Population sampling RSS6 2014
- 1. Population Sampling Dr Fayssal M Farahat MD, MSc, PhD Public Health Consultant Infection Prevention and Control Department Associate Professor, Faculty of Medicine, Menoufia University, Egypt Research Fellow, Oregon Health & Science University (OHSU), USA
- 2. Complete set of people with a specified set of characteristics SAMPLE = Subset of The population Clinical & demographic Teenagers with asthma Teenagers with asthma living in Jeddah in 2013 Population
- 3. Study subjects Truth in the universe Target population Findings in the study Generalisability Infer
- 4. Study subjects Truth in the universe Target population Generalisability design Specify clinical, demographic, & geographic characteristics Findings in the study Specify accessible population and approach to select them
- 5. Study subjects Target population FIRST Whether the sample differs from the population Workers General population
- 6. Study subjects Truth in the universe Target population Findings in the study SECOND Validity of generalizingfrom study subjects to target population Infer Association bet HTN & CHD in a sample of Jeddah adults Same Association Exists in Saudi adults
- 7. IS IT .. Can Be .. • No sample is the exact mirror image of the population. • Select samples with acceptable errors. Representative Generalized
- 8. Inclusion criteria • Main characteristics of the target population. Clinical Demographic Age, sex, Race Geographic
- 9. A 5-year trial of calcium supplementation for preventing osteoporosis Demographic Clinical Geographic Temporal White females 50 – 60 ys In good general health Patients attending PHC Jeddah Bet Jan 1 – Dec 31 of next year Men Black female HTN Paraplegia Metastatic lung dis
- 10. • Including alcoholics in the osteoporosis study would expand generalizability and allow to study alcohol consumption as a cause of demineralization. • Exclude alcoholics to avoid a big problem due to loss of follow-up.
- 11. • Exclusion in clinical trials is more specific and may be mandated by ethical considerations.
- 12. BE CAREFULL ..! • EXCLUSION might threaten the validity of generalizing the findings to the population.
- 13. Sampling ..
- 14. Sampling .. Minimal cost Maximum speed Maximum accuracy Impossible to examine the entire population
- 15. Terminology • Sampling unit (element) – Subject under observation on which 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. Terminology • Sampling frame – List of all the sampling units from which sample is drawn • Lists: e.g. children < 5 years of age, households, health care units… • Sampling technique – Method of selecting sampling units from sampling frame • Randomly, convenience sample…
- 18. Samples Probability Non Probability Probability of selection KNOWN Probability of selection UNKNOWN
- 19. Non probability samples Convenience Quota Snowball Some elements of the population have no chance of selection “out of coverage” ease of access friend ….etc Specific quota for subgroup PurposivePurpose
- 20. Convenience samples Consecutive design a practical approach for most clinical research projects Entire accessible population over a long enough period Avoid seasonal variations Avoid changes over time
- 21. Generalisability Gold Standard for Probability Sampling
- 22. Each unit has specified chance of selection RANDOM
- 23. the choice of one subject will not affect the chance of other subjects being chosen RANDOM
- 25. Simple Random Sample بسيطة عشوائية عينة Systematic Sample منتظمة عشوائية عينة(متوالية) Stratified Random Sample طبقية عشوائية عينة Cluster Sample عنقودية عشوائية عينة Random Sample Multistage sample احلرالم متعددة عشوائية عينة
- 26. Simple Random Sample بسيطة عشوائية عينة Ideal Bowel Random number table Computer-generated
- 29. 57172 42088 70098 11333 26902 29959 43909 49607 33883 87680 28923 15659 09839 45817 89405 70743 77950 67344 10609 87119 15859 74577 42791 75889 11607 11596 01796 24498 17009 67119 00614 49529 56149 55678 38169 47228 49931 94303 67448 31286 80719 65101 77729 83949 83358 75230 56624 27549 93809 19505 82000 79068 45552 86776 48980 56684 40950 86216 48161 17646 24164 35513 94057 51834 12182 59744 65695 83710 41125 14291 74773 66391 13382 48076 73151 48724 35670 38453 63154 58116 38629 94576 48859 75654 17152 66516 78796 73099 60728 32063 12431 23898 23683 10853 04038 75246 01881 99056 46747 08846 01331 88163 74462 14551 23094 29831 95387 23917 07421 97869 88092 72201 15243 21100 48125 05243 16181 39641 36970 99522 53501 58431 68149 25405 23463 49168 02048 31522 07698 24181 01161 01527 17046 31460 91507 16050 22921 25930 79579 43488 13211 71120 91715 49881 68127 00501 37484 99278 28751 80855 02035 10910 55309 10713 36439 65660 72554 77021 46279 22705 92034 90892 69853 06175 61221 76825 18239 47687 50612 84077 41387 54107 09190 74305 68196 75634 81415 98504 32168 17822 49946 37545 47201 85224 38461 44528 30953 08633 08049 68698 08759 45611 07556 24587 88753 71626 64864 54986 38964 83534 60557 50031 75829 05622 30237 77795 41870 26300 Table of random numbers 1200 Students 100
- 32. EXCEL
- 33. Systematic Sample منتظمة عشوائية عينة(متوالية) Random Sample Select sample at regular intervals based on sampling fraction.
- 34. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 46 47 48 49 50 51 52 53 54 55 ……..
- 35. • N = 1200, and n = 60 1200/60 = 20 • List persons from 1 to 1200 • Randomly select a number between 1 and 20 (ex : 8) 1st person selected = the 8th on the list 2nd person = 8 + 20 = the 28th etc ..... Systematic Sample منتظمة عشوائية عينة(متوالية)
- 37. Stratified Random Sample طبقية عشوائية عينة Random Sample
- 38. Stratified sampling • When the sampling frame contains clearly different categories (strata) – Males and females – Social classes • What we do : – Classify population into internally homogeneous subgroups (strata) – Draw sample in each strata
- 40. Stratified sampling (example) • N=12000 K=3 • N1=6000 N2=4000 N3=2000 • N=240 • n1= (240×6000)/12000 = 120 • n2= (240×4000)/12000 = 80 • n3= (240×2000)/12000 = 40
- 41. Cluster Sample عنقودية عشوائية عينة Random Sample
- 42. –In selected clusters, all units or proportion (sample) of units included. –All students in a classroom. Cluster Sample عنقودية عشوائية عينة
- 43. Random Sample Multistage sample احلرالم متعددة عشوائية عينة
- 44. – 1rst stage : drawing regions – 2nd stage : drawing city from each region. – 3rd stage : drawing areas from each city. – 4th stage: drawing houses from each area. Multistage sample احلرالم متعددة عشوائية عينة Determine vaccination coverage in a country
- 45. Section 4 Section 5 Section 3 Section 2Section 1
- 46. Simple Random Sample بسيطة عشوائية عينة Systematic Sample منتظمة عشوائية عينة(متوالية) Stratified Random Sample طبقية عشوائية عينة Cluster Sample عنقودية عشوائية عينة Random Sample Multistage sample احلرالم متعددة عشوائية عينة
- 47. • Random sample of the gallbladder surgery patients. • Reviewing hospital records of patients with lung cancer from allover the country.
- 48. The use of random numbers is generally preferable to using systematic random. Agree Dis-agree
- 49. The use of random numbers is generally preferable to using systematic random. Agree The regularity of selection can coincide by chance with some unforeseen regularity in the presentation of the material for study – Hospital appointments being made from patients from certain practices on certain days of the week
- 50. 2nd Advanced Course on Applied Medical Research and Biostatistics 22 – 24 March 201050 The Errors of Research No study is free of errors The goal is to maximize the validity The best is to prevent errors from occurring (design & Implementation) Errors can be addressed in the analysis
- 51. 2nd Advanced Course on Applied Medical Research and Biostatistics 22 – 24 March 201051 Random Error Wrong result due to chance 20% 18 19 21 22 28 12 Sample Size precision
- 52. 2nd Advanced Course on Applied Medical Research and Biostatistics 22 – 24 March 201052 Systematic Error Wrong result due to BIAS Sample (respondents) or Measurement (unclear Q) OR Accuracy Sample size
- 53. Response rate = proportion of eligible persons who agree to enter the study. People difficult to reach. People refused to enter. ….. ? 25%
- 54. • Acquire additional information on the non- respondents. or best • Deal with non-response bias at the outset
- 55. Deal with non-response bias at the outset • Series of repeated contacts (mail, telephone, home visit). • Choosing a design that avoids invasive and uncomfortable tests. • Using brochures and discussion to minimize the anxiety and discomfort. • Providing incentives (reimbursing the costs of transportation and providing the results of the tests).
- 56. To anticipate .. • Pre-test help to estimate the response rate and how much to increase to get your required sample. • During the actual study, monitor the non- response and find solutions to overcome before continue to next sample.
- 57. Who will be included Technique of selection How many ..?
- 58. Practical issues • Allow for drop-outs and non-consent when planning sample size, particularly when subjects are being followed up for a long period of time. • A pilot study may be necessary to obtain suitable estimates.
- 59. Dr. Fayssal Farahat Thank you Email: farahatfa@ngha.med.sa fmfayssal@yahoo.com