BACKGROUND

Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values (HSUVs) from data obtained from another health-related quality of life (HRQoL) measure. There have been several methodological developments in this area since a previous review up to 2007.

OBJECTIVE

To provide an updated review of all mapping studies that map from HRQoL measures to target generic preference-based measures (EQ-5D measures, SF-6D, HUI measures, QWB, AQoL measures, 15D/16D/17D, CHU-9D) published from January 2007 to October 2018.

DATA SOURCES

A systematic review of English language articles using a variety of approaches: searching electronic and utilities databases, citation searching, targeted journal and website searches.

STUDY SELECTION

Full papers of studies that mapped from one health measure to a target preference-based measure using formal statistical regression techniques.

DATA EXTRACTION

Undertaken by four authors using predefined data fields including measures, data used, econometric models and assessment of predictive ability.

RESULTS

There were 180 papers with 233 mapping functions in total. Mapping functions were generated to obtain EQ-5D-3L/EQ-5D-5L-EQ-5D-Y (n = 147), SF-6D (n = 45), AQoL-4D/AQoL-8D (n = 12), HUI2/HUI3 (n = 13), 15D (n = 8) CHU-9D (n = 4) and QWB-SA (n = 4) HSUVs. A large number of different regression methods were used with ordinary least squares (OLS) still being the most common approach (used ≥ 75% times within each preference-based measure). The majority of studies assessed the predictive ability of the mapping functions using mean absolute or root mean squared errors (n = 192, 82%), but this was lower when considering errors across different categories of severity (n = 92, 39%) and plots of predictions (n = 120, 52%).

CONCLUSIONS

The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions.

Frequentist and Bayesian meta-regression of health state utilities for multiple myeloma incorporating systematic review and analysis of individual patient data

This analysis presents the results of a systematic review for health state utilities in multiple myeloma, as well as analysis of over 9,000 observations taken from registry and trial data. The 27 values identified from 13 papers are then synthesised in a frequentist nonparametric bootstrap model and a Bayesian meta-regression. Results were similar between the frequentist and Bayesian models with low utility on disease diagnosis (approximately 0.55), raising to approximately 0.65 on first line treatment and declining slightly with each subsequent line. Stem cell transplant was also found to be a significant predictor of health-related quality of life in both individual patient data and meta-regression, with an increased utility of approximately 0.06 across different models. The work presented demonstrates the feasibility of Bayesian methods for utility meta-regression, whilst also presenting an internally consistent set of data from the analysis of registry data. To facilitate easy updating of the data and model, data extraction tables and model code are provided as Data S1. The main limitations of the model relate to the low number of studies available, particularly in highly pretreated patients.

Use of Bayesian methods to model the SF-6D health state preference based data

Background

Conventionally, models used for health state valuation data have been frequentists. Recently a number of researchers have investigated the use of Bayesian methods in this area. The aim of this paper is to put on the map of modelling a new approach to estimating SF-6D health state utility values using Bayesian methods. This will help health care professionals in deriving better health state utilities of the original UK SF-6D for their specialized applications.

Methods

The valuation study is composed of 249 SF-6D health states valued by a representative sample of the UK population using the standard gamble technique. Throughout this paper, we present four different models, including one simple linear regression model and three random effect models. The predictive ability of these models is assessed by comparing predicted and observed mean SF-6D scores, R²/adjusted R² and RMSE. All analyses were carried out using Bayesian Markov chain Monte Carlo (MCMC) simulation methods freely available in the specialist software WinBUGS.

Results

The random effects model with interaction model performs best under all criterions, with mean predicted error of 0.166, R²/adjusted R² of 0.683 and RMSE of 0.218.

Conclusions

The Bayesian models provide flexible approaches to estimate mean SF-6D utility estimates, including characterizing the full range of uncertainty inherent in these estimates. We hope that this work will provide applied researchers with a practical set of tools to appropriately model outcomes in cost-effectiveness analysis.

Greater Spending Associated with Improved Survival for Some Cancers in OCM-Defined Episodes

BACKGROUND

Previous research finds significant variation in spending and utilization across regions, with little evidence of differences in outcomes. While such findings have been interpreted as evidence that spending can be reduced without compromising patient outcomes, the link between spending variation and outcomes remains a critical question.

OBJECTIVE

To use evidence from geographic variations in spending and an individual-level survival analysis to test whether spending within oncology care episodes is associated with survival, where episodes are defined as in the Center for Medicare and Medicaid Innovation's Oncology Care Model (OCM).

METHODS

In this retrospective cohort analysis, patient data from the Surveillance, Epidemiology and End Results Medicare (SEER-Medicare) database for 2007-2013 were linked to hospital referral regions (HRRs) using ZIP codes. Patients in the SEER program are a part of selected population-based cancer registries throughout the United States whose records are linked to Medicare enrollment and claims data (93% of elderly registry patients were successfully linked to Medicare data). Episodes of cancer care were defined as in the OCM: 6 months following a triggering chemotherapy claim. We analyzed episodes of care for 5 tumor types: advanced breast cancer (BC), non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), multiple myeloma (MM), and chronic myeloid leukemia (CML). We removed the effects of differentials in Medicare payment rates, which were mostly geographic. Regression analysis was then used to calculate standardized spending levels for each HRR, that is, spending adjusted for differences in patient and episode characteristics. To examine the effect of spending during OCM-defined episodes on individual-level survival, we used Cox regression with patient characteristics and standardized HRR spending per episode as covariates. To address concerns that may arise from multiple comparisons across the 5 tumor types, we used the Benjamini-Hochberg procedure to control the false discovery rate.

RESULTS

Our analysis showed significant differences in standardized spending across HRRs. Compared with spending at the 20th percentile episode, spending at the 80th percentile ranged from 25% higher ($57,392 vs. $45,995 for MM) to 47% higher ($36,920 vs. $24,127 for RCC), indicating practice style variation across regions. The hazard of dying for patients with NSCLC and MM statistically significantly decreased by 7% (HR = 0.93, P = 0.006) and 13% (HR = 0.87, P = 0.019), respectively, for a $10,000 increase in standardized spending (in 2013 U.S. dollars). For the 3 other cancers, spending effects were not statistically significant. After using the Benjamini-Hochberg procedure with a 5% false discovery rate, the effects of increased spending on improved survival for NSCLC and MM remained statistically significant.

CONCLUSIONS

The association we found between spending and survival suggests caution may be warranted for physicians, pharmacists, other health care professionals, and policymakers involved in efforts to reduce across-the-board spending within OCM-defined episodes for at least 2 of the 5 cancers studied.

DISCLOSURES

Funding for this research was provided by Novartis Pharmaceuticals to Precision Health Economics in support of research design, analysis, and technical writing services. The funder provided input on study design and comments on the draft report. Baumgardner, Shahabi, and Linthicum are employees of Precision Health Economics (PHE), a health care consultancy to the insurance and life science industries, including firms that market oncology therapies. Vine was an employee of PHE at the time of this research. Zacker is an employee of and shareholder in Novartis Pharmaceuticals. Lakdawalla is a consultant to PHE and holds equity in its parent company, Precision Medicine Group.

Decision Making and the Limits of Evidence: A Case Study of Acute Stroke in Pregnancy

We report the case of a pregnant woman treated for acute ischemic stroke and review the literature on acute stroke treatment in pregnancy. To our knowledge, this is the first case reporting the successful use of intravenous tissue plasminogen activator and a stent retriever for acute stroke in pregnancy. We then use this case to consider the way medical knowledge is used in therapeutic decision making and argue that decision making necessarily extends beyond the limits of clinical trial evidence.