A predictive multi-modal imaging marker for designing efficient and robust AD clinical trials Vikas Singh † ,⋆ Ozioma Okonkwo ∗ Sterling C. Johnson ‡ , ∗ Vamsi K. Ithapu ⋆ ⋆ Computer Sciences † Biostatistics and Medical Informatics ∗ Medicine ‡ William S Middleton Memorial VA Hospital University of Wisconsin Madison Wisconsin Alzheimer’s Disease Research Center (WADRC) November 21, 2014 (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 1 / 21
Landscape of AD Clinical Trials As of October 2014, Clinicaltrials . gov lists 255 studies that are recruiting 125 in US and 86 in Europe; 35 are in Phase III (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 2 / 21
Landscape of AD Clinical Trials As of October 2014, Clinicaltrials . gov lists 255 studies that are recruiting 125 in US and 86 in Europe; 35 are in Phase III More than 400 trials since 2002 – Very little success (Cummings 2014) (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 2 / 21
Landscape of AD Clinical Trials As of October 2014, Clinicaltrials . gov lists 255 studies that are recruiting 125 in US and 86 in Europe; 35 are in Phase III More than 400 trials since 2002 – Very little success (Cummings 2014) Right Target, Right Drug, Right Stage (Sperling 2011) ... why do we keep testing drugs aimed at the initial stages of the disease process in patients at the end-stage of the illness ? (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 2 / 21
Landscape of AD Clinical Trials As of October 2014, Clinicaltrials . gov lists 255 studies that are recruiting 125 in US and 86 in Europe; 35 are in Phase III More than 400 trials since 2002 – Very little success (Cummings 2014) Right Target, Right Drug, Right Stage (Sperling 2011) ... why do we keep testing drugs aimed at the initial stages of the disease process in patients at the end-stage of the illness ? Choosing the Right Stage Mild Cognitive Impairment (MCI), Pre-symptomatic or Pre-clinical (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 2 / 21
Landscape of AD Clinical Trials – Right Stage However, the annual conversion rate from MCI to AD is 3 − 20 % (Mitchell 2009) (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 3 / 21
Landscape of AD Clinical Trials – Right Stage However, the annual conversion rate from MCI to AD is 3 − 20 % (Mitchell 2009) The effect of a drug (if there is one) is diminished. = ⇒ Right stage is not right enough! (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 3 / 21
Landscape of AD Clinical Trials – Right Stage However, the annual conversion rate from MCI to AD is 3 − 20 % (Mitchell 2009) The effect of a drug (if there is one) is diminished. = ⇒ Right stage is not right enough! According to FDA, enrichment entails to ... the prospective use of any “patient characteristic” to select a study population in which detection of a drug effect (if one is in fact present) is more likely than it would in an unselected population. (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 3 / 21
Population enrichment – continued Enrichment Cut-Off Discarded Included Healthy AD Worsening Disease (Enrichment Criterion) (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 4 / 21
Population enrichment – continued Enrichment Cut-Off Discarded Included Healthy AD Worsening Disease (Enrichment Criterion) Good enrichment criterion ⇐ ⇒ High correlation with disease Practical enrichment criterion ⇐ ⇒ High predictive power (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 4 / 21
Population enrichment – Existing Work CSF based markers (Holland 2012) Normalization issues (i.e., batch processing), preferred as outcome (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 5 / 21
Population enrichment – Existing Work CSF based markers (Holland 2012) Normalization issues (i.e., batch processing), preferred as outcome Imaging based ROI summaries (Lorenzi 2010) Not voxel–wise, too much information loss, Mostly use future time-point data (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 5 / 21
Population enrichment – Existing Work CSF based markers (Holland 2012) Normalization issues (i.e., batch processing), preferred as outcome Imaging based ROI summaries (Lorenzi 2010) Not voxel–wise, too much information loss, Mostly use future time-point data Voxel–wise summaries (Kohannim 2010) Mostly use future time-point data Designed for classification/regression problems, not trial enrichment (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 5 / 21
Population enrichment – Existing Work CSF based markers (Holland 2012) Normalization issues (i.e., batch processing), preferred as outcome Imaging based ROI summaries (Lorenzi 2010) Not voxel–wise, too much information loss, Mostly use future time-point data Voxel–wise summaries (Kohannim 2010) Mostly use future time-point data Designed for classification/regression problems, not trial enrichment Not explicitly designed to result in small sample (high power) trials (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 5 / 21
Designing a good enricher In this work, we construct a predictive multi-modal imaging marker that is explicitly designed for conducting “efficient” clinical trial and uses only baseline data (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 6 / 21
Designing a good enricher In this work, we construct a predictive multi-modal imaging marker that is explicitly designed for conducting “efficient” clinical trial and uses only baseline data Consider the setup of a clinical trial δ – the change in trial outcome for the placebo group η – the hypothesized decrease in the change of outcome due to the drug/treatment (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 6 / 21
Designing a good enricher In this work, we construct a predictive multi-modal imaging marker that is explicitly designed for conducting “efficient” clinical trial and uses only baseline data Consider the setup of a clinical trial δ – the change in trial outcome for the placebo group η – the hypothesized decrease in the change of outcome due to the drug/treatment To detect this drug effect of 1 − η with statistical power of 1 − β , Samples per arm, s = 2( Z α + Z 1 − β ) σ 2 (1 − η ) 2 δ 2 σ 2 – pooled variance of the outcome (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 6 / 21
Designing a good enricher – continued Samples per arm, s = 2( Z α + Z 1 − β ) σ 2 (1 − η ) 2 δ 2 s decreases if δ increases and σ 2 decreases (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 7 / 21
Designing a good enricher – continued Samples per arm, s = 2( Z α + Z 1 − β ) σ 2 (1 − η ) 2 δ 2 s decreases if δ increases and σ 2 decreases H 0 = δ pb − δ tr = 0 H 0 = δ pb − δ tr = (1 − η ) δ Power = 1 − β α/ 2 α/ 2 β Critical Value (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 7 / 21
Designing a good enricher – continued Samples per arm, s = 2( Z α + Z 1 − β ) σ 2 (1 − η ) 2 δ 2 s decreases if δ increases and σ 2 decreases H 0 = δ pb − δ tr = 0 H 0 = δ pb − δ tr = (1 − η ) δ Power = 1 − β α/ 2 α/ 2 β Critical Value (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 8 / 21
Designing a good enricher – continued = ⇒ we want the trial outcome to have large δ and small σ (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 9 / 21
Designing a good enricher – continued = ⇒ we want the trial outcome to have large δ and small σ = ⇒ The trial population should be “filtered” with some enrichment criterion that results in large δ and small σ for any given outcome (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 9 / 21
Designing a good enricher – continued = ⇒ we want the trial outcome to have large δ and small σ = ⇒ The trial population should be “filtered” with some enrichment criterion that results in large δ and small σ for any given outcome A good inclusion criterion should small variance on the trial population correlate very strongly to spectrum of dementia ( unbiased ) i.e. a minimum variance unbiased (MVUB) estimator is desired (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 9 / 21
randomized denoising autoencoders (rDA) Novel machine learning model based on Stacked Denoising Autoencoders (Bengio 2009) (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 10 / 21
randomized denoising autoencoders (rDA) Novel machine learning model based on Stacked Denoising Autoencoders (Bengio 2009) L-layered Stacked Denoising Autoencoder : Layer 1 Layer 2 Layer 3 Layer L Inputs Outputs (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 10 / 21
randomized denoising autoencoders (rDA) (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 11 / 21
randomized denoising autoencoders (rDA) T outputs T networks L layered SDA L layered SDA Block 1 L layered SDA (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 11 / 21
randomized denoising autoencoders (rDA) T outputs T networks L layered SDA L layered SDA Block 1 L layered SDA T outputs Block 2 T networks L layered SDA L layered SDA L layered SDA (UW-Madison, WADRC) rDAm for enrichment November 21, 2014 11 / 21
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