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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Background: Previous studies suggest that hemodiafiltration reduces mortality compared to hemodialysis in patients with end-stage kidney disease (ESKD), but controversy surrounding its benefits remain and it is unclear to what extent individual patients benefit from hemodiafiltration. This study aimed to develop and validate a treatment effect prediction model to determine which patients would benefit most from hemodiafiltration compared to hemodialysis in terms of all-cause mortality.
Methods: Individual participant data from four randomized controlled trials comparing hemodiafiltration with hemodialysis on mortality were used to derive a Royston-Parmar model for prediction of absolute treatment effect of hemodiafiltration based on pre-specified patient and disease characteristics. Validation of the model was performed using internal-external cross validation.
Results: The median predicted survival benefit was 44 (Q1-Q3: 44-46) days for every year of treatment with hemodiafiltration compared to hemodialysis. The median survival benefit with hemodiafiltration ranged from 2 to 48 months. Patients who benefited most from hemodiafiltration were younger, less likely to have diabetes or a cardiovascular history and had higher serum creatinine and albumin levels. Internal-external cross validation showed adequate discrimination and calibration.
Conclusion: Although overall mortality is reduced by hemodiafiltration compared to hemodialysis in ESKD patients, the absolute survival benefit can vary greatly between individuals. Our results indicate that the effects of hemodiafiltration on survival can be predicted using a combination of readily available patient and disease characteristics, which could guide shared decision-making.
Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models' performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain.
Background: With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies.
Objectives: To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research.
Methods: We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice.
Results: Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses.
Conclusion: This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods.
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.
While the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are applied and disseminated in daily practice. This scoping review aimed to identify actionable guidance for those closely involved in AI-based prediction model (AIPM) development, evaluation and implementation including software engineers, data scientists, and healthcare professionals and to identify potential gaps in this guidance. We performed a scoping review of the relevant literature providing guidance or quality criteria regarding the development, evaluation, and implementation of AIPMs using a comprehensive multi-stage screening strategy. PubMed, Web of Science, and the ACM Digital Library were searched, and AI experts were consulted. Topics were extracted from the identified literature and summarized across the six phases at the core of this review: (1) data preparation, (2) AIPM development, (3) AIPM validation, (4) software development, (5) AIPM impact assessment, and (6) AIPM implementation into daily healthcare practice. From 2683 unique hits, 72 relevant guidance documents were identified. Substantial guidance was found for data preparation, AIPM development and AIPM validation (phases 1-3), while later phases clearly have received less attention (software development, impact assessment and implementation) in the scientific literature. The six phases of the AIPM development, evaluation and implementation cycle provide a framework for responsible introduction of AI-based prediction models in healthcare. Additional domain and technology specific research may be necessary and more practical experience with implementing AIPMs is needed to support further guidance.
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.
Context: While urinary incontinence (UI) commonly occurs after radical prostatectomy (RP), it is unclear what factors increase the risk of UI development.
Objective: To perform a systematic review of patient- and tumour-related prognostic factors for post-RP UI. The primary outcome was UI within 3 mo after RP. Secondary outcomes included UI at 3-12 mo and ≥12 mo after RP.
Evidence acquisition: Databases including Medline, EMBASE, and CENTRAL were searched between January 1990 and May 2020. All studies reporting patient- and tumour-related prognostic factors in univariable or multivariable analyses were included. Surgical factors were excluded. Risk of bias (RoB) and confounding assessments were performed using the Quality In Prognosis Studies (QUIPS) tool. Random-effects meta-analyses were performed for all prognostic factor, where possible.
Evidence synthesis: A total of 119 studies (5 randomised controlled trials, 24 prospective, 88 retrospective, and 2 case-control studies) with 131 379 patients were included. RoB was high for study participation and confounding; moderate to high for statistical analysis, study attrition, and prognostic factor measurement; and low for outcome measurements. Significant prognostic factors for postoperative UI within 3 mo after RP were age (odds ratio [OR] per yearly increase 1.04, 95% confidence interval [CI] 1.03-1.05), membranous urethral length (MUL; OR per 1-mm increase 0.81, 95% CI 0.74-0.88), prostate volume (PV; OR per 1-ml increase 1.005, 95% CI 1.000-1.011), and Charlson comorbidity index (CCI; OR 1.28, 95% CI 1.09-1.50).
Conclusions: Increasing age, shorter MUL, greater PV, and higher CCI are independent prognostic factors for UI within 3 mo after RP, with all except CCI remaining prognostic at 3-12 mo.
Patient summary: We reviewed the literature to identify patient and disease factors associated with urinary incontinence after surgery for prostate cancer. We found increasing age, larger prostate volume, shorter length of a section of the urethra (membranous urethra), and lower fitness were associated with worse urinary incontinence for the first 3 mo after surgery, with all except lower fitness remaining predictive at 3-12 mo.
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.
In non-randomised studies, inferring causal effects requires appropriate methods for addressing confounding bias. Although it is common to adopt propensity score analysis to this purpose, prognostic score analysis has recently been proposed as an alternative strategy. Whilst both approaches were originally introduced to estimate causal effects for binary interventions, the theory of propensity score has since been extended to the case of general treatment regimes. Indeed, many treatments are not assigned in a binary fashion, and require a certain extent of dosing. Hence, researchers may often be interested in estimating treatment effects across multiple exposures. To the best of our knowledge, the prognostic score analysis has not been yet generalised to this case. In this article, we describe the theory of prognostic scores for causal inference with general treatment regimes. Our methods can be applied to compare multiple treatments using non-randomised data, a topic of great relevance in contemporary evaluations of clinical interventions. We propose estimators for the average treatment effects in different populations of interest, the validity of which is assessed through a series of simulations. Finally, we present an illustrative case in which we estimate the effect of the delay to Aspirin administration on a composite outcome of death or dependence at 6 months in stroke patients.
