Methods for Estimating Reference Intervals

Abstract

Reference intervals (RIs) are sets of percentiles that outline the range of laboratory test results belonging to healthy individuals. They are essential for the interpretation of laboratory test results. A wide variety of factors affect the validity of RIs. Among them are the statistical methods used to estimate RIs. However, little investigation has gone into the effect that different statistical methods have on the resulting RIs. This is particularly needed as the complexity of paediatric data makes it difficult to estimate RIs. These difficulties, however, can be addressed using appropriate statistical techniques, provided that there is an outline of scenarios under which these techniques are truly “appropriate”. The objective of this thesis is to provide a thorough investigation into the effect of different statistical methods on RIs. A systematic review was first conducted with a focus on paediatric RIs. The results of this review revealed that critical analysis steps are often overlooked due to complicated paediatric data. Even though a guideline addressing the establishment of RIs is available, there is great heterogeneity in the statistical methods chosen to estimate paediatric RIs. An extensive simulation involving the three most commonly used approaches to estimate RIs (the parametric, non-parametric, and robust methods) was also conducted to investigate and compare the performance of the different methods. The simulation results show that, when data follows a Gaussian distribution, or close to it, the parametric method provides the best estimates. The non-parametric method did not provide the best estimates of RIs (compared to the parametric method) unless data was highly skewed and/or large sample sizes were used. In addition, the bias and MSE associated with the parametric method when data follows a Gaussian distribution was mathematically derived, which may lead to the development of a bias corrected and more precise approach in the future.