Computational protocol: Predicting the location of the hip joint centres, impact of age group and sex

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Protocol publication

[…] Regression equations were developed to estimate the location of the HJC from linear combinations of predictors with intercept. Data from the right side of each subject was utilized to generate equations and the best predictors were identified from a best subset regression analysis (Minitab 17, Minitab Inc., USA) between the predictors IA and LL. Pelvic depth was not included in the best subset regression analysis as the purpose was to develop equations with clinical utility. Although pelvic depth, derived from medical images, may be a good predictor for the position of the HJC, the presence of skin and adipose tissue between the skin surface and the bony landmarks introduces an unknown magnitude of error in the corresponding clinical examination measurement and, as a consequence, in the estimate of the location of the HJC. Regression equations including PD were derived and provided for comparison purposes only. The subjects’ age was also not included in the regression model since age did not display a linear trend with respect to the response variables across groups (cf. in ) and age was kept as a categorical variable (1: children, 2: adolescents and 3: adults).For each equation, the goodness of fit was estimated with the coefficient of determination (R2) of the multiple linear regression and the prediction accuracy by the mean absolute error from leave-one-out cross-validation (LOOCV). LOOCV provides a means to estimate the prediction accuracy of the regression equations for new samples. It is a particular case of k-fold cross-validation when k = 1. Given n data points, LOOCV derives n regression equations each based on n-1 data points, hence for each equation, one data point has been removed from the dataset and the absolute difference between the data point value and its estimate from the regression equation provides the prediction error.To determine whether equations with increasing numbers of predictors led to significant improvement, the absolute prediction errors from the equations with increasing number of predictors were compared to those of the single predictor equation with paired t-test at α = 0.01. A stepwise regression analysis was also performed to investigate whether the two categorical variables, age group and sex, may be considered in the regression model. The model included the predictors, the categorical variables and the interactions between predictors and categorical variables. […]

Pipeline specifications

Software tools Minitab, K-Fold
Application Protein structure analysis
Organisms Homo sapiens