Published papers

© 2018 Elsevier Ltd Background: To date, few large-scale comparative effectiveness studies of influenza vaccination have been conducted in Japan, since marketing authorization for influenza vaccines in Japan has been granted based only on the results of seroconversion and safety in small-sized populations in clinical trial phases not on the vaccine effectiveness. We evaluated the clinical effectiveness of influenza vaccination for children aged 1–15 years in Japan throughout four influenza seasons from 2010 to 2014 in the real world setting. Methods: We conducted a cohort study using a large-scale claims database for employee health care insurance plans covering more than 3 million people, including enrollees and their dependents. Vaccination status was identified using plan records for the influenza vaccination subsidies. The effectiveness of influenza vaccination in preventing influenza and its complications was evaluated. To control confounding related to influenza vaccination, odds ratios (OR) were calculated by applying a doubly robust method using the propensity score for vaccination. Results: Total study population throughout the four consecutive influenza seasons was over 116,000. Vaccination rate was higher in younger children and in the recent influenza seasons. Throughout the four seasons, the estimated ORs for influenza onset were statistically significant and ranged from 0.797 to 0.894 after doubly robust adjustment. On age stratification, significant ORs were observed in younger children. Additionally, ORs for influenza complication outcomes, such as pneumonia, hospitalization with influenza and respiratory tract diseases, were significantly reduced, except for hospitalization with influenza in the 2010/2011 and 2012/2013 seasons. Conclusions: We confirmed the clinical effectiveness of influenza vaccination in children aged 1–15 years from the 2010/2011 to 2013/2014 influenza seasons. Influenza vaccine significantly prevented the onset of influenza and was effective in reducing its secondary complications.

© 2019, The Institute of Statistical Mathematics, Tokyo. Issues regarding missing data are critical in observational and experimental research. Recently, for datasets with mixed continuous–discrete variables, multiple imputation by chained equation (MICE) has been widely used, although MICE may yield severely biased estimates. We propose a new semiparametric Bayes multiple imputation approach that can deal with continuous and discrete variables. This enables us to overcome the shortcomings of MICE; they must satisfy strong conditions (known as compatibility) to guarantee obtained estimators are consistent. Our simulation studies show the coverage probability of 95% interval calculated using MICE can be less than 1%, while the MSE of the proposed can be less than one-fiftieth. We applied our method to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, and the results are consistent with those of the previous works that used panel data other than ADNI database, whereas the existing methods, such as MICE, resulted in inconsistent results.