**Scott S. Emerson, M.D., Ph.D.**

Professor of Biostatistics

Department of Biostatistics

University of Washington

**Online: Introductory Applied Biostatistics**

This course provides an introduction to the statistical analysis of data.

The major objectives of this course are to explore the ways in which statistical methods can be used to address scientific questions,
to present simple data analysis methods, and
to teach a general approach to a data analysis problem.
To those ends, this course will stress the general abstraction of descriptive and inferential statistics to
address a scientific question. We primarily address methods for the setting of one response variable and
one grouping variable. This includes one and two sample problems, one way analysis of variance, and simple
regression. Late in the course we address stratified analyses.

Topics covered will include definition of common descriptive techniques,
estimation and testing
for continuous, discrete, and censored response variables in parametric models, and semiparametric and
nonparametric alternatives to those tests, including Monte Carlo methods. Emphasis will be placed on the
similarity among the various forms of analyses.

The materials for this course are primarily re-packaging of the handouts and lectures from Biost 514 / 517
at the University of Washington, especially
as taught in fall 2012.

Tutorial on Viewing Video Courses (mp4) |

Detailed Syllabus (pdf) |

Supplementary Materials (including R) |

Datasets |

References |

Lectures | Handouts | Video | Homework | Reading |
---|---|---|---|---|

L01: Overview |
4/pg 1/pg | 16:16 |
HW #01 (Key R Stata) |
Detailed Syllabus |

L02: Five Questions Answered with Statistics |
4/pg 1/pg | |||

A: Scientific setting | Slides 01 - 06 | 05:11 | ||

B: General classification | Slides 07 - 08 | 07:41 | ||

C: Q1: Clustering cases | Slides 09 - 11 | 05:13 | ||

D: Q2: Clustering variables | Slides 12 - 15 | 07:17 | ||

E: Q3: Quantifying distributions | Slides 16 - 20 | 08:11 | ||

F: Q4a: Hypothesizing associations | Slides 21 - 26 | 08:31 | ||

G: Q4b: Detecting associations | Slides 27 - 32 | 22:40 | ||

H: Q4c: Subgroups / Effect modification | Slides 33 - 36 | 20:56 | ||

I: Q5: Prediction | Slides 37 - 45 | 13:34 | ||

J: Statistical Tasks | Slides 46 - 52 | 10:55 | ||

L03: Overview of Descriptive Statistics |
4/pg 1/pg | |||

A: Purpose; Errors, Materials | Slides 01 - 07 | 09:48 | ||

B: Purpose: Validity of Assumptions | Slides 08 - 14 | 20:29 | ||

C: Sampling Scheme | Slides 15 - 25 | 16:43 | ||

D: Association | Slides 26 - 32 | 12:17 | Reporting Associations: sec 1-2 | |

E: Classifying Variables; Binary | Slides 33 - 40 | 08:06 | ||

F: Categorical; Quantitative | Slides 41 - 48 | 09:41 | ||

G: Censored Variables | Slides 49 - 50 | 03:45 | ||

H: Types of Summary Measures | Slides 51 - 66 | 04:55 |

**Possible reference texts ** (I use no text, because none seems sufficiently distribution-free in focus)

R | Rosner B: Fundamentals of Biostatistics, 5th ed. | |

PG | Pagano, Gauvreau: Principles of Biostatistics | |

FvBHL | Fisher, van Belle, Heagerty, Lumley: Biostatistics: A Methodology for the Health Sciences, 2nd ed. | |

F | Fleiss J: Statistical Methods for Rates and Proportions | |

KK | Kleinbaum, Klein: Survival Analysis: A Self-Learning Text | |

PM | Parmar MKB, Machin D: Survival Analysis: A Practical Approach | |

KM | Klein JP, Moeschberger ML: Survival Analysis: Techniques for Censored and Truncated Data |