Nontraditional Approaches to Big Data Analyses: A Case Study in Rare Disease

Random errors could have a disproportionate effect on smaller clinical trials. This is because as the sample size becomes larger in any trial, outliers and missed events will have less of an effect and the model will still be accurate, as illustrated by Landray. Susan Ward, founder and executive director, the Collaborative Trajectory Analysis Program (cTAP), pointed out that in smaller trials, however, small variations in data can have an enormous impact on the model. This effect is particularly seen in trials involving rare diseases like Duchenne muscular dystrophy (DMD). In a DMD trial, Ward and colleagues noted large variations in the trial’s primary endpoint (6-minute walk distance). To address the variance, Ward and her collaborators at cTAP applied latent class trajectory analysis. This methodology was developed in social sciences and health care economics to handle variance due to heterogeneity. The method assumes that a single mean exists for a “class,” finds the optimal number of classes by minimizing variance, and allows visualization of multiple clusters of data (2). Ward pointed out that this technique used for rare disease could also be applied to more common diseases. Common diseases are increasingly recognized as groupings of heterogeneous diseases with a set of common symptoms.

Techniques such as the one used by cTAP could help to tailor treatment to a particular subset of patients based on covariance or other factors, or to clarify a more significant effect for a treatment under evaluation.


(2) – Ward provided additional resources for the workshop participants: Leoutsakos et al.,
2012; Muthen and Asparouhov, 2014; Muthen and Brown, 2009.

pp 19-20 in: National Academies of Sciences, Engineering, and Medicine. 2016. Advancing the discipline of regulatory science for medical product development: An update on progress and a forward-looking agenda: Workshop summary. Washington, DC: The National Academies Press.
doi: 10.17226/23438.