Random effects Cox models: A Poisson modelling approach

Ma, Renjun

doi:10.1093/biomet/90.1.157

Cited by 91 publications

(88 citation statements)

References 35 publications

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“…Finally, sensitivity analyses of the main findings were undertaken using generalized relative risk models for survival data (33), and using a random-effects Cox model originally developed for air pollution research in the CPS-II cohort (18,34). General relative risk models for survival time data were fitted to compare relative risk estimates obtained from linear versus log-linear models using SAS PROC NLP (33) ) with an average value (SD) of 53.5 (38.0) Bq/m 3 .…”

Section: Discussionmentioning

confidence: 99%

Radon and Lung Cancer in the American Cancer Society Cohort

Turner

Krewski

Chen

et al. 2011

Cancer Epidemiology, Biomarkers &Amp; Prevention

103

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Background: Case-control studies conducted in North America, Europe, and Asia provided evidence of increased lung cancer risk due to radon in homes. Here, the association between residential radon and lung cancer mortality was examined in a large-scale cohort study.Methods: Nearly 1.2 million Cancer Prevention Study-II participants were recruited in 1982. Mean countylevel residential radon concentrations were linked to study participants according to ZIP code information at enrollment [mean (SD) ¼ 53.5 Bq/m 3 (38.0)]. Cox proportional hazards regression models were used to obtain

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Section: Discussionmentioning

confidence: 99%

Radon and Lung Cancer in the American Cancer Society Cohort

Turner

Krewski

Chen

et al. 2011

Cancer Epidemiology, Biomarkers &Amp; Prevention

103

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“…We plotted the relationship between PM 2.5 and deaths from all cancers using the natural splines command COXPH in R 3.0.1 with two degrees of freedom. As a sensitivity analysis, we also used models with random effects set at the intercepts to take account of possible intradistrict correlations (25,26).…”

Section: Cancer Epidemiol Biomarkers Prev; 25(5) May 2016mentioning

confidence: 99%

Cancer Mortality Risks from Long-term Exposure to Ambient Fine Particle

Wong

Tsang

Lai

et al. 2016

Cancer Epidemiology, Biomarkers &Amp; Prevention

164

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Background: Few studies have assessed long-term effects of particulate matter (PM) with aerodynamic diameter < 2.5 mm (PM 2.5 ) on mortality for causes of cancer other than the lung; we assessed the effects on multiple causes. In Hong Kong, most people live and work in urban or suburban areas with high-rise buildings. This facilitates the estimation of PM 2.5 exposure of individuals, taking into account the height of residence above ground level for assessment of the long-term health effects with sufficient statistical power.Methods: We recruited 66,820 persons who were !65 in 1998 to 2001 and followed up for mortality outcomes until 2011. Annual concentrations of PM at their residential addresses were estimated using PM 2.5 concentrations measured at fixed-site monitors, horizontal-vertical locations, and satellite data. We

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“…We used a hazard model to measure the odds of receiving services over time. A Poisson distribution was used because this approach has been shown to produce similar results to the commonly used Cox proportional hazard (Whitehead, 1980) and has been extended to include random error terms that account for hierarchical data (Ma, Krewski, & Burnett, 2003). A benefit of hazard modeling is its ability to accommodate right censored observations.…”

Section: Discussionmentioning

confidence: 99%