Semiparametric regression models for detecting effect modification in matched case-crossover studies

2011 ◽  
Vol 30 (15) ◽  
pp. 1837-1851 ◽  
Author(s):  
Inyoung Kim ◽  
Hae-Kwan Cheong ◽  
Ho Kim
Keyword(s):  
Regression Models ◽  
Semiparametric Regression ◽  
Effect Modification ◽  
Case Crossover ◽  
Crossover Studies ◽  
Matched Case

Flexible derivative time-varying model in matched case-crossover studies for a small number of geographical locations among the participants

2020 ◽  
pp. 096228022096817
Author(s):  
Ana M Ortega-Villa ◽  
Inyoung Kim
Keyword(s):  
Logistic Regression ◽  
Conditional Logistic Regression ◽  
Effect Modification ◽  
Fixed Number ◽  
Time Varying ◽  
Model Framework ◽  
Conditional Logistic Regression Model ◽  
Case Crossover ◽  
Crossover Studies ◽  
Matched Case

In matched case-crossover studies, any stratum effect is removed by conditioning on the fixed number of case–control sets in the stratum, and hence, the conditional logistic regression model is not able to detect any effects associated with matching covariates. However, some matching covariates such as time and location often modify the effect of covariates, making the estimations obtained by conditional logistic regression incorrect. Therefore, in this paper, we propose a flexible derivative time-varying coefficient model to evaluate effect modification by time and location, in order to make correct statistical inference, when the number of locations is small. Our proposed model is developed under the Bayesian hierarchical model framework and allows us to simultaneously detect relationships between the predictor and binary outcome and between the predictor and time. Inference is proposed based on the derivative function of the estimated function to determine whether there is an effect modification due to time and/or location, for a small number of locations among the participants. We demonstrate the accuracy of the estimation using a simulation study and an epidemiological example of a 1–4 bidirectional case-crossover study of childhood aseptic meningitis with drinking water turbidity.

Semiparametric time varying coefficient model for matched case-crossover studies

2016 ◽  
Vol 36 (6) ◽  
pp. 998-1013 ◽  
Author(s):  
Ana Maria Ortega-Villa ◽  
Inyoung Kim ◽  
H. Kim
Keyword(s):  
Time Varying ◽  
Varying Coefficient Model ◽  
Varying Coefficient ◽  
Case Crossover ◽  
Crossover Studies ◽  
Matched Case

scholarly journals Association of Infrastructure and Route Environment Factors with Cycling Injury Risk at Intersection and Non-Intersection Locations: A Case-Crossover Study of Britain

2021 ◽  
Vol 18 (6) ◽  
pp. 3060
Author(s):  
Rachel Aldred ◽  
Georgios Kapousizis ◽  
Anna Goodman
Keyword(s):  
Injury Risk ◽  
Conditional Logistic Regression ◽  
Traffic Signals ◽  
Control Groups ◽  
Class A ◽  
Case Crossover ◽  
Main Class ◽  
Road Intersections ◽  
Matched Case ◽  
Safety In Numbers

Objective: This paper examines infrastructural and route environment correlates of cycling injury risk in Britain for commuters riding in the morning peak. Methods: The study uses a case-crossover design which controls for exposure. Control sites from modelled cyclist routes (matched on intersection status) were compared with sites where cyclists were injured. Conditional logistic regression for matched case–control groups was used to compare characteristics of control and injury sites. Results: High streets (defined by clustering of retail premises) raised injury odds by 32%. Main (Class A or primary) roads were riskier than other road types, with injury odds twice that for residential roads. Wider roads, and those with lower gradients increased injury odds. Guard railing raised injury odds by 18%, and petrol stations or car parks by 43%. Bus lanes raised injury odds by 84%. As in other studies, there was a ‘safety in numbers’ effect from more cyclists. Contrary to other analysis, including two recent studies in London, we did not find a protective effect from cycle infrastructure and the presence of painted cycle lanes raised injury odds by 54%. At intersections, both standard and mini roundabouts were associated with injury odds several times higher than other intersections. Presence of traffic signals, with or without an Advanced Stop Line (‘bike box’), had no impact on injury odds. For a cyclist on a main road, intersections with minor roads were riskier than intersections with other main roads. Conclusions: Typical cycling environments in Britain put cyclists at risk, and infrastructure must be improved, particularly on busy main roads, high streets, and bus routes.

scholarly journals Risk of injury after alcohol consumption from case-crossover studies in five countries from the Americas

Addiction ◽  
2012 ◽  
Vol 108 (1) ◽  
pp. 97-103 ◽  
Author(s):  
Guilherme Borges ◽  
Ricardo Orozco ◽  
Maristela Monteiro ◽  
Cheryl Cherpitel ◽  
Eddy Pérez Then ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Case Crossover ◽  
Risk Of Injury ◽  
Crossover Studies

Interpretation and bias in case-crossover studies

1997 ◽  
Vol 50 (11) ◽  
pp. 1281-1287 ◽  
Author(s):  
Donald A. Redelmeier ◽  
Robert J. Tibshirani
Keyword(s):  
Case Crossover ◽  
Crossover Studies

scholarly journals Causal Interaction and Effect Modification: Same Model, Different Concepts

2017 ◽  
Author(s):  
Luke Keele ◽  
Randolph T. Stevenson
Keyword(s):  
Political Science ◽  
Regression Models ◽  
Interaction Analysis ◽  
Effect Modification ◽  
The Political ◽  
Science Literature ◽  
Causal Interaction ◽  
Single Treatment ◽  
Social Scientists ◽  
The Difference

Social scientists use the concept of interactions to study effect dependency. Such analyses can be conducted using standard regression models. However, an interaction analysis may represent either a causal interaction or effect modification. Under causal interaction, the analyst is interested in whether two treatments have differing effects when both are administered. Under effect modification, the analysts investigates whether the effect of a single treatment varies across levels of a baseline covariate. Importantly, the identification assumptions for these two types of analysis are very different. In this paper, we clarify the difference between these two types of interaction analysis. We demonstrate that this distinction is mostly ignored in the political science literature. We conclude with a review of several applications.

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