Functional data modelling approach for analysing and predicting trends in incidence rates-an application to falls injury
- Ullah, Shahid, Finch, Caroline
- Authors: Ullah, Shahid , Finch, Caroline
- Date: 2010
- Type: Text , Journal article
- Relation: Osteoporosis International Vol. 21, no. 12 (2010), p. 2125-2134
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Full Text:
- Reviewed:
- Description: Summary: Policy decisions about the allocation of current and future resources should be based on the most accurate predictions possible. A functional data analysis (FDA) approach improves the understanding of current trends and future incidence of injuries. FDA provides more valid and reliable long-term predictions than commonly used methods. Introduction: Accurate information about predicted future injury rates is needed to inform public health investment decisions. It is critical that such predictions derived from the best available statistical models to minimise possible error in future injury incidence rates. Methods: FDA approach was developed to improve long-term predictions but is yet to be widely applied to injury epidemiology or other epidemiological research. Using the specific example of modelling age-specific annual incidence of fall-related severe head injuries of older people during 1970-2004 and predicting rates up to 2024 in Finland, this paper explains the principles behind FDA and demonstrates their superiority in terms of prediction accuracy over the more commonly reported ordinary least squares (OLS) approach. Results: Application of the FDA approach shows that the incidence of fall-related severe head injuries would increase by 2.3-2.6-fold by 2024 compared to 2004. The FDA predictions had 55% less prediction error than traditional OLS predictions when compared to actual data. Conclusions: In summary, FDA provides more accurate predictions of long-term incidence trends than commonly used methods. The production of FDA prediction intervals for future injury incidence rates gives likely guidance as to the likely accuracy of these predictions. © 2010 International Osteoporosis Foundation and National Osteoporosis Foundation.
- Authors: Ullah, Shahid , Finch, Caroline
- Date: 2010
- Type: Text , Journal article
- Relation: Osteoporosis International Vol. 21, no. 12 (2010), p. 2125-2134
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Full Text:
- Reviewed:
- Description: Summary: Policy decisions about the allocation of current and future resources should be based on the most accurate predictions possible. A functional data analysis (FDA) approach improves the understanding of current trends and future incidence of injuries. FDA provides more valid and reliable long-term predictions than commonly used methods. Introduction: Accurate information about predicted future injury rates is needed to inform public health investment decisions. It is critical that such predictions derived from the best available statistical models to minimise possible error in future injury incidence rates. Methods: FDA approach was developed to improve long-term predictions but is yet to be widely applied to injury epidemiology or other epidemiological research. Using the specific example of modelling age-specific annual incidence of fall-related severe head injuries of older people during 1970-2004 and predicting rates up to 2024 in Finland, this paper explains the principles behind FDA and demonstrates their superiority in terms of prediction accuracy over the more commonly reported ordinary least squares (OLS) approach. Results: Application of the FDA approach shows that the incidence of fall-related severe head injuries would increase by 2.3-2.6-fold by 2024 compared to 2004. The FDA predictions had 55% less prediction error than traditional OLS predictions when compared to actual data. Conclusions: In summary, FDA provides more accurate predictions of long-term incidence trends than commonly used methods. The production of FDA prediction intervals for future injury incidence rates gives likely guidance as to the likely accuracy of these predictions. © 2010 International Osteoporosis Foundation and National Osteoporosis Foundation.
Applications of functional data analysis : A systematic review
- Ullah, Shahid, Finch, Caroline
- Authors: Ullah, Shahid , Finch, Caroline
- Date: 2013
- Type: Text , Journal article
- Relation: BMC Medical Research Methodology Vol. 13, no. 43 (2013), p.1-12
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Full Text:
- Reviewed:
- Description: Background Functional data analysis (FDA) is increasingly being used to better analyze, model and predict time series data. Key aspects of FDA include the choice of smoothing technique, data reduction, adjustment for clustering, functional linear modeling and forecasting methods. Methods A systematic review using 11 electronic databases was conducted to identify FDA application studies published in the peer-review literature during 1995–2010. Papers reporting methodological considerations only were excluded, as were non-English articles. Results In total, 84 FDA application articles were identified; 75.0% of the reviewed articles have been published since 2005. Application of FDA has appeared in a large number of publications across various fields of sciences; the majority is related to biomedicine applications (21.4%). Overall, 72 studies (85.7%) provided information about the type of smoothing techniques used, with B-spline smoothing (29.8%) being the most popular. Functional principal component analysis (FPCA) for extracting information from functional data was reported in 51 (60.7%) studies. One-quarter (25.0%) of the published studies used functional linear models to describe relationships between explanatory and outcome variables and only 8.3% used FDA for forecasting time series data. Conclusions Despite its clear benefits for analyzing time series data, full appreciation of the key features and value of FDA have been limited to date, though the applications show its relevance to many public health and biomedical problems. Wider application of FDA to all studies involving correlated measurements should allow better modeling of, and predictions from, such data in the future especially as FDA makes no a priori age and time effects assumptions.
- Authors: Ullah, Shahid , Finch, Caroline
- Date: 2013
- Type: Text , Journal article
- Relation: BMC Medical Research Methodology Vol. 13, no. 43 (2013), p.1-12
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Full Text:
- Reviewed:
- Description: Background Functional data analysis (FDA) is increasingly being used to better analyze, model and predict time series data. Key aspects of FDA include the choice of smoothing technique, data reduction, adjustment for clustering, functional linear modeling and forecasting methods. Methods A systematic review using 11 electronic databases was conducted to identify FDA application studies published in the peer-review literature during 1995–2010. Papers reporting methodological considerations only were excluded, as were non-English articles. Results In total, 84 FDA application articles were identified; 75.0% of the reviewed articles have been published since 2005. Application of FDA has appeared in a large number of publications across various fields of sciences; the majority is related to biomedicine applications (21.4%). Overall, 72 studies (85.7%) provided information about the type of smoothing techniques used, with B-spline smoothing (29.8%) being the most popular. Functional principal component analysis (FPCA) for extracting information from functional data was reported in 51 (60.7%) studies. One-quarter (25.0%) of the published studies used functional linear models to describe relationships between explanatory and outcome variables and only 8.3% used FDA for forecasting time series data. Conclusions Despite its clear benefits for analyzing time series data, full appreciation of the key features and value of FDA have been limited to date, though the applications show its relevance to many public health and biomedical problems. Wider application of FDA to all studies involving correlated measurements should allow better modeling of, and predictions from, such data in the future especially as FDA makes no a priori age and time effects assumptions.
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