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Clayson PE. Beyond single paradigms, pipelines, and outcomes: Embracing multiverse analyses in psychophysiology. Int J Psychophysiol 2024; 197:112311. [PMID: 38296000 DOI: 10.1016/j.ijpsycho.2024.112311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 01/02/2024] [Accepted: 01/24/2024] [Indexed: 02/10/2024]
Abstract
Psychophysiological research is an inherently complex undertaking due to the nature of the data, and its analysis is characterized by many decision points that shape the final dataset and a study's findings. These decisions create a "multiverse" of possible outcomes, and each decision from study conceptualization to statistical analysis can lead to different results and interpretations. This review describes the concept of multiverse analyses, a methodological approach designed to understand the impact of different decisions on the robustness of a study's findings and interpretation. The emphasis is on transparently showcasing different reasonable approaches for constructing a final dataset and on highlighting the influence of various decision points, from experimental design to data processing and outcome selection. For example, the choice of an experimental task can significantly impact event-related brain potential (ERP) scores or skin conductance responses (SCRs), and different tasks might elicit unique variances in each measure. This review underscores the importance of transparently embracing the flexibility inherent in psychophysiological research and the potential consequences of not understanding the fragility or robustness of experimental findings. By navigating the intricate terrain of the psychophysiological multiverse, this review serves as an introduction, helping researchers to make informed decisions, improve the collective understanding of psychophysiological findings, and push the boundaries of the field.
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Affiliation(s)
- Peter E Clayson
- Department of Psychology, University of South Florida, Tampa, FL, USA.
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Rengasamy M, Moriarity D, Kraynak T, Tervo-Clemmens B, Price R. Exploring the multiverse: the impact of researchers' analytic decisions on relationships between depression and inflammatory markers. Neuropsychopharmacology 2023; 48:1465-1474. [PMID: 37336935 PMCID: PMC10425405 DOI: 10.1038/s41386-023-01621-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/28/2023] [Accepted: 05/23/2023] [Indexed: 06/21/2023]
Abstract
In recent years, a replication crisis in psychiatry has led to a growing focus on the impact of researchers' analytic decisions on the results from studies. Multiverse analyses involve examining results across a wide array of possible analytic decisions (e.g., log-transforming variables, number of covariates, or treatment of outliers) and identifying if study results are robust to researchers' analytic decisions. Studies have begun to use multiverse analysis for well-studied relationships that have some heterogeneity in results/conclusions across studies.We examine the well-studied relationship between peripheral inflammatory markers (PIMs; e.g., white blood cell count (WBC) and C-reactive protein (CRP)) and depression severity in the large NHANES dataset (n = 25,962). Specification curve analyses tested the impact of 9 common analytic decisions (comprising of 58,000+ possible combinations) on the association of PIMs and depression severity. Relationships of PIMs and total depression severity are robust to analytic decisions (based on tests of inference jointly examining effect sizes and p-values). However, moderate/large differences are noted in effect sizes based on analytic decisions and the majority of analyses do not result in significant findings, with the percentage of analyses with statistically significant results being 46.1% for WBC and 43.8% for CRP. For associations of PIMs with specific symptoms of depression, some associations (e.g., sleep, appetite) in males (but not females) were robust to analytic decisions. We discuss how multiverse analyses can be used to guide research and also the need for authors, reviewers, and editors to incorporate multiverse analyses to enhance replicability of research findings.
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Affiliation(s)
- Manivel Rengasamy
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Daniel Moriarity
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Thomas Kraynak
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Rebecca Price
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
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Lewis MW, Webb CA, Kuhn M, Akman E, Jobson SA, Rosso IM. Predicting Fear Extinction in Posttraumatic Stress Disorder. Brain Sci 2023; 13:1131. [PMID: 37626488 PMCID: PMC10452660 DOI: 10.3390/brainsci13081131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/27/2023] Open
Abstract
Fear extinction is the basis of exposure therapies for posttraumatic stress disorder (PTSD), but half of patients do not improve. Predicting fear extinction in individuals with PTSD may inform personalized exposure therapy development. The participants were 125 trauma-exposed adults (96 female) with a range of PTSD symptoms. Electromyography, electrocardiogram, and skin conductance were recorded at baseline, during dark-enhanced startle, and during fear conditioning and extinction. Using a cross-validated, hold-out sample prediction approach, three penalized regressions and conventional ordinary least squares were trained to predict fear-potentiated startle during extinction using 50 predictor variables (5 clinical, 24 self-reported, and 21 physiological). The predictors, selected by penalized regression algorithms, were included in multivariable regression analyses, while univariate regressions assessed individual predictors. All the penalized regressions outperformed OLS in prediction accuracy and generalizability, as indexed by the lower mean squared error in the training and holdout subsamples. During early extinction, the consistent predictors across all the modeling approaches included dark-enhanced startle, the depersonalization and derealization subscale of the dissociative experiences scale, and the PTSD hyperarousal symptom score. These findings offer novel insights into the modeling approaches and patient characteristics that may reliably predict fear extinction in PTSD. Penalized regression shows promise for identifying symptom-related variables to enhance the predictive modeling accuracy in clinical research.
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Affiliation(s)
- Michael W. Lewis
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
| | - Christian A. Webb
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
| | - Manuel Kuhn
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
| | - Eylül Akman
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA 02478, USA
| | - Sydney A. Jobson
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA 02478, USA
| | - Isabelle M. Rosso
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
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