This course introduces students to biostatistics, demonstrating how to use the R programming language to carry out inference and data analysis. Topics include the principles of probability, statistical data analysis, the fitting of data distributions, linear regression to allow for interaction, non-inferiority mixed modeling, and the contemplation of cutoffs. Additional topics include multiple regression, covariate adjustment, and the use of confusion matrices and Receiver Operating Characteristic curves.
Overview
Syllabus
Biostatistics Using R: lecture 1 (Introduction to R) _part1_.
Biostatistics Using R: lecture 1 (Introduction to R) _part2_.
Biostatistics Using R: lecture 2 (Introduction to Statistical Data Analysis) _part1_.
Biostatistics Using R: lecture 2 (Introduction to Statistical Data Analysis) _part2_.
Biostatistics Using R: lecture 2 (Introduction to Statistical Data Analysis) _part3_.
Biostatistics Using R: lecture 3 (Probability et al.) _part1_.
Biostatistics Using R: lecture 3 (Probability et al.) _part2_.
Biostatistics Using R: lecture 3 (Probability et al.) _part3_.
Biostatistics Using R: lecture 3 (Probability et al.) _part4_.
Biostatistics Using R: lecture 3 (Probability et al.) _part5_.
Biostatistics Using R: lecture 4 (Inferential et al.) _part1_.
Biostatistics Using R: lecture 4 (Inferential et al.) _part2_.
Biostatistics Using R: lecture 4 (Inferential et al.) _part3_.
Non-inferiority mixed modeling using R.
Fit distribution of data using R.
Account for Interaction in linear regression using R.
Contemplating Cutoffs: Toolkit for cutoffs, confusion matrix and ROC curves using R.
[ARABIC] Multiple regression: covariate adjustment, why ?.
Taught by
Stat Pharm
