title: How do statistical properties influence findings of tracking (maintenance) in epidemiologic studies? An example of research in tracking of obesity.
abstract: There is great interest in studying the tracking (maintenance) of health conditions and risk factors over the life span. Tracking is often defined as the maintenance of a distribution position (e.g., quintile or percentile) in a study population over time. This study investigated how statistical properties might influence research findings of tracking with a special attention on the tracking of extreme ranking. Our results show that when repeated measures over time were positively correlated, the probability of tracking in extreme rankings was greater than other rankings and this was closely influenced by the overall correlation (r) and by the categorization. For example, when r = 0.4, 38% remained in the bottom and upper quintile (Q1, Q5) respectively, while only 22% remained in the middle quintile (Q3); when r = 0.8, the figure became 65% vs. 32%. When r = 0.4 and 0.8, 19 and 50% remained in the upper 95th percentile (or under the 5th percentile), respectively. Our real data show that children in the upper body mass index (= weight(kg)/height(m)(2)) quintile were more likely to maintain their ranking (54%) than others (about 30%), but not significantly higher than the expected (47%, p > 0.05). In conclusion, the overall correlation should be considered when studying tracking. Our proposed methods and predicted probabilities of tracking can help test whether one's observed tracking patterns are different from the statistically predicted ones.
authors: Department of Human Nutrition, Division of Epidemiology and Biostatistics, University of Illinois, Chicago, IL 60612, USA. youfwang@uic.edu
authors: Youfa
authors: Y
authors: Wang
authors: Xiaofei
authors: X
authors: Wang
mesh_terms: D015992:Body Mass Index; D002648:Child; D002681:China / Q000453:epidemiology; D002908:Chronic Disease / Q000453:epidemiology; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D016021:Epidemiologic Studies; D005260:Female; D006801:Humans; D008019:Life Style; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D009765:Obesity / Q000453:epidemiology*; D011159:Population Surveillance; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000706:statistics & numerical data*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric