Advanced_epidemiology_course

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Advanced_epidemiology_course

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Material for advanced epidemiology module in MPH

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# readme

# Advanced epidemiology course

# Github pages

See [Website](https://dmcalli2.github.io/Advanced_epidemiology_course/)

The course will take place at 12-2pm in Clarice Pears building see
[Timetable](https://dmcalli2.github.io/Advanced_epidemiology_course/timetable.html)

# HTML slides

[Competing
risks](https://dmcalli2.github.io/Advanced_epidemiology_course/08_competing_risks.html)

# Course outline

## AIMS

This course will introduce students to more complex epidemiological
concepts and advanced methods employed in modern epidemiological
research.

## LEARNING OUTCOMES

Following successful completion of this course, students will be able
to:

1.  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.
2.  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.
3.  Critically understand how quantitative methods can be used to apply
    effect measures to target populations, as well as the assumptions
    such approaches require.
4.  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).

## COURSE CONTENT

Eleven sessions. The first ten will comprise a lecture and tutorial this
will be followed by a revision session.

## TEACHING METHODS

Lectures and tutorials with some web-based interactive tools.

## ASSESSMENT METHOD

See the Assignment folder above for details.

## Interactive web-apps

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/>.

## Reading list

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.

### Textbooks

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.

### Causation

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?A nother 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.

### Approaches to estimating causal effects

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.

### Study analysis: addressing measured confounding

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.

### Administrative data

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.

### Scale and effect measure modification

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.

### Competing risks

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.

### Applying epidemiological estimates

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.

### Measurement and bias

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.

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