final_program_abstracts[1]

11 IMSC Session Program
Structural modelling of nonlinear exposure-response
relationships for longitudinal data
Monday - Poster Session 4
Xiaoshu Lu and Esa-Pekka Takala
Finnish Institute of Occupational Health, Helsinki, Finland
Exposure–response relationships are of interest in many epidemiological, medical,
meteorological, climatological, and many other applications. Once exposure
information is obtained, following outcomes over time would provide the information
needed to complete the risk assessment. The widespread use of exposure and risk
assessment procedures has produced a diversification and specialisation of different
methodologies, depending on the case under consideration. However, a critical review
reveals that linear model has been widely used in examining exposure-response
relationships. In many applications, data often show a remarkable dynamic and
nonlinear characteristic, which requires a structure-based approach to elucidate the
nonlinear exposure-response relationship behind the data. Standard statistical methods
provide few theories on how to study nonlinear patterns. In this paper, we develop a
new model for longitudinal data to address these challenges. The performance of the
model is demonstrated using the hypothetical data. In the hypothetical data, the
response responds to the exposure only by a phase shift with random errors. Hence
the response is positively correlated with the exposure. A longitudinal analysis with a
standard linear mixed-effects model, for example through SAS's PROC MIXED
procedure, shows that the parameter estimate is statistically discernible at 5% level,
indicating that response is negatively associated with exposure (p