Table 3.

Prediction results based on linear regression models, presented for a select subsample of investigated models

Independent variable(s)	Measures based on those who convert		Survival curve–based measures
	RMSE	Max. abs. err.	${\mathbf{\chi }}^2$	Max. vert. dist.
Age	1.80	6.78	46.6	0.491
RAVLT imm.	1.64	5.52	35.7	0.438
RAVLT imm. + age	1.64	5.46	34.5	0.437
All biomarkers	1.58	4.67	20.5	0.428
All biomarkers + age	1.57	4.63	21.1	0.437
PS	1.48	3.79	2.00	0.453
PS + γ	1.48	3.75	1.72	0.453
PS + age	1.49	3.70	1.68	0.453
Comparison to disease age estimated from other methods
LTJMM [11], $t_{i,j=0}+{\delta }_i$	1.80	6.77	47.7	0.492
GPPM [15], ${\varphi }^j\left(\tau \right)=\tau +d^j$ (subset)	2.20	5.80	18.3	0.466
This article, PS (subset)	2.14	4.21	0.685	0.200

Abbreviations: RMSE, root mean square error; Max. abs. err., maximum absolute error; ${\mathbf{\chi }}^2$, log-rank test statistic; Max. vert. dist., maximum vertical distance between the survival curves based on predicted onset and observed onset; RAVLT imm., Rey Auditory Verbal Learning Test immediate recall (sum across learning trials); PS, progression score.

NOTE. The lower portion of the table presents the predictive performance achieved using disease ages estimated using two existing models of AD progression, Latent Time Joint Mixed effects Model (LTJMM) and Gaussian Process Progression Model (GPPM). Because the GPPM model had to be fitted on a subset of the data, for comparison purposes, we also present the predictive performance of PS computed using our model in this same subset.