Additional file 1 of Broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia
Creators: name: Almilaji, Orouba
name: Webb, Gwilym
name: Maynard, Alec
name: Chapman, Thomas P.
name: Shine, Brian S. F.
name: Ellis, Antony J.
name: Hebden, John
name: Docherty, Sharon
name: Williams, Elizabeth J.
name: Snook, Jonathon
Publisher: name: figshare
Year: 2021
Subjects: subject: Medicine
subject: Biotechnology
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: Cancer
subject: Science Policy

description: Additional file 1: Table S1. Model coefficients before/after applying different regularisation methods. Figure S1. Average 95% confidence interval width for the C-statistic (discrimination) at different effective sample sizes comparing by SD(LP) at fixed base probability = 0.084. Figure S2. Average 95% confidence interval width for the calibration slope at different effective sample sizes (based on number of events) comparing by SD(LP) at fixed base probability =0.084. Table S2. Normal ranges for Hb, MCV, ferritin, T.sat in each lab. Figure S3. Density plots show the distributions of continuous variables per GI presence/absence in each dataset using IDIOM model. Figure S4. Density plots show the distribution of estimated risks per GI presence/absence in each dataset using IDIOM model. Figure S5. Risk groups calibration in the combined validation dataset shows the relation between the estimated risks (on the x-axis) and the observed risks (on the y-axis). Figure S6. Flexible calibration curve in Oxford dataset shows the relation between the estimated risks (on the x-axis) and the observed proportion of events (on the y-axis). Figure S7. Flexible calibration curve in Sheffield dataset shows the relation between the estimated risks (on the x-axis) and the observed proportion of events (on the y-axis). Figure S8. Decision curve analysis for GI investigation using Oxford dataset. Grey line: penalised IDIOM model. Black line: “investigate no-one” strategy. Dashed line: “investigate all” strategy. The vertical axis displays standardized net benefit. The horizontal axis shows the risk thresholds. Figure S9. Decision curve analysis for GI investigation using Sheffield dataset. Grey line: penalised IDIOM model. Black line: “investigate no-one” strategy. Dashed line: “investigate all” strategy. The vertical axis displays standardized net benefit. The horizontal axis shows the risk thresholds. Figure S10. Clinical impact curve for penalised IDIOM risk model using Dorset data, with 95% CIs constructed via bootstrapping. Of 1,000 patients, the heavy black solid line shows the total number who would be deemed low risk for each risk threshold. The blue dashed line shows how many of those would be true negatives. The vertical axis displays standardised net benefit. The two horizontal axes show the correspondence between risk threshold and cost:benefit ratio. Figure S11. Clinical impact curve for penalised IDIOM risk model using combined validation data from Oxford and Sheffield, with 95% CIs constructed via bootstrapping. Of 1,000 patients, the heavy black solid line shows the total number who would be deemed low risk for each risk threshold. The blue dashed line shows how many of those would be true negatives. The vertical axis displays standardised net benefit. The two horizontal axes show the correspondence between risk threshold and cost:benefit ratio. Figure S12. Clinical impact curve for penalised IDIOM risk model using the Oxford dataset, with 95% CIs constructed via bootstrapping. Of 1,000 patients, the heavy black solid line shows the total number who would be deemed low risk for each risk threshold. The blue dashed line shows how many of those would be true negatives. The vertical axis displays standardised net benefit. The two horizontal axes show the correspondence between risk threshold and cost:benefit ratio. Figure S13. Clinical impact curve for penalised IDIOM risk model using the Sheffield dataset, with 95% CIs constructed via bootstrapping. Of 1,000 patients, the heavy black solid line shows the total number who would be deemed low risk for each risk threshold. The blue dashed line shows how many of those would be true negatives. The vertical axis displays standardised net benefit. The two horizontal axes show the correspondence between risk threshold and cost:benefit ratio.