Sample size issues in time series regressions of counts on environmental exposures.
Background: Regression analyses of time series of disease counts on environmental determinants are a prominent component of environmental epidemiology. For planning such studies, it can be useful to predict the precision of estimated coefficients and power to detect associations of given magnitude....
| Publicado en: | BMC Medical Research Methodology Vol. 20; no. 1; pp. 1 - 10 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
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BioMed Central
1/28/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141431923&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141431923 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712288 1CI1 jtl: BMC Medical Research Methodology issn: 14712288 maglogo: N pubinfo: dt: 1/28/2020 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 141431923 141431923 NLM31992211 141431923 10.1186/s12874-019-0894-6 NLM31992211 141431923 ppf: 1 ppct: 9 formats: tig: atl: Sample size issues in time series regressions of counts on environmental exposures. aug: au: Armstrong, Ben G. Gasparrini, Antonio Tobias, Aurelio Sera, Francesco affil: Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine (LSHTM), 15-17 Tavistock Place, WC1H 9SH, London, UK sug: subj: Environment Air Pollution Analysis Environmental Exposure Sample Size Poisson Distribution Forecasting Time Factors Regression Human Models, Statistical Validation Studies Comparative Studies Evaluation Research Multicenter Studies Clinical Assessment Tools ab: Background: Regression analyses of time series of disease counts on environmental determinants are a prominent component of environmental epidemiology. For planning such studies, it can be useful to predict the precision of estimated coefficients and power to detect associations of given magnitude. Existing generic approaches for this have been found somewhat complex to apply and do not easily extend to multiple series studies analysed in two stages. We have sought a simpler approximate approach which can easily extend to multiple series and give insight into factors determining precision.Methods: We derive approximate expressions for precision and hence power in single and multiple time series studies of counts from basic statistical theory, compare the precision predicted by these with that estimated by analysis in real data from 51 cities of varying size, and illustrate the use of these estimators in a realistic planning scenario.Results: In single series studies with Poisson outcome distribution, precision and power depend only on the usable variation of exposure (i.e. that conditional on covariates) and the total number of disease events, regardless of how many days those are spread over. In multiple time series (eg multi-city) studies focusing on the meta-analytic mean coefficient, the usable exposure variation and the total number of events (in all series) are again the sole determinants if there is no between-series heterogeneity or within-series overdispersion. With heterogeneity, its extent and the number of series becomes important. For all but the crudest approximation the estimates of standard errors were on average within + 20% of those estimated in full analysis of actual data.Conclusions: Predicting precision in coefficients from a planned time series study is possible simply and given limited information. The total number of disease events and usable exposure variation are the dominant factors when overdispersion and between-series heterogeneity are low. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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