How can methods for classifying and clustering trajectories be used for prevention trials? An example in Alzheimer's disease area

TL;DR

This study explores the use of clustering and classification methods for analyzing trajectories in Alzheimer's prevention trials to identify subgroups with different responses, aiming to improve detection of effects in heterogeneous populations.

Contribution

It demonstrates the applicability of trajectory clustering methods like k-means for longitudinal data in Alzheimer's trials, highlighting their potential for subgroup analysis.

Findings

k-means identified three distinct trajectory groups

one group showed cognitive decline over three years

methods can be applied directly to primary outcome data

Abstract

Background: Clinical trials are designed to prove the efficacy of an intervention by means of model-based approaches involving parametric hypothesis testing. Issues arise when no effect is observed in the study population. Indeed, an effect may be present in a subgroup and the statistical test cannot detect it. To investigate this possibility, we proposed to change the paradigm to a data-driven approach. We selected exploratory methods to provide another perspective on the data and to identify particular homogeneous subgroups of subjects within which an effect might be detected. In the setting of prevention trials, the endpoint is a trajectory of repeated measures. In the settings of prevention trials, the endpoint is a trajectory of repeated measures, which requires the use of methods that can take data autocorrelation into account. The primary aim of this work was to explore the applicability of different methods for clustering and classifying trajectories. Methods: The Multidomain Alzheimer Preventive Trial (MAPT) was a three-year randomized controlled trial with four parallel arms (NCT00672685). The primary outcome was a composite Z-score combining four cognitive tests. The data were analyzed by quadratic mixed effects model. This study was inconclusive. Exploratory analysis is therefore relevant to investigate the use of data-driven methods for trajectory classification. The methods used were unsupervised: k-means for longitudinal data, Hierarchical Cluster Analysis (HCA), graphic semiology, and supervised analysis with dichotomous classification according to responder status. Results: Using k-means for longitudinal data, three groups were obtained and one of these groups showed cognitive decline over the three years of follow-up. This method could be applied directly to the primary outcome, the composite Z-score with repeated observations over time. With the two others unsupervised methods, we were unable to process longitudinal data directly. It was therefore necessary to choose an indicator of change in trajectories and to consider the rate of change between two measurements. For the HCA method, Ward's aggregation was performed. The Euclidean distance and rates of change were applied for the graphic semiology method. Lastly, as there were no objective criteria to define responder status, we defined our responders based on clinical criteria. Discussion: In the princeps study, the prevention trial was found to be inconclusive, likely due to the heterogeneity of the population, which may have masked a treatment effect later identified in a refined subgroup of high Beta Amyloid subjects. So, we have adopted an alternative unsupervised approach to subject stratification based on their trajectories. We could then identify patterns of similar trajectories of cognitive decline and also highlight the potential problem of a large heterogeneity of the profiles, maybe due to the final endpoint considered.