Reporting Understandable, Useful, and Trustworthy Results of Clinical Prediction Model Studies: Insights from Biomedical Researchers

Abstract

Despite the increasing number of clinical prediction model (CPM) studies, the quality of reporting, especially for preimpact analysis studies focusing on developing and validating CPMs in research papers, remains subpar. This poor reporting quality hinders the progression of CPM studies by impeding follow-up studies, such as external validation, impact analysis studies, and systematic reviews. While the reporting guideline for these studies, TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis), emphasizes transparency, biomedical researchers advocate for CPM study results to additionally embody three quality attributes: understandable, useful, and trustworthy. Yet, the extent to which biomedical researchers perceive and ensure that CPM study results meet these quality attributes, has not been exploredThis dissertation aims to bridge these gaps by identifying challenges, needs, and visualization preferences among biomedical researchers to ensure CPM study results meet these three quality attributes. Each main chapter in this dissertation addresses a specific aim. Aim 1, presented in Chapter 4, uses a mixed-method survey to explore biomedical researchers’ challenges in ensuring that CPM study results meet the three quality attributes as authors and reviewers. Aim 2, detailed in Chapter 5, involves interviews with biomedical researchers to characterize their needs to ensure the three quality attributes in CPM study results. Aim 3, outlined in Chapter 6, based on interviews with biomedical researchers, identifies visualization preferences that could enhance the quality of CPM study results. The concluding Chapter 7 summarizes these findings and their contributions to biomedical informatics, which highlight a novel approach to improve the quality of CPM study results by focusing on the three quality attributes and engaging biomedical researchers beyond traditional expert panels. Furthermore, the dissertation includes foundational chapters setting the research stage. Chapter 1 reviews relevant prior work and outlines my motivations for this study, rooted in my experiences as a primary care clinician and biomedical researcher. Chapter 2 reports on my preliminary work through a primary care provider survey about their use of clinical prediction rules. Chapter 3 describes recruitment strategies that enhanced biomedical researchers' participation in the Chapter 4 survey, utilizing PubMed records for expanded outreach.