Regression-Based Proximal Reconciliation of Randomized Trials with Unmeasured Effect Modifiers

Thomas M. Menino Convention & Exhibition Center 

Conflicting randomized controlled trial (RCT) results complicate evidence synthesis and regulatory decision making. For example, the Meis trial evaluating 17-α-hydroxyprogesterone caproate for preventing recurrent preterm birth (PTB) found a protective benefit, whereas the confirmatory PROLONG trial found no effect despite identical protocols. Differences in study populations are a hypothesized explanation, but existing transportability approaches to assess reconcilability are underpowered and do not account for unmeasured effect modifiers (EMs). We propose a regression-based proximal causal inference framework for reconciling RCTs using proxies for unmeasured EMs. We develop hypothesis tests for reconcilability of conditional causal effects on additive and multiplicative scales under generalized linear models. One test extends transportability methods to account for unmeasured EMs; another directly tests equality of model coefficients for improved power. We also introduce an equivalence test based on the mean squared distance between conditional causal effects. Simulations assess finite-sample performance, and the methods are applied to the PTB trials to evaluate reconcilability.

Proximal causal inference

Reconcilability of randomized trials

Unmeasured effect modification

Structural causal models

Preterm birth

Equivalence testing 

Section on Statistics in Epidemiology