A two-stage model for multiple time series data of counts

Berhane, Kiros; Thomas, Duncan C.

doi:10.1093/biostatistics/3.1.21

Cited by 14 publications

(21 citation statements)

References 18 publications

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“…After consideration of several types of lag-based models for short-term effects of pollution (see the online supplementary data supplement for details), linear distributed lag models were found to be the most appropriate 29. Model selection was based on the Akaike Information Criterion (AIC) 30.…”

Section: Methodsmentioning

confidence: 99%

Longitudinal effects of air pollution on exhaled nitric oxide: the Children's Health Study

et al. 2014

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OBJECTIVES To assess the effects of long-term variations in ambient air pollutants on longitudinal changes in exhaled nitric oxide (FeNO), a potentially useful biomarker of eosinophilic airway inflammation, based on data from the southern California Children’s Health Study. METHODS Based on a cohort of 1,211 schoolchildren from 8 Southern California communities with FeNO measurements in 2006/07 and 2007/08, regression models adjusted for short-term effects of air pollution were fitted to assess the association between changes in annual long-term exposures and changes in FeNO. RESULTS Increases in annual average concentrations of 24-hr average NO2 and PM2.5 (scaled to the interquartile range (IQR) of 1.8 ppb and 2.4 μg/m3, respectively) were associated with a 2.29 ppb (CI=[0.36,4.21]; p =0.02) and a 4.94 ppb (CI=[1.44,8.47]; p = 0.005) increase in FeNO, respectively, after adjustments for short term effects of the respective pollutants. In contrast, changes in annual averages of PM10 and O3 were not significantly associated with changes in FeNO. These findings did not differ significantly by asthma status. CONCLUSIONS Changes in annual average exposure to current levels of ambient air pollutants are significantly associated with changes in FeNO levels in children, independent of short-term exposures and asthma status. Use of this biomarker in population-based epidemiologic research has great potential for assessing the impact of changing real world mixtures of ambient air pollutants on children’s respiratory health.

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Section: Methodsmentioning

confidence: 99%

Longitudinal effects of air pollution on exhaled nitric oxide: the Children's Health Study

et al. 2014

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“…However, if several lags of the antibiotic consumption have an effect, it is reasonable to assume that the effect of nearby lags is similar, that is, that the regression coefficients vary smoothly by lag numbers. We constrain the regression parameters for the antibiotics by the so‐called polynomial distributed lags . This means that the l x + 1 coefficients

β_{j 0}, \dots, β_{j l_{x}}

are assumed to be smooth and follow a polynomial.…”

Section: Ward‐specific Modelsmentioning

confidence: 99%

Antibiotic resistance in hospitals: a ward‐specific random effect model in a low antibiotic consumption environment

Aldrin

Raastad

Tvete

et al. 2012

Statistics in Medicine

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Association between previous antibiotic use and emergence of antibiotic resistance has been reported for several microorganisms. The relationship has been extensively studied, and although the causes of antibiotic resistance are multi-factorial, clear evidence of antibiotic use as a major risk factor exists. Most studies are carried out in countries with high consumption of antibiotics and corresponding high levels of antibiotic resistance, and currently, little is known whether and at what level the associations are detectable in a low antibiotic consumption environment. We conduct an ecological, retrospective study aimed at determining the impact of antibiotic consumption on antibiotic-resistant Pseudomonas aeruginosa in three hospitals in Norway, a country with low levels of antibiotic use. We construct a sophisticated statistical model to capture such low signals. To reduce noise, we conduct our study at hospital ward level. We propose a random effect Poisson or binomial regression model, with a reparametrisation that allows us to reduce the number of parameters. Inference is likelihood based. Through scenario simulation, we study the potential effects of reduced or increased antibiotic use. Results clearly indicate that the effects of consumption on resistance are present under conditions with relatively low use of antibiotic agents. This strengthens the recommendation on prudent use of antibiotics, even when consumption is relatively low.

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“…This essentially reduces the number of parameters to fit in the model, at the cost of reducing the number of time points available to feed into the model. In contrast, if the population under study was genetically heterogeneous, we would treat the response slope differently for different individuals and would employ the mixed-effects model as suggested by Berhane and Thomas [37] for combining time series. In that case, we wouldn't need to normalize data for each individual, and as a result there would be an increase in the number of parameters to fit as well as an increase in the available data points.…”

Section: Discussionmentioning

confidence: 99%

“…There are several theoretical studies related to combining multiple time series in a general regression frame work, including for instance that of Berhane & Thomas [37] and Guerrero & Pena [46]. Berhane & Thomas [37] proposed to use a mixed-effects model to combine time series from different locations, while Guerrero & Pena [46] outlined a weighted least squares approach. In both approaches, some constraints were applied after a number of assumptions were made.…”

Section: Methodsmentioning

confidence: 99%

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Characterizing Dynamic Changes in the Human Blood Transcriptional Network

et al. 2010

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Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes. Previously, we showed that food intake has a large impact on blood gene expression patterns and that these responses, either in terms of gene expression level or gene-gene connectivity, are strongly associated with metabolic diseases. In this study, we explored which genes drive the changes of gene expression patterns in response to time and food intake. We applied the Granger causality test and the dynamic Bayesian network to gene expression data generated from blood samples collected at multiple time points during the course of a day. The simulation result shows that combining many short time series together is as powerful to infer Granger causality as using a single long time series. Using the Granger causality test, we identified genes that were supported as the most likely causal candidates for the coordinated temporal changes in the network. These results show that PER1 is a key regulator of the blood transcriptional network, in which multiple biological processes are under circadian rhythm regulation. The fasted and fed dynamic Bayesian networks showed that over 72% of dynamic connections are self links. Finally, we show that different processes such as inflammation and lipid metabolism, which are disconnected in the static network, become dynamically linked in response to food intake, which would suggest that increasing nutritional load leads to coordinate regulation of these biological processes. In conclusion, our results suggest that food intake has a profound impact on the dynamic co-regulation of multiple biological processes, such as metabolism, immune response, apoptosis and circadian rhythm. The results could have broader implications for the design of studies of disease association and drug response in clinical trials.

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A two-stage model for multiple time series data of counts

Cited by 14 publications

References 18 publications

Longitudinal effects of air pollution on exhaled nitric oxide: the Children's Health Study

Longitudinal effects of air pollution on exhaled nitric oxide: the Children's Health Study

Antibiotic resistance in hospitals: a ward‐specific random effect model in a low antibiotic consumption environment

Characterizing Dynamic Changes in the Human Blood Transcriptional Network

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