ArticleBMC medical research methodology2017
Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies.
Article in BMC medical research methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
34 citing papers in PubMed.
- A Neutral Comparison of Multivariate Hypothesis Tests for Parallel Group Designs in Rare Disease Settings.Statistics in medicine · 2026Article
- Methods for Adjusting for Covariate Measurement Error in Flexible Modeling of Functional Form: Designing a Blinded, Controlled Neutral Comparison Simulation Study.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Variable selection for clinical prediction models in low-dimensional data - a simulation study comparing traditional regression and machine learning methods.BMC medical research methodology · 2026Article
- REFINE2: a simplified simulation tool to help epidemiologists evaluate the suitability and sensitivity of effect estimation within user-specified data.American journal of epidemiology · 2026Article
- Editorial: Promoting replicability: empowering method and applied researchers in driving reliable results.Frontiers in psychology · 2026Article
- On "Confirmatory" Methodological Research in Statistics and Related Fields.Statistics in medicine · 2025Article
- Rethinking the Handling of Method Failure in Comparison Studies.Statistics in medicine · 2025Article
- Characterizing the omics landscape based on 10,000+ datasets.Scientific reports · 2025Article
- Proposal of an alternative way of reporting the results of comparative simulation studies.Frontiers in psychology · 2025Article
- A simulation study of the performance of statistical models for count outcomes with excessive zeros.Statistics in medicine · 2024Article
- Replicability of simulation studies for the investigation of statistical methods: the RepliSims project.Royal Society open science · 2024Article
- Evaluating variable selection methods for multivariable regression models: A simulation study protocol.PloS one · 2024Article
- Systematic comparison of approaches to analyze clustered competing risks data.BMC medical research methodology · 2023Article
- Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering.PLoS computational biology · 2023Article
- Evaluation of approaches for accommodating interactions and non-linear terms in multiple imputation of incomplete three-level data.Biometrical journal. Biometrische Zeitschrift · 2022Article
- Simulation Tests of Methods in Evolution, Ecology, and Systematics: Pitfalls, Progress, and Principles.Annual review of ecology, evolution, and systematics · 2022Article
- Joint analysis of PK and immunogenicity outcomes using factorization model - a powerful approach for PK similarity study.BMC medical research methodology · 2022Article
- A scoping methodological review of simulation studies comparing statistical and machine learning approaches to risk prediction for time-to-event data.Diagnostic and prognostic research · 2022Article
- Factors influencing the statistical planning, design, conduct, analysis and reporting of trials in health care: A systematic review.Contemporary clinical trials communications · 2022Review
- It's time! Ten reasons to start replicating simulation studies.Frontiers in epidemiology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe goal of medical research is to develop interventions that are in some sense superior, with respect to patient outcome, to interventions currently in use. Similarly, the goal of research in methodological computational statistics is to develop data analysis tools that are themselves superior to the existing tools. The methodology of the evaluation of medical interventions continues to be discussed extensively in the literature and it is now well accepted that medicine should be at least partly "evidence-based". Although we statisticians are convinced of the importance of unbiased, well-thought-out study designs and evidence-based approaches in the context of clinical research, we tend to ignore these principles when designing our own studies for evaluating statistical methods in the context of our methodological research. MAIN MESSAGE: In this paper, we draw an analogy between clinical trials and real-data-based benchmarking experiments in methodological statistical science, with datasets playing the role of patients and methods playing the role of medical interventions. Through this analogy, we suggest directions for improvement in the design and interpretation of studies which use real data to evaluate statistical methods, in particular with respect to dataset inclusion criteria and the reduction of various forms of bias. More generally, we discuss the concept of "evidence-based" statistical research, its limitations and its impact on the design and interpretation of real-data-based benchmark experiments.
conclusionWe suggest that benchmark studies-a method of assessment of statistical methods using real-world datasets-might benefit from adopting (some) concepts from evidence-based medicine towards the goal of more evidence-based statistical research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.