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INSERM Workshop, St. Raphael Mixture modeling for longitudinal data Introduction

This workshop, led by Bruno Falissard from Univ. Paris-Sud and INSERM U669, focuses on the application of mixture modeling techniques for analyzing longitudinal data. Emphasizing their importance in biomedical research, the sessions cover how to understand typical patterns of evolution over time and differentiate between good and bad responders in randomized controlled trials. Attendees will engage with user-friendly methods that intersect biostatistics, computer science, and social sciences, gaining confidence in these innovative approaches for robust data interpretation.

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INSERM Workshop, St. Raphael Mixture modeling for longitudinal data Introduction

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  1. INSERM Workshop, St. RaphaelMixture modeling for longitudinal dataIntroduction Bruno Falissard Univ. Paris-Sud, INSERM U669

  2. Mixture modeling for longitudinal data • Everybody says that longitudinal data are essential • Most often • T1, T2, T3, T4 • T4 explained by T1 • Another perspective : typical patterns of evolution across time

  3. Mixture modeling for longitudinal data People with fever receiving an antibiotic Temperature time

  4. 39 Mixture modeling for longitudinal data People with fever receiving an antibiotic 39 Temperature 37 time

  5. Mixture modeling for longitudinal data People with fever receiving an antibiotic Temperature time

  6. Mixture modeling for longitudinal data People with fever receiving an antibiotic Virus Temperature Bacteria time

  7. Mixture modeling for longitudinal data •  good or bad responders in RCTs •  developmental perspective • …

  8. Mixture modeling for longitudinal data • Why this workshop today and not several years ago? • The questions did exist several years ago • But • Somewhat exploratory approach (not classical in biomedical research) • Tools more or less efficient (big sample sizes, transversal data)

  9. Mixture modeling for longitudinal data • But the first applications appeared… • Trajectory of aggression in young children • With user-friendly routines • With some elements of robustness

  10. Mixture modeling for longitudinal data • These models are somewhat different from the classical techniques used in biomedical research • “Bottom up” as opposed to “top down” • Intersection of numerous methodological fields • Biostatistics • Computer science • Social sciences • Psychometrics

  11. Mixture modeling for longitudinal data • Statistics are not only mathematics, statistics are highly dependant on the background of application (culture) • Unique opportunity to confront different type of approaches • With statistical and practical considerations • With the objective to be confident with these methods, and to be able to explain them to reviewers

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