The course will take place at 2.00-4.00 p.m. on Tuesday afternoons in the 2nd semester. It will take place in Lecture Room 2 in Lilybank Gardens (lecture room is in House 2, enter building through House 1).
This course will introduce students to more complex epidemiological concepts and advanced methods employed in modern epidemiological research.
Following successful completion of this course, students will be able to:
- Both critique and design epidemiological research informed by an understanding of counterfactual thinking and causal diagrams, including a critical understanding of the limits of these approaches.
- Recognise important biases and understand how these affect interpretation of findings, understand how such biases can be dealt with through study design and/or statistical analysis and have a critical understanding of the relative strengths and limitations of different methodological approaches.
- Critically understand how quantitative methods can be used to apply effect measures to target populations, as well as the assumptions such approaches require.
- Critically understand the major methodological issues in natural experiment studies, administrative data analyses and life-course epidemiology and relate these to major theories across the wider field (i.e. collider bias, confounding etc).
Eleven sessions. The first ten will comprise a lecture and tutorial this will be followed by a revision session.
Lectures and tutorials with some web-based interactive tools.
See the Assignment folder above for details.
Some of the couse will be taught using interactive web-apps which we are developing. The first of which is available at https://ihwph-hehta.shinyapps.io/competing_risks/.
It is easy to be overwhelmed by reading lists. This list is very much optional.
The following reading list includes both texts which closely reflect the content of the course, and broader/secondary reading. As in any field, there are current controversies in epidemiology. Where this is the case, we have sought to include writers on either side of a controversial issue - eg the counterfactual in causal inference.
Rothman KJ. Epidemiology: an introduction: Oxford University Press; 2012. Pearl J, Mackenzie D. The Book of Why: The New Science of Cause and Effect: Basic Books; 2018. Dunning T. Natural experiments in the social sciences: a design-based approach: Cambridge University Press; 2012.
Davey Smith G, Phillips AN, Neaton JD. Smoking as "independent" risk factor for suicide: illustration of an artifact from observational epidemiology? The Lancet. 1992;340(8821):709-12. Lawlor DA, Smith GD, Bruckdorfer KR, Kundu D, Ebrahim S. Those confounded vitamins: what can we learn from the differences between observational versus randomised trial evidence? The Lancet. 2004;363(9422):1724-7. Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15(5):615-25. Vandenbroucke JP, Broadbent A, Pearce N. Causality and causal inference in epidemiology: the need for a pluralistic approach. Int J Epidemiol. 2016. Munafò MR, Smith GD. Robust research needs many lines of evidence. Nature. 2018;553:399-401. Sterne JAC, Cox DR, Smith GD. Sifting the evidence—what's wrong with significance tests?Another comment on the role of statistical methods. BMJ (Clinical research ed). 2001;322(7280):226-31. Richiardi L, Bellocco R, Zugna D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol. 2013;42(5):1511-9.
Study design: addressing unmeasured confounding Craig P, Katikireddi SV, Leyland AH, Popham F. Natural experiments: An overview of methods, approaches and contribution to public health intervention research. Annu Rev Public Health. 2017;38(1):39-56. Craig P, Cooper C, Gunnell D, Haw S, Lawson K, Macintyre S, et al. Using natural experiments to evaluate population health interventions: new Medical Research Council guidance. JECH. 2012;66:1182-6. Katikireddi SV, Green MJ, Taylor AE, Davey Smith G, Munafò MR. Assessing causal relationships using genetic proxies for exposures: an introduction to Mendelian randomization. Addiction. 2018;113(4):764-74. Bouttell J, Craig P, Lewsey J, Robinson M, Popham F. Synthetic control methodology as a tool for evaluating population-level health interventions. J Epidemiol Community Health. 2018;72(8):673-8. Basu S, Meghani A, Siddiqi A. Evaluating the health impact of large-scale public policy changes: classical and novel approaches. Annu Rev Public Health. 2017;38:351-70. Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies. Epidemiology. 2010;21(3):383-8. Humphreys DK, Gasparrini A, Wiebe DJ. Evaluating the impact of florida’s “stand your ground” self-defense law on homicide and suicide by firearm: An interrupted time series study. JAMA Internal Medicine. 2017;177(1):44-50.
Freemantle N, Marston L, Walters K, Wood J, Reynolds MR, Petersen I. Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research. BMJ (Clinical research ed). 2013;347.
Gilbert R, Lafferty R, Hagger-Johnson G, Harron K, Zhang LC, Smith P, et al. GUILD: GUidance for Information about Linking Data sets. J Public Health (Oxf). 2017:1-8. Doidge JC, Harron K. Demystifying probabilistic linkage: Common myths and misconceptions. International Journal of Population Data Science. 2018;3(1). Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement. PLoS Med. 2015;12(10):e1001885. Thygesen LC, Ersbøll AK. When the entire population is the sample: strengths and limitations in register-based epidemiology. Eur J Epidemiol. 2014;29(8):551-8. Herrett E, Shah AD, Boggon R, Denaxas S, Smeeth L, van Staa T, et al. Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ: British Medical Journal. 2013;346. Agniel D, Kohane IS, Weber GM. Biases in electronic health record data due to processes within the healthcare system: retrospective observational study. BMJ. 2018;361.
Corraini P, Olsen M, Pedersen L, Dekkers OM, Vandenbroucke JP. Effect modification, interaction and mediation: an overview of theoretical insights for clinical investigators. Clinical Epidemiology. 2017;9:331. Knol MJ, VanderWeele TJ. Recommendations for presenting analyses of effect modification and interaction. Int J Epidemiol. 2012;41(2):514-20. Richiardi L, Bellocco R, Zugna D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol. 2013;42(5):1511-9. Mansournia MA, Etminan M, Danaei G, Kaufman JS, Collins G. Handling time varying confounding in observational research. BMJ. 2017;359. Diderichsen F, Hallqvist J, Whitehead M. Differential vulnerability and susceptibility: how to make use of recent development in our understanding of mediation and interaction to tackle health inequalities. Int J Epidemiol. 2018:dyy167-dyy.
Andersen PK, Geskus RB, de Witte T, Putter H. Competing risks in epidemiology: possibilities and pitfalls. Int J Epidemiol. 2012;41(3):861-70. Hernán MA, Alonso A, Logan R, Grodstein F, Michels KB, Willett WC, et al. Observational Studies Analyzed Like Randomized Experiments: An Application to Postmenopausal Hormone Therapy and Coronary Heart Disease. Epidemiology. 2008;19(6):766-79.
Mansournia MA, Altman DG. Population attributable fraction. BMJ. 2018;360. Hoffmann R, Eikemo TA, Kulhanova I, Dahl E, Deboosere P, Dzurova D, et al. The potential impact of a social redistribution of specific risk factors on socioeconomic inequalities in mortality: illustration of a method based on population attributable fractions. J Epidemiol Community Health. 2013;67(1):56-62. Purshouse RC, Meier PS, Brennan A, Taylor KB, Rafia R. Estimated effect of alcohol pricing policies on health and health economic outcomes in England: an epidemiological model. The Lancet. 2010;375(9723):1355-64.
Hartge P. Participation in Population Studies. Epidemiology. 2006;17(3):252-4 10.1097/01.ede.0000209441.24307.92. Rothman KJ, Gallacher JE, Hatch EE. Why representativeness should be avoided. Int J Epidemiol. 2013;42(4):1012-4. Munafò MR, Tilling K, Taylor AE, Evans DM, Davey Smith G. Collider scope: when selection bias can substantially influence observed associations. Int J Epidemiol. 2017:dyx206-dyx. Sterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. Fewell Z, Davey Smith G, Sterne JAC. The Impact of Residual and Unmeasured Confounding in Epidemiologic Studies: A Simulation Study. Am J Epidemiol. 2007;166(6):646-55.