Talks will be available at methodology.psu.edu

A demonstration of the time-varying effect model (TVEM):

The dynamic role of dependence on craving during quit attempts

Stephanie T. LanzaThe Methodology Center

Penn StateMegan Piper

Center for Tobacco Research and InterventionUniversity of Wisconsin

Supported by Award Numbers P50-DA010075, P50-CA84724,P50-DA0197, and M01-RR03186 from the National Institutes of Health

95% of smoking cessation attempts end in relapse The majority of smokers report withdrawal symptoms

as a reason for returning to smoking Improved understanding of withdrawal and how

treatments can alleviate withdrawal symptoms could:◦ Lead to the development of new treatments◦ Allow for tailored treatments

Background

New technology to collect data◦ Palmtop computers, smart phones, interactive voice response

software programs◦ Can collect real-world data◦ Frequent assessments – both proactive and reactive

New analytic methods provide a way to analyze intensive longitudinal data and allow researchers to ask new questions

The Role of Methodology

Does treatment continue to suppress withdrawal over the long-term?

Do individual difference variables exert differential effects at various points in the cessation process?

How are constructs such as craving and negative affect related to cessation fatigue?

Which withdrawal symptoms, or combination of symptoms, present the greatest relapse risk? Do these differ based on duration of cessation?

How do we deal with initial lapses in understanding the withdrawal process?

New Questions

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:◦ Placebo◦ Nicotine lozenge◦ Nicotine patch◦ Bupropion SR◦ Bupropion SR + nicotine lozenge◦ Nicotine patch + nicotine lozenge

Summary of TTURC2

Palmtop computers 4 prompts per day◦Waking◦ 2 random during the day (separated by at least 1 hour)◦ Prior to going to bed

2 weeks pre-quit and 2 weeks post-quit◦ Analyzed data 10 days pre-quit and 10 days post-quit

Assessed withdrawal symptoms (craving, affect, hunger, restlessness), smoking, motivation, self-efficacy, and fatigue

EMA Protocol

To demonstrate how to use TVEM in your own research

To study changes in the effect of baseline dependence during first two weeks of quit attempt, and how treatment impacts that time-varying effect

To facilitate discussion of types of research questions that can be addressed using TVEM

Goals of talk

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: Treatment group◦ Placebo versus five treatment conditions

Control: Any cigarette use during two weeks◦ Intensively assessed via EMA

Measures

Organize data◦ Use all available data during time window◦ 14 days post-quit◦ (Megan focused on 10 days pre- and post-quit)

Decide how to handle multiple-groups analysis◦ Separate by treatment group◦ Form interaction terms

Analytic step 1: Organize data

Total N = 1504 Placebo Group Treatment GroupN never quit 15 184N relapsed* 7 17N successful 138 975

Descriptive statistics

Variable Mean (SD)Assessments per day (range 1-4) 3.0 (1.0)Assessments per individual 25.5 (13.0)Days assessed (of first 14) 8.5 (3.5)

* relapse defined as 7 consecutive smoking days

How to incorporate treatment group?

What varies with time?◦Mean urge (intercept function)◦Effect of negative affect◦Effect of cigarette use

Analytic step 2: Specify model

0 1 2 3* * * ( )Urge t t t NA t t FTND t t Cignum t

With TVEM, complex functions can be approximated well if a sufficient number of splitting points (knots) is specified◦ Fewer knots smoother curves◦More knots more complex functions

Model selection involves comparing models with different numbers of knots (and thus different complexity)◦ Use AIC, BIC (lower is better)

Analytic step 3: Model selection

Analytic step 4: Interpretation 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

Results: Craving over time “Intercept

function” shows mean craving when all covariates are at zero

By group

Treatment

Placebo

Results: Craving over time “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.

Results: Effect of negative affect Time-varying

effect of time-varying covariate on craving

By group

TreatmentPlacebo

Results: Effect of negative affect Time-varying

effect of time-varying covariate on craving

By group

TreatmentPlacebo

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.

Results: Effect of dependence Time-varying

effect of baseline characteristic on craving

By group

Treatment

Placebo

Results: Effect of dependence Time-varying

effect of baseline characteristic on craving

By group

Treatment

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.

Results: Effect of cigarette use Time-varying

effect of lapses over time on craving

By group

Treatment

Placebo

Results: Effect of cigarette use Time-varying

effect of lapses over time on craving

By group

Treatment

Placebo

Interpretation: For both groups, smoking lapse is positively associated with craving between days 2 and 12. Association remains significant for Treatment group but weakens in Placebo group at Days 12-14.

Syntax for final model: Placebo%TVEM_normal( mydata = temp_V1, id = subject, time = time, dep = urge1, cov = int_t1 int NA_t1 NA FTND, tcov = cignum, cov_knots = 2, deg = 1, outfilename = V1.csv);

Syntax for final model: Treatment%TVEM_normal( mydata = temp_V2, id = subject, time = time, dep = urge1, cov = na_t1 na FTNDtot_t3 FTNDtot_t2 FTNDtot_t1 FTNDtot cignum_t3 cignum_t2 cignum_t1 cignum, tcov = int, cov_knots = 3, evenly = 1,outfilename = V2.csv);

Step 1. Register as user on Methodology Center website: http://methodology.psu.edu/

How do I get started?

Step 2. Download %TVEM macro suite (and user’s guide), extract into folder

How do I get started?

Step 3. Get data into SAS

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/s11121-011-0264-z

How do I get started?

Let’s see how to estimate a model in SAS

Demonstration

These analyses enable us to think differently about treatment effects◦ How do effects of dependence on craving vary over time?◦ Treatment changes the relationship between dependence and

craving Does treatment weaken the association between

negative affect and craving over time? What are the implications for understanding treatment

effects?

Implications for smoking cessation

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

Future treatment research should continue to include ILD assessments of withdrawal and other key constructs

Implications for smoking cessation