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MEDICINE: MIND THE GAPAn NIH Office of Disease Prevention Webinar Series
Joint Models of Longitudinal and Time-to-Event Data for
Informing Multi-Stage Decision Making in mHealth
Presented byWalter Dempsey, Ph.D.
University of Michigan
Methods: Mind the Gap Webinar Series
If youβre a person with a disability or using assistive technology and are having difficulty accessing any of the content in this file, please contact Natasha Oyedele at [email protected].
β’ Mobile health: an introduction
β’ Joint models: an introduction
β’ Assessing association (Decision making, stage 1)
β’ Defining risk strata (Decision making, stage 2)
β’ Assessing time-varying treatment effects (Decision making, stage 3)
β’ Conclusions: towards incorporating joint models in mHealthdecision making
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Outline
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Mobile HealthβThe delivery of healthcare services via mobile
communication devicesβ β Foundation for the National Institutes of Health (FNIH)
β’ Active data involves explicitly asking participants for information, preferences, and/or opinions
β’ Ecological Momentary Assessments (EMAs)o Administrative: fixed self-report times
β’ E.g., Daily dairyo Random:
β’ 1 EMA uniformly sent in 4-hour time blocksβ’ Can depend on observed history
o Event-contingent: Participant initiatedβ’ Engages in NSSIβ’ Engages in smoking
β’ How many questions and how often you ask depends on o Attendant burden and intrusivenesso Behavioral constructs of interest
β’ E.g., subset of a baseline battery assessment asked multiple times per day
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Active data
β’ Passive data, participant has little awareness of the data collection effort
β’ How many sensors and data depends on o Attendant burden and
intrusivenesso Behavioral constructs of
interest
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Passive data
β’ Example: Sense2Stopβ’ Toβ¦
o Monitor stress continuouslyo Decide whether/how to intervene based on level of
stresso Deliver the intervention as soon as stress occurs
β’ In the personβs natural environment
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Why are we collecting data?
β’ Andβ¦o Monitor Context o Ensure that the context enables the
person to receive and employ the intervention
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Why are we collecting so much data?
β’ Goal is delivery of the right type of support at the right time (JITAIs) while minimizing disruptionso Data-driven decision making
β’ Both using collected data and in experimental design for intervention developmento Recall mHealth most useful when we minimize attendant burden and maximize therapeutic
effects of JITAIs
β’ Research Questions:o How strong is association between behavioral biomarker and event outcome of interest?
β’ How strong is the association between stress and the risk of lapse?o Can we use passive and active data to discriminate between patients of low and high risk?o Are the digital phenotyping biomarkers good?
β’ E.g., is stress a good biomarker? β’ If treatment improves stress, does it also lower risk of lapse?
Example: A-CHESS
β’ Smartphone application to support recovery from alcoholism (A-CHESS)o Mobile phone administered
β’ A-CHESS had both static content (e.g., audio-guided relaxation) and interactive features
β’ If an individual is near a high-risk location (e.g., a bar she used to frequent), GPS initiated alert will ask if patient wants to be there.
β’ Event can be passively-detected via GPS.
(Gustafson et al., 2011 & 2014)
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Joint models for longitudinal and survival data
Methods overview for mHealth
β’ Association: How strong is the association between behavioral biomarker and instantaneous risk rate of event? o Does the association vary over time?o How are behavioral biomarkers related to each other?
β’ Prediction: Can we improve predictions of event risk by considering behavioral biomarkers?
β’ Treatment effect: Does treatment improve the behavioral biomarker?o What is the direct and indirect effect of treatment on hazard rate of event?
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Research questions: Multiple outcomes
β’ Repeated measurements on the same individual are expected to be (positively) correlated
π¦π¦ππ = πππ½π½ + ππππππππ + ππππ,
ππππ~ππ(0,π·π·) and ππππ~ππ(0,ππ2πΌπΌππππ)
β’ Whereo π¦π¦ππ is the outcomeo ππ is the design matrix for fixed effectso Z is the design matrix for random effectso ππππ β₯ ππππ
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Linear Mixed Model
β’ Assume there exists subject-specific coefficients: π½π½ππ~ππ(π½π½,π·π·)
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Linear Mixed Model
Rizopolous (2016), Tutorial: Joint model for Longitudinal and Survival Data
β’ Notation:o ππππβ: βtrueβ time-to-evento πΆπΆππ: censoring time
β’ Observable datao ππππ = min ππππ ,πΆπΆππ : β observed event timeo Ξππ = 1 ππππ = ππππ :β censoring indicator
β’ Goal: Valid inference for ππππβ using observed data
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Time-to-event data
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Time-to-event analysis
β’ Distribution functions
πΉπΉ π‘π‘ = ππ ππβ < π‘π‘ ;ππ π‘π‘ =πππππ‘π‘πΉπΉ(π‘π‘)
β’ Survival functionππ π‘π‘ = ππ ππβ β₯ π‘π‘
β’ Hazard function
β π‘π‘ = limπΏπΏβ0
ππ π‘π‘ β€ ππβ < π‘π‘ + πΏπΏ | ππ β₯ π‘π‘πΏπΏ
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Time-to-event analysis
β’ Relative risk modelβππ π‘π‘ = β0 π‘π‘ exp πΎπΎ1π€π€ππ1 + β―+ πΎπΎπππ€π€ππππ
log βππ π‘π‘ = log β0 π‘π‘ + πΎπΎ1π€π€ππ1 + β―+ πΎπΎπππ€π€ππππβ’ Where
o βππ π‘π‘ denotes the hazard for patient i at time to β0 π‘π‘ denotes the baseline hazardo π€π€ππ1, β¦ ,π€π€ππππ a set of covariates
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Cox model: baseline covariates
β’ Cox model: no assumptions for baseline hazard
β’ Example: PBC datasetβππ π‘π‘ = β0 π‘π‘ exp πΎπΎ1πππππππππ‘π‘πππππππ‘π‘ππ + πΎπΎ2πΉπΉππππππππππππ + πΎπΎ3π΄π΄π΄π΄ππππ
Variable Value HR Std. Err. Z-value P-value
πΎπΎ1 -0.138 0.871 0.156 -0.882 0.378
πΎπΎ2 -0.493 0.611 0.207 -2.379 0.017
πΎπΎ3 0.021 1.022 0.008 2.784 0.005
Rizopolous (2016), Tutorial: Joint model for Longitudinal and Survival Data
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Time dependent covariates
β’ We often wish to answer questions related to time-dependent biomarker measures
β’ There are two types of time-dependent covariates (Kalbfleischand Prentice, 2002):
o Exogenous/External: Future trajectory of the variable is not affected by occurrence of an event:
ππ ππππ π‘π‘ ππππ π π ,ππππβ β₯ π π ) = ππ ππππ π‘π‘ ππππ π π ,ππππβ = π π )
o Endogenous: not externalβ’ Biomarkers are almost ALWAYS endogenous
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Extended Cox Modelβ’ Extend the Cox model to handle time-dependent
covariatesβππ π‘π‘ | ππππ π‘π‘ ,π€π€ππ = β0 π‘π‘ π π ππ(π‘π‘) exp πΎπΎβ€π€π€ππ + πΌπΌπ¦π¦ππ(π‘π‘)
β’ Whereo π π ππ(π‘π‘) is the at-risk indicatoro exp πΌπΌ is relative risk translating a one unit increase in π¦π¦ππ(π‘π‘) at the same time point
β’ Parameters can be estimated based on partial likelihoodo Assumes no measurement erroro Step-function patho Existence of covariate not related to failure status
β’ Extended Cox model valid ONLY for exogenous variableso Treating endogenous variables as exogenous may produce spurious results
β’ Special feature of endogenous covariates leads to a new class of models
Joint models for longitudinal and time-to-event data
β’ Intuitive idea behind the standard joint modelo Use an appropriate model to describe the evolution of the
marker in time in each patiento The estimated evolutions are then used in a Cox model
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Joint modeling framework
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Method 1: Shared Random Effect Modelβ’ Assume a latent trajectory (typically referred
to as βtrue biomarker trajectoryβ)
β’ Build longitudinal model for biomarkers given true trajectory
β’ Build survival model depends on true trajectory
β’ Conditional independent processes given the true trajectory
β’ Requires knowledge of trajectory and association
β’ Typically flexible parametric modeling assumptions
β’ Assume a true and unobserved biomarker trajectory:
ππππ π‘π‘ = ππππ π π , 0 β€ π π < π‘π‘ longitudinal history
β’ Define relative risk model
βππ π‘π‘ | ππππ π‘π‘ = β0 π‘π‘ exp πΎπΎβ€π€π€ππ + πΌπΌππππ(π‘π‘)
β’ Define mixed effects model
π¦π¦ππ π‘π‘ = ππππ π‘π‘ + ππππ π‘π‘ = π₯π₯ππ π‘π‘ β€π½π½ + π§π§ππ π‘π‘ β€ππππ + ππππ(π‘π‘)
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Method 1: Shared Random Effect Model
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Method 2: Random Sampling Methodβ’ Use random EMA design instead
of modeling assumptions
β’ Construct a design-unbiased estimator of the cumulative hazard
β’ Requires knowledge of the EMA design
β’ Parametric modeling assumptions
β’ Quantifiable sources of variance
β’ Assume incorrectly specified biomarker model (linear when truth is quadratic)
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Simulation comparison
n π π Bias SD CR Bias SD CR
300 16 0.009 0.062 95.5 0.092 0.065 71.4
8 0.011 0.068 95.3 0.099 0.067 68.9
4 0.011 0.072 94.4 0.102 0.068 71.0
100 16 0.024 0.110 93.7 0.104 0.117 89.0
8 0.033 0.119 94.3 0.112 0.119 88.2
4 0.033 0.134 91.1 0.109 0.122 89.7
Randomization-based Joint Modeling
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Assessing associationDecision making, stage 1
β’ Claim: Biomarker is strongly associated with a particular negative behavioral event outcomeo Craving β¦ Drug Lapseo Stress β¦ Smoking Lapseo Suicidal ideation β¦ Distress, Attempts
β’ In support of: interventions aimed at biomarkero Coping with Craving and Urges module β¦ reduce craving β¦ reduce risk of
substance useo Reminder to practice mindfulness β¦ reduce stress β¦ reduce risk of lapseo Automated call to increase social support β¦ reduce loneliness β¦ reduce risk
of STBs
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Research Question: Assessing association
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Case Study: Smoking Cessation Study Covariates Estimate S.E. Z P-value
Treatment (0,1) 0.2129 0.1706 1.25 0.2120
Craving (1-11) 0.4234 0.0273 15.51 <0.0001
Negative Affect (-2.31, 5.85) 0.4941 0.0714 6.92 <0.0001
Restlessness (1-11) 0.0631 0.0285 2.21 0.0268
Attention Disturbance (1-11) -0.0573 0.0410 1.40 0.1602
Positive Affect (-3.43, 2.84) 0.0856 0.0872 0.98 0.3266
Rathbun et al. (2014), J. Royal Stat Soc., Series C
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Building predictions/classifications
Decision making, stage 2
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Method 3: Leveraging behavioral latent constructs
1. Latent behavioral constructso Measurement-error models to connect
observables to the latent stateso GLM: E πππ‘π‘
(ππ) = π΄π΄ πππ‘π‘β²π½π½ = π΄π΄ πππ‘π‘1 π½π½1 + πππ‘π‘
(2)Ξ²2o Use anchor measurements to make latent
constructs identifiable
2. Latent risk model
3. Latent risk connects to observed event outcome of interest
o Probability of reporting drug use = πππ₯π₯πππππ‘π‘ π π π‘π‘
o
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Case Study 2: Recovery Support Services
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Comparison of prediction accurary
Fraction of time stressed*
For hour*
*Adjusted for current talk 31
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Assessing treatment effectsDecision making, stage 3
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Case Study 1: Smoking Cessation Study Covariates Estimate S.E. Z P-value
Treatment (0,1) 0.2129 0.1706 1.25 0.2120
Craving (1-11) 0.4234 0.0273 15.51 <0.0001
Negative Affect (-2.31, 5.85) 0.4941 0.0714 6.92 <0.0001
Restlessness (1-11) 0.0631 0.0285 2.21 0.0268
Attention Disturbance (1-11) -0.0573 0.0410 1.40 0.1602
Positive Affect (-3.43, 2.84) 0.0856 0.0872 0.98 0.3266
Rathbun et al. (2014), J. Royal Stat Soc., Series C
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Current issues in JMs in mHealth
Translating JMs into mHealth research
Episodes
Multiple imputation to produce smoking episodes
with error
Various Measures
Of SmokingEpisodes
AndPuffs
Smoking Puffs
PuffMarker
1
0
Random EMA
NoMissedYes>100
Event-based EMA
5-15
End-of-day EMA
Beginning
Of day
End
Of day
Imputation 1
Imputation 2
Smoking
Topic 1: Multiple noisy measurements
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Topic 2: Sensors + Event outcomesβ’ Participant wears Empatic E4
bracelets
β’ Measure various physiological responses (including EDA)
β’ Self-report via button press moments of distress
β’ Research question: how can we map these high-frequency sensor curves onto instantaneous risk? βππ π‘π‘ | ππππ
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Conclusion
β’ Focused on mHealth (mobile health) studies in which both longitudinal andtime-to-event data are recorded per participant
β’ Discussed how joint models enter into various stages of the intervention development process o Assessing levels of biomarker association with event risk, o Defining risk strata for a stratified micro-randomized trial, o Post-study analysis of the treatment effect on event risk
β’ Discussed how mHealth studies present novel methodological challenges for joint modeling and solutions in several case studies
β’ Data collection and analysis geared toward informing multi-stage decision making in mHealth
The End
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Email me with questions: Walter: [email protected]
Data science for dynamic decision making (d3-) labUniversity of Michigan
Susan Murphy (Harvard University)Inbal Nahum-ShaniDanny AlmirallAmbuj Tewari
β’ Assuming EMAs are sent at random times; that is,
ππππ π‘π‘ = limπΏπΏβ0
πΏπΏβ1P ππππππ[0, π‘π‘ + πΏπΏ) βππππππ[0, π‘π‘) π»π»π‘π‘)
is a known intensity function for sending EMAs.
β’ Random EMAs then ππππ π‘π‘ β 1
β’ Self-correcting point process (Isham and Wescott, 1970):
ππππ π‘π‘ = exp πΌπΌ + π½π½(π‘π‘ β ππππππππ[0, π‘π‘))
corrects when far from target = π‘π‘/ππ.
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Method 2: Random Sampling Method
β’ Recall the likelihood
β’ Where
β’ Key idea: approximate cumulative hazard using random-design:
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Method 2: Random Sampling Method
Case Study 3: Sense2Stopβ’ Participant wears Autosense chest
band + sensors on each wrist
β’ Measure various physiological responses and body movements to robustly assess physiological stress.
β’ Pattern-mining algorithm uses the sensor data to construct a binary time-varying stress classification
β’ Participant is then classified at each minute as either βStressedβ or βDoes not qualify as Stressedβ
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β’ Recovery Support Services (RSS) Studies of individuals with Substance Use Disorders (SUDs) (PI: Scott)o 2 pilot RSS studies on individuals who have been discharged from outpatient, intensive outpatient, or residential
treatmento EMA + EMI usage data
β’ Smoking cessation study (PI: Shiffman)o N=412 volunteer smokerso Randomized at quit date N=188 to nicotine patch, N=136 to placeboo Random EMAs, no passive data
β’ Sense2Stop (PI: Spring)o Stratified micro-randomized trialo N=75 (expected)o Various EMA + sensor data; o Sequential randomization to reminder to practice mindfulness
β’ Suicidal Inpatient study (PI: Nock)o N=91o Wrist sensor (measures EDA and accelerometer)o User-initiated button press to denote times of distress (i.e., recurrent event outcome)
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Motivating studies
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What can digital phenotyping tell us?
Mohr et al. (2017), Annual Reviews of Clinical Psychology
Topic 3: Missing data assumptions
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Topic 4: Multiple treatment effects
Beginning
Of day
End
Of day
Smoking Episodes
Smoking Puffs
ReminderTreatments
Indicatorππ(π‘π‘)
1
0
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Topic 5: EMA + Event outcomes
β’ Participant wears Empatic E4 bracelets
β’ EMA question on suicidal ideation also asked 5-times per day
β’ Research question: can we disentangle βactual riskβ from the risk due to reminder based on EMA and study fatigue?
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Randomization formula with soft-budget constraint
β’ Assume the randomization probability formula, P π΄π΄π‘π‘ = 1 π»π»π‘π‘) =pt π»π»π‘π‘ , is known
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Markov dynamics for latent constructs
1. Partially observable Markov decision process
2. Time-varying exogenous process affects risk (e.g., weather)
3. Actions can impact outcome directly and indirectly
4. Latent state can account for impact of continuing impact of actions on risk