A reliable time-series method for predicting arthritic disease outcomes: New step from regression toward a nonlinear artificial intelligence method

Abstract

Background and objectiveThe 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. MethodsDataset 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. ResultsIn 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. ConclusionsThe 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.