An introduction to medical statistics - Part 3. How do you get the data?

1. Topic 3
Getting the Data!
Dr Luke Kane
April 2014
Topic 3: Getting the Data 1

2. Outline
• Study design
• Data Collection
• Populations
• Sampling
• Types of Study
• Confounders
• Matching
• Placebo
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3. Objectives
• Explain what we mean by study design
• Explain what we mean by data collection
• Understand a sampling frame, populations, errors,
simple random sample, stratified and systematic
sampling, cluster and control sampling
• Understand the types of studies: Case reports, cross
sectional, case control, cohort, clinical trials,
randomised control trials
• Understand what is meant by confounders and
matching
• Understand randomisation and placebo
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4. Study Design
• Study design
– What is the question?
– What is the hypothesis?
– What are the variables?
• What is the outcome variable (the main one)?
– How many subjects do we need to include?
– Who are the subjects? How do we select them?
– How many groups do we need?
– Are we going to intervene or observe?
– Do we need a comparison group?
– When will take measurements? Before, during, after?
– How long will the study take?
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5. Data Collection
• How are we going to collect the data from the
subjects?
• How do we make sure the sample is as
representative as possible?
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6. Sampling: Huh?
• What is sampling?
– Selecting a subset of individuals from a population
to estimate characteristics of total population
• If you want to study rat behaviour
– You can’t watch every rat in the world
– “Sampling” is how you choose which rats to look
at
– You need to make the rats you look at
representative
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7. Sampling: The Sampling Frame
• The information you use to identify your
sample
• Examples:
– List of people in a census
– Telephone directory
– Management list of workers in a plantation
– Maps
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8. Sampling: Populations
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• Best explained with examples:
– Target population: All children with malaria in
Cambodia in 2013
– Study population: All children with malaria in the
main hospital in Phnom Penh, Battambang, Siem Reap
and Sihanoukville in 2013
– Sample population: 200 children from the paediatric
ward of each of the four hospitals in 2013

9. Sampling: Errors
• Can a sample ever be a perfect replica of the
target population?
• NO!
– It is an feature of any sample
– Unless you could measure every single person in a
population (usually impossible)
• Example:
– Total population has a TB prevalence of 1.3%
– Your sample has a prevalence of 0.8%
– The sampling error is 0.5%
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10. Sampling: The Simple Random Sample
• Importance of data being representative
– Most representative sample is usually a simple
random sample
• Only way it will differ from target population is by
chance
– What do we mean by RANDOM
• Each individual has an equal chance of being included
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11. Sampling: Further Types of Random
Sampling
• Can also have stratified and systematic
random sampling
– Stratified: break down sampling frame into strata
• E.g. male/female, smoker/non-smoker etc.
– Systematic: Use a system to pick individuals out of
a sampling frame
• E.g. every 10th on the list
• May be patterns on the list – Randomness!
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12. Sampling: Other Types of Sampling
• Cluster sampling
– Test households for dengue in Phnom Penh
– Difficult to get a list of every house in PP
– So you can look at a map, divide the map up and
take samples from different “clusters” of houses
• What if you look at houses which are all along a canal?
• Contact or consecutive sampling
– Look at patients visiting a clinic
– What if the clinic is in a very rich part of town?
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13. Types of Study
• Case reports
• Cross sectional studies
• Case-control studies (“Retrospective studies”)
• Cohort studies (“Prospective studies”)
• Randomised controlled trials (RCTs)
• Ecological studies
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14. How to Categorise Types of Studies
• Observational Vs. Experimental
– Observing is when you measure, ask questions etc
– Experimentation is when you make an
intervention – A CHANGE – and see what happens
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Observational Experimental
Case Series or Case Report Clinical trials
Cross Section study Randomised controlled trial
Cohort Study
Case Control study

15. Observational: Case Series/Report
• Case report – experience of on patient
• Case series – experience of a group of patients
with a similar diagnosis
– Very good for identifying new disease
– Accumulation of case reports could point to an
epidemic
• Easy, quick
• But very limited, no comparison group
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16. Case Report: Examples
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Am J Cardiol. 1968 Dec;22(6):782-90.
Transplantation of the heart in an infant and
an adult.
Kantrowitz A, Haller JD, Joos H, Cerruti MM,
Carstensen HE.
PMID: 4880223 [PubMed - indexed for
MEDLINE]

17. Observational: Cross Sectional Studies
• Probably the most common type of study
– Sample (cross section) of population interviewed,
tested or studied to answer a question
• Examples:
– What is prevalence of TB in Cambodia?
– Is prevalence of TB affected by age or sex?
• Quick and easy, good for measuring scale of
problem
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18. Observational: Cohort Studies –
“Prospective”
• Descriptive cohort study: follow a group (cohort) of
people with a risk factor and see if they develop a
disease
• Analytic cohort study:
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• Prospective – i.e.
they look forward
• Incidence of
disease

19. Example of Cohort Studies
• Is the risk of lung cancer higher among people
who smoke compared with non smokers?
– Sir Richard Doll’s “British Doctors’ Cohort Study”
• 35,000 British doctors – Smoking and Lung Cancer
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20. Observational: Case Control Studies –
“Retrospective”
• Compare cases (people with a disease) and
controls (people without the disease) to see if
they share a past exposure
– Look backwards to find a cause
• Cases and controls must be as similar as
possible
• This is to account for “confounding” – will talk
about this soon
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21. Case Control Studies: Examples
• Are people with lung cancer more likely to be
smokers than people without lung cancer?
– Define cases:
• people with lung cancer
– Define controls:
• People without lung cancer
– Define exposure:
• Smoking
• Does working in a plantation increase the risk of
malaria?
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22. Example: Malaria & Plantations 1
• Case report:
– A patient in Mondulkiri province has P. falciparum
malaria and he works and lives in a rubber plantation
• Case series:
– There are 15 patients in Mondulkiri with P. falciparum
malaria and they all work in a rubber plantation
• Cross sectional study:
– Test the blood of a samples of workers in 20
plantations in Cambodia to see if they have malaria
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23. Example: Malaria & Plantations 2
• Case control study:
– Ask 500 people with malaria and 500 people without
malaria where they work
• Descriptive cohort study:
– Take 100 new plantation workers who have never
been to a plantation and monitor them to see if they
develop malaria
• Analytic cohort study:
– Take 100 rural workers, assign 50 to work in a rice
paddy, and 50 to work in a plantation. Monitor them
to see if they develop malaria
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24. Confounding
• Before looking at experimental designs…
• Cases and controls must be similar
– Example: Does smoking cause lung cancer?
– Cases: smokers, controls: non-smokers
– difficult to tell if smoking causes lung cancer if
controls are all double the age of the cases
– Because cancer increases with age
– So age is a CONFOUNDER in this example
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25. Confounding
• A confounder is a variable that is associated with
the risk factor and the outcome
• Commonly age and sex
• Important to adjust for or control for confounders
• Rates of drowning increase with ice-cream
consumption
– Confounder is the SUMMER
– i.e. no real relationship between drowning and ice
cream
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26. Matching
• Matching is a way of making cases and controls
more similar
– How you do the matching divides case-control studies
into two types:
• Matched and unmatched designs
• Matched designs
– Each person matched with another person
• Unmatched designs
– Use frequency matching to broadly group cases and
controls
– E.g. same proportion of M/F, same mix of ages
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27. Experimental: Clinical Trials
• Compare treatments between a treatment
group and a control group
– Example is a new drug to treat asthma
– Give half the population the new drug
– Half an old drug
– See what the difference is
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28. Randomisation
• How do you allocate people to each group?
• You can do this randomly
– Like tossing a coin
– Or a random number generator
• So any differences between the groups will
only be by chance
• Gets rid of selection bias
– Researchers choose who to put in each group
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29. Experimental: Randomised Control
Trials (RCTs)
• Randomised clinical trial is called a
randomised control trial
• BLINDING:
– better if patient’s don’t know what group they are
in
• Reduced placebo effect
– Better still if investigator doesn’t know what group
patient is in
• Reduces treatment bias ( you think drug is working)
• Reduces assessment bias ( you think they are better)
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30. Placebo
• Psychological response which can lead to a
physical (i.e. biochemical) response
• Can effect outcomes in studies
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31. Summary
• Study design
• Data Collection
• Populations
• Sampling
• Types of Study
• Confounders
• Matching
• Placebo
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32. References
• Bowers, D. (2008) Medical Statistics from Scratch: An Introduction
for Health Professionals. USA: Wiley-Interscience.
• Grant, A. (2014) “Epidemiology for tropical doctors”. Lecture (S6)
from the Diploma of Tropical Medicine & Hygiene, London School of
Hygiene & Tropical Medicine.
• Greenhalgh, T. (1997) “How to read a paper” British Medical
Journal. Web, accessed April-May 2014 at
<http://www.bmj.com/about-bmj/resources-
readers/publications/how-read-paper>
• Hoskin, T (2012) Parametric and non-parametric: Demystifying the
Terms. Retrieved from <http://www.mayo.edu/mayo-edu-
docs/center-for-translational-science-activities-documents/berd-5-
6.pdf>
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