Identifying Fit-for-Emulation Data: Adaptation of a Structured Data Feasibility Assessment Process for Real-World Oncology Trial Emulations

11/20/2024

Identifying Fit-for-Emulation Data: Adaptation of a Structured Data Feasibility Assessment Process for Real-World Oncology Trial Emulations

ISPOR Europe 2024

Authors Levy N, Campbell U, Sheridan P, Lenis D, Madsen A, O'Doherty I, Estrin A, Iyer M, McDonald S, Becnel L, Belli A, Carrigan G, Chan KA, Chen J, Chia VM, Dhopeshwarkar N, Eckert JC, Fernandes L, Goldstein M, Greshock J, Hendricks-Sturrup R, Huang J, Jiao X, Khosla S, Lunacsek O, McRoy L, Natanzon Y, Ovbiosa O, Pace ND, Pinheiro S, Quinn J, Rees M, Rider J, Rimawi M, Robinson T, Rodriguez-Watson C, Sangli C, Sarsour K, Schneeweiss S, Shapiro M, Stewart M, Taylor A, Wang C, Wasserman A, and Zhang Y

OBJECTIVES

The Coalition to Advance Real-World Evidence through Randomized Controlled Trial Emulation (CARE) Initiative seeks to advance understanding of when real-world data (RWD) can generate valid treatment effectiveness estimates by emulating oncology randomized controlled trials (RCTs) using RWD. Successful emulation requires fit-for-emulation data. We describe learnings from a structured feasibility assessment process to evaluate potential datasets for CARE studies, which may inform other RCT emulations.

METHODS

Candidate RCTs were identified from active comparator trials for common tumor types leading to approvals during 2015-2020. Feasibility assessments included two phases. First, in each potential dataset, we confirmed availability of the RCT indication and outcomes and sample size ≥1.5-times the RCT population, estimated as counts of patients with the indication receiving an RCT treatment or comparator therapy. Second, we modified the Structured Process to Identify Fit-For-Purpose Data (SPIFD2) framework to conduct detailed data fitness assessments. Key RCT design elements (e.g., research question, treatment strategies, eligibility criteria, outcomes, covariates) and potential confounders were identified. The ability to operationalize each element was assessed and ranked (1-low, 5-high), based on reliability/validation of measures and missingness. Overall ratings were calculated by averaging required design element rankings.

RESULTS

Of 49 possible RCT-dataset combinations, 9 passed initial screening and proceeded to detailed feasibility assessment. Key drivers of overall ratings included: availability of performance status, non-cancer diagnoses/treatments, and progression measures; diagnosis date quality; and death data validity. Measurable disease and prognosis eligibility criteria were not captured in any dataset. Two datasets were determined fit-for-emulation of two RCTs (n=3 emulations).

CONCLUSIONS

Oncology RCT emulations require specific eligibility criteria and outcomes that make identifying fit-for-emulation RWD especially challenging. In particular, routinely-captured, non-cancer diagnosis/treatment variables are absent from datasets containing high-quality oncology information. Rigorous feasibility assessments are critical for identifying fit-for-emulation RWD, contextualizing results, and identifying gaps in existing datasets.

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