Chapter 6. Experimental Studies

Transcription

1 Chapter 6 Experimental Studies 1

2 Chapter 6 Outline 6.1 Introduction 6.2 Historical perspective 6.3 General concepts 6.4 Data analysis 2

3 Epi Experiments ( Trials ) Trials - from the French trier (to try) Clinical trial test therapeutic interventions applied to individuals (e.g., chemotherapy trial) Field trial test preventive interventions applied to individuals (e.g., vaccine trial) Community trial test interventions applied at the aggregate level (e.g., fluoridation of public water trial) 3

4 Illustrative Example 6.1 WHI Clinical Trial 40 US clinical centers Recruitment: Exposure randomized, double blinded: estrogen + progestin vs identical looking placebo Average follow-up 5.2 years 1 outcome = Coronary Heart Disease Risk of CHD in the exposed cohort 164 R =19.3 per Risk of CHD in the nonexposed cohort 122 R = 15.1 per

5 Survival curves WHI estrogen trial 5

6 Illustrative Example 6.2 Vitamin A Community Trial 450 Sumatran villages w ith high childhood mortality rates Exposure = Vitamin A supplementation program vs. no intervention Random allocation of intervention: 229 treatment villages, 221 control villages Childhood mortality rate in exposed village R , per 1000 Childhood mortality rate in control villages R per , 231 6

7 Historical perspective Read in text Biblical reference Van Helmont s proposal (1662) James Lind s scurvy experiment (1753) Modern trials Polio trail (1954) MRFIT (1982) WHI (2002) 7

8 Natural Experiments Natural conditions that mimic an experiment Example: French surgeon Paré ( ) ran out of boiling oil to treat wounds forced to use an innocuous lotion for treatment noticed vastly improved results Not a true experiment because the intervention was not allocated by study protocol 8

9 Selected Concepts Experimental Design 1. The control group (and the placebo effect) 2. Randomization & comparability 3. Follow-up and outcome ascertainment 4. Intention-to-treat vs. perprotocol analysis 9

10 The effects of an exposure can only be judged in comparison to what would happen in its absence Treatment Group Exposed to the intervention Control Group Not exposed to intervention 10

11 Illustration: MRFIT Multiple Risk Factor Intervention Trial (1982) 12,855 high risk men, 35- to 57-years-old Randomly assigned multi-factor Intervention ( special intervention ) group or usual care group Study endpoints: Coronary Heart Disease (CHD) mortality and overall mortality Results described here: No significant difference in endpoint rates Also, lower than expected rates in both groups Had no control group had been used, the intervention might have unjustifiably been declared a success 11

12 Polio Field Trial (1954) Polio rates (per 100,000) Placebo group 69 Refusers 46 Vaccinated group 28 Had Refusers been used as the control group effects of the intervention would have been underestimated Am J Pub Health, 1957, 47: Dr. Jonas Salk,

13 The placebo effect Improvements attributed to an inert intervention Despite popular belief, placebos have no real effect False impressions of placebo effects can be explained by spontaneous improvement, fluctuation of symptoms, regression to the mean, additional treatment, conditional switching of placebo treatment, scaling bias, irrelevant response variables, answers of politeness, experimental subordination, conditioned answers, neurotic or psychotic misjudgment, psychosomatic phenomena, misquotation, etc (Kienle & Kiene, 1997 ) 13

14 The Hawthorne Effect Improvements in behavior because subjects know they are being observed effects unrelated to the intervention Initially observed in industrial psychology experiments in the 1930 A comparable attention bias effect is seen in trials 14

15 Randomization and Comparability Randomization works by balancing potential confounding factors in the treatment & control group like-to-like comparisons differences observed at completion of trial due to the treatment or to chance 15

16 Checking Group Comparability WHI Trial 16

17 Follow-up & Outcome Ascertainment Follow-up screening for study outcomes and confirming the outcomes as true (adjudication) Study outcomes based on case definitions (uniform and valid criteria for case ascertainments) The importance of blinding Single blinding Double blinding Triple blinding 17

18 Intention-to-treat vs. per-protocol Intention-to-treat (ITT) = analyze as randomized (regardless of compliance) Per protocol (PP) = analyze only those that completed the protocol Effectiveness = real world effectiveness (including noncompliance) Efficacy = effect under ideal conditions (e.g., complete compliance) analysis

19 Human Subjects Ethics now covered in Ch 5 The Belmont Report Respect for individuals Beneficence Justice IRB oversight Data Safety Monitoring Board (DSMB) Informed consent Equipoise 19

20 Equipoise Equipoise balanced doubt Cannot knowingly expose a participant to harm Cannot withhold known benefit to study subjects What s left? (ANS: equipoise) Is equipoise the over-riding principles of trial ethics? 20

21 Advocacy vs. Scientific Ethics Advocacy, partisan, corporate, advertising, and political ethics: Plan with the end result in mind. Scientific ethics: A bending over backwards to prove oneself wrong. I cannot give any scientist of any age any better advice than this: The intensity of the conviction that a hypothesis is true has no bearing on whether it is true or not. Sir Peter Medewar 21

22 Simple Analysis: Relative Effect Data = WHI trial E = HRT vs. placebo D = CHD (yes or no) Average follow-up: 5.2 years 164 R per R per RR R R per per 1000 How to say it: HRT increased the risk of CHD by 28% in relative terms. 22

23 Simple Analysis: Absolute Effect Data = WHI trial E = HRT vs. placebo D = CHD (yes or no) Average follow-up: 5.2 years 164 R = per 1000 women R = per 1000 women 8102 RD R1 R / / per 1000 How to say it: In absolute terms, there was an additional 4.22 CHD cases for every thousand women using HRT over 5.2 years. 23

24 Simple Analysis: Efficacy same as RRD but without the minus sign 450 Sumatra villages randomly assigned to either a vitamin A supplementation or not 53 R per , R per , per 1000 RR per 1000 Efficacy = 1 RR How to say it: Vitamin A supplementation was 34% effective in preventing childhood mortality. This provides a suitable taking-off point for the discussion of Rothman, K. J., Adami, H. O., & Trichopoulos, D. (1998). Should the mission of epidemiology include the eradication of poverty? Lancet, 352(9130),

25 Simple Analysis: Absolute Effect 450 Sumatra villages randomly assigned to either a vitamin A or control 53 deaths R per 1000R 0 10,919 children 75 deaths 10, 231children 7.33 per 1000 RD R R per per per 1000 How to say it: The effect was to reduce mortality by 2.47 deaths per 1000 children over the period of observation. 25

26 OpenEpi.com for data analysis Counts menu for incidence proportions, prevalences, and casecontrol data Person Time menu for rate data Descriptive and inferential (confidence intervals and P-values) statistics Can be used as a learning tool 26

27 6.1 Bicycle helmet campaign You want to test whether a public awareness campaign about bicycle safety at elementary schools will increase bicycle helmets use among school-aged children. To test this intervention, you identify 12 elementary schools, half of which will be randomly assigned to participate in a school-wide bicycle helmet awareness program. The other 6 schools will serve as controls and will receive no special intervention. Research assistants will determine the percentage of bicyclists wearing helmets at standard locations in neighborhoods of each of the schools before and after the intervention. (A) What is the unit of intervention in this study? (The unit of intervention refers to the level at which the intervention is randomized. This may differ from the unit of observation, which is the unit upon which the outcome is measured.) (B) What is the unit of observation in this study? (C) Even though the intervention was randomized in this study, there were only 6 treatment schools and 6 controls schools. Therefore, there is a good chance that treatment and control schools will differ with respect to important characteristics such as socioeconomic status. Can you think of a way to control for socioeconomic status through a randomization or study design approach? 27

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