Key result
Landmark frailty model outperforms the standard landmark model in assessing cardiovascular risk.
Why the study?
The study was designed to improve the landmark dynamic prediction model by incorporating a frailty parameter and to apply it to assess cardiovascular risk in on-treatment blood pressure patients.
The Landmark frailty model provides improved dynamic prediction of cardiovascular risk in patients on blood pressure treatment compared to the standard Landmark model.
May enhance dynamic CV risk prediction in BP-treated patients; leaves open prospective outcome validation before clinical use.
Landmark model (LM) is a dynamic prediction model that uses a longitudinal biomarker in time-to-event data to make prognosis prediction. This study was designed to improve this model and to apply it to assess the cardiovascular risk in on-treatment blood pressure patients. A frailty parameter was used in LM, landmark frailty model (LFM), to account the frailty of the patients and measure the correlation between different landmarks. The proposed model was compared with LM in different scenarios respecting data missing status, sample size (100, 200, and 400), landmarks (6, 12, 24, and 48), and failure percentage (30, 50, and 100%). Bias of parameter estimation and mean square error as well as deviance statistic between models were compared. Additionally, discrimination and calibration capability as the goodness of fit of the model were evaluated using dynamic concordance index (DCI), dynamic prediction error (DPE), and dynamic relative prediction error (DRPE). The proposed model was performed on blood pressure data, obtained from systolic blood pressure intervention trial (SPRINT), in order to calculate the cardiovascular risk. Dynpred, coxme, and coxphw packages in the R.3.4.3 software were used. It was proved that our proposed model, LFM, had a better performance than LM. Parameter estimation in LFM was closer to true values in comparison to that in LM. Deviance statistic showed that there was a statistically significant difference between the two models. In the landmark numbers 6, 12, and 24, the LFM had a higher DCI over time and the three landmarks showed better performance in discrimination. Both DPE and DRPE in LFM were lower in comparison to those in LM over time. It was indicated that LFM had better calibration in comparison to its peer. Moreover, real data showed that the structure of prognostic process was predicted better in LFM than in LM. Accordingly, it is recommended to use the LFM model for assessing cardiovascular risk due to its better performance.
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Sayadi et al. (2020) studied Cardiovascular disease risk in on-treatment blood pressure patients. Landmark frailty model (LFM) vs. Landmark model (LM) was evaluated on Model performance (bias of parameter estimation, mean square error, deviance statistic, DCI, DPE, DRPE). The proposed landmark frailty model (LFM) demonstrated better performance, discrimination, and calibration than the standard landmark model for assessing cardiovascular risk.
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