Latency Models for Analyses of Protracted Exposures

Richardson, David B.

doi:10.1097/ede.0b013e318194646d

Cited by 30 publications

(29 citation statements)

References 12 publications

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“…The effect summaryˇc ;i corresponds to the true effect from s s .x; t / D P`f s .x t `/ w s .`/, while Ǒ c;i is estimated from the best fitting model selected by AIC and BIC, by using s e .x; t / D P`f e w e .x t `;`/ , given the specific exposure history q h;i of the random subject at the random time. This approach assures that the performance indicators in (13) are evaluated on the whole range of simulated exposure histories, and do not depend on a specific choice. A visual inspection of performance is also provided by computing from the best-fitting models the grid of risk contributions Ǒ x p ;`p ;i defined in (9) composing the exposure-lag-response surface.…”

Section: Evaluation Of Performancesupporting

confidence: 91%

Modeling exposure–lag–response associations with distributed lag non‐linear models

Gasparrini

2013

Statistics in Medicine

598

389

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In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure–lag–response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

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Section: Evaluation Of Performancesupporting

confidence: 91%

Modeling exposure–lag–response associations with distributed lag non‐linear models

Gasparrini

2013

Statistics in Medicine

598

389

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“…The DLNM framework unifies methods proposed to investigate lagged associations in different research fields, beyond time series analysis in environmental research. For instance, these include case‐control studies in cancer epidemiology (Thomas, ; Hauptmann et al, ; Berhane et al, ; Richardson, ) and survival analysis in pharmaco‐epidemiology (Sylvestre and Abrahamowicz, ; Abrahamowicz et al, )…”

Section: Discussionmentioning

confidence: 99%

A Penalized Framework for Distributed Lag Non-Linear Models

et al. 2017

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Summary. Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM framework through the use of penalized splines within generalized additive models (GAM). This extension offers built-in model selection procedures and the possibility of accommodating assumptions on the shape of the lag structure through specific penalties. In addition, this framework includes, as special cases, simpler models previously proposed for linear relationships (DLMs). Alternative versions of penalized DLNMs are compared with each other and with the standard unpenalized version in a simulation study. Results show that this penalized extension to the DLNM class provides greater flexibility and improved inferential properties. The framework exploits recent theoretical developments of GAMs and is implemented using efficient routines within freely available software. Real-data applications are illustrated through two reproducible examples in time series and survival analysis.

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“…Methodological issues addressed in our study also apply to many epidemiological studies of different time-varying exposures. Indeed, the WCE approach was initially proposed for case-control studies of occupational and environmental exposures [13,14], and such applications motivated some recent, elegant developments in this area [45,46]. We hope that our encouraging results will stimulate wider applications of the WCE modeling in (pharmaco-) epidemiological studies of time-varying exposures.…”

Section: Discussionmentioning

confidence: 82%

Comparison of alternative models for linking drug exposure with adverse effects

Abrahamowicz

Beauchamp

Sylvestre

2011

Statistics in Medicine

129

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Pharmacoepidemiology investigates associations between time-varying medication use/dose and risk of adverse events. Applied research typically relies on a priori chosen simple conventional models, such as current dose or any use in the past 3 months. However, different models imply different risk predictions, and only one model can be etiologically correct in any specific applications. We first formally defined several candidate models mapping the time vector of past drug doses (X (t), t = 1, … ,u) into the value of a time-varying exposure metric M(u) at current time u. In addition to conventional one-parameter models, we considered two-parameter models accounting for recent dose increase or withdrawal and a flexible spline-based weighted cumulative exposure (WCE) model that defines M(u) as the weighted sum of past doses. In simulations, we generated event times assuming one of the models was correct and then analyzed the data with all candidate models. We demonstrated that the minimum AIC criterion is able to identify the correct model as the best-fitting model or one of the equivalent (within 4 AIC points of the minimum) models in a vast majority of simulated samples, especially with 500 or more events. We also showed how relying on an incorrect a priori chosen model may largely reduce the power to test for an association. Finally, we demonstrated how the flexible WCE estimates may help with model diagnostics even if the correct model is not WCE. We illustrated the practical advantages of AIC-based a posteriori model selection and WCE modeling in a real-life pharmacoepidemiology example.

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Latency Models for Analyses of Protracted Exposures

Cited by 30 publications

References 12 publications

Modeling exposure–lag–response associations with distributed lag non‐linear models

Modeling exposure–lag–response associations with distributed lag non‐linear models

A Penalized Framework for Distributed Lag Non-Linear Models

Comparison of alternative models for linking drug exposure with adverse effects

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