2015
DOI: 10.1186/s12872-015-0035-z
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Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data

Abstract: BackgroundPhysicians utilize different types of information to predict patient prognosis. For example: confronted with a new patient suffering from severe aortic stenosis (AS), the cardiologist considers not only the severity of the AS but also patient characteristics, medical history, and markers such as BNP. Intuitively, doctors adjust their prediction of prognosis over time, with the change in clinical status, aortic valve area and BNP at each outpatient clinic visit. With the help of novel statistical appr… Show more

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Cited by 27 publications
(23 citation statements)
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“…The comparison of survival prediction between joint modeling and RECIST, at a given time, could be performed in further works. This approach is not specific to cancer immunotherapies and has been advocated in other contexts, such as Parkinson's disease, aortic stenosis, chronic kidney disease, prostate cancer, and some others …”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The comparison of survival prediction between joint modeling and RECIST, at a given time, could be performed in further works. This approach is not specific to cancer immunotherapies and has been advocated in other contexts, such as Parkinson's disease, aortic stenosis, chronic kidney disease, prostate cancer, and some others …”
Section: Discussionmentioning
confidence: 99%
“…This approach is not specific to cancer immunotherapies and has been advocated in other contexts, such as Parkinson's disease, aortic stenosis, chronic kidney disease, prostate cancer, and some others. [28][29][30][31] In the context of mUC and immunotherapies, our predictions of OS relied on baseline clinical characteristics and kinetics of target lesions. In the future, models may benefit from the inclusion of other longitudinal markers that may be associated with changes in treatment efficacy over time.…”
Section: Discussionmentioning
confidence: 99%
“…That is, the CA-125 values that were recorded over time from the same patient are supposed to be more relevant than those between different patients. 6 Moreover, the model explains the biological diversity in the longitudinal outcome. In particular, if we measured CA-125 more than once a day, we might obtain different results.…”
Section: Fit a Linear Mixed-effects Model And Correct Baseline Variablementioning
confidence: 99%
“…Because of the timedependence and discontinuity of CA-125 between follow-ups, providing a longitudinal measurement up to a specific time and assuming survival up to this time were more pertinent and meaningful to calculate the survival probability at a future moment, given that the patient was still at risk until her last follow-up. 6 Adopting this method, we applied the fitted joint model to the 2 patients in the test set and predicted their future survival probabilities. Specifically, patient 8 was a 38.8-year-old Hispanic woman whose menarche was at the age of 12.9 years, menopause was at the age of 48.7 years, and her CA-125 values over time were 20, 13, 11, 56, 112, and 1789 U/mL as measured at 0, 2.1, 6.8, 14.3, 10.5, and 21.7 months.…”
Section: Perform Dynamic Event Predictionsmentioning
confidence: 99%
“…Joint models are gaining favor in the statistical literature for these purposes(9). In a recent example, a joint model was developed to provide a prognostic calculator for risk of prostate cancer recurrence(10).…”
Section: Introductionmentioning
confidence: 99%