Journal article
Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington's disease
BMC Medical Research Methodology, Vol.18(1), 138
11/16/2018
DOI: 10.1186/s12874-018-0592-9
PMCID: PMC6240282
PMID: 30445915
Abstract
Background
Joint modeling is appropriate when one wants to predict the time to an event with covariates that are measured longitudinally and are related to the event. An underlying random effects structure links the survival and longitudinal submodels and allows for individual-specific predictions. Multiple time-varying and time-invariant covariates can be included to potentially increase prediction accuracy. The goal of this study was to estimate a multivariate joint model on several longitudinal observational studies of Huntington’s disease, examine external validity performance, and compute individual-specific predictions for characterizing disease progression. Emphasis was on the survival submodel for predicting the hazard of motor diagnosis.
Methods
Data from four observational studies was analyzed: Enroll-HD, PREDICT-HD, REGISTRY, and Track-HD. A Bayesian approach to estimation was adopted, and external validation was performed using a time-varying AUC measure. Individual-specific cumulative hazard predictions were computed based on a simulation approach. The cumulative hazard was used for computing predicted age of motor onset and also for a deviance residual indicating the discrepancy between observed diagnosis status and model-based status.
Results
The joint model trained in a single study had very good performance in discriminating among diagnosed and pre-diagnosed participants in the remaining test studies, with the 5-year mean AUC = .83 (range .77–.90), and the 10-year mean AUC = .86 (range .82–.92). Graphical analysis of the predicted age of motor diagnosis showed an expected strong relationship with the trinucleotide expansion that causes Huntington’s disease. Graphical analysis of the deviance-type residual revealed there were individuals who converted to a diagnosis despite having relatively low model-based risk, others who had not yet converted despite having relatively high risk, and the majority falling between the two extremes.
Conclusions
Joint modeling is an improvement over traditional survival modeling because it considers all the longitudinal observations of covariates that are predictive of an event. Predictions from joint models can have greater accuracy because they are tailored to account for individual variability. These predictions can provide relatively accurate characterizations of individual disease progression, which might be important in the timing of interventions, determining the qualification for appropriate clinical trials, and general genotypic analysis.
Details
- Title: Subtitle
- Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington's disease
- Creators
- Jeffrey D Long - University of Iowa, PsychiatryJames A. Mills
- Resource Type
- Journal article
- Publication Details
- BMC Medical Research Methodology, Vol.18(1), 138
- Publisher
- BioMed Central Ltd
- DOI
- 10.1186/s12874-018-0592-9
- PMID
- 30445915
- PMCID
- PMC6240282
- ISSN
- 1471-2288
- eISSN
- 1471-2288
- Copyright
- © 2018, Springer Nature
- Grant note
- Jeffrey D. Long receives funding from CHDI Inc., Michael J. Fox Foundation, and the US National Institutes of Health. James A. Mills receives funding from CHDI Inc. and the US National Institutes of Health. PREDICT-HD was supported by the US National Institutes of Health (NIH) under the following grants: 5R01NS040068, 5R01NS054893, 1S10RR023392, 1U01NS082085, 5R01NS050568, 1U01NS082083, and 2UL1TR000442–06 (JS Paulsen principal investigator). This research was also supported by CHDI Foundation grant A3917, and the National Alliance for Medical Image Computing, which provided general data collection/analysis support. ------------------------ Competing Interests: JDL is a paid consultant for Wave Life Sciences USA Inc., Vaccinex Inc., and Azevan Pharmaceuticals Inc. He is also a paid advisory board member for Wave Life Sciences USA Inc., F. Hoffmann-La Roche Ltd., Huntington Study Group (for uniQure biopharma B.V.), and Mitoconix Bio Limited. JAM is a paid consultant for Wave Life Sciences USA Inc.
- Language
- English
- Date published
- 11/16/2018
- Description audience
- Academic
- Academic Unit
- Psychiatry
- Record Identifier
- 9983761184502771
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