No abstract

The statistical quality of trial‐based economic evaluations is often suboptimal, while a comprehensive overview of available statistical methods is lacking. Therefore, this review summarized and critically appraised available statistical methods for trial‐based economic evaluations. A literature search was performed to identify studies on statistical methods for dealing with baseline imbalances, skewed costs and/or effects, correlated costs and effects, clustered data, longitudinal data, missing data and censoring in trial‐based economic evaluations. Data was extracted on the statistical methods described, their advantages, disadvantages, relative performance and recommendations of the study. Sixty‐eight studies were included. Of them, 27 (40%) assessed methods for baseline imbalances, 39 (57%) assessed methods for skewed costs and/or effects, 27 (40%) assessed methods for correlated costs and effects, 18 (26%) assessed methods for clustered data, 7 (10%) assessed methods for longitudinal data, 26 (38%) assessed methods for missing data and 10 (15%) assessed methods for censoring. All identified methods were narratively described. This review provides a comprehensive overview of available statistical methods for dealing with the most common statistical complexities in trial‐based economic evaluations. Herewith, it can provide valuable input for researchers when deciding which statistical methods to use in a trial‐based economic evaluation.

Background and purpose: The posterolateral and direct lateral surgical approach are the 2 most common surgical approaches for performing a hemiarthroplasty in patients with a hip fracture. It is unknown which surgical approach is preferable in terms of (cost-)effectiveness and quality of life.Methods and analysis: We designed a multicenter randomized controlled trial (RCT) with an economic evaluation and a natural experiment (NE) alongside. We will include 555 patients ≥ 18 years with an acute femoral neck fracture. The primary outcome is patient-reported health-related quality of life assessed with the EQ-5D-5L. Secondary outcomes include healthcare costs, complications, mortality, and balance (including fear of falling, actual falls, and injuries due to falling). An economic evaluation will be performed for quality adjusted life years (QALYs). We will use variable block randomization stratified for hospital. For continuous outcomes, we will use linear mixed-model analysis. Dichotomous secondary outcome measures will be analyzed using chi-square statistics and logistic regression models. Primary analyses are based on the intention-to-treat principle. Additional as treated analyses will be performed to evaluate the effect of protocol deviations.Study summary: (i) Largest RCT addressing the health-related patient outcome of the main surgical approaches of hemiarthroplasty. (ii) Focus on outcomes that are important for the patient. (iii) Pragmatic and inclusive RCT with few exclusion criteria, e.g., patients with dementia can participate. (iv) Natural experiment alongside to amplify the generalizability. (v) The first study conducting a costutility analysis comparing both surgical approaches.

Objective: Information on the pregnancy rate after successive in-vitro fertilization (IVF) cycles and their associated costs is relevant for couples undergoing assisted reproduction treatments (ARTs). This study, therefore, sought to investigate the effectiveness and the costeffectiveness of two ARTs, the minimal ovarian stimulation IVF (MS-IVF) compared to the conventional ovarian stimulation IVF (C-IVF) from the payer's perspective.Methods: A 10-months follow-up prospective observational study was conducted in a sample of couples who sought ARTs in a private clinic in Southern Brazil. Women had to satisfy the Bologna Criteria and be older than 35 years. The effect outcome was pregnancy rate per initiated cycle. Medication costs were based on medical records. Costs and effect differences were estimated using seemingly unrelated regressions adjusted for the propensity score estimated based on women's characteristics.Results: All 84 eligible women who agreed to participate received a total of 92 IVF cycles (MS-IVF, n=27[35 cycles]; C-IVF n=57[57 cycles]. The effect difference between MS-IVF and C-IVF was -5.1% (95%CI, -13.2 to 5.2). Medication costs of MS-IVF were significantly lower than C-IVF by €-1260 (95%CI, -1401 to -1118). The probabilities of MS-IVF being cost-effective compared to C-IVF ranged from 1 to 0.76 for willingness-to-pay of €0 to €15,000 per established pregnancy, respectively.Conclusions: Even though there were no positive effect differences between groups, MS-IVF might be costeffective compared to C-IVF from the payer's perspective due to its relatively large cost savings compared to C-IVF. However, further investigation is needed to confirm these findings in a larger sample.

The aim of this article was to perform a scoping review of methods available for dealing with confounding when analyzing the effect of health care treatments with single‐point exposure in observational data. We aim to provide an overview of methods and their performance assessed by simulation studies indexed in PubMed. We searched PubMed for simulation studies published until January 2021. Our search was restricted to studies evaluating binary treatments and binary and/or continuous outcomes. Information was extracted on the methods' assumptions, performance, and technical properties. Of 28,548 identified references, 127 studies were eligible for inclusion. Of them, 84 assessed 14 different methods (ie, groups of estimators that share assumptions and implementation) for dealing with measured confounding, and 43 assessed 10 different methods for dealing with unmeasured confounding. Results suggest that there are large differences in performance between methods and that the performance of a specific method is highly dependent on the estimator. Furthermore, the methods' assumptions regarding the specific data features also substantially influence the methods' performance. Finally, the methods result in different estimands (ie, target of inference), which can even vary within methods. In conclusion, when choosing a method to adjust for measured or unmeasured confounding it is important to choose the most appropriate estimand, while considering the population of interest, data structure, and whether the plausibility of the methods' required assumptions hold.