Multimethod quantitative benefit-risk assessment of treatments for moderate-to-severe osteoarthritis

Abstract

Objective: Demonstrate how benefit-risk profiles of systemic treatments for moderate-to-severe osteoarthritis (OA) can be compared using a quantitative approach accounting for patient preference.

Study design and setting: This study used a multimethod benefit-risk modelling approach to quantifiably compare treatments of moderate-to-severe OA. In total four treatments and placebo were compared. Comparisons were based on four attributes identified as most important to patients. Patient Global Assessment of Osteoarthritis was included as a favourable effect. Unfavourable effects, or risks, included opioid dependence, nonfatal myocardial infarction and rapidly progressive OA leading to total joint replacement. Clinical data from randomized clinical trials, a meta-analysis of opioid dependence and a long-term study of celecoxib were mapped into value functions and weighted with patient preferences from a discrete choice experiment.

Results: Lower-dose NGFi had the highest weighted net benefit-risk score (0.901), followed by higher-dose NGFi (0.889) and NSAIDs (0.852), and the lowest score was for opioids (0.762). Lower-dose NGFi was the highest-ranked treatment option even when assuming a low incidence (0.34% instead of 4.7%) of opioid dependence (ie, opioid benefit-risk score 808) and accounting for both the uncertainty in clinical effect estimates (first rank probability 46% vs 20% for NSAIDs) and imprecision in patient preference estimates (predicted choice probability 0.26, 95% confidence interval [CI] 0.25-0.28 vs 0.21, 95% CI 0.19-0.23 for NSAIDs).

Conclusion: The multimethod approach to quantitative benefit-risk modelling allowed the interpretation of clinical data from the patient perspective while accounting for uncertainties in the clinical effect estimates and imprecision in patient preferences.

Keywords: benefit-risk assessment; nerve growth factor inhibitor; opioid; osteoarthritis; patient preference.

FIGURE 1

Schematic of the comparative benefit‐risk approach. The overall analysis approach for the quantitative benefit‐risk assessment was adapted from Postmus et al. First, based on structured benefit‐risk assessment principles and using attribute preferences elicited from a representative population, favourable (PGA‐OA) and unfavourable (opioid dependence, nonfatal MI and joint safety [RPOA 2]) attributes were selected that can differentiate the included treatment options. Next, source data were extracted from published articles and the discrete choice experiment, and organized in the effects table framework, with favourable and unfavourable attributes mapped into clinical value scores for efficacy (OA symptom relief) and safety (dependence, cardiovascular safety and joint safety). Finally, quantitative benefit‐risk assessment was performed using multicriteria decision, stochastic multicriteria acceptability and predicted choice probability analyses. CV, cardiovascular; MI, myocardial infarction; OA, osteoarthritis; PGA‐OA, Patient Global Assessment of Osteoarthritis; RPOA 2, rapidly progressive osteoarthritis type 2

FIGURE 2

Net benefit‐risk scores of osteoarthritis treatment options (Supporting Information Table S1). Safer, more efficacious treatments score higher than treatments with less safety and efficacy. The contribution to net benefit from a given attribute is the same when two treatments' comparative clinical performance on that attribute is equal. MI, myocardial infarction; NGFi, nerve growth factor inhibitor; NSAID, nonsteroidal anti‐inflammatory drug; PGA, Patient Global Assessment; RPOA 2, rapidly progressive osteoarthritis type 2

FIGURE 3

Sensitivity analysis: effect of imprecision in patient preference estimates. One‐way sensitivity analysis was conducted on patient preference weighting of joint safety (rapidly progressive osteoarthritis type 2). CI, confidence interval; NGFi, nerve growth factor inhibitor; NSAID, nonsteroidal anti‐inflammatory drug; PCP, predicted choice probability

FIGURE 4

Sensitivity analysis: uncertainty in clinical effect estimates. Uncertainty in all clinical data was tested simultaneously using stochastic multicriteria acceptability analysis. Shown are the probabilities of each ranking for each treatment. NGFi, nerve growth factor inhibitor; NSAID, nonsteroidal anti‐inflammatory drug