How is the Limit of Detection (LOD) estimated and how is this handled in the data analysis?

Limit of detection (LOD) is calculated separately for each Olink assay and sample plate. The LOD is based on the background, estimated from negative controls included on every plate, plus three standard deviations. The standard deviation is assay specific and estimated during product validation for every panel.

Limit of detection (LOD) is provided for Olink® Target. For Olink® Explore HT and Explore 384/3072, LOD is not reported in customer runs, read this FAQ to learn more about the reasoning.

Consider excluding assays with low detection from analysis
Olink recommends that assays with a large proportion of samples below LOD is excluded from the analysis. The limit for exclusion should be decided on a study basis and consider design including sample size and experimental variables. Suitable exclusion limits may be in the range of less than 25-50% of samples above LOD.

Characteristics of data below LOD
As with all affinity based assays, data from the Olink platform has a S-curve (sigmoid) relationship with the true protein concentration in a sample. Data below LOD have a higher risk to be in the non-linear phase of the S-curve meaning that 1 NPX difference may not correspond to 2x protein concentration in this region. This may bias estimates including data below LOD and should be considered when interpreting any results that are based on data below LOD.

Strategies for handling data below LOD in data analysis
Several strategies exist for handling data below LOD that varies in complexity. Olink delivers data below LOD to allow researches to choose the strategy that is best for their study and interpret results with the complexity of data below LOD in mind. Some examples of strategies include:

  • Use actual data below LOD: As data below LOD may be non-linear this data should be interpreted cautiously. However, especially in large multiplate studies LOD is a conservative measurement. Using actual data may increase the statistical power and gives a more normal distribution of the data. Including data under LOD does commonly not increase false positives as there is generally no significant difference between groups under LOD (values tend to be condensed to a very small range).
  • Replace data below LOD: Data values below the LOD may be replaced with a fixed value, commonly the LOD. This will truncate the lower end of the data distribution rendering a less normal distribution. Estimates of e.g. the mean will be biased and parametric statistical tests may have lower statistical power.
  • Impute data below LOD: A more complex approach for handling data below LOD is to impute the true value. There exist various approaches for imputation of data below LOD.

Olink recommends to not remove data points below LOD (i.e. treating data under LOD as missing data), as some of the most distinct biomarkers may be low in some groups analyzed but high in other groups.

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