Talks will be available at methodology.psu.edu

1. Talks will be available at methodology.psu.edu

2. A demonstration of the time-varying effect model (TVEM):The dynamic role of dependence on craving during quit attempts Stephanie T. Lanza The Methodology Center Penn State Megan Piper Center for Tobacco Research and Intervention University of Wisconsin Supported by Award Numbers P50-DA010075, P50-CA84724, P50-DA0197, and M01-RR03186 from the National Institutes of Health

3. Background • 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

4. The Role of Methodology • 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

5. New Questions • 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?

6. Summary of TTURC2 • 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

7. EMA Protocol • 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

8. Goals of talk • 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

9. 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: Treatment group • Placebo versus five treatment conditions • Control: Any cigarette use during two weeks • Intensively assessed via EMA

10. Analytic step 1: Organize data • 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

11. Descriptive statistics * relapse defined as 7 consecutive smoking days

12. Analytic step 2: Specify model • How to incorporate treatment group? • What varies with time? • Mean urge (intercept function) • Effect of negative affect • Effect of cigarette use

13. Analytic step 3: Model selection • 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)

14. 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

15. Results: Craving over time • “Intercept function” shows mean craving when all covariates are at zero • By group Treatment Placebo

16. 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.

17. Results: Effect of negative affect • Time-varying effect of time-varying covariate on craving • By group Placebo Treatment

18. Results: Effect of negative affect • Time-varying effect of time-varying covariate on craving • By group Placebo Treatment 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.

19. Results: Effect of dependence • Time-varying effect of baseline characteristic on craving • By group Treatment Placebo

20. 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.

21. Results: Effect of cigarette use • Time-varying effect of lapses over time on craving • By group Treatment Placebo

22. 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.

23. 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 );

24. 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 FTNDtotcignum_t3 cignum_t2 cignum_t1 cignum, tcov= int, cov_knots = 3, evenly = 1, outfilename = V2.csv );

25. How do I get started? • Step 1. Register as user on Methodology Center website: http://methodology.psu.edu/

26. How do I get started? • Step 2. Download %TVEM macro suite (and user’s guide), extract into folder

27. 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

28. Demonstration • Let’s see how to estimate a model in SAS

29. Implications for smoking cessation • 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?

30. 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