AI-NLME: A New Artificial Intelligence-Driven Nonlinear Mixed Effect Modeling Approach for Analyzing Longitudinal Data in Randomized Placebo-Controlled Clinical Trials

Abstract

A propensity weighted (PSW) methodology was recently proposed for assessing the treatment effect conditional to the probability of non-specific response to a treatment (prob-NSRT). Prob-NSRT was estimated using an artificial neural network (ANN) model applied to pre-randomization and study endpoint observations in a placebo arm of a placebo-controlled clinical trial. Placebo data were initially used to estimate prob-NSRT, then the ANN model was applied to the data of each individual in each treatment arm (placebo + active) for estimating the individual prob-NSRT, and finally all data in the trial enriched by the prob-NSRT values were used to assess the treatment effect. One of the major limitations of this methodology was that the ANN model was developed and applied to analyze data in the same dataset. To overcome this limitation, a new artificial intelligence driven nonlinear mixed effect modeling approach (AI-NLME) is proposed. This approach involves the development of the ANN model using a dataset that is independent from the dataset used to estimate the treatment effect. A case study is presented using data from a randomized, placebo-controlled trial in major depressive disorders. The AI-NLME approach provided an effective tool for controlling the confounding effect of treatment non-specific response, for increasing signal detection, for decreasing heterogeneity in the response, for increasing the effect size, for better assessing the responder rate, and for providing a reliable estimate of the “true” treatment effect. These findings provide convergent evidence on the potential role of AI-NLME to become the reference approach for analyzing placebo-controlled clinical trials.