Welcome to our research page featuring recent publications in the field of biostatistics and epidemiology! These fields play a crucial role in advancing our understanding of the causes, prevention, and treatment of various health conditions. Our team is dedicated to advancing the field through innovative studies and cutting-edge statistical analyses. On this page, you will find our collection of research publications describing the development of new statistical methods and their application to real-world data. Please feel free to contact us with any questions or comments.
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Predicting future outcomes of patients is essential to clinical practice, with many prediction models published each year. Empirical evidence suggests that published studies often have severe methodological limitations, which undermine their usefulness. This article presents a step-by-step guide to help researchers develop and evaluate a clinical prediction model. The guide covers best practices in defining the aim and users, selecting data sources, addressing missing data, exploring alternative modelling options, and assessing model performance. The steps are illustrated using an example from relapsing-remitting multiple sclerosis. Comprehensive R code is also provided.
Objectives: Among ID studies seeking to make causal inferences and pooling individual-level longitudinal data from multiple infectious disease cohorts, we sought to assess what methods are being used, how those methods are being reported, and whether these factors have changed over time.
Study design and setting: Systematic review of longitudinal observational infectious disease studies pooling individual-level patient data from 2+ studies published in English in 2009. 2014, or 2019. This systematic review protocol is registered with PROSPERO (CRD42020204104).
Results: Our search yielded 1,462 unique articles. Of these, 16 were included in the final review. Our analysis showed a lack of causal inference methods and of clear reporting on methods and the required assumptions.
Conclusion: There are many approaches to causal inference which may help facilitate accurate inference in the presence of unmeasured and time-varying confounding. In observational ID studies leveraging pooled, longitudinal IPD, the absence of these causal inference methods and gaps in the reporting of key methodological considerations suggests there is ample opportunity to enhance the rigor and reporting of research in this field. Interdisciplinary collaborations between substantive and methodological experts would strengthen future work.
Introduction: Causal methods have been adopted and adapted across health disciplines, particularly for the analysis of single studies. However, the sample sizes necessary to best inform decision-making are often not attainable with single studies, making pooled individual-level data analysis invaluable for public health efforts. Researchers commonly implement causal methods prevailing in their home disciplines, and how these are selected, evaluated, implemented and reported may vary widely. To our knowledge, no article has yet evaluated trends in the implementation and reporting of causal methods in studies leveraging individual-level data pooled from several studies. We undertake this review to uncover patterns in the implementation and reporting of causal methods used across disciplines in research focused on health outcomes. We will investigate variations in methods to infer causality used across disciplines, time and geography and identify gaps in reporting of methods to inform the development of reporting standards and the conversation required to effect change.
Methods and analysis We will search four databases (EBSCO, Embase, PubMed, Web of Science) using a search strategy developed with librarians from three universities (Heidelberg University, Harvard University, and University of California, San Francisco). The search strategy includes terms such as "pool*", "harmoniz*", "cohort*", "observational", variations on "individual-level data". Four reviewers will independently screen articles using Covidence and extract data from included articles. The extracted data will be analysed descriptively in tables and graphically to reveal the pattern in methods implementation and reporting. This protocol has been registered with PROSPERO (CRD42020143148).
Ethics and dissemination No ethical approval was required as only publicly available data were used. The results will be submitted as a manuscript to a peer-reviewed journal, disseminated in conferences if relevant, and published as part of doctoral dissertations in Global Health at the Heidelberg University Hospital.
Objective: To determine whether assessment tools for non-randomised studies (NRS) address critical elements that influence the validity of NRS findings for comparative safety and effectiveness of medications.
Design: Systematic review and Delphi survey.
Data sources: We searched PubMed, Embase, Google, bibliographies of reviews and websites of influential organisations from inception to November 2019. In parallel, we conducted a Delphi survey among the International Society for Pharmacoepidemiology Comparative Effectiveness Research Special Interest Group to identify key methodological challenges for NRS of medications. We created a framework consisting of the reported methodological challenges to evaluate the selected NRS tools.
Study selection Checklists or scales assessing NRS.
Data extraction: Two reviewers extracted general information and content data related to the prespecified framework.
Results: Of 44 tools reviewed, 48% (n=21) assess multiple NRS designs, while other tools specifically addressed case-control (n=12, 27%) or cohort studies (n=11, 25%) only. Response rate to the Delphi survey was 73% (35 out of 48 content experts), and a consensus was reached in only two rounds. Most tools evaluated methods for selecting study participants (n=43, 98%), although only one addressed selection bias due to depletion of susceptibles (2%). Many tools addressed the measurement of exposure and outcome (n=40, 91%), and measurement and control for confounders (n=40, 91%). Most tools have at least one item/question on design-specific sources of bias (n=40, 91%), but only a few investigate reverse causation (n=8, 18%), detection bias (n=4, 9%), time-related bias (n=3, 7%), lack of new-user design (n=2, 5%) or active comparator design (n=0). Few tools address the appropriateness of statistical analyses (n=15, 34%), methods for assessing internal (n=15, 34%) or external validity (n=11, 25%) and statistical uncertainty in the findings (n=21, 48%). None of the reviewed tools investigated all the methodological domains and subdomains.
Conclusions: The acknowledgement of major design-specific sources of bias (eg, lack of new-user design, lack of active comparator design, time-related bias, depletion of susceptibles, reverse causation) and statistical assessment of internal and external validity is currently not sufficiently addressed in most of the existing tools. These critical elements should be integrated to systematically investigate the validity of NRS on comparative safety and effectiveness of medications.
Systematic review protocol and registration: https://osf.io/es65q.
