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1.
Almog-Bar Michal Ashkenazi-Anor Mayrav Hersberger-Langloh Sophie E. Compion Sara Butcher Jacqueline 《Voluntas: International Journal of Voluntary and Nonprofit Organizations》2022,33(3):483-496
VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Age has long been understood as a strong demographic determinant of volunteering. However, to date, limited literature... 相似文献
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Cancho Vicente G. Macera Márcia A. C. Suzuki Adriano K. Louzada Francisco Zavaleta Katherine E. C. 《Lifetime data analysis》2020,26(2):221-244
Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there... 相似文献
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Stephen J. Ruberg Frank E. Harrell Jr. Margaret Gamalo-Siebers Lisa LaVange J. Jack Lee Karen Price 《The American statistician》2019,73(1):319-327
ABSTRACTThe cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of “no treatment effect” are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a “successful” trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared “unsuccessful” due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a “prior” for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making. 相似文献
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Archila Bustos Maria Francisca Hall Ola Niedomysl Thomas Ernstson Ulf 《Population and environment》2020,42(2):255-277
Population and Environment - Human activity is a major driver of change and has contributed to many of the challenges we face today. Detailed information about human population distribution is... 相似文献
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Journal of Population Research - This paper details efforts to link administrative records from the Internal Revenue Service (IRS) to American Community Survey (ACS) and 2010 Census microdata for... 相似文献
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Thomas K. Kenemore 《Child and Adolescent Social Work Journal》2002,19(4):269-270