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Evaluation ViewAttendees 6

Suicide and Self-Injury
Symposia

Symposium 104 - Use of mixture modeling to advance the understanding of suicide-related outcomes

Saturday, November 16, 2024
4:30 PM – 6:00 PM EST
Location: Grand Ballroom F, Level 5, Downtown Marriott
CE: 1.5

Keywords: Suicide, Statistics, Prevention
Level of Familiarity: Moderate to advanced
Recommended Readings: Muthén, B., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55(2), 463–469. https://doi.org/10.1111/j.0006-341x.1999.00463.x,  

Miettunen, J., Nordström, T., Kaakinen, M., & Ahmed, A. O. (2016). Latent variable mixture modeling in psychiatric research--a review and application. Psychological Medicine, 46(3), 457–467. https://doi.org/10.1017/S0033291715002305

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Petersen, K. J., Qualter, P., & Humphrey, N. (2019). The Application of Latent Class Analysis for Investigating Population Child Mental Health: A Systematic Review. Frontiers in Psychology, 10, 1214. https://doi.org/10.3389/fpsyg.2019.01214

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Allan, N. P., Gros, D. F., Lancaster, C. L., Saulnier, K. G., & Stecker, T. (2019). Heterogeneity in short‐term suicidal ideation trajectories: Predictors of and projections to suicidal behavior. Suicide and Life-Threatening Behavior, 49(3), 826–837. https://doi.org/10.1111/sltb.12480

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Díez-Gómez, A., Pérez-Albéniz, A., Sebastián-Enesco, C., & Fonseca-Pedrero, E. (2020). Suicidal Behavior in Adolescents: A Latent Class Analysis. International Journal of Environmental Research and Public Health, 17(8), 2820. https://doi.org/10.3390/ijerph17082820

Heterogeneity is a strikingly common feature of many mental health phenomena, making it challenge to predict who will experience certain symptoms. Accordingly, considerable research has sought to leverage mixture modeling approaches, such as latent class analyses and latent profile analyses, to parse this heterogeneity and identify clusters of people that share similar features. This approach has been used across mental health conditions and is increasingly being applied to advance the understanding of suicide-related outcomes, such as suicidal thoughts and behaviors. With suicide rates consistently on the rise, there is a need to continue to leverage cutting-edge statistical approaches to better understand which individuals may be at heightened risk for suicide outcomes. Such information can support clinicians in optimizing their treatment depending on the symptom profiles of a particular client.

First, Presenter 1, using a nationally representative database of over 40,000 participants, will describe the use of latent class analysis to identify classes of different DSM-IV depression symptom presentations among adults endorsing depressive symptoms and explore differences among classes in suicidal thoughts, plans, and attempts.

Next, Presenter 2 will describe analyses from an ecological momentary assessment study of college students aimed to advance the understanding of the link between suicide urges and emotion/distress-coping behaviors using latent profile analysis.

Presenter 3 will then describe the use of latent profile analysis in a cohort of depressed older adults to identify distinct ideation profiles and their clinical correlates as well as to test their associations with the risk of suicidal behavior longitudinally.

Finally, Presenter 4 will discuss the use of latent class analysis among a sample of adolescents to advance understanding of features of social media usage and its implications on suicide prevention and intervention.

The four presentations will be summarized and discussed by a leading expert in the use of advanced statistical methods to understand and prevent suicidal thoughts and behaviors. His experience using large datasets and complex analyses to study predictors of suicide risk position him to provide insightful commentary on the presented research. This discussion will address the value of using mixture modeling to study suicidal thoughts and suicide risk factors as well as the opportunities for innovation in this research area. Clinical implications with regards to using mixture modeling will be emphasized.

Learning Objectives:

At the end of this session, the learner will be able to:
  1. Describe mixture modeling using examples of specific analyses that exemplify this statistical approach
  2. Explain the value of mixture modeling in understanding clinical outcomes
  3. Describe the use of mixture modeling within the context of advancing suicide prevention research
  4. Discuss research directions that leverage mixture modeling methodologies.
  5. Explain ways they could mixture modeling approaches to understand a clinical outcome specific to their research.

Presentations:

  • 4:30 PM – 6:00 PM EST
    (SYM 104) Latent Class Analysis of Depressive Symptoms and Associations with Suicidal Ideation, Plans, and Attempts Among a National Sample of Depressed Adults

    Speaker: Annabelle M. Mournet, M.S. – Rutgers University

    Co-author: Annabelle M. Mournet, M.S. – Rutgers University

    Co-author: Evan M. Kleiman, Ph.D. (he/him/his) – Rutgers University

  • 4:30 PM – 6:00 PM EST
    (SYM 104) Latent Profiles of Emotion Coping Behaviors Among College Students with Suicide Thinking

    Speaker: Qingqing Yin, M.S. (she/her/hers) – Rutgers University

    Co-author: Qingqing Yin, M.S. (she/her/hers) – Rutgers University

    Co-author: Annabelle M. Mournet, M.S. – Rutgers University

    Co-author: Evan M. Kleiman, Ph.D. (he/him/his) – Rutgers University

    Co-author: Shireen L. Rizvi, ABPP, Ph.D. – Rutgers University

  • 4:30 PM – 6:00 PM EST
    (SYM 104) Can Latent Profile Analysis of Suicidal Ideation Identify Who Is at Risk for Suicidal Behavior?

    Speaker: Morgan Buerke, MA – Louisiana State University

    Co-author: Morgan Buerke, MA – Louisiana State University

    Co-author: Katalin Szanto, MD (she/her/hers) – University of Pittsburgh Medical Center

    Co-author: Cary Ni, MS (he/him/his) – Columbia University

    Co-author: Hanga Galfalvy, PhD (she/her/hers) – Columbia University

  • 4:30 PM – 6:00 PM EST
    (SYM 104) Understanding Social Media Use and Mental Health in Teens: Using Latent Class Analysis to Identify Patterns of Teen Social Media Use

    Speaker: Samuel Seidman, M.A. (he/him/his) – Rutgers University

    Co-author: Samuel Seidman, M.A. (he/him/his) – Rutgers University

    Co-author: Simone Imani Boyd, M.A., M.S. – Rutgers, the State University of New Jersey

    Co-author: Taylor A. Burke, Ph.D. (she/her/hers) – Harvard Medical School / Massachusetts General Hospital

    Co-author: Sophia Choukas-Bradley, Ph.D. – University of Pittsburgh

    Co-author: Jacqueline Nesi, Ph.D. – Alpert Medical School of Brown University

    Co-author: Jessica L. Hamilton, Ph.D. (she/her/hers) – Rutgers University