Dataset Information

Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline.

ABSTRACT: This study develops a comprehensive method to assess seasonal influences on a longitudinal marker and compare estimates between cohorts. The method extends existing approaches by (i) combining a sine-cosine model of seasonality with a specialized covariance function for modeling longitudinal correlation; (ii) performing mediation analysis on a seasonality model. An example dataset and R code are provided. The bundle of methods is referred to as seasonality, mediation and comparison (SMAC). The case study described utilizes lung function as the marker observed on a cystic fibrosis cohort but SMAC can be used to evaluate other markers and in other disease contexts. Key aspects of customization are as follows.•This study introduces a novel seasonality model to fit trajectories of lung function decline and demonstrates how to compare this model to a conventional model in this context.•Steps required for mediation analyses in the seasonality model are shown.•The necessary calculations to compare seasonality models between cohorts, based on estimation coefficients, are derived in the study.

SUBMITTER: Gecili E

PROVIDER: S-EPMC8374306 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline.

Gecili Emrah E Palipana Anushka A Brokamp Cole C Huang Rui R Andrinopoulou Eleni-Rosalina ER Pestian Teresa T Rasnick Erika E Keogh Ruth H RH Ni Yizhao Y Clancy John P JP Ryan Patrick P Szczesniak Rhonda D RD

MethodsX 20210321

This study develops a comprehensive method to assess seasonal influences on a longitudinal marker and compare estimates between cohorts. The method extends existing approaches by (i) combining a sine-cosine model of seasonality with a specialized covariance function for modeling longitudinal correlation; (ii) performing mediation analysis on a seasonality model. An example dataset and R code are provided. The bundle of methods is referred to as seasonality, mediation and comparison (SMAC). The c ...[more]

PMID: 34434833

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Project description:BackgroundAttenuated decreases in lung function can signal the onset of acute respiratory events known as pulmonary exacerbations (PEs) in children and adolescents with cystic fibrosis (CF). Univariate joint modeling facilitates dynamic risk prediction of PE onset and accounts for measurement error of the lung function marker. However, CF is a multi-system disease and the extent to which simultaneously modeling growth and nutrition markers improves PE predictive accuracy is unknown. Furthermore, it is unclear which routinely collected clinical indicators of growth and nutrition in early life predict PE onset in CF.MethodsUsing a longitudinal cohort of 17,100 patients aged 6-20 years (US Cystic Fibrosis Foundation Patient Registry; 2003-2015), we fit a univariate joint model of lung-function decline and PE onset and contrasted its predictive performance with a class of multivariate joint models that included combinations of growth markers as additional submodels. Outcomes were longitudinal lung function (forced expiratory volume in 1 s of % predicted), percentiles of body mass index, weight-for-age and height-for-age and PE onset. Relevant demographic/clinical covariates were included in submodels. We implemented a univariate joint model of lung function and time-to-PE and four multivariate joint models including growth outcomes.ResultsAll five joint models showed that declining lung function corresponded to slightly increased risk of PE onset (hazard ratio from univariate joint model: 0.97, P < 0.0001), and all had reasonable predictive accuracy (cross-validated area under the receiver-operator characteristic curve > 0.70). None of the growth markers alongside lung function as outcomes in multivariate joint modeling appeared to have an association with hazard of PE. Jointly modeling only lung function and PE onset yielded the most accurate (area under the receiver-operator characteristic curve = 0.75) and precise (narrowest interquartile range) predictions. Dynamic predictions were accurate across forecast horizons (0.5, 1 and 2 years) and precision improved with age.ConclusionsIncluding growth markers via multivariate joint models did not yield gains in prediction performance, compared to a univariate joint model with lung function. Individualized dynamic predictions from joint modeling could enhance physician monitoring of CF disease progression by providing PE risk assessment over a patient's clinical course.

Dataset Information

Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline.

Publications

Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline.

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