Additional file 1 of Predicting patient-reported outcomes following lumbar spine surgery: development and external validation of multivariable prediction models
Creators: name: Halicka, Monika
name: Wilby, Martin
name: Duarte, Rui
name: Brown, Christopher
Publisher: name: figshare
Year: 2023
Subjects: subject: Statistics
subject: FOS: Mathematics
subject: Artificial Intelligence and Image Processing
subject: FOS: Computer and information sciences

description: Additional file 1: Methods S1. Data processing and handling of predictors. Methods S2. Handling missing data (including Fig. S1. Trace lines representing means and SDs of imputed values at each iteration. Fig. S2. Kernel density estimates for the marginal distributions of the observed data and the densities per variable calculated from the imputed data. Fig. S3. Each variable with missing data plotted against its propensity score for observed and imputed values. Fig. S4. Distributions of the residuals of regression models of each variable with missing data on its propensity score, grouped by observed and imputed data. Table S1. Results overview of the logistic regression models on the probability of missingness of each variable with missing data), Methods S3. Random forest hyperparameter tuning (including Table S2. Random forest hyperparameters), Results S1. Participants (including Fig. S5. The flow of surgery cases and participants through the selection process, Table S3. Characteristics of included and excluded participants on continuous and categorical factors, Fig. S6. Mean with 95% CI change in COMI, back pain, and leg pain outcomes at each follow-up interval; Proportions of patients who achieved MCID on these outcomes at each follow-up interval. Table S4. Participant characteristics and differences between the development and validation samples). Results S2. Regression diagnostics (including Fig. S7. Scatterplots with fitted smooth loess regression line with 95% confidence interval, and Pearson’s correlation coefficients, illustrating any relationships among continuous predictors and between continuous predictors and outcomes. Fig. S8. Scatterplots with fitted smooth loess regression line with 95% confidence interval illustrating the relationships between each continuous predictor and logit of outcome in the development data. Fig. S9. Diagnostic plots for logistic regression models on Minimal Clinically Important Difference (MCID) in Core Outcome Measures Index (COMI), back pain, and leg pain. Fig. S10. Component residual plots. Fig. S11. Diagnostic plots for linear regression models on change in Core Outcome Measures Index (COMI), back pain, and leg pain). Fig. S12. Odds Ratios and standardised regression coefficients with 95% confidence intervals for predictors of Minimal Clinically Important Difference (MCID) and continuous change scores in Core Outcome Measures Index (COMI), back pain, and leg pain intensity. Fig. S13. Discrimination ability of the logistic regression and random forest classification models when fitted to the development data for MCID in COMI, back pain, and leg pain. Fig. S14. Calibration plots for logistic regression, linear regression, random forest (RF) classification, and RF regression models when fitted to the development data for each outcome. Fig. S15. Flexible calibration curves fitted to ungrouped observed vs. predicted MCID probabilities for logistic regression and random forest (RF) classification on the validation data. Fig. S16. Flexible calibration curves fitted to ungrouped observed vs. predicted change outcome values for linear and random forest (RF) regression on the validation data. Results S3. Individual predictions formulae (including Table S5. Results of the multivariate logistic regression models on MCID outcomes (log-odds) and multivariate linear regression models on continuous outcomes (unstandardised beta coefficients) in the development data). Results S4. Sensitivity complete case analysis (including Table S6. Comparison of the results from the primary regression analyses using imputed predictor data and from the sensitivity analyses using only cases with complete predictor data). Fig. S17. Random forests relative variable importance plots for Core Outcome Measures Index (COMI), back pain, and leg pain Minimal Clinically Important Difference (MCID), and continuous change on these outcomes in the development data.