OBJECTIVES: We conducted a systematic literature review and network meta-analysis (NMA) to assess complete clearance of actinic keratosis by a new drug, tirbanibulin 1% ointment, compared with existing treatments in Europe.

METHODS: A feasibility assessment (FA) was conducted to assess the similarity of populations, interventions, comparators, outcomes and design among the 40 eligible randomized controlled trials (RCTs) relevant for European approved posologies.

RESULTS: Five RCTs were deemed unsuitable for the NMA following FA. In the 35 remaining trials, potential treatment-effect modifiers were: number of treatment cycles, treatment area size, timepoint of response assessment, and type of placebo/vehicle. Sensitivity analyses were conducted to assess the impact of these variables.

In the primary analysis, all odds ratios credible intervals overlapped, suggesting similar efficacy across treatments (tirbanibulin, imiquimod 5%, imiquimod 3.75%, fluorouracil 0.5% + salicylic acid, diclofenac sodium 3%, fluorouracil 5%, fluorouracil 4%, ingenol mebutate 0.015%, cryosurgery, photodynamic therapy with aminolevulinic acid and with methyl aminolevulinate). All treatments had higher odds than topical placebo/vehicle.

When analysing studies assessing a single course of treatment or treatment area of ≤25cm2 or when pooling topical and photodynamic therapy placebo nodes we obtained results consistent with the primary analysis. When including studies assessing clearance ≥8 weeks from the end of treatment, the only impact was for imiquimod 5%: decreased performance, possibly due to decreased size of the evidence base.

Assessment of inconsistency showed that indirect posterior odds ratios were generally consistent with direct posterior odds.

CONCLUSIONS: Results of the primary analysis suggested that tirbanibulin can achieve similar complete clearance to other treatments for AK. The treatment-effect modifiers were found to have little impact on the results, increasing confidence in their generalizability within the population of interest.

Identifying potential treatment-effect modifiers and quantitatively assessing their impact on the results helps to draw more robust conclusions from NMAs.