Abstract
The analysis of extreme diagnostic measurements in clinical trials relies on reference intervals that help drug developers quickly determine whether a particular value is typical or atypical. The distribution of diagnostic variables is often greatly influenced by various covariates and it is important to properly account for this influence in the analysis of extreme measurements. This paper discusses three approaches to constructing covariate-adjusted reference intervals for quantitative diagnostic data: global quantile smoothing, local quantile smoothing, and stepwise quantile approximations based on recursive partitioning. A detailed review of methods for optimizing the quantile estimation procedures is provided. The paper presents algorithms for selecting the degree of a polynomial approximation in global smoothing, bandwidth parameter in local smoothing, and number of strata in recursive partitioning. The described methods for computing covariate-adjusted reference intervals are applied to the analysis of electrocardiographic data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
