Title: A general conceptual and statistical framework to model non-linear and delayed exposure-response relationships and combi
Funder: Medical Research Council

Abstract: Background Epidemiological multi-city studies on the health effects of environmental factors, such as air pollution and temperature, have known an intense development in the last few years. Within this study design, two methodological issues are prominent among those that need to be addressed: how to specify exposure-response relationships describing potentially non-linear and delayed effects in each city, and how to combine such complex associations across different cities. Interestingly, the same problems apply in different research areas involving different study designs and data structures. The extension and further development of statistical methodologies already proposed in time series analysis would provide a valuable tool in a wide range of research fields. Purpose The aim of this project is to develop a general conceptual and statistical framework to model non-linear and delayed exposure-response relationships, and to propose appropriate approaches to combine such complex associations across studies. Specific objectives: 1. to define a general conceptual framework to formalize the idea of delayed effects. 2. to provide a unified statistical approach to model non-linear and delayed effects by extending existing methods developed in time series analysis. 3. to review, compare and extend meta-analytic techniques to synthesize non-linear and/or delayed associations. 4. to provide a complete software implementation of the methodologies described above in the R statistical software, together with detailed and comprehensive documentation. Research plan This research is comprised of sub-projects Sub-P1 and Sub-P2. Sub-P1 addresses objectives 1-2-4. Existing methods in time series analysis, based on distributed lag non-linear models, will be improved and extended, algebraically and conceptually, to different designs such as cohort and longitudinal analysis. The framework will be implemented by extending the R package dlnm developed by the applicant. Sub-P2 addresses objectives 3-4. In particular, two existing techniques, meta-smoothing and multivariate meta-analysis, will be compared and extended, assessing their flexibility, applicability, and reliability of their results through simulations and applications to real data. A specific R package will be developed to implement these models. Collaborations The research involves collaborations with high-calibre and experienced researchers across three different institutions (LSHTM, MRC-Cambridge and University of Southern California). Research outputs The research project aims to provide a unified methodological approach to model and pool non-linear and delayed dependencies, through a general conceptual and statistical framework. The research project emphasizes generality and usability, specifying a systematic and widely applicable methodology, which encompasses different study designs and data structures, and providing a comprehensive software implementation and documentation.


Research activity codes: 2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE