Stephanie T. Lanza
Scientific Director, The Methodology Center
Research Associate Professor, HHD
http://methodology.psu.edu/
Work in collaboration with:
Runze Li
The Methodology Center and Department of Statistics
Megan Piper
Center for Tobacco Research and Intervention
University of Wisconsin

What is EMA data?

An empirical example: Smoking cessation

How to get started with TVEM

A hands-on demonstration
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Conclusions
◦ The need for methodological advances
◦ Step-by-step analysis

Ecological

Momentary
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Assessment
◦ Real-world environments & experience
◦ Provides ecological validity
◦ Real-time assessment & focus
◦ Avoids recall bias
◦ Self-report or automatic (physiological)
◦ Repeated, intensive, longitudinal
◦ Allows analysis of physiological/psychological/behavioral
processes over time
(Stone & Shiffman, 1994)
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Palmtop computers
Smart phones
Biological/physiological devices
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Assessments may be proactive (event-driven) or
reactive (when beeped)
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Another type of intensive longitudinal data (ILD) repeated Internet-based assessments
◦ Daily or weekly assessments over long time period
◦ Longitudinal burst designs
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Remote and Intensive Data (RAID), SSRI
Nilam Ram
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Aaron Pincus
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Dave Almeida
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Martin Sliwinski
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Bo Cleveland
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Josh Smyth
◦ Interactions, daily activities, feelings, well-being
◦ Achievement motivation, interpersonal behavior
◦ Stressful experiences
◦ Stress, cognitive impairment
◦ Mood, stress, craving during drug abuse recovery
◦ Stress, affect, health

Natural range of behaviors, states, and context as they
occur and in response to naturally occurring events
(Smyth & Stone, 2003)
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Designed to capture a process as it unfolds
◦ Important to match speed of process with frequency of
assessment
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Provide incredibly rich information about dynamic
processes… yet sheer volume of data can be
overwhelming to analyze
Individuals may not be assessed at same times
Traditonal Longitudinal Data
6
5
Subject
4
3
2
1
0
0
1
2
3
4
5
Times
6
7
8
9
10
Sparse Irregular Longitudinal Data
6
5
Subject
4
3
2
1
0
0
1
2
3
4
5
Times
6
7
8
9
10
Intensive Longitudinal Data
6
5
Subject
4
3
2
1
0
0
1
2
3
4
5
Times
6
7
8
9
10
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New analytic methods provide a way to analyze EMA
data, allow researchers to ask new questions
Walls & Schafer (Eds.), 2006
Models for Intensive Longitudinal Data
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Multilevel models
◦ Assessments within days, days within individuals
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State space models
Time series analysis
Functional data analysis
Dynamical systems modeling
Control systems models
Location-scale models
Time-varying effect model (TVEM)
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95% of smoking cessation
attempts end in relapse
Majority of smokers
report withdrawal
symptoms as a reason
Improved understanding
of withdrawal and how
treatments can alleviate withdrawal symptoms could:
◦ Lead to the development of new treatments
◦ Allow for tailored treatments
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Does treatment continue to suppress withdrawal over
the long-term?
Do baseline characteristics exert differential effects at
various points in the cessation process?
Which withdrawal symptoms present greatest relapse
risk?
◦ Do these differ based on duration of cessation?
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How do we deal with initial lapses in understanding the
withdrawal process?
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To demonstrate how to use TVEM in your own
research
To study changes in the effect of baseline nicotine
dependence on craving during first two weeks of quit
attempt
◦ Treatment as a moderator

To examine the time-varying effect of negative affect
on craving during first two weeks of quit attempt
◦ Treatment as a moderator
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1504 (58.2% women) daily smokers enrolled in a
randomized double-blind placebo controlled smoking
cessation trial
Received counseling and one of the following
medications:
1.
2.
3.
4.
5.
6.
Placebo
Nicotine lozenge
Nicotine patch
Bupropion SR
Bupropion SR + nicotine lozenge
Nicotine patch + nicotine lozenge

4 weeks of EMA
◦ 2 weeks pre-quit
◦ 2 weeks post-quit
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Palmtop computers
4 prompts per day
◦ Waking
◦ 2 random during the day (separated by at least 1 hour)
◦ Prior to going to bed
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2 weeks pre-quit and 2 weeks post-quit
◦ Analyzed data 14 days post-quit
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Assessed withdrawal symptoms (e.g. craving), smoking,
motivation, self-efficacy, fatigue
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Outcome: Craving during first two weeks of quit
attempt
◦ Intensively assessed via EMA
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Predictors:
◦ Baseline nicotine dependence (not time-varying, but effect
can be!)
◦ Negative affect (time-varying)
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Moderator: Treatment group
◦ Placebo versus five treatment conditions
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Control: Any cigarette use during two weeks
◦ Intensively assessed via EMA
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Organize data
◦ Define time window
◦ Use all available data during time window
◦ Our study: 14 days post-quit

Decide how to handle multiple-groups analysis
◦ Interaction terms or separate analysis by group
◦ Our study: Separate by treatment group
Total N = 1504
N never quit
N relapsed*
N successful
Placebo Group
15
7
138
Variable
Assessments per day (range 1-4)
Assessments per individual
Days assessed (of first 14)
Treatment Group
184
17
975
Mean (SD)
3.0 (1.0)
25.5 (13.0)
8.5 (3.5)
* relapse defined as 7 consecutive smoking days

How to incorporate treatment group?
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What varies with time?
◦ Mean urge (intercept function)
◦ Effect of negative affect
◦ Effect of cigarette use

Familiar multilevel model (urge as function of time)
Urgeti =+
β 0i β1iTIMEti + β 2i NAti + β3i FTNDti + β 4i CIGNUM ti + ε ti

β=
γ 00 + r0i
0i
β=
γ 20 + r2i
2i
β=
γ 10 + r1i
1i
β=
γ 30 + r3i
3i
Time-varying effect model (TVEM)
Urgeit =
β 0 ( t ) + β1 ( t ) NAit + β 2 ( t ) FTNDi + β3 ( t ) CIGNUM it + ε it

Complex functions can be approximated well if
sufficient number of splitting points (knots) is specified

Model selection involves comparing models with
different numbers of knots (and thus different
complexity)
◦ Use AIC, BIC (lower is better)
◦ Depending on smoothing technique, this step may be
unnecessary
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Coefficients are not single-number summaries, but are
expressed as functions of time
Interpretation must take time into account
Confidence intervals guide interpretation
Helpful to plot multiple-groups results on same axes
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“Intercept
function” shows
mean craving
when all
covariates are at
zero
By group
Treatment
Placebo
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“Intercept
function” shows
mean craving
when all
covariates are at
zero
By group
Treatment
Placebo
Interpretation: Craving levels when there has been no smoking are
lower in the Placebo group than in the Treatment group. Craving
decreases fairly linearly for both groups during days 2-14, dropping by
nearly half initial craving levels.
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Time-varying
effect of timevarying
covariate on
craving
By group
Placebo
Treatment
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Time-varying
effect of timevarying
covariate on
craving
Placebo
Treatment
By group
Interpretation: Negative affect is positively associated with craving
during entire two-week window for both groups. Some evidence that
treatment weakens the association during second week of quit attempt.

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Time-varying
effect of
baseline
characteristic on
craving
Treatment
By group
Placebo


Time-varying
effect of
baseline
characteristic on
craving
Treatment
By group
Placebo
Interpretation: Baseline dependence is significantly related to craving in
Treatment group; effect remains in place during entire two-week
window. Baseline dependence not associated with craving in Placebo
group.
%TVEM_normal(
method
mydata
id
time
dep
tcov
cov_knots
);
=
=
=
=
=
=
=
P_spline,
urge_trt1,
subject,
time,
urge,
int NA FTND CIGNUM,
5 5 5 5
%TVEM_normal(
method
mydata
id
time
dep
tcov
cov_knots
);
=
=
=
=
=
=
=
P_spline,
urge_trt2,
subject,
time,
urge,
int NA FTND CIGNUM,
5 5 5 5

Step 1. Register as user on Methodology Center
website: http://methodology.psu.edu and login

Step 2. Download %TVEM macro suite (and user’s
guide), extract into folder
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Step 3. Get data into SAS
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Step 4. Use %INCLUDE statement to point to macro, then
specify model

A good reference:
◦ Shiyko, M. P., Lanza, S. T., Tan, X., Li, R., & Shiffman, S.
(2012). Using the Time-Varying Effect Model (TVEM) to
examine dynamic associations between negative affect
and self confidence on smoking urges: Differences
between successful quitters and relapsers. Prevention
Science. Advance online publication. doi: 10.1007/s11121011-0264-z

Let’s see how to estimate a model in SAS

These analyses enable us to think differently about
treatment effects

Effect of treatment can strengthen or weaken with
time
Treatment changes the relationship between
dependence and craving over time

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These findings illustrate that the effect of “baseline”
variables can change over time
Could lead to not only tailoring treatment, but
adaptive treatment designs and strategies

TVEM SAS macro suite has been expanded to address
questions about outcomes over time that are
◦ Normally distributed (craving, mood)
◦ Counts (number of cigarettes since last assessment)
◦ Binary (did/did not smoke, binge, have sex, etc.)

Mixture TVEM under development
◦ organize complex EMA data while respecting heterogeneity
◦ identify subgroups defined by dynamics that unfold during
smoking cessation attempts

Find out more about TVEM and download the
SAS software at:
http://methodology.psu.edu