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Background: Evidence-based complementary treatment options for multiple sclerosis (MS) are limited. Objective: To investigate the effect of hippotherapy plus standard care versus standard care alone in MS patients. Methods: A total of 70 adults with MS were recruited in five German centers and randomly allocated to the intervention group (12 weeks of hippotherapy) or the control group. Primary outcome was the change in the Berg Balance Scale (BBS) after 12 weeks, and further outcome measures included fatigue, pain, quality of life, and spasticity. Results: Covariance analysis of the primary endpoint resulted in a mean difference in BBS change of 2.33 (95% confidence interval (CI): 0.03–4.63, p = 0.047) between intervention (n = 32) and control (n = 38) groups. Benefit on BBS was largest for the subgroup with an Expanded Disability Status Scale (EDSS) ⩾ 5 (5.1, p = 0.001). Fatigue (−6.8, p = 0.02) and spasticity (−0.9, p = 0.03) improved in the intervention group. The mean difference in change between groups was 12.0 (p < 0.001) in physical health score and 14.4 (p < 0.001) in mental health score of Multiple Sclerosis Quality of Life-54 (MSQoL-54). Conclusion: Hippotherapy plus standard care, while below the threshold of a minimal clinically important difference, significantly improved balance and also fatigue, spasticity, and quality of life in MS patients.

Background: Evidence-based complementary treatment options for multiple sclerosis (MS) are limited. Objective: To investigate the effect of hippotherapy plus standard care versus standard care alone in MS patients. Methods: A total of 70 adults with MS were recruited in five German centers and randomly allocated to the intervention group (12 weeks of hippotherapy) or the control group. Primary outcome was the change in the Berg Balance Scale (BBS) after 12 weeks, and further outcome measures included fatigue, pain, quality of life, and spasticity. Results: Covariance analysis of the primary endpoint resulted in a mean difference in BBS change of 2.33 (95% confidence interval (CI): 0.03–4.63, p = 0.047) between intervention (n = 32) and control (n = 38) groups. Benefit on BBS was largest for the subgroup with an Expanded Disability Status Scale (EDSS) ⩾ 5 (5.1, p = 0.001). Fatigue (−6.8, p = 0.02) and spasticity (−0.9, p = 0.03) improved in the intervention group. The mean difference in change between groups was 12.0 (p < 0.001) in physical health score and 14.4 (p < 0.001) in mental health score of Multiple Sclerosis Quality of Life-54 (MSQoL-54). Conclusion: Hippotherapy plus standard care, while below the threshold of a minimal clinically important difference, significantly improved balance and also fatigue, spasticity, and quality of life in MS patients.

Evidence-based complementary treatment options for multiple sclerosis (MS) are limited.

Background: Estimating input costs for Markov models in health economic evaluations requires health state-specific costing. This is a challenge in mental illnesses such as depression, as interventions are not clearly related to health states. We present a hybrid approach to health state-specific cost estimation for a German health economic evaluation of antidepressants. Methods: Costs were determined from the perspective of the community of persons insured by statutory health insurance (“SHI insuree perspective”) and included costs for outpatient care, inpatient care, drugs, and psychotherapy. In an additional step, costs for rehabilitation and productivity losses were calculated from the societal perspective. We collected resource use data in a stepwise hierarchical approach using SHI claims data, where available, followed by data from clinical guidelines and expert surveys. Bottom-up and top-down costing approaches were combined. Results: Depending on the drug strategy and health state, the average input costs varied per patient per 8-week Markov cycle. The highest costs occurred for agomelatine in the health state first-line treatment (FT) (“FT relapse”) with €506 from the SHI insuree perspective and €724 from the societal perspective. From both perspectives, the lowest costs (excluding placebo) were €55 for selective serotonin reuptake inhibitors in the health state “FT remission.” Conclusion: To estimate costs in health economic evaluations of treatments for depression, it can be necessary to link different data sources and costing approaches systematically to meet the requirements of the decision-analytic model. As this can increase complexity, the corresponding calculations should be presented transparently. The approach presented could provide useful input for future models.

Background: The National Institute for Quality and Efficiency in Health Care (IQWiG) employs an efficiency frontier (EF) framework to facilitate setting maximum reimbursable prices for new interventions. Probabilistic sensitivity analysis (PSA) is used when yes/no reimbursement decisions are sought based on a fixed threshold. In the IQWiG framework, an additional layer of complexity arises as the EF itself may vary its shape in each PSA iteration, and thus the willingness-to-pay, indicated by the EF segments, may vary. Objectives: To explore the practical problems arising when, within the EF approach, maximum reimbursable prices for new interventions are sought through PSA. Methods: When the EF is varied in a PSA, cost recommendations for new interventions may be determined by the mean or the median of the distances between each intervention’s point estimate and each EF. Implications of using these metrics were explored in a simulation study based on the model used by IQWiG to assess the cost-effectiveness of 4 antidepressants. Results. Depending on the metric used, cost recommendations can be contradictory. Recommendations based on the mean can also be inconsistent. Results (median) suggested that costs of duloxetine, venlafaxine, mirtazapine, and bupropion should be decreased by €131, €29, €12, and €99, respectively. These recommendations were implemented and the analysis repeated. New results suggested keeping the costs as they were. The percentage of acceptable PSA outcomes increased 41% on average, and the uncertainty associated to the net health benefit was significantly reduced. Conclusions: The median of the distances between every intervention outcome and every EF is a good proxy for the cost recommendation that would be given should the EF be fixed. Adjusting costs according to the median increased the probability of acceptance and reduced the uncertainty around the net health benefit distribution, resulting in a reduced uncertainty for decision makers.

Evidence-based complementary treatment options for multiple sclerosis (MS) are limited.

Background: Estimating input costs for Markov models in health economic evaluations requires health state-specific costing. This is a challenge in mental illnesses such as depression, as interventions are not clearly related to health states. We present a hybrid approach to health state-specific cost estimation for a German health economic evaluation of antidepressants. Methods: Costs were determined from the perspective of the community of persons insured by statutory health insurance (“SHI insuree perspective”) and included costs for outpatient care, inpatient care, drugs, and psychotherapy. In an additional step, costs for rehabilitation and productivity losses were calculated from the societal perspective. We collected resource use data in a stepwise hierarchical approach using SHI claims data, where available, followed by data from clinical guidelines and expert surveys. Bottom-up and top-down costing approaches were combined. Results: Depending on the drug strategy and health state, the average input costs varied per patient per 8-week Markov cycle. The highest costs occurred for agomelatine in the health state first-line treatment (FT) (“FT relapse”) with €506 from the SHI insuree perspective and €724 from the societal perspective. From both perspectives, the lowest costs (excluding placebo) were €55 for selective serotonin reuptake inhibitors in the health state “FT remission.” Conclusion: To estimate costs in health economic evaluations of treatments for depression, it can be necessary to link different data sources and costing approaches systematically to meet the requirements of the decision-analytic model. As this can increase complexity, the corresponding calculations should be presented transparently. The approach presented could provide useful input for future models.

Background: The National Institute for Quality and Efficiency in Health Care (IQWiG) employs an efficiency frontier (EF) framework to facilitate setting maximum reimbursable prices for new interventions. Probabilistic sensitivity analysis (PSA) is used when yes/no reimbursement decisions are sought based on a fixed threshold. In the IQWiG framework, an additional layer of complexity arises as the EF itself may vary its shape in each PSA iteration, and thus the willingness-to-pay, indicated by the EF segments, may vary. Objectives: To explore the practical problems arising when, within the EF approach, maximum reimbursable prices for new interventions are sought through PSA. Methods: When the EF is varied in a PSA, cost recommendations for new interventions may be determined by the mean or the median of the distances between each intervention’s point estimate and each EF. Implications of using these metrics were explored in a simulation study based on the model used by IQWiG to assess the cost-effectiveness of 4 antidepressants. Results. Depending on the metric used, cost recommendations can be contradictory. Recommendations based on the mean can also be inconsistent. Results (median) suggested that costs of duloxetine, venlafaxine, mirtazapine, and bupropion should be decreased by €131, €29, €12, and €99, respectively. These recommendations were implemented and the analysis repeated. New results suggested keeping the costs as they were. The percentage of acceptable PSA outcomes increased 41% on average, and the uncertainty associated to the net health benefit was significantly reduced. Conclusions: The median of the distances between every intervention outcome and every EF is a good proxy for the cost recommendation that would be given should the EF be fixed. Adjusting costs according to the median increased the probability of acceptance and reduced the uncertainty around the net health benefit distribution, resulting in a reduced uncertainty for decision makers.