Statistical Thinking Experiments in the Real World Chapter 6 Experiments in the Real World Chapter 6 Chapter 6

Clinical Trials Experiments that study the effectiveness of medical treatments on actual patients. Chapter 6

Statistical Thinking Thought Question 1 Suppose you are interested in determining if drinking a glass of red wine each day helps prevent heartburn. You recruit 40 adults age 50 and older to participate in an experiment. You want half of them to drink a glass of red wine each day and the other half to not do so. You ask them which they would prefer, and 20 say they would like to drink the red wine and the other 20 say they would not. You ask each of them to record how many cases of heartburn they have in the next six months. At the end of that time period, you compare the results reported from the two groups. Give three reasons why this is not a good experiment. Chapter 6 Chapter 6

Randomization in Expts. Completely Randomized Design The experimental units are divided into different groups through a process of random selection. Chapter 6

Blocking Refers to the idea of only making comparisons within relatively similar groups of subjects In the smoking lung cancer example, we could “block” age, subjects in a block will have similar ages. Completely Randomized Block Design Chapter 6

Blocking and randomization in expts. Block to ensure fair comparisons with respect to factors known to be important Randomize to try to obtain comparability with respect to unknown factors Randomization also allows the calculation of how much the estimates made from the study data are likely to be in error Chapter 6

Blocking and randomization in expts. Block to ensure fair comparisons with respect to factors known to be important. “Block what you can and randomize what you cannot.” Chapter 6

“Blocking” vs “stratification” word used in describing an experimental design “Stratification” used in describing a survey or observational study Both refer to idea of only making comparisons within relatively similar groups of subjects Chapter 6

Blinding: Preventing people involved in an experiment from knowing which experimental subjects have received which treatment One may be able to blind subjects themselves people administering the treatments people measuring the results Double blind: Both the subjects and those administering the treatments have been blinded. Chapter 6

Statistical Thinking Chapter 6 Chapter 6

Hawthorne, Placebo, and Experimenter Effects Statistical Thinking Hawthorne, Placebo, and Experimenter Effects The problem: people may respond differently when they know they are part of an experiment. The solution: use placebos, control groups, and double-blind studies when possible. Chapter 6 Chapter 6

Hawthorne, Placebo, and Experimenter Effects : Case Study I Statistical Thinking Hawthorne, Placebo, and Experimenter Effects : Case Study I 1920’s Experiment by Hawthorne Works of the Western Electric Company What changes in working conditions improve productivity of workers? More lighting? Less lighting? Other changes? All changes improved productivity! Chapter 6 Chapter 6

Hawthorne, Placebo, and Experimenter Effects : Case Study II Statistical Thinking Hawthorne, Placebo, and Experimenter Effects : Case Study II Experimenter Effects in Behavioral Research (Rosenthal, 1976, Irvington Pub., p. 410) Teachers given a list of student names told these were students “who would show unusual academic development.” IQ was measured at end of year first graders on list: 15 points higher second graders on list: 9.5 points higher older: no striking difference Great expectations = self-fulfilling prophecy students were randomly selected (did not have high IQ) Chapter 6 Chapter 6

Experiments: Some Techniques Statistical Thinking Experiments: Some Techniques Matched Pairs to reduce a source of variability in responses the same or similar subjects receive each treatment Chapter 6 Chapter 6

(not) Double-Blinded: Case Study Statistical Thinking (not) Double-Blinded: Case Study Mozart, Relaxation and Performance on Spatial Tasks (Nature, Oct. 14, 1993, p. 611) Variables: Explanatory: Relaxation condition assignment Response: Stanford-Binet IQ measure Not double-blinded Participants know their treatment group Single-blinded Those measuring the IQ Chapter 6 Chapter 6

Double-Blinded: Case Study Statistical Thinking Double-Blinded: Case Study Quitting Smoking with Nicotine Patches (JAMA, Feb. 23, 1994, pp. 595-600) Variables: Explanatory: Treatment assignment Response: Cessation of smoking (yes/no) Double-blinded Participants do not know which patch they received Nor do those measuring smoking behavior Chapter 6 Chapter 6

Experiments: Difficulties and Disasters Statistical Thinking Experiments: Difficulties and Disasters Extraneous variables Confounding variables (in chapter 5) Interacting variables Hawthorne, placebo, and experimenter effects Refusals, non-adherers, dropouts Extending the results (generalizing) Chapter 6 Chapter 6

Interacting Variables Statistical Thinking Interacting Variables The problem: effect of explanatory variable on response variable may vary over levels of other variables. The solution: measure and study potential interacting variables. does the relationship between explanatory and response variables change for different levels of these interacting variables? if so, report results for different groups defined by the levels of the interacting variables. Chapter 6 Chapter 6

Interacting Variables: Case Study Statistical Thinking Interacting Variables: Case Study Quitting Smoking with Nicotine Patches (JAMA, Feb. 23, 1994, pp. 595-600) Researchers considered: smoker at home found this to be an interacting variable: Percent quitting Nicotine Placebo Smoker at home 31% 20% No smoker at home 58% 20% other variables: age, weight, depression no interactions found Chapter 6 Chapter 6

Extending the Results ( Can We Generalize? ) Statistical Thinking Extending the Results ( Can We Generalize? ) The problem: lack of generalizability due to: unrealistic treatments unnatural settings sample that is not representative of population The solution: Researchers should use natural settings with a properly chosen sample. Chapter 6 Chapter 6

Extending the Results : Case Study Statistical Thinking Extending the Results : Case Study Does Aspirin Prevent Heart Attacks? (NEJM, Jan. 28, 1988, pp. 262-264) Participants were measured in their natural setting (at home) Only healthy male physicians were participants Results may not apply to: male physical labourers women Chapter 6 Chapter 6

Example: # 6.16 Page111 The progress of a type of cancer differs in men and women. A clinical experiment to compare four therapies for this cancer therefore treats sex as a blocking variable. You have 500 male and 300 female patients who are willing to serve as subjects. Use a diagram to outline a block design for this experiment. Chapter 6

Example Cont. (b) What are the advantages of a block design over a completely randomized design using these 800 subjects? A block design allows the researchers to control for differences between men and women. If, for example, the treatment is effective for men but not women (or vice versa), the treatment might be found to be ineffective overall if both genders are mixed together. Chapter 6

Key Concepts Double-Blind Experiment Difficulties and Disasters Statistical Thinking Key Concepts Double-Blind Experiment Difficulties and Disasters Experimental Designs Completely Randomized Design Matched Pairs Design Block Design Chapter 6 Chapter 6

Quiz In what sense does random allocation make for comparisons that are fair or unbiased? What is a completely randomized design? What is blocking? Why do we use blocking in designing experiments? What is a control group and why are control groups used? What is a placebo effect? Chapter 6