ArticleBMC medical research methodology2020
Sample size issues in time series regressions of counts on environmental exposures.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- The association between daily-diagnosed COVID-19 morbidity and short-term exposure to PMEnvironmental research · 2022Pooled it
- Mortality and healthcare services in Pennsylvania state prisons following extreme ambient temperatures.Nature communications · 2026Article
- Comparing mortality burdens attributable to ambient temperature and seasonal variation across major causes of death in Spain.Environmental epidemiology (Philadelphia, Pa.) · 2025Article
- Short-term effects of air pollution on the infectious disease spectrum in Shanghai, China: a time-series analysis from 2013 to 2019.Frontiers in public health · 2025Article
- The Multi-Country Multi-City Collaborative Research Network: An international research consortium investigating environment, climate, and health.Environmental epidemiology (Philadelphia, Pa.) · 2024Article
- Associations of weather and air pollution with objective physical activity and sedentary time before and after bariatric surgery: a secondary analysis of a prospective cohort study.Environmental research communications · 2024Article
- Constrained groupwise additive index models.Biostatistics (Oxford, England) · 2023Article
- Extended two-stage designs for environmental research.Environmental health : a global access science source · 2022Article
- Temperature-sensitive morbidity indicator: consequence from the increased ambulance dispatches associated with heat and cold exposure.International journal of biometeorology · 2021Article
- Impact of biometeorological conditions and air pollution on influenza-like illnesses incidence in Warsaw.International journal of biometeorology · 2021Article
- Alternative adjustment for seasonality and long-term time-trend in time-series analysis for long-term environmental exposures and disease counts.BMC medical research methodology · 2021Article
- Evaluating Mortality Response Associated with Two Different Nordic Heat Warning Systems in Riga, Latvia.International journal of environmental research and public health · 2020Article
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Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundRegression 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.
methodsWe 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.
resultsIn 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.
conclusionsPredicting 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.