How do you reduce sample variability and bias in biomarker studies?

1 Choose the right sample type and size

The first step in any biomarker study is to select the appropriate sample type and size for your research question and assay method. Different sample types may have different advantages and disadvantages in terms of availability, invasiveness, stability, and specificity of the biomarkers. For example, blood samples are widely used and can reflect systemic changes, but they may also contain interfering substances or degrade over time. Urine samples are easy to collect and store, but they may have low biomarker concentration or variation due to hydration or diurnal cycles. Tissue samples can provide localized information, but they may be difficult to obtain or standardize. You should also consider the minimum sample size required to achieve sufficient statistical power and accuracy for your study, and avoid over- or under-sampling.

2 Follow standardized protocols and best practices

The next step is to adhere to standardized protocols and best practices for sample collection, storage, processing, and analysis. These protocols should be based on the current literature, guidelines, and recommendations for your specific biomarker and sample type. It’s important to ensure consistency across all samples and sites involved in your study. When it comes to best practices, you should use appropriate containers, labels, and materials for sample collection and handling. Additionally, it’s essential to minimize pre-analytical factors such as fasting or stress that may affect the biomarker levels or stability. Furthermore, samples should be stored at optimal temperature and conditions as well as processed as soon as possible or with stabilizing agents or preservatives to prevent degradation or contamination. Finally, analyze samples using validated and calibrated methods with quality control and assurance procedures.

3 Apply statistical methods and tools

The final step is to use statistical methods and tools to analyze your data and account for any variability or bias in your samples or results. These techniques can help you identify any outliers, missing values, or errors in the data and decide how to handle them. Additionally, you can normalize, standardize, or transform the data to reduce the effects of scale, distribution, or heterogeneity. Furthermore, you can adjust for confounding or covariate factors that may influence the biomarker levels or associations. Lastly, it is important to perform appropriate tests or models to compare or correlate your biomarker measurements with your outcomes or hypotheses and report your findings with confidence intervals, p-values, or effect sizes. By following these tips, you can reduce sample variability and bias in your biomarker studies and enhance the quality and reliability of your data and conclusions.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?