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The Case Time Series Design.

ABSTRACT: Modern data linkage and technologies provide a way to reconstruct detailed longitudinal profiles of health outcomes and predictors at the individual or small-area level. Although these rich data resources offer the possibility to address epidemiologic questions that could not be feasibly examined using traditional studies, they require innovative analytical approaches. Here we present a new study design, called case time series, for epidemiologic investigations of transient health risks associated with time-varying exposures. This design combines a longitudinal structure and flexible control of time-varying confounders, typical of aggregated time series, with individual-level analysis and control-by-design of time-invariant between-subject differences, typical of self-matched methods such as case-crossover and self-controlled case series. The modeling framework is highly adaptable to various outcome and exposure definitions, and it is based on efficient estimation and computational methods that make it suitable for the analysis of highly informative longitudinal data resources. We assess the methodology in a simulation study that demonstrates its validity under defined assumptions in a wide range of data settings. We then illustrate the design in real-data examples: a first case study replicates an analysis on influenza infections and the risk of myocardial infarction using linked clinical datasets, while a second case study assesses the association between environmental exposures and respiratory symptoms using real-time measurements from a smartphone study. The case time series design represents a general and flexible tool, applicable in different epidemiologic areas for investigating transient associations with environmental factors, clinical conditions, or medications.

SUBMITTER: Gasparrini A

PROVIDER: S-EPMC7611753 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

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The Case Time Series Design.

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