Introduction: Differences in psoriatic arthritis (PsA) treatment response between sexes for ixekizumab, an interleukin-17A antagonist, are largely unexplored. This analysis used data from randomized clinical trials (RCTs) evaluating ixekizumab to study differences in treatment response between male and female patients with PsA. Methods: We used pooled data from patients enrolled in SPIRIT-P1 and SPIRIT-P2 (NCT01695239 and NCT02349295, respectively), phase 3 RCTs evaluating ixekizumab every 4 and 2 weeks in patients with active PsA. Subgroups of patients were defined by sex (male, female). Efficacy was measured by the proportion of male and female patients achieving American College of Rheumatology 20%/50%/70% improvement criteria (ACR20/50/70), minimal disease activity or very low disease activity (MDA/VLDA), and Disease Activity Index for Psoriatic Arthritis (DAPSA) scores representing low disease activity (LDA) or remission through week 156. Changes from baseline in components of the above measures were also assessed through week 156. Results: Compared to male patients at baseline, female patients were older, had higher body mass index and lower C-reactive protein levels, and had worse tender joint count, Health Assessment Questionnaire Disability Index, and Leeds Enthesitis Index scores. Through week 156, female patients in all treatment arms had lower response rates than male patients in all analyzed composite measures (ACR20/50/70; MDA/VLDA; DAPSA LDA/remission), with significant differences observed at multiple timepoints in both ixekizumab treatment arms. Female patients also had smaller numeric changes from baseline in the composite measures’ individual components. Conclusion: Compared to female patients, male patients had greater response rates in ACR20/50/70, MDA/VLDA, and DAPSA LDA/remission and numerically larger improvements in these measures’ individual components, although clinical significance is unclear. Continued efforts to understand sex differences in treatment response may provide insights that can help optimize clinical decision making.