Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1402
- Type
- D - Journal article
- DOI
-
10.1177/0962280216674496
- Title of journal
- STATISTICAL METHODS IN MEDICAL RESEARCH
- Article number
- -
- First page
- 2060
- Volume
- 27
- Issue
- 7
- ISSN
- 0962-2802
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work presents novel longitudinal treatment monitoring from markers of various types (continuous, nominal and ordinal) that current methods do not combine. A novel monitoring method is proposed, in the framework of multivariate generalised mixed effect model, with mixture of normal distribution for the random effects to robustify the model, and developed Markov Chain Monte Carlo for estimation of the posterior of parameters. Supported by MRC Methodology Research Panel funding (£334K, DiALog, MR/L010909/1) this has led to a reliable, early predictor of seizure reoccurrence in epilepsy patients for more effective patient management and counselling (Neurology, 91 :E2035-E2044 DOI: https://doi.org/10.1212/WNL.0000000000006564 ).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
