Please use this identifier to cite or link to this item: http://hdl.handle.net/11434/1174
Title: Quantile regression: predicting more than the mean.
Epworth Authors: McKenzie, Dean
Gwini, Stella
Fahey, Michael
Roberts, Caroline
Fedele, Bianca
Olver, John
Farr, Babak
Keywords: Quantile Regression
Whole Data Distribution
Graphical Data Analysis
Statistical Data Analysis
Prediction of Percentiles
Percentile Measurements
Interval Measurements
Mean
Median
Standard Deviation
Asymmetric Distributions
Data Summaries
Current Research Limitations
Interquartile Range
Upper Quartile
Lower Quartile
Statistical Software Packages
R (Programming Language)
Stata
Monash-Epworth Rehabilitation Research Centre (MEERC), Epworth HealthCare, Melbourne, Australia
Epworth Research Institute, Epworth HealthCare, Victoria, Australia
Epworth Prostate Centre, Epworth Healthcare, Victoria, Australia
Epworth Monash Rehabilitation Unit (EMReM), Epworth HealthCare, Richmond, Victoria, Australia.
Issue Date: Jun-2017
Citation: Epworth Research Institute Research Week 2017; Poster 27: pp 51
Conference: Epworth Research Institute Research Week 2017
Conference Location: Epworth Research Institute, Victoria, Australia
Abstract: INTRODUCTION: Interval measurements such as length of hospital stay, depression inventory scores and overall functioning, are typically summarized by the mean with its standard deviation. In instances of asymmetric, highly skewed distributions such as length of hospital stay, the data is often summarized using the 50th percentile (i.e. the median) together with the 25th and 75th percentiles, known as the lower and upper quartiles. The latter are often reported as the "interquartile range" (a measure of the middle 50% of the distribution). However, other percentiles such as the 10th and 90th appear far less frequently. In other words, the top and bottom 25% of data is not captured - reflecting a current research limitation pointed out by Vasista et al (2014). Recent studies have demonstrated the importance of evaluating the upper and lower ends of the distribution together with the median. For example, in a recent study by Westerlund et al (2014), researchers showed that although the overall median body mass index (BMI) was similar between patients with short (<=5hr) and medium length (6-8hr) sleep durations, the 90th percentile of BMI was much higher in the former than in the latter sleep duration. This example illustrates the importance of assessing more than just the median and to consider the whole data distribution. AIMS: To present graphical and statistical methods of examining the higher and lower ends of a given data distribution using quantile regression. METHODOLOGY: Originally formulated in the 18th century by Fr Roger Boscovich SJ, physicist, astronomer and mathematician, but implemented much more recently with the development of fast computers and sophisticated software, quantile regression allows the ready prediction of percentiles such as the 90th, 75th, 25th and 10th as well as the median. Quantile regression is available in standard statistical software packages such as R and Stata. RESULTS: Illustrative published medical data and graphs will be presented in order to demonstrate ways of evaluating the entire distribution of data. [See poster]
URI: http://hdl.handle.net/11434/1174
Type: Conference Poster
Type of Clinical Study or Trial: Review
Appears in Collections:Health Informatics

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