Types of Studies

1. Types of Studies

2. Research classifications • Observational vs. Experimental Observational – researcher collects info on attributes or measurements of interest, but does not influence results. Experimental – researcher deliberately influences events and investigates the effects of the intervention, e.g. clinical trials and laboratory experiments. We often use these when we are interested in studying the effect of a treatment on individuals or experimental units.

3. Experiments & Observational Studies We conduct an experiment when it is (ethically, physically etc) possible for the experimenter to determine which experimental units receive which treatment.

4. Experiments & Observational Studies Experiment Terminology Experimental Unit Treatment Response • patient drug cholesterol • heroin addict rehab program relapse • student prior knowledge instructorrating of lecture • patient magnets foot pain

5. Experiments & Observational Studies In an observational study, we compare the units that happen to have received each of the treatments.

6. Unit “Treatment” Response patient smoking lung cancer inmate race Sentence Length subject gender opinion Experiments & Observational Studies e.g. You cannot set up a control (non-smoking) group and treatment (smoking) group. Observational Study

7. Experiments & Observational Studies Note: Only a well-designed and well-executedexperiment can reliably establish causation. An observational study is useful for identifying possible causes of effects, but it cannot reliably establish causation.

8. Basic Experimental Design

9. 1. Completely Randomized Design The treatments are allocated entirely by chance to the experimental units.

10. 1. Completely Randomized Design Example: Which of two varieties of tomatoes (A & B) yield a greater quantity of market quality fruit? Factors that may affect yield: • different soil fertility levels • exposure to wind/sun • soil pH levels • soil water content etc.

11. (B) (B) (B) (B) What if the field sloped upward from left to right? 1. Completely Randomized Design Divide the field into plots and randomly allocate the tomato varieties (treatments) to each plot (unit). 8 plots – 4 get variety A UPHILL (A) (B) (A) (A) (A) (B) (B) (A) (A) (B) Randomly assign A & B varieties in each strip of similar elevation. Discuss for ½ Minute

12. 1. Completely Randomized Design Note: Randomization is an attempt to make the treatment groups as similar as possible — we can only expect to achieve this when there is a large number of plots or experimental units.

13. 2. Blocking Group (block) experimental units by some known factor and then randomize within each block in an attempt to balance out the unknown factors. Use: • blocking for known factors (e.g. slope of field in previous example) and • randomization for unknown factors to try to “balance things out”.

14. 2. Blocking Example continued: It is recognized that there are two areas in the field – well drained and poorly drained. Partition the field into two blocks and then randomly allocate the tomato varieties to plots within each block.

15. 1 (A) 2 (B) 3 (A) 4 (B) 2. Blocking Well drained Poorly drained How should we allocate varieties to plots? Discuss in groups for 1/2 minute. 1 (B) 2(A) 4 (B) 3(A) 6(A) 5(A) 8 (B) 7 (B) Randomly assign types to 4 well drained plots and then to the 8 poorly drained plots.

16. 2. Blocking Example 2: Comparing Three Pain Relievers for Headache Sufferers • How could blocking be used to increase precision of a designed experiment to compare the three pain relievers? • What are some other design issues?

17. Example 3: Comparing 17 Different Leg Wraps/Boots Used on Race Horses • 17 “boots” tested & each boot is tested n = 5 times. Why? • Because of the time constraints all boots were not tested on the same day. • 8 tested 1st day, 5 tested 2nd day, 4 tested 3rd day. • Leg was placed in freezer and thawed before the 2nd and 3rd days of testing. Days 1 and 2 were about a week apart and days 2 and 3 were a few days apart.

18. Forces readings obtained from cadaver leg when no boot was used. Horse Boots (cont’d) • What problems do you foresee with this experimental design? Discuss • What actually happened? What are the implications of these results? Discuss

19. Horse Leg Wraps (cont’d) FINAL BOOT COMPARISONS

20. Horse Legs Wraps (cont’d) • What should have been done? Discuss

21. 3. People as Experimental Units Example: Cholesterol Drug Study – Suppose we wish to determine whether a drug will help lower the cholesterol level of patients who take it. How should we design our study? Discuss for two minutes in groups.

22. Polio Vaccine Example

23. Polio Vaccine Example Dr. Jonas Salk, vaccine pioneer 1914-95 Iron Lung

24. The Salk Vaccine Field Trial • 1954 Public Health Service organized an experiment to test the effectiveness of Salk’s vaccine. • Need for experiment: • Polio, an epidemic disease with cases varying considerably from year to year. A drop in polio after vaccination could mean either: • Vaccine effective • No epidemic that year

25. The Salk Vaccine Field Trial Subjects: 2 million, Grades 1, 2, and 3 • 500,000 were vaccinated • (Treatment Group) • 1 million deliberately not vaccinated • (Control Group) • 500,000 not vaccinated - parental permission denied

26. The Salk Vaccine Field Trial NFIP Design • Treatment Group: Grade 2 • Control Group: Grades 1 and 3 + No Permission Flaws ? Discuss for 30 seconds. • Polio contagious, spreading through contact. i.e. incidence could be greater in Grade 2 (bias against vaccine), or vice-versa. • Control group included children without parental permission (usually children from lower income families) whereas Treatment group could not (bias against the vaccine).

27. The Salk Vaccine Field Trial Double-Blinded Randomized Controlled Experimental Design • Control group only chosen from those with parental permission for vaccination • Random assignment to treatment or control group • Use of placebo (control group given injection of salted water) • Diagnosticians not told which group the subject came from (polio can be difficult to diagnose) • i.e., a double-blind randomized controlled experiment

28. Size of Rate per (NFIP rate) group 100,000 Treatment 200,000 28 (25) Grade 2 Control 200,000 71 (54) Grade1/3 Noconsent 350,000 46 (44) Grade 2 The Salk Vaccine Field Trial The double-blind randomized controlled experiment (and NFIP) results

29. 3. People as Experimental Units • control group: • Receive no treatment or an existing treatment • blinding: • Subjects don’t know which treatment they receive • double blind: • Subjects and administers / diagnosticians are blinded • placebo: • Inert dummy treatment

30. 3. People as Experimental Units • placebo effect: • A common response in humans when they believe they have been treated. • Approximately 35% of people respond positively to dummy treatments - the placebo effect

31. Observational Studies • There are two major types of observational studies: prospective and retrospective studies

32. Observational Studies 1. Prospective Studies • (looking forward) • Choose samples now, measure variables and follow up in the future. • E.g., choose a group of smokers and non-smokers now and observe their health in the future.

33. Observational Studies • Looks back at the past. • E.g., a case-control study • Separate samples for cases and controls (non-cases). • Look back into the past and compare histories. • E.g. choose two groups: lung cancer patients and non-lung cancer patients. Compare their smoking histories. • 2. Retrospective Studies • (looking back)

34. Observational Studies Important Note: 1. Observational studies should use some form of random sampling to obtain representative samples. • Observational studies cannot reliably establish causation.

35. Controlling for various factors • A prospective study was carried out over 11 years on a group of smokers and non-smokers showed that there were 7 lung cancer deaths per 100,000 in the non-smoker sample, but 166 lung cancer deaths per 100,000 in the smoker sample. • This still does not show smoking causes lung cancer because it could be that smokers smoke because of stress and that this stress causes lung cancer.

36. Controlling for various factors • To control for this factor we might divide our samples into different stress categories. We then compare smokers and non-smokers who are in the same stress category. • This is called controlling for a confounding factor.

37. Example 1 • “Home births give babies a good chance” NZ Herald, 1990 • An Australian report was stated to have said that babies are twice as likely to die during or soon after a hospital delivery than those from a home birth. • The report was based upon simple random samples of home births and hospital births. Q: Does this mean hospitals are dangerous places to have babies in Australia? Why or why not?Discuss for 1 minute in groups.

38. Example 2 • “Lead Exposure Linked to Bad Teeth in Children” ~ USA Today The study involved 24,901 children ages 2 and older. It showed that the greater the child’s exposure to lead, the more decayed or missing teeth. Q: Does this show lead exposure causes tooth decay in children? Why or why not? Discuss for 1 minute.

39. Example 2 ~ cont’d • “Lead Exposure Linked to Bad Teeth in Children” ~ USA Today Researcher: “We controlled for income level, the proportion of diet due to carbohydrates, calcium in the diet and the number of days since the last dental visit.”

40. Figure 1.5 pg. 9

41. Additional Example 1 – Determine Whether Age at 1st Pregnancy is a Risk Factor for Cervical Cancer How might we proceed? Discuss

42. Additional Example 2 – Determine whether prior knowledge about an instructor effects the rating given to a lecture presentation. How might we proceed? Discuss

43. Additional Example 3 – Identify factors related to fall-to-fall retention of WSU students. How might we proceed? Discuss

44. Surveys and Polls(and the errors inherent in them)

45. Sampling/Chance/ Random Errors Nonsampling Errors Selection bias Interviewer effects Non-response bias Behavioural considerations Self selection Transfer findings Question effects Survey-format effects Sampling

46. sample population Sources of Nonsampling Errors Selection bias Population sampled is not exactly the population of interest. e.g. KARE 11 poll, telephone interviews

47. Sources of Nonsampling Errors Non-response bias People who have been targeted to be surveyed do not respond. Non-respondents tend to behave differently to respondents with respect to the question being asked.

48. 1936 U.S. Election • Country struggling to recover from the Great Depression • 9 million unemployed • 1929-1933 real income dropped by 1/3

49. 1936 U.S. Election • Candidates: • Franklin D Roosevelt (Democrat) • Deficit financing - “Balance the budget of the people before balancing the budget of the Nation” • Albert Landon (Republican) • “The spenders must go!”

50. 1936 U.S. Election • Roosevelt’s percentage • Digest prediction of the election result • Gallup’s prediction of the Digest prediction • Gallup’s prediction of the election result • Actual election result 43% 44% 56% 62% • Digest sent out 10 million questionnaires to people on club membership lists, telephone directories etc. • received 2.4 million responses • Gallup Poll used another sample of 50,000 • Gallup used a random sample of 3,000 from the Digest lists to predict Digest outcome