A hierarchical Bayesian entry time realignment method to study the long-term natural history of diseases

Abstract

A major question in clinical science is how to study the natural course of a chronic disease from inception to end, which is challenging because it is impractical to follow patients over decades. Here, we developed BETR (Bayesian entry time realignment), a hierarchical Bayesian method for investigating the long-term natural history of diseases using data from patients followed over short durations. A simulation study shows that BETR outperforms an existing method that ignores patient-level variation in progression rates. BETR, when combined with a common Bayesian model comparison tool, can identify the correct disease progression function nearly 100% of the time, with high accuracy in estimating the individual disease durations and progression rates. Application of BETR in patients with geographic atrophy, a disease with a known natural history model, shows that it can identify the correct disease progression model. Applying BETR in patients with Huntington’s disease demonstrates that the progression of motor symptoms follows a second order function over approximately 20 years.

Establishing a natural history of X-linked dystonia parkinsonism

“…To align participants on a common temporal scale, we model progression as a function of age minus an estimated age at onset. This approach, sometimes referred to as ‘entry time realignment’, 27 has been employed to analyse disease progression for a variety of long-term natural history studies, including Alzheimer’s disease, 28 exudative age-related macular degeneration, 29 autosomal recessive Stargardt disease, 30 Parkinson’s disease 31 and Huntington disease. 32 At first, it might seem more natural to model progression as a function of age.…”

“…Note that entry time realignment has been successfully applied to studies where only symptom measures are available. 27 The model also involves estimating a rate of progression for each symptomatic gene-positive participant. The estimated rate of progression depends on a participant’s observed symptom measures and estimated age at onset.…”