Pharmacological and residual effects in randomized placebo-controlled trials. A structural causal modelling approach

Abstract

Background: Distinguishing between pharmacological and residual effects, this paper considers the problem of causal assessment in the case of a particular model, namely a Sure Outcome of Random Events (SORE) model developed for the analysis of data from a randomized placebo-controlled double-blind trial of a drug.

Method: This model takes into account two kinds of observable effects, a therapeutic effect and a side-effect. For each observable effect, two latent factors are considered, i.e. a pharmacological (or explained) factor and a residual (or unexplained) one.

Results: The model presents a plausible mechanism generating the observed and latent outcomes, recursively decomposed into an ordered sequence of sub-mechanisms.

Conclusions: The characteristics of this model leads to a novel assessment of causality that evaluates the effect of latent variables and of the bias resulting from ignoring the structural features of the data generating process. This approach is illustrated by a numerical example, along with a case study based on a secondary analysis of real data.

Keywords: Attribution causale; Causal assessment; Causal modelling; Directed acyclic graph; Essai avec placebo randomisé; Graphe acyclique orienté; Latent variables; Modèle structurel; Modélisation causale; Randomized placebo-controlled trials; Structural modelling; Variables latentes.