Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multiple imputation (MI) is increasingly used to deal with missing data in medical studies, whilst variable selection and prediction on multiply-imputed data is an area under intense research in statistics. A commonly used strategy is to select a single top model based on the Rubin's rules (RR). Hover, such approaches do not take the model uncertainty into consideration, which might lead to over-confident inferences. ,  extended the Bayesian model averaging method to perform variable selection and prediction under multiple imputation (MI-BMA), which takes into account the uncertainties originated from both the missing data and the model selection.  applied the MI-BMA method to simulated datasets as ll as a real data set from a prospective cohort, and demonstrated the advantage of our method as compared with the classical RR stepwise method.
['Guozhi Jiang', 'Claudia H. T. Tam', 'Andrea Luk', 'Alice P.S. Kong', 'Wing Yee So', 'Juliana C. N. Chan', 'Ronald C.W. Ma', 'Xiaodan Fan']
I am writing a research paper on a idea Variable selection and prediction of clinical outcome with multiply-imputed data via Bayesian model averaging
[{"from": "human", "value": "I am writing a research paper on a idea Variable selection and prediction of clinical outcome with multiply-imputed data via Bayesian model averaging"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multiple imputation (MI) is increasingly used to deal with missing data in medical studies, whilst variable selection and prediction on multiply-imputed data is an area under intense research in statistics. A commonly used strategy is to select a single top model based on the Rubin's rules (RR). Hover, such approaches do not take the model uncertainty into consideration, which might lead to over-confident inferences. ,  extended the Bayesian model averaging method to perform variable selection and prediction under multiple imputation (MI-BMA), which takes into account the uncertainties originated from both the missing data and the model selection.  applied the MI-BMA method to simulated datasets as ll as a real data set from a prospective cohort, and demonstrated the advantage of our method as compared with the classical RR stepwise method."}]