Patient characteristics as effect modifiers for psoriasis biologic treatment response: an assessment using network meta-analysis subgroups

Ros Wade,Sahar Sharif-Hurst,Sofia Dias

doi:10.1186/s13643-020-01395-6

Abstract

BackgroundNetwork meta-analyses (NMAs) of psoriasis treatments, undertaken as part of the NICE Single Technology Appraisal (STA) process, have included heterogeneous studies. When there is inconsistency or heterogeneity across the different comparisons or trials within the network of studies, the results of the NMA may not be valid. We explored the impact of including studies with heterogeneous patient characteristics on the results of NMAs of psoriasis treatments.MethodsAll NMAs undertaken for psoriasis STAs were identified and the included studies tabulated, including patient characteristics that may influence relative treatment effects. In addition to the original network of all studies using licensed treatment doses, a range of smaller, less heterogeneous networks were mapped: ‘no previous biologic use’ (< 25% patients had prior biologic therapy exposure), ‘Psoriasis Area and Severity Index score ≤ 25’, ‘weight ≤ 90 kg’ and ‘white ethnicity’ (≥ 90% patients were white).ResultsSixty-nine studies were included in our synthesis (34,924 participants). A random effects model with a log-normal prior distribution was chosen for each of the subgroup NMAs. Heterogeneity was reduced for the four smaller networks. There were no significant differences in the relative treatment effect (PASI 75 response) for each treatment across the five NMAs, with all credible intervals overlapping, although there were noticeable differences. Treatment rankings based on the median relative risks were also generally consistent across the networks. However, the NMA that included only studies in which < 25% patients had prior biologic therapy exposure had slightly different treatment rankings; the anti-TNF therapies certolizumab pegol and infliximab ranked higher in this network than any other network, although credible intervals were large.ConclusionsThis work has highlighted potential differences in treatment response for biologic-naïve patients. When conducting NMAs in any area, heterogeneity in patient characteristics of included trials should be carefully assessed and effect modification related to certain patient characteristics investigated through clinically relevant subgroup analyses.

Highlights

Network meta-analyses (NMAs) of psoriasis treatments, undertaken as part of the National Institute for Health and Care Excellence (NICE) Single Technology Appraisal (STA) process, have included heterogeneous studies
Patient characteristics that may contribute to heterogeneity in relative treatment effects Sensitivity analyses undertaken alongside the STA NMAs related to the following study/patient characteristics: size of the trial; licensed and NICE approved treatment doses; timing of primary outcome assessment; patients’ baseline Psoriasis Area and Severity Index (PASI) score; patients’ baseline Dermatology Life Quality Index (DLQI) score; duration of disease; and prior exposure to biologic therapy
Whilst this could reflect the fact that studies in which a higher proportion of patients had prior exposure to a biologic therapy had used an antiTNF as the prior therapy, this may be an important effect modifier

Summary

Introduction

Network meta-analyses (NMAs) of psoriasis treatments, undertaken as part of the NICE Single Technology Appraisal (STA) process, have included heterogeneous studies. When there is inconsistency or heterogeneity across the different comparisons or trials within the network of studies, the results of the NMA may not be valid. We explored the impact of including studies with heterogeneous patient characteristics on the results of NMAs of psoriasis treatments. There can be differences in trial specific features, such as country of origin and trial design If these differences are effect modifiers, they can result in between-study heterogeneity and create biased comparisons. It is important to adjust for effect modifiers in a NMA; this can be done by restricting inclusion in the NMA to certain subgroups of patients with similar characteristics or by conducting meta-regression. When conducting network meta-regression, a sufficient number of studies is needed to estimate independent coefficients for each treatment comparison. It is often more useful to identify clinically meaningful discrete participant and study characteristics which could be expected to lead to different decisions, and restrict inclusion in the NMA

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