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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Hernán, using a hypothetical example, argues that policies that prevent researchers from conducting underpowered observational studies using existing databases are misguided explaining that "[w]hen a causal question is important, it is preferable to have multiple studies with imprecise estimates than having no study at all." While we do not disagree with the sentiment expressed, caution is warranted. Small observational studies are a major cause of distrust in science, mainly because their results are often selectively reported. The hypothetical example used to justify Hernán's position is too simplistic and overly optimistic. In this short response, we reconsider Hernán's hypothetical example and offer a list of other factors - beyond simply the importance of the question - that are relevant when deciding whether or not to pursue underpowered research.
As real world evidence on drug efficacy involves non-randomised studies, statistical methods adjusting for confounding are needed. In this context, prognostic score (PGS) analysis has recently been proposed as a method for causal inference. It aims to restore balance across the different treatment groups by identifying subjects with a similar prognosis for a given reference exposure ('control'). This requires the development of a multivariable prognostic model in the control arm of the study sample, which is then extrapolated to the different treatment arms. Unfortunately, large cohorts for developing prognostic models are not always available. Prognostic models are therefore subject to a dilemma between overfitting and parsimony; the latter being prone to a violation of the assumption of no unmeasured confounders when important covariates are ignored. Although it is possible to limit overfitting by using penalization strategies, an alternative approach is to adopt evidence synthesis. Aggregating previously published prognostic models may improve the generalizability of PGS, while taking account of a large set of covariates - even when limited individual participant data are available. In this article, we extend a method for prediction model aggregation to PGS analysis in non- randomised studies. We conduct extensive simulations to assess the validity of model aggregation, compared with other methods of PGS analysis for estimating marginal treatment effects. We show that aggregating existing PGS into a 'meta-score' is robust to misspecification, even when elementary scores wrongfully omit confounders or focus on different outcomes. We illustrate our methods in a setting of treatments for asthma.
Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognostic endpoint. Such models may be derived from data from various studies in the context of a meta-analysis. We describe and propose approaches for assessing heterogeneity in predictor effects and predictions arising from models based on data from different sources. These methods are illustrated in a case study with patients suffering from traumatic brain injury, where we aim to predict 6-month mortality based on individual patient data using meta-analytic techniques (15 studies, n = 11022 patients). The insights into various aspects of heterogeneity are important to develop better models and understand problems with the transportability of absolute risk predictions.
