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Boosted regression trees require the parameters learning rate and tree complexity. It is worth noting that these terms are also referred to as shrinkage parameter and tree complexity, respectively. The learning rate controls how much each tree contributes to the model as it develops. Typically, a smaller learning rate provides better prediction than a larger learning rate. The tree complexity sets the number of interactions fitted in the model, where a tree complexity of two allows for two-way interactions, three allows for three-way interactions, and so on.26 Creating reproducible results in the BRT model requires setting a random seed, as the process used to create the BRT model involves random subsampling of data. Whereas the single trees produced by the CART analysis are appealing, they are less able to predict linear  relationships, are very sensitive to small variations in data, and may provide an oversimplification  of the ��real�� model.30 In contrast, the BRT analysis is better able to describe linear relationships and is more robust in terms of predictive accuracy, although interpretability suffers as a result.26 Using both CART and BRT models provides complementary inference��one is simple but provides interpretability, the other provides complexity and robustness, but with reduced interpretability. CART and BRT models were applied to the case study data, using per cent data missing per row as the response variable and the following explanatory variables: Site, UIN  (unique identifying number), Sex, Type (of data), Date, FVC, FVC%, FEV1, FEV1%, FEV1%, FVC%, SEG Primary, Age, BMI, Code, Systolic Blood Pressure, Diastolic Blood Pressure, HDL Cholesterol, Total Cholesterol, Cardiac Risk Score, Smoking, Epworth Sleeping Scale, Secondary SEG, K10 Depression, ETOH Alcohol Scale, BHL, Repeated Visits, Exercise Per Week, Weight, Height, Waist, Blood Sugar Level, Pulse, Concentration, LAeq. These variables can be seen in table form in online supplementary table S1. The statistical software package ��R�� and the graphical user interface, ��RStudio�� were employed for the analyses.31 32 R [http://www.selleckchem.com/products/CAL-101.html GS-1101] packages ��rpart�� and ��gbm�� were used for the CART and BRT analyses.27 33 The rpart model handles missing values by using surrogate splits: when a value for a variable is missing, and that variable needs to be used for a split, an alternative variable with a similar splitting property is used to determine the direction of the split. The gbm function also uses a surrogate split method. The current analysis generated CART models using the default values specified in ��rpart��27 and BRT models using the guidelines provided by Reference 26, which build on the package ��gbm��.33 The BRT model was run assuming a Gaussian error distribution for the response, an interaction depth of 5, learning rate of 0.01, and bagging (fraction of training set observations randomly selected) set to 0.5.