Statistical Reasoning for Public Health: Estimation, Inference, & Interpretation

开始时间: 04/22/2022 持续时间: 8 weeks

所在平台: CourseraArchive 课程类别: 医学 大学或机构: Johns Hopkins University(约翰•霍普金斯大学) 授课老师： John McGready

课程主页: https://www.coursera.org/course/statreasoning

课程评论：没有评论

第一个写评论        关注课程

课程详情

In this data centric era, statistics has become an essential tool for processing information from the realm of public health and medical research.  Understanding both the implications and limitations of the results from such research is tantamount to making informed treatment decisions, public health practice protocols and policy recommendations.  In this class, a conceptual and interpretive approach is applied to some of the most used methods from basic statistics.  The course will detail appropriate summary measures for quantifying the health of single populations, and comparing such outcomes between populations using results from representative but imperfect data samples.  Additionally, the role of uncertainty in sample based estimated will be covered, allowing for conclusions to be drawn regarding the larger populations under study while recognizing the imperfection in the study estimates.  All topics will be considered through a “conceptual lens” allowing students to focus on the “what”, “why”, and “so what” with regards to the implications of research results.

课程大纲

• study designs for comparing populations
  
• single sample numerical summary measures for continuous, binary and time-to-event outcomes
  
• visual displays for continuous and time-to-event outcomes
  
• the normal (Gaussian) distribution
  
• measures of association: mean differences, risk differences, relative risks, odds ratios and incidence rate ratios
  
• confidence intervals for means, mean differences, proportions, relative risks, odds ratios, incidence rates and incidence rate ratios
  
• hypothesis testing: paired and two-sample t-tests, the chi-square test, Fisher's exact test, and the log-rank test
  
• power and sample size 
  

课程评论(0条)