Combining previously published studies with current data in Bayesian logistic regression model: an example for identifying susceptibility genes related to lung cancer in humans.
A general analysis method is proposed that utilizes meta-analysis to incorporate similar studies in addition to our current investigation in order to obtain informative prior effect parameters in a logistic regression model. It is common in epidemiological studies that data from similar previous studies are available. The case of gene susceptibility association with increased lung cancer frequency was used to demonstrate this methodology. Results of Markov chain Monte Carlo (MCMC) iterations provided a more precise estimation of the regression coefficient in a logistic model with informative prior distribution compared to the noninformative prior distribution model. In situations where similar historical data are available, it is proposed to include as much relevant information as previously published results in the analysis of current data.
Yu
Xiao-Jin
XJ
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Jiangsu, People's Republic of China.
Xun
Peng-Cheng
PC
Hu
Zhi-Bin
ZB
Liu
Pei
P
Shen
Hong-Bing
HB
Chen
Feng
F
D001499:Bayes Theorem; D016022:Case-Control Studies; D002681:China; D004268:DNA-Binding Proteins / Q000235:genetics*; D005260:Female; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D008297:Male; D015201:Meta-Analysis as Topic; D016017:Odds Ratio; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012907:Smoking / Q000009:adverse effects* / Q000235:genetics; D000076105:X-ray Repair Cross Complementing Protein 1
D004268:DNA-Binding Proteins; D000076105:X-ray Repair Cross Complementing Protein 1