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

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No Stats

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

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

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

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

Combined Time & Event Sampling Situational Associations with Smoking

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

Craving and Smoking… and Craving…

Downward Spiral of Self-Efficacy as Lapses Lead to Relapse

Time is a Crucial Element in EMA Analysis

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, 2795-2804

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

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

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, 366-379

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), 497-523

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, 104-116

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), 192-201

Not just different analytic units – different psychological meaning and process

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, 993-1002

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, 187-197

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

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

5 Subjects’ EMA Data

5 Subjects’ EMA Data

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

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

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