Margaret J Moore1, Nele Demeyere2. 1. Department of Experimental Psychology, University of Oxford, Radcliffe Observatory Quarter, Oxford, OX2 6GG, UK. 2. Department of Experimental Psychology, University of Oxford, Radcliffe Observatory Quarter, Oxford, OX2 6GG, UK. nele.demeyere@psy.ox.ac.uk.
Abstract
OBJECTIVE: Multiverse analysis provides an ideal tool for understanding how inherent, yet ultimately arbitrary methodological choices impact the conclusions of individual studies. With this investigation, we aimed to demonstrate the utility of multiverse analysis for evaluating generalisability and identifying potential sources of bias within studies employing neurological populations. METHODS: Multiverse analysis was used to evaluate the robustness of the relationship between post-stroke visuospatial neglect and poor long-term recovery outcome within a sample of 1113 (age = 72.5, 45.1% female) stroke survivors. A total of 25,600 t-test comparisons were run across 400 different patient groups defined using various combinations of valid inclusion criteria based on lesion location, stroke type, assessment time, neglect impairment definition, and scoring criteria across 16 standardised outcome measures. RESULTS: Overall, 33.9% of conducted comparisons yielded significant results. 99.9% of these significant results fell below the null specification curve, indicating a highly robust relationship between neglect and poor recovery outcome. However, the strength of this effect was not constant across all comparison groups. Comparisons which included < 100 participants, pre-selected patients based on lesion type, or failed to account for allocentric neglect impairment were found to yield average effect sizes which differed substantially. Similarly, average effect sizes differed across various outcome measures with the strongest average effect in comparisons involving an activities of daily living measure and the weakest in comparisons employing a depression subscale. CONCLUSIONS: This investigation demonstrates the utility of multiverse analysis techniques for evaluating effect robustness and identifying potential sources of bias within neurological research.
OBJECTIVE: Multiverse analysis provides an ideal tool for understanding how inherent, yet ultimately arbitrary methodological choices impact the conclusions of individual studies. With this investigation, we aimed to demonstrate the utility of multiverse analysis for evaluating generalisability and identifying potential sources of bias within studies employing neurological populations. METHODS: Multiverse analysis was used to evaluate the robustness of the relationship between post-stroke visuospatial neglect and poor long-term recovery outcome within a sample of 1113 (age = 72.5, 45.1% female) stroke survivors. A total of 25,600 t-test comparisons were run across 400 different patient groups defined using various combinations of valid inclusion criteria based on lesion location, stroke type, assessment time, neglect impairment definition, and scoring criteria across 16 standardised outcome measures. RESULTS: Overall, 33.9% of conducted comparisons yielded significant results. 99.9% of these significant results fell below the null specification curve, indicating a highly robust relationship between neglect and poor recovery outcome. However, the strength of this effect was not constant across all comparison groups. Comparisons which included < 100 participants, pre-selected patients based on lesion type, or failed to account for allocentric neglect impairment were found to yield average effect sizes which differed substantially. Similarly, average effect sizes differed across various outcome measures with the strongest average effect in comparisons involving an activities of daily living measure and the weakest in comparisons employing a depression subscale. CONCLUSIONS: This investigation demonstrates the utility of multiverse analysis techniques for evaluating effect robustness and identifying potential sources of bias within neurological research.
Authors: Ziad S Nasreddine; Natalie A Phillips; Valérie Bédirian; Simon Charbonneau; Victor Whitehead; Isabelle Collin; Jeffrey L Cummings; Howard Chertkow Journal: J Am Geriatr Soc Date: 2005-04 Impact factor: 5.562
Authors: Julie Bernhardt; Kathryn S Hayward; Gert Kwakkel; Nick S Ward; Steven L Wolf; Karen Borschmann; John W Krakauer; Lara A Boyd; S Thomas Carmichael; Dale Corbett; Steven C Cramer Journal: Int J Stroke Date: 2017-07 Impact factor: 5.266