Outliers and their impact on Clinical Data Management

• Is your clinical data facing issues with accuracy?

• Are data quality issues skewing your clinical results? Identifying them is key to ensuring accuracy and quality.

Outliers are hidden threats that can compromise clinical data integrity and skew trial results.

Identifying them is key to ensuring accuracy and quality

Incorrect Data: Data entry errors such as typographical mistakes or highly inaccurate values entered in the study database, if not caught through validation procedures or overlooked during reviews (eg. "Vital signs" such as blood pressure or heart rate or "demographic data" such as age) can significantly distort analysis and skew results, leading to misinterpretation of study outcomes.

Non-compliance/Protocol Violation: Subjects not following the study protocol including missed medication doses, incorrect drug administration, or failure to adhere to diet or activity restrictions may result in responses that differ significantly from the rest of the study population, causing misleading results or inaccurate conclusions about the treatment's effects.

Demographic Characteristics: Group of subject characteristics if far outside the norm for the condition under study (e.g., an unusually high percentage of elderly or pediatric patients) may reflect inherent differences in health status, response to treatment, or comorbid conditions and if these are not representative of the broader patient population, they can undermine the generalizability of the study results.

Misclassification of Data: Misclassified or data recorded in wrong categories like subjects classified into an incorrect treatment group, may lead to bias, especially in randomized controlled trials (RCTs), and can distort comparisons between treatment groups.

Data Imputation Issues: If missing data is imputed incorrectly (e.g., using unrealistic values like extremes of the range), can distort the results of analyses, leading to inaccurate estimations of treatment effects or misleading conclusions.

Natural Extreme Values: Natural extremes in the data that are not usually caused by error or non-compliance like rare side effect or an unusually high or low response to a treatment which affects statistical measures like mean, variance, and regression analysis, and may influence study outcomes if not properly handled.

Instrumental or Measurement Errors: Faulty instruments used or errors in the way clinical measurements are recorded like incorrect blood pressure readings due to a malfunctioning sphygmomanometer or inaccurate lab test, results due to equipment failure may misrepresent the true patient status, especially in terms of vital signs or lab tests, and affect conclusions about treatment efficacy or safety.

Having identified these major outliers, please stay tuned for a comprehensive article that is going to follow with more details on the methodologies and strategies in mitigating and ensuring a clean and quality clinical Data base.