Journal of Biometrics & Biostatistics

Ming Wang

Publications

• Research Article
  Piecewise Negative Binomial Regression in Analyzing Hypoglycemic Events with Missing Observations
  Author(s): Ming Wang, Junxiang Luo, Haoda Fu and Yongming QuMing Wang, Junxiang Luo, Haoda Fu and Yongming Qu
  
  In diabetes clinical trials, hypoglycemia can be captured. Negative binomial regression is emerging as a standard method for analyzing hypoglycemic events by considering overdispersion. However, in negative binomial regression for hypoglycemic events, variability of the subjects lost to follow up due to dropout is adjusted through an offset parameter, which assumes that dropout is missing completely at random and constant hypoglycemia rate over time. This assumption is vulnerable because dropout may be due to the excessive observed hypoglycemic events and the hypoglycemic event rate may change over time. In addition, the traditional way of using negative binomial regression to analyze hypoglycemic events only compares the counts of hypoglycemic events during a specified period. However, researchers may be interested in comparing hypoglycemic event rates between treatment groups at dif.. Read More»
  DOI: 10.4172/2155-6180.1000195

  Abstract PDF