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Topic 3
Getting the Data!
Dr Luke Kane
April 2014
Topic 3: Getting the Data 1
Outline
• Study design
• Data Collection
• Populations
• Sampling
• Types of Study
• Confounders
• Matching
• Placebo
Topic 3: Getting the Data 2
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
Topic 3: Getting the Data 3
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?
Topic 3: Getting the Data 4
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?
Topic 3: Getting the Data 5
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
Topic 3: Getting the Data 6
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
Topic 3: Getting the Data 7
Sampling: Populations
Topic 3: Getting the Data 8
• 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
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%
Topic 3: Getting the Data 9
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
Topic 3: Getting the Data 10
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!
Topic 3: Getting the Data 11
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?
Topic 3: Getting the Data 12
Types of Study
• Case reports
• Cross sectional studies
• Case-control studies (“Retrospective studies”)
• Cohort studies (“Prospective studies”)
• Randomised controlled trials (RCTs)
• Ecological studies
Topic 3: Getting the Data 13
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
Topic 3: Getting the Data 14
Observational Experimental
Case Series or Case Report Clinical trials
Cross Section study Randomised controlled trial
Cohort Study
Case Control study
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
Topic 3: Getting the Data 15
Case Report: Examples
Topic 3: Getting the Data 16
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]
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
Topic 3: Getting the Data 17
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:
Topic 3: Getting the Data 18
• Prospective – i.e.
they look forward
• Incidence of
disease
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
Topic 3: Getting the Data 19
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
Topic 3: Getting the Data 20
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?
Topic 3: Getting the Data 21
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
Topic 3: Getting the Data 22
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
Topic 3: Getting the Data 23
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
Topic 3: Getting the Data 24
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
Topic 3: Getting the Data 25
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
Topic 3: Getting the Data 26
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
Topic 3: Getting the Data 27
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
Topic 3: Getting the Data 28
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)
Topic 3: Getting the Data 29
Placebo
• Psychological response which can lead to a
physical (i.e. biochemical) response
• Can effect outcomes in studies
Topic 3: Getting the Data 30
Summary
• Study design
• Data Collection
• Populations
• Sampling
• Types of Study
• Confounders
• Matching
• Placebo
Topic 3: Getting the Data 31
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>
Topic 3: Getting the Data 32

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Statistics for the Health Scientist: Basic Statistics III

  • 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 Topic 3: Getting the Data 2
  • 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 Topic 3: Getting the Data 3
  • 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? Topic 3: Getting the Data 4
  • 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? Topic 3: Getting the Data 5
  • 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 Topic 3: Getting the Data 6
  • 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 Topic 3: Getting the Data 7
  • 8. Sampling: Populations Topic 3: Getting the Data 8 • 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% Topic 3: Getting the Data 9
  • 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 Topic 3: Getting the Data 10
  • 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! Topic 3: Getting the Data 11
  • 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? Topic 3: Getting the Data 12
  • 13. Types of Study • Case reports • Cross sectional studies • Case-control studies (“Retrospective studies”) • Cohort studies (“Prospective studies”) • Randomised controlled trials (RCTs) • Ecological studies Topic 3: Getting the Data 13
  • 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 Topic 3: Getting the Data 14 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 Topic 3: Getting the Data 15
  • 16. Case Report: Examples Topic 3: Getting the Data 16 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 Topic 3: Getting the Data 17
  • 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: Topic 3: Getting the Data 18 • 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 Topic 3: Getting the Data 19
  • 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 Topic 3: Getting the Data 20
  • 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? Topic 3: Getting the Data 21
  • 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 Topic 3: Getting the Data 22
  • 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 Topic 3: Getting the Data 23
  • 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 Topic 3: Getting the Data 24
  • 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 Topic 3: Getting the Data 25
  • 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 Topic 3: Getting the Data 26
  • 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 Topic 3: Getting the Data 27
  • 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 Topic 3: Getting the Data 28
  • 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) Topic 3: Getting the Data 29
  • 30. Placebo • Psychological response which can lead to a physical (i.e. biochemical) response • Can effect outcomes in studies Topic 3: Getting the Data 30
  • 31. Summary • Study design • Data Collection • Populations • Sampling • Types of Study • Confounders • Matching • Placebo Topic 3: Getting the Data 31
  • 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> Topic 3: Getting the Data 32