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Statistics for the Health Scientist: Basic Statistics III

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

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Statistics for the Health Scientist: Basic Statistics III

  1. 1. Topic 3 Getting the Data! Dr Luke Kane April 2014 Topic 3: Getting the Data 1
  2. 2. Outline • Study design • Data Collection • Populations • Sampling • Types of Study • Confounders • Matching • Placebo Topic 3: Getting the Data 2
  3. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 31. Summary • Study design • Data Collection • Populations • Sampling • Types of Study • Confounders • Matching • Placebo Topic 3: Getting the Data 31
  32. 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

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