Sampling Techniques for epidemiological studies

1. Sampling Techniques for epidemiological studies Biagio Pedalino

2. Objectives • To decide whether to conduct sampling • To choose among a list of sampling techniques • To define sampling • To describe sampling techniques

3. Approach We are normally interested in: - Distribution of a variable of interest in a specific population

4. Definition of population A population is defined by: • Its nature (an individual, housing, a firm etc.) • Its intrinsic characteristics (gender, housing type, industries) • Its localisation (city, neighbourhood etc.) who, where and when

5. Examples of populations • The inhabitants of London in 2010 • French nationality women living in Paris in 2010 • Children in elementary schools in France in 2010 • HIV seropositive patients in hospital centers in France in 2006 • Individuals recently entered in the French prisons in 2010

6. Variable of interest • We are interested in a (non random) variable yin a population U (of kunits) • It must bedefinedcarefully and accurately (i.e. vaccination status) Example • We are interested in HIV (variable y) prevalence in a population. The variable of interest is defined, for each unit k in the population by: y= 1 (HIV seropositive); 0 (otherwise, i.e. negative, not known, etc)

7. Example of research question • What is the proportion of individuals vaccinated against Hepatitis B in Lazareto, in October 2012 ? • Variable of interest? • Population? • Time? • How to obtain the information about the variable ? • Census • Build a sample

8. Example of research question • What is the proportion of individuals vaccinated against Hepatitis B in Lazareto, in October 2012 ? • Variable of interest? • Population? • Time? • How to obtain the information about the variable ? • Census • Build a sample

9. Example of research question • What is the proportion of children vaccinated against Hepatitis B in Minorca, in 2012 ? • Variable of interest? • Population? • Time? • How to obtain the information about the variable ? • Census • Build a sample

10. Example of research question • What is the proportion of children vaccinated against Hepatitis B in Minorca, in 2012 ? • Variable of interest? • Population? • Time? • How to obtain the information about the variable ? • Census • Build a sample

11. Definition of sampling Sampling is the process of selecting units from a specific population to collect information on a variable of interest

12. Sample Sampling frame Target population

13. Why bother in the first place? Get information from large populations with: • Reduced costs • Reduced field time • Increased accuracy

14. Definition of sampling terms Sampling frame • List of all the sampling units from which sample is drawn • Lists: e.g. all children < 5 years of age, households, health care units… Sampling scheme • Method of selecting sampling units from sampling frame • Randomly, convenience sample…

15. Definition of sampling terms Sampling unit (element) • Subject under observation from whom information is collected • Example: children <5 years, hospital discharges, health events… Sampling fraction • Ratio between sample size and population size • Example: 100 out of 2000 (5%)

16. Sampling errors • Systematic error (or bias) • Representativeness (validity) • Information bias • Sampling error (random error) • Precision

17. Validity • Sample should accurately reflect the distribution of relevant variable in population • Person (age, sex) • Place (urban vs. rural) • Time (seasonality) • Representativeness essential to generalise

18. Representativeness • Often used as synonym of validity of a sample • General rule: to build a sample representative of the whole population of interest • Erroneous

19. Which is the correct sample? Populations Women Men Women Men Women Men 50% 50% 50% 50% 50% 50% All of them are correct !!! Samples Women Men Women Men Women Men 50% 50% 70% 30% 70% 30%

20. Example: question • Aim of the study is to • estimate the national prevalence of elevated Blood Lead Level (BLL 100μg/L) • determine the risk factors associated to elevated BLL • Among children aged 1 to 6 years in Minorca in 2008-2009 We want to recruit 3000 children through hospitals Is the best design to select each unit with the same inclusion probability?

21. Example: answer • No! • ... because if the expected prevalence of elevated BLL is 1% • With a sample size = 3000, we expect to have 30 children with an elevated BLL in the sample • Small number to achieve the second objective of the study, i.e. identification of risk factors It is one of the reasons why we do not perform surveys with equally-representedindividuals in epidemiology

22. Example: solution • We want to over-represent children with an elevated BLL in the sample • If we know that some hospitals stand in areas where the risk of lead exposure in the dwellings is high, then we will over-represent hospitals in these high risk areas

23. Representativeness: take home messages • A sample is correct if randomly built • It is not necessary that the distributions in the sample and in the population are the same

24. Information bias • Systematic problem in collecting information • Inaccurate measuring • Scales (weight), ultrasound, lab tests(dubious results) • Badly asked questions • Ambiguous, not offering right options…

25. Sampling error (random error) • No sample is an exact mirror image of the population • Standard error depends on • size of the sample • distribution of character of interest in population • Size of error • can be measured in probability samples • standard error

26. Precision but no validity No precision Random error Systematicerror (bias) Quality of a sampling estimate Precision & validity

27. Survey errors: example Measuring height: • Measuring tape held differentlyby different investigators → loss of precision → large standard error • Tape too short → systematic error → bias (cannot be correctedretrospectively)

28. Types of sampling Non-probability samples Convenience samples Biased Subjective samples Based on knowledge In the presence of time/resource constraints Probability samples Random only method that allows valid conclusions about population and measurements of sampling error

29. Non-probability samples • Convenience samples (ease of access) • Snowball sampling (friend of friend….etc.) • Purposive sampling (judgemental) • You chose who you think should be in the study Probability of being chosen is unknown Cheaper- but unable to generalise, potential for bias

30. Example of a non-probability sample Take a sample of the population of Minorca to ask about possible exposures following a gastroenteritis outbreak Sampling frame: people walking aroundthe Es Castel harbour at noon on a Monday

31. Probability samples • Random sampling • Each unit has a known probability of being selected • Allows application of statistical sampling theory to results in order to: • Generalise • Test hypotheses

32. Methods used in probability samples • Simple random sampling • Systematic sampling • Stratified sampling • Multi-stage sampling • Cluster sampling

33. Simple random sampling • Principle • Equal chance/probability of each unitbeing drawn • Procedure • Take sampling population • Need listing of all sampling units (“sampling frame”) • Number all units • Randomly draw units

35. Simple random sampling 5 20 27 29 32 40

36. Simple random sampling • Advantages • Simple • Sampling error easily measured • Disadvantages • Need complete list of units • Units may be scattered and poorly accessible • Heterogeneous population important minorities might not be taken into account

37. Systematic sampling Principle Select sampling units at regular intervals(e.g. every 20th unit) Procedure Arrange the units in some kind of sequence Divide total sampling population by the designated sample size (eg 1200/60=20) Choose a random starting point (for 20, the starting point will be a random number between 1 and 20) Select units at regular intervals (in this case, every 20th unit), i.e. 4th, 24th, 44th etc.

38. Systematic sampling Advantages Ensures representativity across list Easy to implement Disadvantages Need complete list of units Periodicity-underlying pattern may be a problem (characteristics occurring at regular intervals)

39. More complex sampling methods

40. Stratified sampling When to use Population with distinct subgroups Procedure Divide (stratify) sampling frame into homogeneous subgroups (strata) e.g. age-group, urban/rural areas, regions, occupations Draw random sample within each stratum

41. Selecting a sample with probability proportional to size Stratified sampling Area Population Proportion Sample size Sampling size fraction 1000 x 0.7 = 700 10 % Urban 7000 70% Rural 3000 30% 1000 x 0.3 = 300 10 % 1000 Total 10000

42. Stratified sampling Advantages Can acquire information about whole population and individual strata Precision increased if variability within strata is smaller (homogenous) than between strata Disadvantages Sampling error is difficult to measure Different strata can be difficult to identify Loss of precision if small numbers in individual strata (resolved by sampling proportional to stratum population)

44. Multiple stage sampling Principle: • Consecutive sampling • Example : sampling unit = household • 1st stage: draw neighbourhoods • 2nd stage: draw buildings • 3rd stage: draw households

45. Cluster sampling • Principle • Whole population divided into groups e.g. neighbourhoods • A type of multi-stage sampling where all units at the lower level are included in the sample • Random sample taken of these groups (“clusters”) • Within selected clusters, all units e.g. households included (or random sample of these units) • Provides logistical advantage

48. Number of cluster needed=25

49. Number of cluster needed=25