Statistical methods for Medical Research (2.5cu / 3 cu)

This page contains the material for the course on Statistical Methods for Medical Research given at UH. It is given for students of the Degree programme in Medicine and Dentistry as well as to PhD students at UH. Students must register through Sisu.

For MD students, the course code is MED-VAL08 and the course gives 2.5cu. This course cannot be included among 15cu needed from "toukokuun syventävät" but can be included among the 10cu needed from "vapaasti valittavat opintojaksot". (Previous codes have been MED-TOU09 and 3757114).
For PhD students, the course code is DPBM-300 (previously DPBM-207) and the course gives 3 cu. The difference compared to the MD students is that from the PhD students more home exercises are required to be completed.

The course is given in a fully remote teaching mode in fall of 2023, starting on week 38 (Mon 18.9.2023).

Teacher: Matti Pirinen.

Practice

The course consists of 8 topics. Each topic has a set of home exercises to be done with the R language. The solutions to the home exercises are returned by Monday 9am each week. An exercise session will be available in Moodle the same Monday at 10am latest. In case you find something to correct in your solutions after watching the exercise session, the corrected version of the exercises should be returned to Moodle by the following Monday 9am (the same deadline as for the home exercises of the following week).

Weekly tasks on weeks 1 — 8.

Week 1 ("Topic 1")

1. Get Rstudio up and running following 'RunningR.pdf' slides 1-3.
2. Go through Topic 1 Learn R: Open Topic1_learnR.R in Rstudio.
3. Get to know Rmarkdown following 'RunningR.pdf', slide 4 and go through Rmarkdown_intro.Rmd in Rstudio.
4. Take Topic 1 Lecture: Open Topic1_lecture as html (in web browser) or pdf for reading, or open source code Topic1_lecture.Rmd in Rstudio to get familiar with the source code and compiling it yourself.
5. Do Topic 1 Home Exercises and return by 09.00 Mon 25.9 to Moodle.
6. Watch Topic 1 exercise session (available 10.00am Mon 25.9 in Moodle). If your solutions need correcting, return a corrected version to Moodle by Mon 2.10.

Week 2 ("Topic 2")

1. Go through Topic 2 Learn R: Open Topic2_learnR.R in Rstudio.
2. Take Topic 2 Lecture: Open Topic2_lecture as html (in web browser) or pdf for reading, or open the Topic2_lecture.Rmd source code in Rstudio and compile it yourself.
3. Do Topic 2 Home Exercises and return by 09.00 Mon 2.10 to Moodle.
4. Watch Topic 2 exercise session (available 10.00am Mon 2.10. in Moodle). If your solutions need correcting, return a corrected version to Moodle by Mon 9.10.

Week 3 ("Topic 3")

1. Go through Topic 3 Learn R: Open Topic3_learnR.R in Rstudio.
2. Take Topic 3 Lecture: Open Topic3_lecture as html (in web browser) or pdf for reading, or open the Topic3_lecture.Rmd source code in Rstudio and compile it yourself.
3. Do Topic 3 Home Exercises and return by 09.00 Mon 9.10. to Moodle.
4. Watch Topic 3 exercise session (available 10.00am Mon 9.10. in Moodle). If your solutions need correcting, return a corrected version to Moodle by Mon 16.10.

Week 4 ("Topic 4")

1. Go through Topic 4 Learn R: Open Topic4_learnR.R in Rstudio.
2. Take Topic 4 Lecture: Open Topic4_lecture as html (in web browser) or pdf for reading, or open the Topic4_lecture.Rmd source code in Rstudio and compile it yourself.
3. Do Topic 4 Home Exercises and return by 09.00 Mon 16.10. to Moodle.
4. Watch Topic 4 exercise session (available 10.00am Mon 16.10. in Moodle). If your solutions need correcting, return a corrected version to Moodle by Mon 23.10.

Week 5 ("Topic 5")

1. Go through Topic 5 Learn R: Open Topic5_learnR.R in Rstudio.
2. Take Topic 5 Lecture: Open Topic5_lecture as html (in web browser) or pdf for reading, or open the Topic5_lecture.Rmd source code in Rstudio and compile it yourself.
3. Do Topic 5 Home Exercises and return by 09.00 Mon 23.10. to Moodle.
4. Watch Topic 5 exercise session (available 10.00am Mon 23.10. in Moodle). If your solutions need correcting, return a corrected version to Moodle by Mon 30.10.

Week 6 ("Topic 6")

1. Go through Topic 6 Learn R: Open Topic6_learnR.Rmd in Rstudio.
2. Take Topic 6 Lecture: Open Topic6_lecture as html (in web browser) or pdf for reading, or open the Topic6_lecture.Rmd source code in Rstudio and compile it yourself.
3. Do Topic 6 Home Exercises and return by 09.00 Mon 30.10. to Moodle.
4. Watch Topic 6 exercise session (available 10.00am Mon 30.10. in Moodle). If your solutions need correcting, return a corrected version to Moodle by Mon 6.11.

Week 7 ("Topic 7")

1. Go through Topic 7 Learn R: Open Topic7_learnR.R in Rstudio.
2. Take Topic 7 Lecture: Open Topic7_lecture as html (in web browser) or pdf for reading, or open the Topic7_lecture.Rmd source code in Rstudio and compile it yourself.
3. Do Topic 7 Home Exercises and return by 09.00 Mon 6.11. to Moodle.
4. Watch Topic 7 exercise session (available 10.00am Mon 6.11. in Moodle). If your solutions need correcting, return a corrected version to Moodle by Mon 13.11.

Week 8 ("Topic 8")

1. Go through the R Cheat Sheet.
2. Take Topic 8 Lecture: Open Topic8_lecture as html (in web browser) or pdf for reading, or open the Topic8_lecture.Rmd source code in Rstudio and compile it yourself.
3. Do Topic 8 Home Exercises and return by 09.00 Mon 13.11. to Moodle.
4. Watch Topic 8 exercise session (available 10.00am Mon 13.11. in Moodle). If your solutions need correcting, return a corrected version to Moodle by Mon 20.11.

Materials

Materials in table below (except some of the data files) are by Matti Pirinen and distributed under CC-BY-SA license. Sources of the data files are given in the corresponding home exercises. To save the files to your computer, set your cursor on top of the filename and right-click and "Save As". On a Mac touchpad, "right-click" is a two-finger click. If you view lecture files with a web browser, click "Refresh" from your browser to make sure you are always looking at the most recent version.

Week	Topic	Learn R	Lecture	Home Exercises	Other files
1	1.Intro	Rscript	browser pdf Rmd	Rmd pdf	RunningR.pdf Rmarkdown_intro.Rmd systbp_ldlc.txt
2	2.P-value	Rscript	browser pdf Rmd	Rmd pdf
3	3.Estimation	Rscript	browser pdf Rmd	Rmd pdf
4	4.Continuous data	Rscript	browser pdf Rmd	Rmd pdf	beers.csv prostate.txt systbp_ldlc.txt
5	5.Statistical power	Rscript	browser pdf Rmd	Rmd pdf	hypothesis_testing.pdf
6	6.Linear regression I	Rmd	browser pdf Rmd	Rmd pdf	Davis_height_weight.txt prostate.txt systbp_ldlc.txt
7	7.Linear regression II	Rscript	browser pdf Rmd	Rmd pdf	Davis_height_weight.txt prostate.txt UN98.csv
8	8.Logistic regression	Cheat sheet	browser pdf Rmd	Rmd pdf	Arrests.csv Wells.csv
Extra	Statistical thinking		browser pdf Rmd

If you have problems with R, first try google your problem, like "splitting plotting area in R", and most likely you will find quickly an answer.
In Finnish, a detailed R tutorial used on the UHelsinki course "Tilastotiede tutuksi" is here.
R tutorial by Kelly Black.
For statistics, UH ebooks contain e.g. Machin D., Campbell M.J., Walters S.J. Medical Statistics 4th ed.

Contact: matti.pirinen'at'helsinki.fi

Updated: 17-Sep-2023