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Conceptual Considerations for Analysis of EMA Data

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

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Conceptual Considerations for Analysis of EMA Data

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  1. Conceptual Considerations for Analysis of EMA Data Saul Shiffman, Ph.D. University of Pittsburgh ___ 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 • “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) • Ecological • 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 • 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

  8. Combined Time & Event SamplingSituational Associations with Smoking

  9. Why Bother? • Ecological 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

  10. Craving and Smoking…and Craving…

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

  12. Time is a Crucial Element in EMA Analysis

  13. Collapsing Time:Between-Subject Analyses Craving reportedby abstinent smokerstreated withnicotine patch vs placebo 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, 2795-2804

  14. 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 vsnot smoking,among non-dailysmokers identified as“social smokers” 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

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

  16. Negative Affect in Background, Temptations & First Lapses 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, 366-379

  17. Pre-Post Event Self-Efficacy,before and after a Temptation vsa Lapse episode 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), 497-523

  18. Event-Anchored Calendar Time Craving intensity amongabstinent smokers,temptation episodes vsrandom moments, over days sincequitting • 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, 104-116

  19. Event-Anchored ReverseCalendar & Clock Time Negative affectamong abstinentsmokers, 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), 192-201

  20. Time as Risk Time to relapse, after a first lapse, by pleasantness ofsmoking in the lapse 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, 993-1002

  21. Repeated Events over Time Accelerating time-to-re - lapse times oversuccessive lapses,initially slowed bynicotine patch treatment Kirchner, T.R., Shiffman, S., Wileyto, P. (2012). Relapse dynamics during smoking cessation: Recurrent abstinence violation effects and lapse-relapse progression. Journal ofAbnormal Psychology, 121, 187-197

  22. 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

  23. Data Analysis • Effort: • 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

  24. 5 Subjects’ EMA Data

  25. 5 Subjects’ EMA Data

  26. 304 Subjects’ EMA Data N=304 subjects, 191,841 observations

  27. 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

  28. 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

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