Therefore, most studies have focused on the more challenging problem of predicting conversion from mild cognitive impairment (MCI) to AD. Good performance has been well-established for discriminating healthy controls (CTL) and AD patients. To model the spatial pattern of atrophy and predict conversion to AD, pattern recognition (PR) has been extensively applied, in particular to the Alzheimer’s disease neuroimaging initiative (ADNI) dataset –. The anatomical hallmarks of AD, cerebral atrophy and ventricular expansion, can be detected using magnetic resonance imaging (MRI). In addition to devastating cognitive impairment, AD is characterized by progressive cerebral atrophy. A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive.Īlzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive dementia that occurs in later life. reverters and healthy controls who later progressed to MCI), moderately late converters (12–24 months) and late converters (24–36 months). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. We showed that this measure significantly correlated with MMSE at 12 months (ρ = –0.64, ADNI and ρ = –0.59, AddNeuroMed).
The ORCHID score was computed for all subjects. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed.
Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression – the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. A much more cumbersome alternative would be to understand what stored results the command ovtest uses, creating locals with the same name and containing the result your need, but since the ovtest is a simple linear regression, it is probably much simpler to run the test by yourself, and also safer than using a blackbox command which might interact strangely with your previous estimation.We propose a novel approach to predicting disease progression in Alzheimer’s disease (AD) – multivariate ordinal regression – which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. This is probably something you should discuss with your tutor. However, I really don't know whether it would be a good idea and what the statistical properties of the test would be if you did this. It should be feasible to include weights when running the test by using standard options (since the test itself is a regression), or even maybe using repest to run the test to include the appropriate weights. If predict does not work after you use repest (although it probably should work since e(b) is stored, according to the help file), this discussion might be helpful to find a way to compute the fitted values. All you need to do is to compute the fitted values after your regression, which you should be able to do since repest stores e(b). With respect to ovtest, I think that the best solution is probably to run the test by yourself, without using the command ovtest, since the test essentially boils down to a regression. Otherwise, repest stores e(b) and e(V), so you could also manually compute the t test statistics, but this probably would be cumbersome with a high risk of making some mistake.
You can check a statistical table to verify whether, in the case of your regression, N-k is large enough to use Z statistics. With respect to the difference between Z and t-statistics, for N-k sufficiently large, they are equivalent, but Z statistics are only appropriate asymptotically, i.e.
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