title: Multiple patient samples of an analyte improve detection of changes in clinical status.
abstract: BACKGROUND
  When comparing results over time, biologic variation must be statistically incorporated into the evaluation of laboratory results to identify a physiologic change. Traditional methods compare the difference in 2 values with the standard deviation (SD) of the biologic variation to indicate whether a "true" physiologic change has occurred.
  
  
  OBJECTIVE
  To develop methodology to reduce the effect of biologic variation on the difference necessary to detect changes in clinical status in the presence of biologic variation.
  
  
  METHODS
  The standard test for change compares the difference between 2 points with the 95% confidence limit, given as +/- 1.96 x square root of 2 x SD. We examined the effect of multiple data pairs on the confidence limit.
  
  
  RESULTS
  Increasing the number of data pairs using the formula 1.96 x square root of 2 x SD, where n = number of data pairs, significantly reduces the difference between values necessary to achieve a 95% confidence limit.
  
  
  CONCLUSIONS
  Evaluating multiple paired sets of patient data rather than a single pair results in a substantial decrease in the difference between values necessary to achieve a given confidence interval, thereby improving the sensitivity of the evaluation. A practice of using multiple patient samples results in enhanced power to detect true changes in patient physiology. Such a testing protocol is warranted when small changes in the analyte precede serious clinical events or when the SD of the biologic variation is large.
authors.affiliation: Department of Laboratory Medicine, Boston Medical Center, Division of Pathology and Laboratory Medicine, Boston University School of Medicine, 88 E Newton St, Boston, MA 02118, USA. Martin.kroll@bmc.org
authors.forename: Martin H
authors.initials: MH
authors.lastname: Kroll
mesh_terms: D000704:Analysis of Variance; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D018450:Disease Progression; D006801:Humans; D008954:Models, Biological; D010338:Pathology, Clinical / Q000706:statistics & numerical data*; D010361:Patients / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity