A reliable time-series method for predicting arthritic disease outcomes: New step from regression toward a nonlinear artificial intelligence method.
Arthritis, Rheumatoid
/ drug therapy
Arthroplasty, Replacement, Hip
Arthroplasty, Replacement, Knee
Artificial Intelligence
Hip
/ physiopathology
Humans
Incidence
Interrupted Time Series Analysis
/ statistics & numerical data
Knee
/ physiopathology
Linear Models
Outcome Assessment, Health Care
/ methods
Research Design
Artificial intelligence
GS-GMDH
Hip replacement
Interrupted time-series
Knee replacement
Rheumatoid arthritis
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
Jun 2020
Jun 2020
Historique:
received:
30
09
2019
accepted:
04
01
2020
pubmed:
24
1
2020
medline:
25
3
2021
entrez:
24
1
2020
Statut:
ppublish
Résumé
The interrupted time-series (ITS) concept is performed using linear regression to evaluate the impact of policy changes in public health at a specific time. Objectives of this study were to verify, with an artificial intelligence-based nonlinear approach, if the estimation of ITS data could be facilitated, in addition to providing a computationally explicit equation. Dataset were from a study of Hawley et al. (2018) in which they evaluated the impact of UK National Institute for Health and Care Excellence (NICE) approval of tumor necrosis factor inhibitor therapies on the incidence of total hip (THR) and knee (TKR) replacement in rheumatoid arthritis patients. We used the newly developed Generalized Structure Group Method of Data Handling (GS-GMDH) model, a nonlinear method, for the prediction of THR and TKR incidence in the abovementioned population. In contrast to linear regression, the GS-GMDH yields for both THR and TKR prediction values that almost fitted with the measured ones. These models demonstrated a low mean absolute relative error (0.10 and 0.09 respectively) and high correlation coefficient values (0.98 and 0.78). The GS-GMDH model for THR demonstrated 6.4/1000 person years (PYs) at the mid-point of the linear regression line post-NICE, whereas at the same point linear regression is 4.12/1000 PYs, a difference of around 35%. Similarly for the TKR, the linear regression to the datasets post-NICE was 9.05/1000 PYs, which is lower by about 27% than the GS-GMDH values of 12.47/1000 PYs. Importantly, with the GS-GMDH models, there is no need to identify the change point and intervention lag time as they simulate ITS continually throughout modelling. The results demonstrate that in the medical field, when looking at the estimation of the impact of a new drug using ITS, a nonlinear GS-GMDH method could be used as a better alternative to regression-based methods data processing. In addition to yielding more accurate predictions and requiring less time-consuming experimental measurements, this nonlinear method addresses, for the first time, one of the most challenging tasks in ITS modelling, i.e. avoiding the need to identify the change point and intervention lag time.
Sections du résumé
BACKGROUND AND OBJECTIVE
OBJECTIVE
The interrupted time-series (ITS) concept is performed using linear regression to evaluate the impact of policy changes in public health at a specific time. Objectives of this study were to verify, with an artificial intelligence-based nonlinear approach, if the estimation of ITS data could be facilitated, in addition to providing a computationally explicit equation.
METHODS
METHODS
Dataset were from a study of Hawley et al. (2018) in which they evaluated the impact of UK National Institute for Health and Care Excellence (NICE) approval of tumor necrosis factor inhibitor therapies on the incidence of total hip (THR) and knee (TKR) replacement in rheumatoid arthritis patients. We used the newly developed Generalized Structure Group Method of Data Handling (GS-GMDH) model, a nonlinear method, for the prediction of THR and TKR incidence in the abovementioned population.
RESULTS
RESULTS
In contrast to linear regression, the GS-GMDH yields for both THR and TKR prediction values that almost fitted with the measured ones. These models demonstrated a low mean absolute relative error (0.10 and 0.09 respectively) and high correlation coefficient values (0.98 and 0.78). The GS-GMDH model for THR demonstrated 6.4/1000 person years (PYs) at the mid-point of the linear regression line post-NICE, whereas at the same point linear regression is 4.12/1000 PYs, a difference of around 35%. Similarly for the TKR, the linear regression to the datasets post-NICE was 9.05/1000 PYs, which is lower by about 27% than the GS-GMDH values of 12.47/1000 PYs. Importantly, with the GS-GMDH models, there is no need to identify the change point and intervention lag time as they simulate ITS continually throughout modelling.
CONCLUSIONS
CONCLUSIONS
The results demonstrate that in the medical field, when looking at the estimation of the impact of a new drug using ITS, a nonlinear GS-GMDH method could be used as a better alternative to regression-based methods data processing. In addition to yielding more accurate predictions and requiring less time-consuming experimental measurements, this nonlinear method addresses, for the first time, one of the most challenging tasks in ITS modelling, i.e. avoiding the need to identify the change point and intervention lag time.
Identifiants
pubmed: 31972347
pii: S0169-2607(19)31677-3
doi: 10.1016/j.cmpb.2020.105315
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
105315Informations de copyright
Copyright © 2020. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of Competing Interest H. Bonakdari, J-P. Pelletier, and J. Martel-Pelletier have no conflicts of interest for this study.