Table 3.

Suggested minimal reporting requirements for biomarker publications.

Consideration	Assay characteristic	Comments
Biospecimen protocols	Specimen collection, handling and storage conditions and duration	Could include details on number of freeze-thaw cycles; fasting/non-fasting status of individual
Analytical Considerations	LOD	Concentration that is reliably distinguished from “analytical noise”
	LLOQ	May be higher than the LOD and is the lowest concentration that is acceptably quantified by a particular assay; typically refers to the concentration of lowest standard on the calibration curve; alternatively, the lowest concentration at which the CV of the calculated concentration is <20% and the value within 80-120% of the known value
	Data handling method below LOD/LLOQ	For instance imputation, if so what type of imputation; or exclusion of sample, patient or biomarker from analysis
	ULOQ	This is the highest calibrator concentration; alternatively, the highest concentration at which the CV of the calculated concentration is <20% and the value is within 80-120% of the known value.
	Data handling method above ULOQ	For instance, re-run sample at higher dilution or impute or exclude
	Inter-assay and/or intra-assay CV	Inter-assay (between plates) and/or intra-assay (within plate) CV
	Specific analyzer and/or assay manufacturer	Include manufacturer product number
	Duplicate measurements performed for each sample	If duplicate analyses cannot be performed due to limited availability of sample volume, then either a pooled sample control should be run in duplicate on all plates and/or a small number of samples with sufficient volume should be run in duplicate on all plates
Statistical Considerations	Understanding the assumptions needed for valid statistical inference and assessing validity of those assumptions for the data at hand	For instance, assessing normality of biomarker data before performing a t-test
	Adjustment for multiple testing	Avoid p-value hacking
	Assessing stability of results using cross-validation and/or bootstrapping	For instance, cross validation of areas under the curve [AUC] from receiver operator characteristic curves
	Accounting for paired/longitudinal study design	For instance, use of generalized estimating equations (GEEs) to control for inter-individual correlation when examining synovial fluid biomarker results from both knees of the same subject
	Sensitivity analysis	For instance, assessing how results change when outliers are included versus excluded

LLOQ, lower limit of quantification; LOD, limit of detection; ULOQ, upper limit of quantification; CV, coefficient of variation