1
POPH90242 Epidemiology 2
Semester 2, 2024
Assignment 2
Task type:
Written task
Task length:
2000 words (+10% allowed and can be less than 1750
words – no penalties for less)
Weighting:
40%
Due Date/Time:
• 22nd September, 11.59pm. A penalty of 5%
per day will be applied for every day after the
due date.
Submission:
• Submit as a Microsoft Word document
electronically via Turnitin and Gradescope
Learning Outcomes:
• Apply standardisation, inverse probability weighting and g-computation to control for
confounding
• Apply quantitative bias techniques to quantify the direction and magnitude of bias
• Critique experimental and observational epidemiological studies
Task Purpose:
In this assessment task you will apply many of the epidemiological analytic techniques that you
have learnt in Epidemiology 2. In addition to these specific analyses, the general skills of thinking
through and conducting an epidemiological analysis is a core skill in epidemiology. Hence, this
assessment will give you the opportunity to practice those skills.
Additionally, a very common task in epidemiology is reading and analysing previous studies. This
is a key skill if you work in government or non-government organisations or research. In
government or non-government organisations you need to know when to trust the results of a
study and develop programs or policies based on study finding and when not to do so. Hence
there is a critical appraisal of an article in this assessment. We will do another critical appraisal in
the next assessment as well, so this is a good chance to learn and improve. It takes practice.
Section A.
Learning Outcomes:
2
• Apply standardisation, regression, propensity scores, and g-computation to control for
confounding
Research question
• Does living rurally, compared to urban living increase systolic blood pressure over a five
year period in adults living in the United States?
Methods
Study design: Cohort study, follow-up period of 5 years.
Population: Adults living in the United States
Participants: randomly selected individuals from across the USA for the NHANES study
longitudinal study https://www.cdc.gov/nchs/nhanes-ls/index.htm.
Data information
Variable Definition Measurement Categorisation Use in this study
sampl Individual id
number
- - Individual
identification
number
rural Living rurally /
urban setting
Based on
categorization
of addresses
Urban=0
Living rurally=1
Exposure
age_grp 6 age groups in 10
year brackets
Questionnaire
data at baseline
Categorical Potential
confounding
factor
sex1 Dichotomous sex
variable (USA
categorisations)
Questionnaire
data at baseline
Male=0
Female=1
Potential
confounding
factor
race1 Dichotomous race
variable (USA
categorisations)
Questionnaire
data at baseline
White=0
African American=1
Potential
confounding
factor
bmi Body mass index
(kg/m2)
Questionnaire
data at baseline
Continuous
measure
Potential
confounding
factor
bpsystol_2 Systolic blood
pressure at follow-
up (mmHg)
Questionnaire
data at follow-
up
Continuous
measure
Outcome
bpsystol_1 Systolic blood
pressure at baseline
(mmHg)
Questionnaire
data at baseline
Continuous
measure
Potential
confounding
factor
For the purposes of questions 1 and 2 that there are no other potential confounding
factors of the association between living rurally and blood pressure. Do not assume
this for question 3.
Results
Table 1. Participant characteristics
3
Participants (%)
(n=10,137)
Urban (%)
(n=3,897)
Rural (%)
(n=6,240)
Sex
Male (%) 4,806 (47.4) 1,793 (46.0) 3,013 (48.3)
Female (%) 5,331 (52.6) 2,104 (54.0) 3,227 (51.7)
Age groups (%)
20 - 29 years 2,261 (22.3) 933 (23.9) 1,328 (21.3)
30 - 39 years 1,589 (15.7) 612 (15.7) 977 (15.7)
40 - 49 years 1,242 (12.3) 469 (12.0) 773 (12.4)
50 - 59 years 1,267 (12.5) 491 (12.6) 776 (12.4)
60 - 69 years 2,804 (27.7) 1036 (26.6) 1,768 (28.3)
70+ years 974 (9.6) 356 (9.1) 618 (9.9)
Race
Identifies as white (%) 8,548 (84.3) 2,435 (62.5) 6,113 (98.0)
Identifies as African
American (%)
1,589 (15.7) 1,462 (37.5) 127 (2.0)
Mean BMI (SD) 25.6 (4.9) 25.6 (5.1) 25.5 (4.8)
Mean baseline systolic
blood pressure (SD)
127.7 (12.9) 128.3 (13.2) 127.4 (12.7)
Mean follow-up systolic
blood pressure (SD)
130.9 (23.4) 131.4 (23.9) 130.6 (22.9)
SD: standard deviation, BMI: body mass index.
This table has been included so you do not need to repeat the table in your assignment.
Questions
Question 1
Write a plan for your analysis (see module 5.6 for a guide). Choose either IPW or G-
computation to address the research question.
Question 2
Write up the results from your analytic plan. Include:
• a short summary of the key findings from your descriptive results (from Table 1 above)
(i.e., 3 sentences)
• the results of your analyses
• interpretations of the results from your IPW or G computation analyses
Question 3
Do you think the four causal conditions have been met in this analysis? Explain your answer
by exploring each of the four causal conditions separately.
Question 4
Briefly write three to four sentences of Discussion for this analysis, taking into account your
answers to questions 2 and 3. Include a summary of your findings, limitations and a
recommendation for future research.
4
Section B
Learning outcome:
• Critique experimental and observational epidemiological studies
All Questions in Section B will refer to this study:
Petit D, Touchette E, Pennestri MH, Paquet J, Côté S, Tremblay RE, Boivin M,
Montplaisir JY. Nocturnal sleep duration trajectories in early childhood and school
performance at age 10 years. J Sleep Res. 2023 Oct;32(5):e13893. doi:
10.1111/jsr.13893. Epub 2023 Mar 27. PMID: 36973015.
https://onlinelibrary.wiley.com/doi/full/10.1111/jsr.13893
Questions
Question 5
Answer the questions in Domain 1, Domain 5 and Domain 6 the ROBINS-E Adapted for
POPH90242 Epidemiology 2 document. Copy and paste the question number and your
answer into your assessment answer page.
A1 is filled in below, otherwise the initial sections are not included in this assessment. Include
what you think is relevant from these sections (i.e., important confounding factors) in the
responses to the Domain questions. This will make it cleaner to write and read.
A1. Specify the numerical result being assessed
Association between sleep trajectory 1 and Reading level: OR: 2.4 (95%CI 1.3-4.6), p value 0.007.
Taken from Table 4 in the study above.
Question 6
Based on your answer to Question 5 answer the questions in the ‘Overall risk of bias’ section
of the ROBINS-E Adapted for POPH90242 Epidemiology 2. Copy and paste this section into
your assessment answer page.
Section C.
Learning outcome:
• Apply quantitative bias techniques to quantify the direction and magnitude of bias
In this section of the assignment we will be looking at this study:
Bruinsma FJ, Jordan S, Bassett JK, et al. Analgesic use and the risk of renal cell
carcinoma - Findings from the Consortium for the Investigation of Renal Malignancies
(CONFIRM) study. Cancer Epidemiol. 2021 Dec;75:102036. doi:
10.1016/j.canep.2021.102036. Epub 2021 Sep 22. PMID: 34562747.
The question we will focus on is:
5
Is there an increased risk of incident renal cell carcinoma (RCC) in Australian adults
with a higher paracetamol intake compared to those with a lower paracetamol intake?
Methods
Participants: This study was conducted across Victoria and Queensland in Australia, using
Cancer Registry data.
• Cases with a renal cell carcinoma diagnosis on the Cancer Registries of participating
states were invited to participate.
• Controls were family members of the case; a sibling or spouse.
Measurement: Regular paracetamol intake was measured through a questionnaire.
Participants who used paracetamol for least five times per month, for six months or more
were defined as regular users.
Analysis: Odds ratios were calculated and adjustment for age, sex, smoking and hypertension
was undertaken.
Results:
Regular paracetamol use was associated with increased odds of renal cell carcinoma (OR 1.32,
95%CI 1.09, 1.61)*
Table 1. The number of cases and controls using paracetamol regularly.
Cases Controls
Regular paracetamol
users
514 300 814
Non regular
paracetamol users
550 424 974
1064 724 1788
You decide to explore the role bias may play in this finding by completing a quantitative bias
analysis.
The information below will help you plan your bias analysis:
• Recall of over-the-counter medications is often prone to measurement error. You find
that the sensitivity and specificity of the self-report of use of these medications is low
(1). Using the information from this published study you decide that you will conduct
a QBA under the hypothesis that in controls the sensitivity and specificity is 0.55
(95%CI 0.50, 0.60) and 0.89 (95%CI 0.81, 0.94), respectively. You think those with
RCC are more likely to ‘recall’ their taking of these medications, hence in the cases you
estimate that the sensitivity will be 5% higher and specificity 5% lower in cases,
compared to controls
• To begin the study there were logically 3484 potential case-control pairs that could
participate. In the final analysis 12111064/3484 (34.8030.5%) cases and 724/3484
(20.8%) controls had data available. You are concerned that low education is cause of
6
paracetamol use (2), and that two causes of low participation are low education and
being a control participant. Hence you hypothesise that the participation fraction for
the controls taking paracetamol is 1% to 2% lower than those not taking paracetamol.
Question
Question 7
Write-up your bias analysis plan, include a DAG of all potential biases discussed above.
Notes:
• we will return to complete this analysis in Assessment 3.
• do not include Stata commands.
• *Supplementary Table 3. Note there are some differences in the confidence intervals
between our analysis and that published due to differences in the data available.
References
1. Lacasse A, Ware MA, Bourgault P, Lanctôt H, Dorais M, Boulanger A, Cloutier C, Shir Y,
Choinière M. Accuracy of Self-reported Prescribed Analgesic Medication Use: Linkage
Between the Quebec Pain Registry and the Quebec Administrative Prescription Claims
Databases. Clin J Pain. 2016 Feb;32(2):95-102. doi: 10.1097/AJP.0000000000000248.
PMID: 25924096.
2. Algarni, M., Hadi, M.A., Yahyouche, A. et al. A mixed-methods systematic review of the
prevalence, reasons, associated harms and risk-reduction interventions of over-the-
counter (OTC) medicines misuse, abuse and dependence in adults. J of Pharm Policy
and Pract 14, 76 (2021). https://doi.org/10.1186/s40545-021-00350-7
