Flexible dose-response models for Japanese atomic bomb survivor data: Bayesian estimation and prediction of cancer risk

2004 ◽  
Vol 43 (4) ◽  
pp. 233-245 ◽  
Author(s):  
James Bennett ◽  
Mark P. Little ◽  
Sylvia Richardson
2017 ◽  
Vol 57 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Helmut Schöllnberger ◽  
Markus Eidemüller ◽  
Harry M. Cullings ◽  
Cristoforo Simonetto ◽  
Frauke Neff ◽  
...  

Abstract The scientific community faces important discussions on the validity of the linear no-threshold (LNT) model for radiation-associated cardiovascular diseases at low and moderate doses. In the present study, mortalities from cerebrovascular diseases (CeVD) and heart diseases from the latest data on atomic bomb survivors were analyzed. The analysis was performed with several radio-biologically motivated linear and nonlinear dose–response models. For each detrimental health outcome one set of models was identified that all fitted the data about equally well. This set was used for multi-model inference (MMI), a statistical method of superposing different models to allow risk estimates to be based on several plausible dose–response models rather than just relying on a single model of choice. MMI provides a more accurate determination of the dose response and a more comprehensive characterization of uncertainties. It was found that for CeVD, the dose–response curve from MMI is located below the linear no-threshold model at low and medium doses (0–1.4 Gy). At higher doses MMI predicts a higher risk compared to the LNT model. A sublinear dose–response was also found for heart diseases (0–3 Gy). The analyses provide no conclusive answer to the question whether there is a radiation risk below 0.75 Gy for CeVD and 2.6 Gy for heart diseases. MMI suggests that the dose–response curves for CeVD and heart diseases in the Lifespan Study are sublinear at low and moderate doses. This has relevance for radiotherapy treatment planning and for international radiation protection practices in general.


2015 ◽  
Vol 81 ◽  
pp. 137-140 ◽  
Author(s):  
Edward J. Calabrese ◽  
Dima Yazji Shamoun ◽  
Jaap C. Hanekamp

2019 ◽  
Vol 60 ◽  
pp. 179-184
Author(s):  
Gertraud Maskarinec ◽  
Atsuko Sadakane ◽  
Hiromi Sugiyama ◽  
Alina Brenner ◽  
Yoshimi Tatsukawa ◽  
...  

Author(s):  
Nicola Orsini

Recognizing a dose–response pattern based on heterogeneous tables of contrasts is hard. Specification of a statistical model that can consider the possible dose–response data-generating mechanism, including its variation across studies, is crucial for statistical inference. The aim of this article is to increase the understanding of mixed-effects dose–response models suitable for tables of correlated estimates. One can use the command drmeta with additive (mean difference) and multiplicative (odds ratios, hazard ratios) measures of association. The postestimation command drmeta_graph greatly facilitates the visualization of predicted average and study-specific dose–response relationships. I illustrate applications of the drmeta command with regression splines in experimental and observational data based on nonlinear and random-effects data-generation mechanisms that can be encountered in health-related sciences.


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