The statistical approach in trial-based economic evaluations matters: get your statistics together!

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.

Cost-effectiveness of combination therapy (Mechanical Diagnosis and Treatment and Transforaminal Epidural Steroid Injections) among patients with an indication for a Lumbar Herniated Disc surgery: Protocol of a randomized controlled trial.

OBJECTIVES: The general consensus is that surgical treatment is advised when conservative methods fail in patients with lumbosacral radicular syndrome (LRS). Preliminary evidence from our pilot study indicates that combination therapy (mechanical diagnosis therapy and transforaminal epidural injections) can prevent surgical treatment in patients on the waiting list for surgery. The pilot study lacked a control group, and therefore, firm conclusions pertaining to effects could not be made. This study aims to determine if combination therapy, performed while being on the waiting list for lumbar herniated disc surgery, is effective and cost-effective compared with usual care (i.e., no intervention while being on the waiting list) among patients with a magnetic resonance imaging (MRI)-confirmed indication for a lumbar herniated disc surgery. METHODS: A randomized controlled trial will be conducted with an economic evaluation. Patients aged 18 and above with incapacitating LRS, with leg pain and an MRI confirmed indication for lumbar disc hernia surgery, will be recruited from seven Dutch hospitals. While being on the waiting list for lumbar herniated disc surgery, patients will be randomized to either the combination therapy or usual care group. The primary outcome measure is the number of patients undergoing lumbar disc surgery during 12-month follow-up. Secondary outcomes include back and leg pain intensity (numeric pain rating scale), physical functioning (Roland Morris Disability Questionnaires-23), self-perceived recovery (global perceived effect), and health-related quality of life (EuroQol Five Dimensions Health Questionnaire (EQ-5D-5L) and 12-Item Short Form Health Survey (SF-12)). For the economic evaluation, societal and health care costs will be measured. Measurements moments are baseline, 1, 2, 4, 6, 9, and 12 months. Data will be analysed according to the intention-to-treat principle. CONCLUSION: No randomized controlled trials have evaluated the effectiveness and cost-effectiveness of combination therapy compared with usual care in patients with an indication for lumbar herniated disc surgery, which emphasizes the importance of this study.

Predictive factors of high societal costs among chronic low back pain patients.

BACKGROUND: Societal costs of low back pain (LBP) are high, yet few studies have been performed to identify the predictive factors of high societal costs among chronic LBP patients. This study aimed to determine which factors predict high societal costs in patients with chronic LBP. METHODS: Data of 6,316 chronic LBP patients were used. In the main analysis, high societal costs were defined as patients in the top 10% of cost outcomes. Sensitivity analyses were conducted using patients in the top 5% and top 20% of societal costs. Potential predictive factors included patient expectations, demographic factors (e.g. age, gender, nationality), socio-economic factors (e.g. employment, education level) and health-related factors (e.g. body mass index [BMI], general health, mental health). The final prediction models were obtained using backward selection. The model's prognostic accuracy (Hosmer-Lemeshow X2 , Nagelkerke's R2 ) and discriminative ability (area under the receiver operating curve [AUC]) were assessed, and the models were internally validated using bootstrapping. RESULTS: Poor physical health, high functional disability, low health-related quality of life, high impact of pain experience, non-Dutch nationality and decreasing pain were found to be predictive of high societal costs in all models, and were therefore considered robust. After internal validation, the models' fit was good, their explained variance was relatively low (≤14.1%) and their AUCs could be interpreted as moderate (≥0.71). CONCLUSION: Future studies should focus on understanding the mechanisms associated with the identified predictors for high societal costs in order to design effective cost reduction initiatives. SIGNIFICANCE: Identifying low back pain patients who are at risk (risk stratification) of becoming high-cost users and making appropriate initiatives could help in reducing high costs.