TY  - JOUR
A1  - Fehr, Jana
A1  - Piccininni, Marco
A1  - Kurth, Tobias
A1  - Konigorski, Stefan
T1  - Assessing the transportability of clinical prediction models for cognitive impairment using causal models
T2  - BMC medical research methodology
N2  - Background
Machine learning models promise to support diagnostic predictions, but may not perform well in new settings. Selecting the best model for a new setting without available data is challenging. We aimed to investigate the transportability by calibration and discrimination of prediction models for cognitive impairment in simulated external settings with different distributions of demographic and clinical characteristics.

Methods
We mapped and quantified relationships between variables associated with cognitive impairment using causal graphs, structural equation models, and data from the ADNI study. These estimates were then used to generate datasets and evaluate prediction models with different sets of predictors. We measured transportability to external settings under guided interventions on age, APOE & epsilon;4, and tau-protein, using performance differences between internal and external settings measured by calibration metrics and area under the receiver operating curve (AUC).

Results
Calibration differences indicated that models predicting with causes of the outcome were more transportable than those predicting with consequences. AUC differences indicated inconsistent trends of transportability between the different external settings. Models predicting with consequences tended to show higher AUC in the external settings compared to internal settings, while models predicting with parents or all variables showed similar AUC.

Conclusions
We demonstrated with a practical prediction task example that predicting with causes of the outcome results in better transportability compared to anti-causal predictions when considering calibration differences. We conclude that calibration performance is crucial when assessing model transportability to external settings.
KW  - Alzheimer's Disease
KW  - Clinical risk prediction
KW  - DAG
KW  - Causality;
KW  - Transportability
Y1  - 2023
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/64346
SN  - 1471-2288
VL  - 23
IS  - 1
PB  - BMC
CY  - London
ER  -