medicine: mind the gap - time-varying effect modeling to ... · o a developmental perspective 4....
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Medicine: Mind the Gap
Time-varying effect modeling to study developmental and dynamic processes STEPHANIE T. LANZA, PHD JANUARY 29, 2016
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Outline 1. Background
2. Study I: Nicotine addiction o Recovery is dynamic
3. Study II: E-cigarette use o A developmental perspective
4. Next steps
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1.
2. Study I: Nicotine addiction o Recovery is dynamic
3. Study II: E-cigarette use o A developmental perspective
4. Next steps
Background
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Outline
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Human behavior as it relates to health is dynamic Orientation is relevant for understanding
◦ Behavioral change – across age, time ◦ Changes in process – across age, time ◦ Differential intervention effects – across age, time
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A motivating example We know that negative affect and craving to smoke are tightly linkedamong addicted smokers. What does recovery look like?
TVEM can address questions such as: Is negative affect differentially associated with craving at various points in the smoking cessation process?
How does a smoking cessation intervention affect that link over time?
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A thought exercise Suppose an intervention was conducted to reduce alcohol abuse 2-arm RCT
Post-baseline measurement ◦ One time point ◦ Two time points ◦ Multiple time points (moving window)
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Effect of intervention: One time
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
Time 1
Placebo Intervention
Rate of heavy episodic drinking
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Effect of intervention: Two times Rate of heavy episodic drinking
0.8 0.7 0.6
0.1 0.0
Time 1 Time 2
Placebo Intervention
0.2 0.3 0.4 0.5
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Effect of intervention: Multiple times
0.2 0.3 0.4 0.5 0.6 0.7 0.8
Rate of heavy episodic drinking
0.1 0.0
Time 1 Time 2 Time 3 Time 4 Time 5 Time 6 Time 7 Time 8 Time 9 Time 10
Placebo Intervention
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TVEM: Direct extension of regression P50-DA039838: Center for Complex Data to Knowledge (CD2K) in Drug Abuse and HIV Behavioral Science
Why collect longitudinal data? ◦ capture temporal changes in an outcome and time-varying covariates
Natural to expect that the associations between covariates and outcome may change over time
TVEM is designed to evaluate whether and how associations change over time
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TVEM: Direct extension of regression
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Regression coefficients express associations between variables
Traditional regression predicting outcome (Y) from covariate (X)
𝑌𝑌=𝛽𝛽0 + 𝛽𝛽1𝑋𝑋 + 𝑒𝑒
TVEM allows coefficients to be dynamic
𝑌𝑌=𝛽𝛽0 𝒕𝒕 + 𝛽𝛽1 𝒕𝒕 𝑋𝑋 + 𝑒𝑒
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Coefficient functions are estimated TVEM estimates regressioncoefficients as flexible function of continuous time ◦ Intercept ◦ Slopes
Use figure to interpret a“coefficient function”
-1.2
-0.2
0.8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
REG
RESS
ION
CO
EFFI
CIEN
T
TIME
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Brief history of TVEM 1990’s ◦ Functional regression analysis introduced in statistical literature (Hastie &
Tibshirani, 1993; Hoover et al., 1998)
2010 ◦ SAS software released (under direction of Runze Li)
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Brief history of TVEM 2012 ◦ Demonstration paper – Prevention Science ◦ Pre-conference workshop – Society for Research on Nicotine and Tobacco ◦ NCI R01 – Smoking cessation dynamics
2013 ◦ Pre-conference workshop – Society for Prevention Research ◦ NCI, OBSSR funds supplemental issue of Nicotine and Tobacco Research ◦ Application paper – Drug and Alcohol Dependence
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Brief history of TVEM 2014 ◦ Supplemental issue published
(Lanza, Piper, & Shiffman, Eds.)
◦ Other researchers picking up TVEM
2015 ◦ Summer Institute on Innovative Methods ◦ Pre-conference workshop – Society for
Ambulatory Assessments ◦ Software extended: random effects ◦ NIDA R01 – Epidemiology of substance use
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Outline 1. Background
2. Study I: Nicotine addiction Recovery is dynamic o
3. Study II: E-cigarette use o A developmental perspective
4. Next steps
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Nicotine addiction Tobacco use is leading cause of preventable death globally
95% cessation attempts end in relapse; withdrawal symptomsprimary reason
Improved understanding of withdrawal symptoms and howtreatments alleviate them could: ◦ Lead to new treatments ◦ Inform tailored treatments (to people, to time)
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Nicotine addiction Overall goal:
To apply innovative methods to existing data from an RCT to gainknowledge that can inform next generation of smoking interventions
R01-CA168676: Advancing Tobacco Research by Integrating Systems Science andMixture Models
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Wisconsin Smokers’ Health Study 1504 daily smokers enrolled in smoking cessation RCT ◦ Funded by P50-CA84724
Placebo group ◦ Counseling only
Treatment group ◦ Five combinations of Bupropion, lozenge, patch
Real-time assessment of dynamic phenomena (withdrawal symptoms, mood, behavior)
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Study design EMA: 4 assessments per day ◦ Upon waking ◦ 2 random times ◦ At bedtime
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Time-varying effects of smoking intervention Goal 1: Study the underlying dynamics of craving during cessation attempt
Goal 2: Estimate effect of intervention on decoupling craving from itskey drivers (e.g., negative affect)
From Lanza et al. (2014) Nicotine and Tobacco Research
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Measures Outcome: Craving during first two weeks of quit attempt ◦ Intensively assessed via EMA
Predictors: ◦ Baseline nicotine dependence (not time-varying, but effect can be!) ◦ Negative affect (time-varying)
Moderator: Intervention group
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Specify model Within each intervention group, what varies with time? ◦ Mean craving (intercept function) ◦ Negative affect ◦ Effect of negative affect (slope function) ◦ Effect of baseline dependence (slope function)
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑡𝑡 + 𝛽𝛽1 𝑡𝑡 𝐶𝐶𝐴𝐴𝐴𝐴𝐴𝐴𝐶𝐶𝐴𝐴𝑖𝑖𝑖𝑖 + 𝛽𝛽2 𝑡𝑡 𝐷𝐷𝐴𝐴𝐷𝐷𝑖𝑖 + 𝜖𝜖𝑖𝑖𝑖𝑖
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Effect on craving: Negative affect
Placebo
Treatment
Lanza et al. (2014) Nicotine and Tobacco Research
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Effect on craving: Baseline dependence
Placebo
Treatment
Lanza et al. (2014) Nicotine and Tobacco Research
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Implications for smoking cessation Think differently about intervention effects
With time, intervention changes the relationship between baselinedependence and craving
Intervention diffuses role of negative affect– a key driver of craving – early in quit attempt
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Broader implications Effects of static “baseline” variables can change over time Effect of treatment in standard RCT may be time-varying ◦ Model intervention processes we posit
Could inform tailoring of treatment to individuals and to time (adaptive intervention designs)
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2. Study I: Nicotine addiction o Recovery is dynamic
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Outline 1. Background
3. Study II: E-cigarette use A developmental perspective o
4. Next steps
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NIDA R01 Overall goal:
To apply TVEM to existing national data to study etiology ofsubstance use, co-use, comorbidity with mental health problems,and health disparities
R01-DA039854: Age-Varying Effects in the Epidemiology of Drug Abuse
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E-cigarette use among adolescents Developed as “reduced harm product” thus often considered safealternative to traditional cigarettes (Cobb et al., 2010)
Inhalation-activated devices; heat produced which turns solution(nicotine, other additives) into vapor ◦ Eliminates combustion/smoke, but long-term effects of use inconclusive
(Chapman & Wu, 2014; Cobb et al., 2010; Pepper & Brewer, 2014, Williams & Talbot, 2011)
Rate of adolescent use rising rapidly ◦ Lack of FDA regulations ◦ Gateway to traditional cigarettes?
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National Youth Tobacco Study Cross-sectional data from 2014
CDC to assess “tobacco-related beliefs, attitudes, behaviors, and exposure to pro- and anti-tobacco influences”
22,007 US middle- and high-school students ◦ ages 11-19 (mean 14.5) ◦ 49% female ◦ 29% Hispanic, 48% NH White, 17% NH Black
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Etiology of traditional and e-cigarette use Goal 1: Estimate disparities in rates of use across adolescence for sexand race/ethnicity population subgroups
Goal 2: Estimate rate of use of both products as continuous function of age
From Lanza et al. (under review)
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Measures Current traditional cigarette smoking ◦ Coded 1 if use in past 30 days, 0 otherwise (6.4% yes)
Current e-cigarette smoking ◦ Coded 1 if use in past 30 days, 0 otherwise (9.2% yes)
Age (to nearest year) Sex, Race/ethnicity (moderators)
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Specify model (logistic TVEM) What varies with age? ◦ Probability of cig use ◦ Probability of e-cig use ◦ Effects of sex, race/ethnicity ◦ Effect of cig on e-cig (age-varying odds ratio)
𝑝𝑝(𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖)𝑙𝑙𝑙𝑙 = 𝛽𝛽0 𝑎𝑎𝑎𝑎𝑒𝑒 + 𝛽𝛽1 𝑎𝑎𝑎𝑎𝑒𝑒 𝑆𝑆𝐴𝐴𝑋𝑋𝑖𝑖1 − 𝑝𝑝(𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖)
𝑝𝑝(𝐴𝐴_𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖)𝑙𝑙𝑙𝑙 = 𝛽𝛽0 𝑎𝑎𝑎𝑎𝑒𝑒 + 𝛽𝛽1 𝑎𝑎𝑎𝑎𝑒𝑒 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖1 − 𝑝𝑝(𝐴𝐴_𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖)
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E-cigarette and traditional cigarette use: Sex differences (ages 11-19)
E-Cigarette Use Traditional Cigarette Use
Female
Male
Female
Male
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E-cigarette and traditional cigarette use:Race/ethnicity differences (ages 11-19)
Black
White
Hispanic
Black
White
Hispanic
E-Cigarette Use Traditional Cigarette Use
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Use of both products (odds ratio, ages 11-19)
Among those age 12, adolescents using e-cigarettes are >40 times
as likely to use traditional cigarettes compared to those not using e-cigarettes
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Implications for policy and prevention Identification of key ages of risk can inform targeted, age-appropriate intervention
Traditional and e-cigarette use go hand in hand, particularly in very early adolescence
Early use of e-cigarettes significantly more likely among Hispanicyouth, suggesting greater risk for future nicotine dependence
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2. Study I: Nicotine addiction
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Outline 1. Background
o Recovery is dynamic
3. Study II: E-cigarette use o A developmental perspective
4. Next steps
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New information contained in contemporary data sources Intensive longitudinal data (ILD) ◦ EMA, wearable devices
Electronic medical records (EMR) Genetic data
Big data, complex data = big opportunity Adaptive interventions, Mobile interventions, Precision medicine ◦ Stress, mood, context, health behaviors
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SEND QUESTIONS TO [email protected] USE @NIHPREVENTS & #NIHMTG ON TWITTER
TVEM can unlock new knowledge from existing data Complex processes unfolding with time
Dynamic effects of interventions
Developmental associations Associations across historical time
Complex link between age-of-onset and later outcomes
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SEND QUESTIONS TO [email protected] USE @NIHPREVENTS & #NIHMTG ON TWITTER
John Dziak Runze Li Michael Russell Sara Vasilenko
KEY TVEM COLLABORATORS
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SEND QUESTIONS TO [email protected] USE @NIHPREVENTS & #NIHMTG ON TWITTER
P50-DA010075, P50-DA039838, R01-DA039854
NATIONAL CANCER INSTITUTE R01-CA168676
THANK YOU!
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EXTRA SLIDES
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Getting started with TVEM
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TVEM is freely available Download SAS macro and user’s guide at methodology.psu.edu
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Data requirements for TVEM
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Data requirements (intensive longitudinal data)
Intensive Longitudinal Data
0 1 2 3 4 5 6 7 8 9 0
1
2
3
4
5
6
Times 10
Subj
ect
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Data requirements (cross-sectional and panel studies) Only one or a few waves, but many ages sampled
Example: The National Longitudinal Study of Adolescent to Adult Health (Add Health) ◦ Nationally representative sample ◦ 4 waves of data collected from 1996-2008 ◦ N~12,000 (core sample) ◦ 34,562 person-times (spans ages 12-32)
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Add Health: Coverage across age
14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Age
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TVEM to examine change over historical time
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Rates of use over time: By race Black Youth White Youth
Lanza et al. (2015) Journal of Adolescent Health
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Rates of co-use over time: Black youth
Lanza et al. (2015) Journal of Adolescent Health
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Rates of co-use over time: White youth
Lanza et al. (2015) Journal of Adolescent Health
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TVEM to understand age-of-onset
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Rate of dependence as function of age of onset
Estim
ated
Pre
vale
nce
of D
epen
denc
e
43%
23%
Age of Onset Age 10
Lanza & Vasilenko (2015) Journal of Adolescent Health
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Rate of dependence as function of age of onset: By sex
Estim
ated
Pre
vale
nce
of D
epen
denc
e
Females
Males
Age of Onset
Lanza & Vasilenko (2015) Journal of Adolescent Health