Statistical Methods for Wearable Technology in CNS Trials

Transcription

1 Statistical Methods for Wearable Technology in CNS Trials Andrew Potter, PhD Division of Biometrics 1, OB/OTS/CDER, FDA ISCTM 2018 Autumn Conference Oct. 15, 2018 Marina del Rey, CA

2 Disclaimer This presentation reflects the views of the author and should not be construed to represent FDA s views or policies. 2

3 Outline Data Statistical Methods Signal Processing Feature Selection Modeling of treatment effect evolution over time Simulated case study in sleep medicine 3

4 Movement Data from Acceleration Sensors 0 min 8 min Gyllensten, IC, Physical Activity Recognition in Daily Life using a Triaxial Accelerometer, Master s Thesis,

5 Converting Acceleration Sensor Data to Health Outcomes Dense information on a person s movement while the device is recording At least 100 measurements per day Days to weeks of data What are the important features of the signal? How does a feature relate to a disease state? How do features change over time? How to compare between people and groups? How to define a drug effect? How to identify features? Have subject tag events in real time on the device? Can machine learning automate this task? 5

6 An Important Feature: Circadian Variation in Sensor Data Blood flow data from a ventricular assist device recorded every 15 min. Circadian patterns present in multiple types of sensor data 6

7 Total Sleep Time Derived from Acceleration Sensor High device use Low device use Calendar Day 7

8 Weekday to Weekend Variability In Total Sleep Time Weekday mornings Weekend mornings 8

9 Extracting Features Fourier Transform Focuses on periodic features in a signal Represents the strength of a signal over a range of frequencies Signals with circadian variation have a peak at 1 cycle/day Spectral representation of EEG Circadian Cycle Feature 9

10 Extracting Features Smoothing Signal in Time Source: Wang et al. Journal of Circadian Rhythms 2011, 9:

11 Feature Selection - LASSO LASSO least absolute shrinkage and selection operator Extension of regression Automatically selects covariates Subset of all covariates most predictive of outcome Shrinks covariate coefficients towards zero - regularization See The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman for more details 11

12 Feature Selection Neural Networks Automatic techniques that selects a combination of features associated with a class membership Creates features from the data Applications - Digital Phenotypes, detection of cancer in radiology images, classification of sleep states in polysomnography Automated classification of PSG and sleep events Source: Nielsen, Neural Networks and Deep Learning, 12

13 Feature Selection Neural Networks Multiple models using different feature of the PSG Examples: Supratak et al, DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25 (11), pp Chambon et al, A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26 (4), pp Olsen et al, Automatic, electrocardiographic-based detection of autonomic arousals and their association with cortical arousals, leg movements, and respiratory events in sleep, Sleep, Volume 41, Issue 3, 1 March 2018, zsy006, Source: Chambon et al. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26 (4), pp

14 Modeling Features Functional Linear Models Method for analyzing curves Extends regression to curves Multiple cases: Cross-sectional observation of a single curve per patient on a single outcome measurement Longitudinal observations of the same curve on a single outcome measurement within a patient Cross-sectional and longitudinal of multiple curves on the same patient AHI Sleep Apnea Severity Source: Wang et al. Journal of Circadian Rhythms 2011, 9:

15 Case Study - Sleep Wearable sensors introduce two statistical challenges Analysis of the within day data recorded densely Analysis of the longitudinal evolution of the daily sensor Illustrate an approach to analyzing longitudinal evolution using total sleep time (TST) as a summary measure of daily sensor data Compare changes in TST between a new sleep medication to placebo over four weeks Focus on modeling the linear trend in TST in both groups Use all observed data Calculation of TST at specific time points conducted after statistical modeling Framework extends to multiple sleep parameters and functional models 15

16 Case Study - Sleep Simulated data: 300 patients 30 minute improvement in TST by day 15 Similar change in TST to several NDAs submitted to FDA Measure treatment effect by: Difference in TST at four weeks Average TST trajectory in each group focus on the linear trend Use two statistical models Linear mixed model with random slopes strong assumption on covariance between days Generalized estimating equation (GEE) model robust to misspecification of covariance between days 16

17 Simulated Clinical Trial The Data Example Subjects Subject Specific Change from Baseline in TST Triangles Weekday Circles - Weekend 17

18 Population Average Total Sleep Time Trajectories 18

19 The Linear Mixed Model Results Estimate 95% Confidence Interval Intercept Average TST Trajectories Day Treatment Tue Wed Thurs Fri Sat Sun Treatment by Day Week 4 Placebo Subtracted Treatment Effect

20 The GEE Results Estimate 95% Confidence Interval Intercept Day Treatment Tue Wed Thurs Fri Sat Sun Treatment by Day Week 4 Placebo Subtracted Treatment Effect Average TST Trajectories 20

21 Final Thoughts Rich new data source Contains new information about neurology and psychiatry diseases Existing statistical method provide starting point Explore new methods to show population and individual drug effects 21

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