A Novel Method to Estimate Long‐Term Chronological Changes From Fragmented Observations in Disease Progression

Abstract

Clinical observations of patients with chronic diseases are often restricted in terms of duration. Therefore, obtaining a quantitative and comprehensive understanding of the chronology of chronic diseases is challenging, because of the inability to precisely estimate the patient's disease stage at the time point of observation. We developed a novel method to reconstitute long-term disease progression from temporally fragmented data by extending the nonlinear mixed-effects model to incorporate the estimation of "disease time" of each subject. Application of this method to sporadic Alzheimer's disease successfully depicted disease progression over 20 years. The covariate analysis revealed earlier onset of amyloid-β accumulation in male and female apolipoprotein E ε4 homozygotes, whereas disease progression was remarkably slower in female ε3 homozygotes compared with female ε4 carriers and males. Simulation of a clinical trial suggests patient recruitment using the information of precise disease time of each patient will decrease the sample size required for clinical trials.

Predicting time to dementia using a quantitative template of disease progression

“…In addition to these substantial improvements to our previously described model, our work addresses several limitations of previously described disease progression models. (1) Existing disease progression score models formulated in a Bayesian framework [11,14,15] specify the latent disease progression variable using a single unknown parameter, the time‐shift, meaning that these models do not take into account that disease progression may accelerate over time, limiting their accuracy when working with individual‐level data over long periods of time. Our proposed progression score (PS) takes this phenomenon into account through the use of an additional parameter in the construction of PSs.…”

“…Our proposed progression score (PS) takes this phenomenon into account through the use of an additional parameter in the construction of PSs. (2) Current models either assume independence across biomarkers [15], resulting in biased estimates of latent disease scores [7], or impose linear [11] or double exponential [14] parametric trajectories on biomarkers, limiting the possible functions that can be estimated, potentially resulting in mischaracterization of the time evolution of biomarkers. We model biomarker time courses using flexible monotonic nonlinear functions characterized by basis functions.…”

“…These models emphasize the interpretability of model results rather than optimization of predictive performance, where discriminative methods might outperform but produce results that are not as easily interpretable. Existing generative models of AD progression can be divided into two major categories based on the granularity of their characterization of the latent disease progression variable, either as a sequence of discrete events [3][4][5] or as a continuous variable [6][7][8][9][10][11][12][13][14]. In addition to characterizing changes in biomarker trajectories as a function of latent disease stages, these statistical models provide individualized information that can be used for personalized disease staging and monitoring.…”

Predicting time to dementia using a quantitative template of disease progression

“…These models emphasize the interpretability of model results rather than optimization of predictive performance, where discriminative methods might outperform but produce results that are not as easily interpretable. Existing generative models of AD progression can be divided into two major categories based on the granularity 30 of their characterization of the latent disease progression variable, either as a sequence of discrete events [3][4][5] or as a continuous variable [6][7][8][9][10][11][12][13][14]. In addition to characterizing changes in biomarker trajectories as a function of latent disease stages, these statistical models provide individualized information that can be used for personalized disease staging and monitoring.…”

“…In addition to characterizing changes in biomarker trajectories as a function of latent disease stages, these statistical models provide individualized information that can be used for personalized disease staging and monitoring. 35 Recent work in this area has focused on Bayesian reformulations of statistical approaches to biomarker trajectory modeling [11,14,15] to enable probabilistic estimates of trajectories and better characterization of the individual-level uncertainty in disease progression variables. These improvements can lead to better disease monitoring and progression prediction at the individual level, 40 thereby providing useful tools for clinical trial recruitment and assessment.…”