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Table 3 Reporting recommendations

From: A consensus-based framework for conducting and reporting osteoarthritis phenotype research

General study characteristics
 Availability of a prespecified research protocol
 Study design: observational cohort, case-control, clinical trial, animal study, other
 Primary goal and setup of the original study, when the phenotype approach is not the primary goal of the study. Cite references/registrations when available
 Intended goal(s) and context(s) of the pursued phenotype classification (e.g., to have prognostic or therapeutic consequences)
 Position of the study with respect to its stage in phenotyping (i.e., study of assessment method, hypothesis-setting, hypothesis-testing, narrow validation, broad validation or impact analysis)
Study population
 Setting: general population, general practitioner, rheumatological and/or orthopedic practice, etc.
 Flow diagram of participants selection process/sampling
 Sample size, dropouts
 Demographics
 Clinical OA characteristics (e.g., pain, function)
 Structural OA characteristics (e.g., radiographic parameters)
Data collection
 Variable(s) for the assumed pathobiological and/or pain mechanisms under study
 • Explanation of how and why the variable(s) is (are) anticipated to reflect the mechanism(s)
 • Statement of the quality of the variable(s), when available
 Follow-up time points for each of the variables (longitudinal studies)
 Outcome parameter(s) (i.e., structural and functional consequences of the phenotype(s))
Statistical analysis
 Availability of a prespecified statistical analysis plan
 Analytical approach (supervised, unsupervised*) and rationale
 Power, sample size considerations
 Methods to adjust for potential confounders/effect modifiers and to handle missing data
 Criteria for the distinction between phenotypes and whether these were predefined
 Criteria for clinical relevance and/or applicability and whether these were predefined
 Any sensitivity analyses
 Methods to determine reproducibility/consistency
 Availability of datasets and syntaxes to other investigators (e.g., website, contact details)
Appraisal
 Underlying pathobiological and/or pain mechanisms
 Potential clinical relevance and applicability
 Internal validity, potential sources of bias
 External validity, generalizability
 Comparison with other phenotype classifications/literature data, when possible
 Relevance and consequences of the present work for future research
 Financial/commercial interests, funding sources
  1. *Supervised statistical methods require output variables to be available and serve to estimate functions that best approximate the relationship between the input and output variables in the dataset (e.g., linear regression). Unsupervised statistical methods are not provided with output variables but are concerned with uncovering structures within datasets without prior knowledge of how the data are organized (e.g., principal component analysis)