SPPH 504-007: Application of Epidemiological Methods

Ehsan Karim

Description

This PhD-level course teaches research trainees emerging and advanced epidemiological methods, including statistical approaches for confounding, missingness and complex surveys towards the development of analysis plans, the analyzes and interpretation of real-world epidemiologic data and the communication of findings.

Contact

• Feel free to email me for comments / suggestions.

Lab schedule

#	Part	Topic	Lab Links	Source type
1	1	Data manipulation	Lab 1: Introduction to R	HTML
2	1	NHANES	Lab 2a: Creating Analytic dataset from NHANES	HTML
2	2		Lab 2b: Working with Analytic dataset	HTML
3	1	Model diagnostics	Lab 3a: Regression diagnostics	HTML
3	2		Lab 3b: Avoiding overfitting	HTML
4	1	Survey data analysis	Lab 4a: Creating the analytic survey dataset	HTML
4	2		Lab 4b: Checking the analytic survey dataset	HTML
4	3		Lab 4c: Analyzing the analytic survey dataset	HTML
5	1	Propensity Score Matching	Lab 5a: Matching	HTML
5	2		Lab 5b: Propensity score Matching in CCHS	HTML
5	3		Lab 5c: Propensity score matching in NHANES	HTML
5	4		Lab 5d: Propensity score weighting in NHANES	HTML
5	5		Lab 5e: Estimating propensity score weighting for multiple treatments	HTML
6	1	Missing data analysis	Lab 6a: Missing data and Imputation	HTML
6	2		Lab 6b: Missing values in survey data (binary variable with missing values)	HTML
6	3		Lab 6c: Missing values in survey data (multiple variables with missing values)	HTML
6	4		Lab 6d: Missing values in propensity score analysis involving survey data	HTML
6	5		Lab 6e: Propensity score sensitivity analysis, when weights are too large - 1	HTML
6	6		Lab 6f: Propensity score sensitivity analysis, when weights are too large - 2	HTML
6	7		Lab 6g: Model performance from multiple imputed datasets	HTML
7	1	Machine learning	Lab 7: Machine learning and Application in Propensity Score Analysis	HTML
8	1	RMarkdown for Scientific Writing	Lab 8: RMarkdown	Video
9	1	Complex outcomes	Lab 9a: Matched data	HTML
9	2		Lab 9b: Polytomous and Ordinal Regression	HTML
9	3		Lab 9c: Poisson and Negative binomial regression	HTML
9	4		Lab 9d: Survival analysis	HTML
10	1	Mediation Analysis	Lab 10a: Ideas of Mediation analysis on CCHS	HTML
10	2		Lab 10b: Mediation analysis on CCHS	HTML
10	3		Lab 10c: Mediation analysis (3 category mediator) on CCHS	HTML
10	4		Lab 10d: Mediation analysis using various R packages	HTML
11	1	Longitudinal Data (optional)	Lab 11a: Mixed Effects Models	HTML
11	2		Lab 11b: GEE	HTML

References

UBC Library Online Course Reserves

Computing resources

• Kaggle Kernel
• Google Colab
• Jupyter for UBC

Acknowledgements

• Derek Ouyang/SPPH (acted as GTA for 2019-2020),
• M Atiquzzaman/Pharm (acted as GTA for 2018-2019) and
• course participants from 2019-2020 and 2018-2019.

Teaching-related web-Apps

• Propensity scores by example
• Inverse probability weights by example