Semi Parametric and Parametric Approaches in the Analysis of Short-Term Effects of Air Pollution on Health

Baccini, Michela; Biggeri, A; Lertxundi, Aitana; Sáez, Marta Pascual

doi:10.1097/00001648-200309001-00143

Cited by 3 publications

(4 citation statements)

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“…These B ‐splines are unpenalized. Complex model selection procedures exist for non‐penalized models (Baccini et al ., ). A thorough comparison of penalized and non‐penalized models is beyond the scope of our paper.…”

Section: Discussionmentioning

confidence: 97%

Flexible Distributed Lags for Modelling Earthquake Data

Obermeier

Scheipl

Heumann

et al. 2014

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary Heavy long‐lasting rainfall can trigger earthquake swarms. We are interested in the specific shape of lagged rain influence on the occurrence of earthquakes at different depths at Mount Hochstaufen, Bavaria. We present a novel penalty structure for interpretable and flexible estimates of lag coefficients based on spline representations. We provide an easy‐to‐use implementation of our flexible distributed lag approach that can be used directly in the established R package mgcv for estimation of generalized additive models. This allows our approach to be immediately included in complex additive models for generalized responses even in hierarchical or longitudinal data settings, making use of established stable and well‐tested inference algorithms. The benefit of flexible distributed lag modelling is shown in a detailed simulation study.

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Section: Discussionmentioning

confidence: 97%

Flexible Distributed Lags for Modelling Earthquake Data

Obermeier

Scheipl

Heumann

et al. 2014

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…Recently, interesting alternatives based on penalized regression with low-rank smoothers have been proposed to deal with non-linear effects [ 32 , 33 ], and also applied to describe the distributed lag curve [ 6 , 22 ]. Although completely parametric approaches seems to be preferred to control for season and long-term trend in time series data [ 27 , 34 , 35 ], the penalized methods could show some advantage in the bi-dimensional framework of DLNM. This issue represents an opportunity for further development, and could benefit from the research already carried out on penalized tensor-product smoothers [ 25 , 36 ].…”

Section: Discussionmentioning

confidence: 99%

Distributed lag non‐linear models

Gasparrini

Armstrong

Kenward

2010

Statistics in Medicine

1,575

1,252

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Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure–response dependencies and delayed effects. This methodology is based on the definition of a ‘cross-basis’, a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987–2000. Copyright © 2010 John Wiley & Sons, Ltd.

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“…RS method has been frequently used for performing nonparametric curve estimation (Friedman and Silverman, 1989;Koo, 1997;Kooperberg et al 1997;Stone et al 1997). As described by Baccini et al (2007), RS fitting of a GAM has certain advantages over backfitting algorithm and other spline methods. In particular, they found that the inference of the parametric components in a GAM is more robust to the misspecification of the degree of smoothness in RS, as compared with the alternative spline methods.…”

Section: Model and Notationmentioning

confidence: 99%

“…There has been research showing that simpler and more standard estimation methods, such as the approach based on the specification of a GAM with regression spline (RS), can be preferred (Baccini et al 2007). RS smoothing is one popular method for performing nonparametric curve estimation (Friedman and Silverman, 1989;Koo, 1997;Kooperberg, Bose, and Stone, 1997;Stone et al 1997;Baccini et al 2007). In this article, we adopt the GAM1RS approach when modeling the complete outcome in the nonignorable selection model.…”

Section: Introductionmentioning

confidence: 99%

Adjusting for Nonignorable Missingness When Estimating Generalized Additive Models

Xie

2010

Biometrical J

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Generalized additive models (GAMs) have been widely used for flexible modeling of various types of outcomes. When the outcome in a GAM is subject to missing, practical analyses often assume that missingness is missing at random (MAR). This assumption can be of suspicion when the missingness is not by design. Evaluating the potential effects of alternative nonignorable missing data mechanism on the MAR inference from a GAM can be important but often challenging due to the complicatedness of alternative nonignorable models. We apply the index approach to local sensitivity (Troxel, Ma, and Heitjan (2004). Statistica Sinica 14, 1221-1237) to evaluate the potential changes of the GAM estimates in the neighborhood of the MAR model. The approach avoids fitting any complicated nonignorable GAM. Only MAR estimates are required to calculate the resulting sensitivity index and adjust the GAM estimates to account for nonignorable missingness. Thus the proposed approach is considerably simpler to conduct, as compared with the alternative methods. The simulation study shows that the index provides valid assessment of the local sensitivity of the GAM estimates to nonignorable missingness. We then illustrate the method using a rheumatoid arthritis clinical trial data set.

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Semi Parametric and Parametric Approaches in the Analysis of Short-Term Effects of Air Pollution on Health

Cited by 3 publications

References 0 publications

Flexible Distributed Lags for Modelling Earthquake Data

Flexible Distributed Lags for Modelling Earthquake Data

Distributed lag non‐linear models

Adjusting for Nonignorable Missingness When Estimating Generalized Additive Models

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