Phases of methodological research in biostatistics—Building the evidence base for new methods
Authors:
- Georg HeinzeGeorg Heinze
- Anne‐Laure BoulesteixABAnne‐Laure Boulesteix
- Tim P. MorrisTMTim P. Morris
- The Simulation Panel Of The Stratos InitiativeTIThe Simulation Panel Of The Stratos Initiative
Top Cited Papers
Open Access
- 3 February 2023
- journal article
- research article
- Published by Wiley in Biometrical Journal
- Vol. 66 (1) , e2200222
Abstract
Although new biostatistical methods are published at a very high rate, many of these developments are not trustworthy enough to be adopted by the scientific community. We propose a framework to think about how a piece of methodological work contributes to the evidence base for a method. Similar to the well‐known phases of clinical research in drug development, we propose to define four phases of methodological research. These four phases cover (I) proposing a new methodological idea while providing, for example, logical reasoning or proofs, (II) providing empirical evidence, first in a narrow target setting, then (III) in an extended range of settings and for various outcomes, accompanied by appropriate application examples, and (IV) investigations that establish a method as sufficiently well‐understood to know when it is preferred over others and when it is not; that is, its pitfalls. We suggest basic definitions of the four phases to provoke thought and discussion rather than devising an unambiguous classification of studies into phases. Too many methodological developments finish before phase III/IV, but we give two examples with references. Our concept rebalances the emphasis to studies in phases III and IV, that is, carefully planned method comparison studies and studies that explore the empirical properties of existing methods in a wider range of problems.Keywords
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Funding Information
- Medical Research Council (MC_UU_00004/07)
- Deutsche Forschungsgemeinschaft (BO3139/4‐3)
This publication has 25 references indexed in Scilit:
- Tuning multiple imputation by predictive mean matching and local residual drawsBMC Medical Research Methodology, 2014
- A Plea for Neutral Comparison Studies in Computational SciencesPLOS ONE, 2013
- Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor ResearchPLOS Medicine, 2013
- A technology readiness levels (TRLs) calculator software for systems engineering and technology management toolAdvances in Engineering Software, 2010
- Identifying phases of investigation helps planning, appraising, and applying the results of explanatory prognosis studiesJournal of Clinical Epidemiology, 2008
- A solution to the problem of separation in logistic regressionStatistics in Medicine, 2002
- Methodological challenges in the evaluation of prognostic factors in breast cancerBreast Cancer Research and Treatment, 1998
- Bias reduction of maximum likelihood estimatesBiometrika, 1993
- Multiple Imputation for the Fatal Accident Reporting SystemJournal of the Royal Statistical Society Series C: Applied Statistics, 1991
- Missing-Data Adjustments in Large SurveysJournal of Business & Economic Statistics, 1988