TY - JOUR
T1 - Implementation of the simplified stochastic model of ageing for longitudinal osteoarthritis data assessment
AU - Korostishevsky, Michael
AU - Williams, Frances
AU - Hart, Deborah
AU - Blumenfeld, Orit
AU - Spector, Timothy
AU - Livshits, Gregory
N1 - Funding Information:
The study was supported by Israel Ministry of Health, Chief Scientist Grant #3-4101.We would like to thank all the participants of the Chingford Women Study and Maxine Daniels and Dr Alan Hakim for their time and dedication and Arthritis Research UK for their funding support to the study.
PY - 2012/5
Y1 - 2012/5
N2 - Background: Occurrence and progression of age-related irreversible degradations of skeletal joints, osteoarthritis (OA), has a stochastic nature. However, it is commonly described using polynomial models, which may not necessarily be optimal.Aim: To implement a stochastic model of gradual accumulation of the distinct changes for estimating individuals' putative age at onset and risk of the process advancing in the OA longitudinal data.Subjects and methods: The model was formulated as a discrete Markov process. It was applied to radiographic knee osteoarthritis (RKOA) data: 243 Kellgren-Lawrence (K/L) and 207 osteophytes (OP) score histories from the 15-year follow-up Chingford study.Results: The model performance was examined in Monte-Carlo simulations. The mean age at onset of knee osteoarthritis was: 53.04 and 53.23 years and the average annual risk of one K/L and one OP grade appearance was: 0.066 and 0.025, respectively. The analysis also suggested that there is 34 years difference between the inferred age at onset and the age when knee osteoarthritis becomes detectable on radiograph.Conclusion: The stochastic model provides more accurate description of the empiric data compared with the corresponding polynomial model. The model-based individual's estimates could be used as an important tool to fit age-related patterns of the corresponding diseases and conditions.
AB - Background: Occurrence and progression of age-related irreversible degradations of skeletal joints, osteoarthritis (OA), has a stochastic nature. However, it is commonly described using polynomial models, which may not necessarily be optimal.Aim: To implement a stochastic model of gradual accumulation of the distinct changes for estimating individuals' putative age at onset and risk of the process advancing in the OA longitudinal data.Subjects and methods: The model was formulated as a discrete Markov process. It was applied to radiographic knee osteoarthritis (RKOA) data: 243 Kellgren-Lawrence (K/L) and 207 osteophytes (OP) score histories from the 15-year follow-up Chingford study.Results: The model performance was examined in Monte-Carlo simulations. The mean age at onset of knee osteoarthritis was: 53.04 and 53.23 years and the average annual risk of one K/L and one OP grade appearance was: 0.066 and 0.025, respectively. The analysis also suggested that there is 34 years difference between the inferred age at onset and the age when knee osteoarthritis becomes detectable on radiograph.Conclusion: The stochastic model provides more accurate description of the empiric data compared with the corresponding polynomial model. The model-based individual's estimates could be used as an important tool to fit age-related patterns of the corresponding diseases and conditions.
KW - Age-dependent degeneration
KW - Kellgren/Lawrence
KW - Knee osteoarthritis
KW - Maximum likelihood
KW - Monte-Carlo simulation
UR - http://www.scopus.com/inward/record.url?scp=84861398985&partnerID=8YFLogxK
U2 - 10.3109/03014460.2012.681801
DO - 10.3109/03014460.2012.681801
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AN - SCOPUS:84861398985
SN - 0301-4460
VL - 39
SP - 214
EP - 222
JO - Annals of Human Biology
JF - Annals of Human Biology
IS - 3
ER -