LATENT SIMILARITY MATCHING FOR EXTERNAL CONTROL ARMS: A PREREGISTERED TARGET TRIAL EMULATION REPRODUCING A RANDOMIZED SPINAL CORD INJURY PLACEBO ARM
Author(s)
Juan Carlos Ocampo, MSc, Miklovana Tuci, MSc, Albert Belenguer Llorens, PhD, Olga Taran, PhD, Catherine Jutzeler, PhD.
ETH Zurich, Zurich, Switzerland.
ETH Zurich, Zurich, Switzerland.
OBJECTIVES: To evaluate whether an external control arm (ECA) constructed using latent similarity matching reproduces the placebo recovery trajectory of the phase 2b Nogo inhibition (NISCI) trial.
METHODS: The placebo arm was emulated using the European Multicenter Study about Spinal Cord Injury registry (EMSCI). An autoencoder trained exclusively on registry baseline covariates learned a latent representation. Placebo patients were projected into this space, and registry controls were identified by nearest-neighbour matching under a severity grade, level of injury, and motor score caliper. Hyperparameters were selected through an outcome-blind, reproducible search. The primary estimand is the difference in mean upper extremity motor score (UEMS) recovery slopes between matched cohorts, reproducing the trial model. Equivalence was evaluated with Two One-Sided Tests at a 4-point UEMS margin. The primary analysis is contextualized by results using covariate-balancing propensity score, Mahalanobis distance, and coarsened exact matching. The preregistration included the target trial protocol, ICH E9(R1) estimand, and directed acyclic graph. The preregistered pipeline was executed under a reduced hyperparameter search.
RESULTS: Outcome-blind selection produced an ECA of 111 controls for all 37 placebo patients, with a post-match standardized mean difference below 0.3 on clinical matching covariates. The difference in mean UEMS was −0.44 [90% CI: −3.2, 2.4], within the pre-specified margin and robust under bootstrap resampling. Under comparable conditions, only covariate-balancing propensity score matching achieved equivalence at 0.65 [90% CI: −3.5, 2.2], whereas Mahalanobis distance matching and coarsened exact matching did not.
CONCLUSIONS: Our proposed approach constructed an external control arm that reproduced the randomized placebo recovery trajectory in this initial evaluation. These findings support the potential of machine learning-derived external controls, combined with transparent target trial emulation, to reduce reliance on concurrent control arms in spinal cord injury trials.
METHODS: The placebo arm was emulated using the European Multicenter Study about Spinal Cord Injury registry (EMSCI). An autoencoder trained exclusively on registry baseline covariates learned a latent representation. Placebo patients were projected into this space, and registry controls were identified by nearest-neighbour matching under a severity grade, level of injury, and motor score caliper. Hyperparameters were selected through an outcome-blind, reproducible search. The primary estimand is the difference in mean upper extremity motor score (UEMS) recovery slopes between matched cohorts, reproducing the trial model. Equivalence was evaluated with Two One-Sided Tests at a 4-point UEMS margin. The primary analysis is contextualized by results using covariate-balancing propensity score, Mahalanobis distance, and coarsened exact matching. The preregistration included the target trial protocol, ICH E9(R1) estimand, and directed acyclic graph. The preregistered pipeline was executed under a reduced hyperparameter search.
RESULTS: Outcome-blind selection produced an ECA of 111 controls for all 37 placebo patients, with a post-match standardized mean difference below 0.3 on clinical matching covariates. The difference in mean UEMS was −0.44 [90% CI: −3.2, 2.4], within the pre-specified margin and robust under bootstrap resampling. Under comparable conditions, only covariate-balancing propensity score matching achieved equivalence at 0.65 [90% CI: −3.5, 2.2], whereas Mahalanobis distance matching and coarsened exact matching did not.
CONCLUSIONS: Our proposed approach constructed an external control arm that reproduced the randomized placebo recovery trajectory in this initial evaluation. These findings support the potential of machine learning-derived external controls, combined with transparent target trial emulation, to reduce reliance on concurrent control arms in spinal cord injury trials.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
MSR268
Topic
Methodological & Statistical Research, Real World Data & Information Systems, Study Approaches
Topic Subcategory
Artificial Intelligence, Machine Learning, Predictive Analytics, Confounding, Selection Bias Correction, Causal Inference
Disease
Injury & Trauma, Rare & Orphan Diseases