Different methods are available for measuring medication adherence. In this paper, we conducted a scoping review to identify and summarize evidence of all studies comparing the Medication Event Monitoring System (MEMS) with alternative methods for measuring medication adherence. A literature search was performed using the open database www.iAdherence.org that includes all original studies reporting findings from the MEMS. Papers comparing methods for measuring adherence to solid oral formulations were included. Data was extracted using a standardized extraction table. A total of 117 articles fulfilled the inclusion criteria, including 251 comparisons. Most frequent comparisons were against self-report (n = 119) and pill count (n = 59). Similar outcome measures were used in 210 comparisons (84%), among which 78 used dichotomous variables (adherent or not) and 132 used continuous measures (adherence expressed as percentage). Furthermore, 32% of all comparisons did not estimate adherence over the same coverage period and 44% of all comparisons did not use a statistical method or used a suboptimal one. Only eighty-seven (35%) comparisons had similar coverage periods, similar outcome measures and optimal statistical methods. Compared to MEMS, median adherence was grossly overestimated by 17% using self-report, by 8% using pill count and by 6% using rating. In conclusion, among all comparisons of MEMS versus alternative methods for measuring adherence, only a few used adequate comparisons in terms of outcome measures, coverage periods and statistical method. Researchers should therefore use stronger methodological frameworks when comparing measurement methods and be aware that non-electronic measures could lead to overestimation of medication adherence.

Objectives The aim of this study was to assess the performance and impact of multilevel modelling (MLM) compared with ordinary least squares (OLS) regression in trial-based economic evaluations with clustered data. Methods Three thousand datasets with balanced and unbalanced clusters were simulated with correlation coefficients between costs and effects of − 0.5, 0, and 0.5, and intraclass correlation coefficients (ICCs) varying between 0.05 and 0.30. Each scenario was analyzed using both MLM and OLS. Statistical uncertainty around MLM and OLS estimates was estimated using bootstrapping. Performance measures were estimated and compared between approaches, including bias, root mean squared error (RMSE) and coverage probability. Cost and effect differences, and their corresponding confidence intervals and standard errors, incremental cost-effectiveness ratios, incremental net-monetary benefits and cost-effectiveness acceptability curves were compared. Results Cost-effectiveness outcomes were similar between OLS and MLM. MLM produced larger statistical uncertainty and coverage probabilities closer to nominal levels than OLS. The higher the ICC, the larger the effect on statistical uncertainty between MLM and OLS. Significant cost-effectiveness outcomes as estimated by OLS became non-significant when estimated by MLM. At all ICCs, MLM resulted in lower probabilities of cost effectiveness than OLS, and this difference became larger with increasing ICCs. Performance measures and cost-effectiveness outcomes were similar across scenarios with varying correlation coefficients between costs and effects. Conclusions Although OLS produced similar cost-effectiveness outcomes, it substantially underestimated the amount of variation in the data compared with MLM. To prevent suboptimal conclusions and a possible waste of scarce resources, it is important to use MLM in trial-based economic evaluations when data are clustered.

Background and ObjectivesThe aim was to systematically review whether the reporting and analysis of trial-based cost-effectiveness evaluations in the field of obstetrics and gynaecology comply with guidelines and recommendations, and whether this has improved over time.Data Sources and Selection CriteriaA literature search was performed in MEDLINE, the NHS Economic Evaluation Database (NHS EED) and the Health Technology Assessment (HTA) database to identify trial-based cost-effectiveness evaluations in obstetrics and gynaecology published between January 1, 2000 and May 16, 2017. Studies performed in middle- and low-income countries and studies related to prevention, midwifery, and reproduction were excluded.Data Collection and AnalysisReporting quality was assessed using the Consolidated Health Economic Evaluation Reporting Standard (CHEERS) statement (a modified version with 21 items, as we focused on trial-based cost-effectiveness evaluations) and the statistical quality was assessed using a literature-based list of criteria (8 items). Exploratory regression analyses were performed to assess the association between reporting and statistical quality scores and publication year.ResultsThe electronic search resulted in 5482 potentially eligible studies. Forty-five studies fulfilled the inclusion criteria, 22 in obstetrics and 23 in gynaecology. Twenty-seven (60%) studies did not adhere to 50% (n = 10) or more of the reporting quality items and 32 studies (71%) did not meet 50% (n = 4) or more of the statistical quality items. As for the statistical quality, no study used the appropriate method to assess cost differences, no advanced methods were used to deal with missing data, and clustering of data was ignored in all studies. No significant improvements over time were found in reporting or statistical quality in gynaecology, whereas in obstetrics a significant improvement in reporting and statistical quality was found over time.LimitationsThe focus of this review was on trial-based cost-effectiveness evaluations in obstetrics and gynaecology, so further research is needed to explore whether results from this review are generalizable to other medical disciplines.Conclusions and Implications of Key FindingsThe reporting and analysis of trial-based cost-effectiveness evaluations in gynaecology and obstetrics is generally poor. Since this can result in biased results, incorrect conclusions, and inappropriate healthcare decisions, there is an urgent need for improvement in the methods of cost-effectiveness evaluations in this field.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-017-0531-3) contains supplementary material, which is available to authorized users.

Background Baseline imbalances, skewed costs, the correlation between costs and effects, and missing data are statistical challenges that are often not adequately accounted for in the analysis of cost-effectiveness data. This study aims to illustrate the impact of accounting for these statistical challenges in trial-based economic evaluations. Methods Data from two trial-based economic evaluations, the REALISE and HypoAware studies, were used. In total, 14 full cost-effectiveness analyses were performed per study, in which the four statistical challenges in trial-based economic evaluations were taken into account step-by-step. Statistical approaches were compared in terms of the resulting cost and effect differences, ICERs, and probabilities of cost-effectiveness. Results In the REALISE study and HypoAware study, the ICER ranged from 636,744€/QALY and 90,989€/QALY when ignoring all statistical challenges to − 7502€/QALY and 46,592€/QALY when accounting for all statistical challenges, respectively. The probabilities of the intervention being cost-effective at 0€/ QALY gained were 0.67 and 0.59 when ignoring all statistical challenges, and 0.54 and 0.27 when all of the statistical challenges were taken into account for the REALISE study and HypoAware study, respectively. Conclusions Not accounting for baseline imbalances, skewed costs, correlated costs and effects, and missing data in trial-based economic evaluations may notably impact results. Therefore, when conducting trial-based economic evaluations, it is important to align the statistical approach with the identified statistical challenges in cost-effectiveness data. To facilitate researchers in handling statistical challenges in trial-based economic evaluations, software code is provided.

Introduction: The statistical quality of many trial-based economic evaluations is poor. When conducting trial-based economic evaluations, researchers often turn to national pharmacoeconomic guidelines for guidance. Therefore, this study reviewed which recommendations are currently given by national pharmacoeconomic guidelines on the statistical analysis of trial-based economic evaluations. Areas covered: 40 national pharmacoeconomic guidelines were identified. Data were extracted on the guidelines' recommendations on how to deal with baseline imbalances, skewed costs, correlated costs and effects, clustering of data, longitudinal data, and missing data in trial-based economic evaluations. Four guidelines (10%) were found to include recommendations on how to deal with baseline imbalances, five (13%) on how to deal with skewed costs, and seven (18%) on how to deal with missing data. Recommendations were very general in nature and recommendations on dealing with correlated costs and effects, clustering of data, and longitudinal data were lacking. Expert opinion: Current national pharmacoeconomic guidelines provide little to no guidance on how to deal with the statistical challenges to trial-based economic evaluations. Since the use of suboptimal statistical methods may lead to biased results, and, therefore, possibly to a waste of scarce resources, national agencies are advised to include more statistical guidance in their pharmacoeconomic guidelines.