## 2 Methods
### Data Extraction and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Section** & **Topic** & **Checklist Item** \\
**1** & **Title** & Nature of study & Identify the report as introducing a predictive model. \\
**2** & Abstract & Structured summary & Background. Objectives. Data sources. Performance metrics of the predictive models (in both point estimates and confidence intervals). Conclusion including the practical value of the developed predictive model or models. \\
**3** & Introduction & Rationale & Identify the clinical goal. Review the current practice and prediction accuracy of any existing models. \\
**4** & & Objectives & State the nature of study being predictive modeling, defining the target of prediction. Identify how the prediction problem may benefit the clinical goal. \\
**5** & Methods & Describe the setting & Identify the clinical setting for the target predictive model: Identify the modeling context in terms of facility type, size, volume, and duration of available data. \\  & & Define a measure for the potential goal. Identify the problem to be prognostic or diagnostic. Determine the form of the prediction model classification, regression, or survival prediction. Explain practical costs of prediction errors. Define quality metrics for prediction models. Define the success criteria for prediction. Identify relevant data sources and quote the ethics approval number for data access. State the inclusion and exclusion criteria for data. Describe the time span of data and the sample or cohort size. Define the observational units on which the response variable and predictor variables are defined. Define the predictor variables. Describe the data pre-processing performed, including data cleaning and transformation. State any criteria used for outlier removal. State how missing values were handled. Describe the basic statistics of the dataset as the ratio of positive to negative classes for a classification problem. Define the model validation strategies. Define the validation metrics. For classification problems, the metrics should include sensitivity, specificity, positive predictive value, range predictive value, area under the ROC curve, and calibrant plot. \\
**8** & & Build the predictive model & Identify independent variables that predominantly take a single value. Identify and remove redundant independent variables. Identify the independent variables that may suffer from the perfect separation problem. Assess whether sufficient data are available for a good fit of the model. Determine a set of candidate modeling techniques is if only one or of model was used, justify the decision for using that model. Define the performance metrics to select the best model. \\  & & Report the predictive performance of the final model in terms of the validation metrics specified in the methods section. If possible, report the parameter estimates in the model and their confidence intervals. When the direct calculation of confidence intervals is not possible, report nonparametric estimates from bootstrap samples. \\  & & Comparison with other models in the literature should be based on confidence intervals. Interpretation of the final model. If possible, report what variables were shown to be predictive of the response variable. State which subpopulation has the best prediction and which subpopulation is most difficult to predict. \\
**10** & Discussion & Clinical implications & Report the clinical implications derived from the obtained predictive performance. \\
**11** & Limitations of the model & Discus the following potential limitations: assumed input and output data format. Potential pitfalls in interpreting the model. Potential bias of the data used in modeling. Generalizability of the data. \\
**12** & Unexpected results during the experiments & Report unexpected signs of coefficients, indicating collinearity or complex interaction between predictor variables. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Items to include when reporting predictive models in biomedical research.