Methodological Approaches for Conducting Matching-Adjusted Indirect Comparisons Involving Multiple Randomized Controlled Trials

C. Cameron1, A. Varu1, T. Disher1, B. Hutton2, 3 1Cornerstone Research Group Inc., Burlington, Ontario, Canada; 2Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; 3School of Epidemiology, Public Health & Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada

INTRODUCTION METHODS 1 Matching-adjusted indirect comparisons (MAICs) typically compare two We ran MAIC analyses using NICE DSU Technical Support Document 18 code to assess treatments and consider two randomized controlled trials(RCTs) – one approaches for conducting MAICs involving multiple RCTs. RCT with individual participant data (IPD) and another with summary- level data. We considered two approaches: 1) conduct multiple independent MAICs using IPD for the treatment of interest in tandem However, for many therapeutic areas (e.g., psoriasis, rheumatoid with multiple RCTs for the comparator, followed by pooling of MAICs using traditional arthritis), there may be multiple RCTs that warrant consideration in MAIC, meta-analysis. and there is limited guidance available for these situations. 2) conduct one MAIC of the treatment of interest versus the comparator wherein the MAIC was based on a weighted average of characteristics and eligibility criteria across OBJECTIVE the set of relevant comparator RCTs. Compare two alternative methods for the conduct of MAICs when there are multiple aggregate comparator trials. We assessed the advantages and disadvantages of each approach.

DESCRIPTION OF THE CASE Table 1: Study characteristics and matching results for all study characteristics STUDY Aggregate Study 1 Aggregate Study 2 Weighted Average of 1 and 2

Suppose you have IPD from one trial (N=500) and Measure of Interest IPD Aggregate Data MAIC Aggregate Data MAIC Aggregate Data MAIC summary-level data from two trials (N=500). IPD N (Neff) 500 500 362 500 117 1000 296 and aggregate studies have a common comparator Age 46.35 (8.74) 48 (6.84) 48 (6.85) 51 (8.51) 51 (8.53) 49.5 (7.72) 49.5 (7.73) thereby allowing an anchored MAIC to be Duration of conducted. 15.86 (4.34) 14.09 (3.89) 14.09 (3.89) 13.52 (3.76) 13.52 (3.77) 13.8 (3.83) 13.81 (3.83) Disease

The case study assumes that inclusion/exclusion Previous Therapy 76.8% 70% 70 % 51% 51 % 60.5% 60.5 % criteria are similar between studies. Clinical Gender, Male 13.2% 12% 12 % 25% 25 % 18.5% 18.5 % advisors rank ordered characteristics from most to least important that are available in either trial: age, Race, Caucasian 88.2% 91% 91 % 85% 85 % 88% 88 % duration of disease, previous therapy, gender, race, weight and baseline score. Weight (kg) 84.94 (5.49) NA NA 82.81 (4.58) 82.81 (4.59) NA NA Baseline Score 13.19 (4.06) NA NA 11.23 (3.88) 11.23 (3.89) NA NA Aggregate studies differed on the number of patient characteristics reported (Table 1). The first study Figure 1: Scenario analyses of MAIC vs Aggregate Study 1 Figure 2: Scenario analyses of MAIC vs Aggregate Study 2 does not report baseline score and weight, while the second reports all available characteristics. For the purpose of this study we will focus on an objective outcome (objective response rate).

ANALYSIS

For each method, we conduct a series of MAIC scenarios by first matching on all available characteristics followed by iteratively dropping the least important characteristic until only the most important remains. See Table 1 for quality of matching when including all characteristics. Favours Active Treatment in Favours Active Treatment in Favours Active Treatment in Favours Active Treatment in Scenarios used in pooling were those that matched Aggregate Study IPD Study Aggregate Study IPD Study on the most variables while maintaining a Neff of at a Scenario A matches on all available summary level characteristics. Each subsequent scenario drops the next least important summary level least 58% of the original IPD sample size. characteristic from the matching. Neff: Effective sample size; CI: Confidence Interval

RESULTS

Our analyses determined that the meta-analytic approach allows for the examination of consistency in findings between MAICs across individual studies (Figures 1,2 and 3 A/B). However, a meta-analysis of these comparisons double count patients from the IPD dataset, thereby overestimating precision (Figure 3).

Figure 3: Comparison of methods for MAIC analyses involving multiple studies

A. Pooling using meta-analysis, all characteristics for both studies B. Pooling using meta-analysis, best matching scenario for both C. Matching on weighted average of common trial characteristics studies

Favours Active Treatment in Favours Active Treatment in Aggregate Study IPD Study Favours Active Treatment in Favours Active Treatment in Aggregate Study IPD Study Favours Active Treatment in Favours Active Treatment in Aggregate Study IPD Study

CONCLUSION REFERENCES There are advantages and disadvantages with methodological approaches for 1. Phillippo DM, Ades AE, Dias S, Palmer S, Abrams KR, Welton NJ. NICE DSU conducting MAICs involving multiple RCTs. Both approaches should be considered Technical Support Document 18: Methods for Population-Adjusted Indirect Comparisons when conducting MAICs involving multiple RCTs given they complement each in Submissions To NICE Report By the Decision Support Unit. 2016. http://research- other. information.bristol.ac.uk/files/94868463/Population_adjustment_ TSD_FINAL.pdf.

Chris Cameron Cornerstone Research Group Inc. 3228 South Service Rd., Suite 204, Burlington, Ontario, Canada L7N 3H8. Tel: 905.637.6231 ext. 239. E-mail: [email protected] www.cornerstone-research.com