Statistical analysis
diarrhoea, illness, asthma, chest signs, vomiting, dry cough, chills
REGRESSION
Logistic regression models were used to assess the prediction of illness. The external validity of the STARWAVe score was assessed using the three STARWAVe classifications of low, normal, or high risk.As the performance of STARWAVe was limited in this population, the authors of the current study then developed a new model using a backwards fitting approach to model the predictors of progression of illness in the current dataset. Backward elimination is preferred among automatic predictor selection techniques because the correlations between predictors are accounted for in the multivariable model.Developing a model with only a few major predictors can increase applicability in clinical practice. The authors therefore explored whether progression of illness could be predicted from the following baseline characteristics: age, the severity of symptoms, heart rate, respiratory rate, temperature, duration of illness before consultation, sex, comorbid conditions, history of asthma, chest signs, feeling generally unwell, oxygen saturation, sputum or a rattly chest, vomiting, dry cough, chills, diarrhoea, disturbed sleep, and passing urine less often.The discrimination of the model in the AUROC is presented using bootstrapping to limit overly optimistic estimates because of overfitting, and a recommended and more efficient approach to internal validation rather than using split samples.