China implemented the two-child policy in 2016, however, potential impacts of this new policy on its population reality have not been adequately understood. Using population census data and 1% population sampling data during the period of 1982–2015, this study develops a fertility simulation model to explore the effects of the two-child policy on women’s total fertility rate, and employs Cohort Component Method in population projections to examine China’s demographic future with different fertility regimes. The fertility simulation results reveal that the two-child policy will make significantly positive effects on China’s total fertility rate through increasing second births, leading to a sharp but temporary increase in the first 5 years after the implementation of the new policy. In addition, population projections using simulated total fertility rates show that the Chinese population would reach its peak value around the middle 2020s and be faced with the reduction of labor force supply and rapid aging process, featured with remarkable increases in both size and share of the elderly population. The findings suggest that the two-child policy would undoubtedly affect China’s fertility rates and demographic future; however, the effects are mild and temporary.
In clinical trials, missing data commonly arise through nonadherence to the randomized treatment or to study procedure. For trials in which recurrent event endpoints are of interests, conventional analyses using the proportional intensity model or the count model assume that the data are missing at random, which cannot be tested using the observed data alone. Thus, sensitivity analyses are recommended. We implement the control‐based multiple imputation as sensitivity analyses for the recurrent event data. We model the recurrent event using a piecewise exponential proportional intensity model with frailty and sample the parameters from the posterior distribution. We impute the number of events after dropped out and correct the variance estimation using a bootstrap procedure. We apply the method to an application of sitagliptin study. 相似文献
In quantitative trait linkage studies using experimental crosses, the conventional normal location-shift model or other parameterizations may be unnecessarily restrictive. We generalize the mapping problem to a genuine nonparametric setup and provide a robust estimation procedure for the situation where the underlying phenotype distributions are completely unspecified. Classical Wilcoxon–Mann–Whitney statistics are employed for point and interval estimation of QTL positions and effects. 相似文献