1. Conceptual Considerations for Analysis of EMA Data
Saul Shiffman, Ph.D.
University of Pittsburgh
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Co-Founder, invivodata, inc.
Consult to GlaxoSmithKline

2. Disclaimer
This talk DOES NOT provide any statistical advice and listeners should consult with their own statistician for advice. This talk provides commentary. The speaker is not a statistician and is not acting as your statistician. The information in the talk is general information and should not be construed as statistical advice to be applied to any specific factual situation. The Terms and Conditions specify that there is no guarantee or warranty and that the speaker is not responsible for any loss, injury, claim, liability, or damage ("damages") related to your use of the information in the talk, from errors or omissions in the content of the talk. Use of information in this talk is governed by our Terms and Conditions; refer to our website for more information.

3. No Stats

4. Self-Report Methods Global self-report Time-bound recall
“Are you the sort of person who…?”
“On average….”
Time-bound recall
“In the past month…”
Episodic recall
“When you first used…”
Momentary assessment

5. Ecological Momentary Assessment (EMA)
Real-world environments & experience
Ecological validity
Momentary
Real-time assessment & focus
Avoid recall
Assessment
Self-report, psychophysiology, biological samples
Repeated, intensive, longitudinal
Allow analysis of process over time
Stone & Shiffman, 1994

6. Characteristics of Ecological Momentary Assessment
Assesses subjects in the natural environment
Assesses phenomena as they occur
Considers assessments to be samples
Gathers many repeated observations

7. Sampling Schemes Event-based Time-based
Record made when event occurs; subject typically initiates
Event triggers assessment
Time-based
Regular intervals or milestones
Daily diary; at every meal
Clock or milestone triggers assessment
Time-based schedules controlled by investigator
Random time sampling or other schemes
Need facilities for scheduling and triggering assessment
Smoking well-suited because of events

8. Combined Time & Event Sampling Situational Associations with Smoking

10. Why Bother? Ecological validity Self-report validity
To study and understand the real world
Self-report validity
To avoid recall error and bias
Reliability through aggregation
To get many observations to achieve reliability, replication
Temporal ordering and resolution
To study how events and processes unfold over time

11. Craving and Smoking… and Craving…

12. Downward Spiral of Self-Efficacy as Lapses Lead to Relapse

13. Time is a Crucial Element in EMA Analysis

14. Collapsing Time: Between-Subject Analyses
Craving reported by abstinent smokers treated with nicotine patch vs
placebo
No time at all – just reliable measurement of people
Shiffman, S. & Ferguson, S.G. (2008). The effect of nicotine patch on cigarette craving over the course of the day: Results from two randomized clinical trials. Current Medical Research and Opinion, 24,

15. Blenderizing Time: Between-Occasion Analyses
THURSDAY 1:00 p.m.–2:30 p.m Grand Ballroom C, Level 4
INCREASING OUR UNDERSTANDING OF NONDAILY SMOKING
% of occasions w/ alcohol consumption, when smoking vs not smoking, among non-daily smokers identified as “social smokers”
“Time” as occasion type, but no sense of precedence or ordering in time
Shiffman, S., Li, X., Dunbar, M., Scholl, S., & Tindle, H. (2012, March). Non-daily smokers = Social smokers? In a symposium on Increasing our understanding of nondaily smoking: Individual patterns, smoking trajectories, and cultural influences (Jasjit Ahluwalia & Saul Shiffman, chairs), presented at the annual meeting of the Society for Research on Nicotine and Tobacco (SRNT), Houston, TX

17. Time as Sequence within subject R T L Prompted Assessment Subject* * *
Assessment
Subject T L
Entries
Preceding
Lapse
Succeeding
Day
Day
Day
R - Random Prompt
T - Temptation
L - Lapse

18. Negative Affect in Background, Temptations & First Lapses
Sequence unique moment
Shiffman, S., Paty, J.A., Gnys, M., Kassel, J.D., & Hickcox, M. (1996). First lapses to smoking: Within-subjects analyses of real-time reports. Journal of Consulting and Clinical Psychology, 64,

19. Pre-Post Event
Self-Efficacy, before and after a Temptation vs a Lapse episode
traditional
Shiffman, S., Hickcox, M., Paty, J.A., Gnys, M., Kassel, J.D., & Richards, T. (1997). The Abstinence Violation Effect following smoking lapses and temptations. Cognitive Therapy and Research, 21 (5),

20. Event-Anchored Calendar Time
Craving intensity among abstinent smokers, temptation episodes vs random moments, over days since quitting
Shiffman, S., Engberg, J., Paty, J.A., Perz, W., Gnys, M., Kassel, J.D., & Hickcox, M. (1997). A day at a time: Predicting smoking lapse from daily urge. Journal of Abnormal Psychology, 106,

21. Event-Anchored Reverse Calendar & Clock Time
Negative affect among abstinent smokers, in the days and hours preceding a first lapse, by lapse trigger
Shiffman, S. & Waters, A. J. (2004). Negative affect and smoking lapses: A prospective analysis. Journal of Consulting and Clinical Psychology, 72 (2),

22. Not just different analytic units – different psychological meaning and process

23. Time as Risk
Time to relapse, after a first lapse, by pleasantness of smoking in the lapse
Time just an opportunity for events
Shiffman, S., Hickcox, M., Paty, J.A., Gnys, M., Kassel, J.D., & Richards, T. (1996). Progression from a smoking lapse to relapse: Prediction from abstinence violation effects and nicotine dependence. Journal of Consulting and Clinical Psychology, 64,

24. Repeated Events over Time
Accelerating time-to-re - lapse times over successive lapses, initially slowed by nicotine patch treatment
X axis is events, not time, Y axis is elapsed time … each dot a survival analysis
Kirchner, T.R., Shiffman, S., Wileyto, P. (2012). Relapse dynamics during smoking cessation: Recurrent abstinence violation effects and lapse-relapse progression. Journal of Abnormal Psychology, 121,

25. Even More Ways to Think About Time in EMA Data
Reciprocal effects
e.g., smoking reduces self-efficacy, which increases smoking, which reduces self-efficacy, which …..
Cumulative effects
e.g., cumulative effort of coping eventually exhausts quitters, leading to relapse

26. Data Analysis Effort: Design envy:
50% thinking about theory and question
30% organizing data to address question
20% statistical analysis (now easier)
Design envy:
Experiments: structure dictates analyses
EMA: Not much structure… Question dictates analysis

27. 5 Subjects’ EMA Data

28. 5 Subjects’ EMA Data

29. 304 Subjects’ EMA Data
N=304 subjects, 191,841 observations

30. Design Envy In traditional design, design dictates analysis
1 or n observations / person
Confounds are limited by design
EMA: We have to work harder to select, arrange, structure data to fit question & analysis
Active
Placebo
Men
Women

31. Summary EMA data unstructured
+ Can address many different questions
- Require hard thinking & effort to shape for analysis
Find structure and statistics to match question (not vice versa)
Consider treatment of time in analysis