SAT0588 COMPARATIVE EFFECTIVENESS RESEARCH IN OBSERVATIONAL SETTINGS: EVALUATING TWO NEW METHODS TO ANALYSE RESPONSE RATES
Authors: Kim Lauper, Denis Mongin, Cem Gabay, Axel Finckh, Delphine Courvoisier
Journal: Annals of the Rheumatic Diseases (2019)

<h3>Background</h3> Researchers typically report the proportion of patients reaching a defined clinical threshold (e.g. EULAR response, low disease activity (LDA) rates) after a set time. Comparing response rates (%rr) in observational settings is hampered by two major threats. 1. Confounding: Patient, disease, and treatment characteristics often differ for each drug. 2. Attrition bias: Assessing%rr after a set time excludes patients who discontinued their treatment, which may overestimate drug effectiveness. Currently, no proposed method accounts for both confounding and attrition. <h3>Objectives</h3> To propose two new methods (3 and 4) to adequately compare%rr in patients with different baseline characteristics, while accounting for attrition and compare them to established methods (1 and 2). <h3>Methods</h3> 1. Complete case (CC) %rr is computed as the percentage of responders on total patients still on treatment at the given time point. 2. LUNDEX<sup>1</sup>: CC%rr is corrected for attrition by multiplying it by the Kaplan Meier estimates of the survival (SKM), thus considering all patients not on treatment as non-responders, irrespective of the reason for drug discontinuation. 3. PSM LUNDEX: Propensity Score (PS) Matching LUNDEX Step A: Select patients in both exposure groups using propensity score matching Step B: Use the LUNDEX to compute the%rr. 4. CARRAC: Confounder-Adjusted Response Rate with Attrition Correction by reason for drug discontinuation Step A: Compute estimates of drug survival for the main reasons of drug discontinuation (e.g., ineffectiveness, adverse events, remission, other reasons). Step B: Estimate%rr using random effect IPD meta-analysis with estimates for each reason of drug discontinuation. Step C: Combine%rr estimates using weights of step A. The methods will be illustrated using CDAI LDA (≤10) rates in data from a collaboration of registries<sup>2</sup>. <h3>Results</h3> We used 3448 patients treated by a biologic, either intravenously (IV: n=2414) or subcutaneously (SC: n=1034). Before matching, the population differed in terms of body mass index, function (Health Assessment Questionnaire, HAQ), concomitant treatments and erythrocyte sedimentation rate (ESR). Adjustment was done using these potential confounders. For PSM LUNDEX, PS matching was done on a 1:1 basis, yielding 561 patients in each group with similar characteristics. Estimated%rr differed by more than 20%, depending on the method used (Figure). Compared to CC%rr, both PSM LUNDEX and LUNDEX methods yielded much lower%rr, while the CARRAC method estimated%rr was in between these estimates. Compared to CC analysis, differences in%rr between the SC and IV groups were smaller for the LUNDEX methods, larger for PSM LUNDEX, and close to CC for the CARRAC method (Figure). <h3>Conclusion</h3> Both LUNDEX methods certainly underestimates the true response rates by considering all patients stopping treatment as non-responders, while complete case method certainly overestimates the%rr by considering patients stopping as having a similar%rr to patients continuing treatment. As expected, the CARRAC method, which accounts for attrition by reason for drug discontinuation, obtained%rr estimates in between complete-case and LUNDEX corrections. Simulation studies are needed to assess the most accurate estimation method. <h3>References</h3> [1] Kristensen, et al. 2006A&amp;R [2] Lauper, et al. 2018RMD Open <h3>Acknowledgement</h3> This work has been supported by an unrestricted research Grant from Abbvie. <h3>Disclosure of Interests</h3> Kim Lauper Grant/research support from: reports an unrestricted research grant from AbbVie and non-financial support from Pfizer, outside the submitted work, Denis Mongin: None declared, Cem Gabay Grant/research support from: Roche, Pfizer, AB2 Bio Ltd, Consultant for: Roche, Pfizer, Lilly, AbbVie, Sanofi, Regeneron, Bristol-Myers Squibb, Novartis, UCB, AB2 Bio Ltd, Debiopharm, Axel Finckh Grant/research support from: Bristol-Myers Squibb, Pfizer Inc, Consultant for: AbbVie, A2Bio, Bristol-Myers Squibb, MSD, Roche, Pfizer Inc, and UCB, Delphine Courvoisier Grant/research support from: has received an unrestricted grant from MSD for this study, Consultant for: has received consulting fees from BMS, Pfizer, AB2 Bio and Janssen., Paid instructor for: Janssen