1 what do these tell us? a higher percentage of people in hospitals die each day than do people not...
DESCRIPTION
3 BASIC EXPERIMENTATION Overview (I)Research Methods Experimentation Other approaches Medical practice (II)Measurement and diagnosis (III) Descriptive and Inferential StatisticsTRANSCRIPT
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What do these tell us?
• A higher percentage of people in hospitals die each day than do people not in hospitals
• Long Island has 3% higher breast cancer rate, so a survey examines environmental pollutants. Results suggest pollutants are not the cause.
• John hears of a new home remedy for Disease X that worked well for two friends so he wants to try it.
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BEHAVIORAL SCIENCE
Experimentation, learning, cognition
Instructor: Brian Ross (2157 Beckman) phone: 244-1095, 333-8745 email: [email protected]
Readings: On reserve in the Medical School Library.
Examination: Based on lecture material.
Lecture orientation: Basic science . . . . . . . . applications.
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BASIC EXPERIMENTATION
Overview
(I) Research Methods
Experimentation
Other approaches
Medical practice
(II) Measurement and diagnosis
(III) Descriptive and Inferential Statistics
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(I) Research Methods
(A) Experimentation allow cause-effect inference
Key Characteristics: Manipulate Independent Variables (IV) & assess
effects on Dependent Variables (DV)
Importance of eliminating confounding factorswhich might co-vary with IVs and influence DVs
Goal to show that change in IV CAUSES change in DV so need to manipulate IV & hold everything else constant
BUT HOW . . . . . . . . . . . . . . . ?
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(A) Experimentation allow cause-effect inference
Potential problems:1) Reactive (Hawthorne) effects
2) Selection bias-- if subjects choose which condition they are in
3) Demand characteristics
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(A) Experimentation allow cause-effect inference
Solutions:Use control group with placebo treatment
Random assignment of subjects to groups (or matching on important factors)
Both subjects (blind) & subjects/experimenters (double blind) don’t know what treatment the subjects have received.
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(B) Other Approaches
(a) Observational (field study) and surveys
* good way to generate hypotheses
* no intervention is required
* can establish relationships among variables
** But … correlation does not imply causation
(b) Case studies * Good way to generate hypotheses
* But - few subjects, no control group, no random selection
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(C) Medical practice as an experimental setting
* No control group - must treat everyone
* Non-random (and small) sample
* Patients don’t always comply with instructions
* Physicians don’t always receive feedback about treatment efficacy
Conclusion: Medical practice provides questionable (at best) scientific data
Hence the need for medical research
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(IV) Measurement and diagnosis (a) Measurement Error
Measured score (DV) = true score + error
(b) Reliability
- consistency of measurements when repeated
- need to know if can rely on the measurement
Varieties: split-half, test-retest
(c) Validity
Does the test measure what you think it does?
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Test-Retest reliability of self-measured blood pressure
Within each 7 day session.87 systolic.80 diastolic
After 4 years
.70 systolic
.61 diastolic
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(IV) Measurement and diagnosis (a) Measurement Error
Measured score (DV) = true score + error
(b) Reliability
- consistency of measurements when repeated
- need to know if can rely on the measurement
Varieties: split-half, test-retest
(c) Validity - Does the test measure what you thing it does?
* Content - representativeness of test items
* Construct - degree to which a test measures a theoretical trait
* Predictive - degree to which test score is associated with an external criterion
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How well does MCAT predict? (correlations from MCAT people, Contemporary
Issues in Medical Education, April 2000)
• Performance in first two years of medical school: about .77
• Performance during clinical training: about .68
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(d) Diagnostic confidenceSensitivity - how well a test identifies people who are really ill
So, take all people who are ill -- what proportion are correctly identified as being ill?
# of true positives
(# of true positives + # of false negatives)
Specificity - how well a test identifies people who are really well (do not have disease)
So, take all people who are well -- what proportion are correctly identified as being well?
# of true negatives
(# of true negatives + # of false positives)
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(d) Diagnostic confidencePositive predictive value - how well a positive test result identifies people who are really ill
So, take all people who show a positive result -- what proportion are correctly identified as being ill?
# of true positives (# of true positives + # of false positives)
Negative predictive value - how well a negative test result identifies people who are really well
So, take all people who show a negative test result -- what proportion are correctly identified as being well?
# of true negatives
(# of true negatives + # of false negatives)
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(d) Diagnostic confidenceIllustration
Have Illness (“truth”) Diagnosis
Positive
Negative
Sensitivity = 85 / (85 + 15) = 85% If ill…
Specificity = 40 /(40 + 60) = 40% If well…
Positive Predictive value = 85/(85 + 60) = 59%If positive…
Negative Predictive value = 40/(40 + 15) = 73%If negative…
85 60
15 40
100 100
True False
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Have Dementia (4 years in future)Diagnosis True False Positive
Negative
11 2 10 202
21 204
Sensitivity = 11 /21 ; Specificity = 202/204 ;Pos. Pred = 11/13; Neg. Pred. = 202/212
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have dyslexiaDiagnosis True False Positive
Negative
22 5 2 19
24 24
Sensitivity = 22/24 ; Specificity = 19/24;Pos. Pred. = 22/27; Neg. Pred. = 19/21
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(IV) Descriptive and Inferential Statistics
(A)Data Types
(a) Nominal - categorical, no underlying continuum
e.g. Numbers on basketball uniforms orSoft tissue wounds - abrasion, laceration,
avulsion
(b) Ordinal - rank ordering but not equal intervals
e.g. class rank orApgar score - composite of cardiac rate,
respiratory rate, muscle tone, reflex irritability, color
(d) Interval/Ratio - equal intervals between scores
e.g. Heart rate, respiration
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(B) Descriptive Statistics
(a) Measures of Central Tendency
What is the middle?
(b) Measures of Variability
How wide is it?
(c) Measures of Relationships
How do the two co-relate?
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(B) Descriptive Statistics
(a) Measures of Central Tendency Consider the following scores: 4 5 7 7 7 8 9 12 15
(a1) Mode - most frequent score ( 7 )
(a2) Median - midpoint of ranked scores ( 7 )
9 scores so 5th from top or bottom
(if even number, sum middle two and divide by 2)
(a3) Mean - (Sum(all scores) / # of scores)
So, 74/9 = 8.22
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Measures of Variability --How wide is it?
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(B) Descriptive Statistics
(a) Measures of Central Tendency
(b) Measures of Variability --How wide is it?
Consider the following scores: 4 5 7 7 7 8 9 12 15
(b1) Range -difference between extreme scores So range = 15 - 4 = 11
(b2) Standard deviation
-”average” distance from middle
Square root (((Sum(each score - mean)2) / N)
So, get squared deviations from mean
(4 - 8.22)2 + (5 - 8.22)2 + (7 - 8.22)2 + …= 93.32
Divide by N (which is 9) = 10.37
Take square root = 3.22
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(B) Descriptive Statistics
(a) Measures of Central Tendency
(b) Measures of Variability
(c) Measures of Relationships
(c1) Scatter plot - way to visualize degree & shape of relationship
(c2) Correlation - quantitative measures of linear relationship
Consists of two parts
Amount of relationship from 0 to 1
Direction of relationship + or - * Ranges from -1 to +1; 0 = no relationship
* Does not imply causation!(Tall children have
taller parents)
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(C) Inferential Statistics
* Enables an investigator to generalize to a population from data collected on a sample
* Statistical significance - degree of “risk” an investigator is willing to assume when rejecting the null hypothesis
** Null hypothesis - no difference between treatment and control group
(e.g., new drug does not help)
** Statistical significance is defined in terms of the probability of making an error in generalization * Types of Errors
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(C) Inferential Statistics * Types of Errors
** Type I error - null hypothesis is true but is rejected
*** usually set at .05 (5%) or .01
** Type II error - null hypothesis accepted when false
Null hyp true Null hyp false
accept
reject
There are lots of different types of inferential tests.
Correct acceptance
Type II
Type I Correctrejection
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Summary/Main points
Research Methods
Experimentation
Other approaches
Medical practice
Measurement and diagnosis
Diagnostic confidence
Descriptive and Inferential Statistics
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Readings/key terms
Readings in Fadem -- Chapters 25, 26
Key terms
experiment independent variable
placebo dependent variable
blind double blind
selection bias reliability
validity sensitivity
specificity mean, median,
mode
correlation standard deviation
Type I, II errors
positive predictive value
negative predictive value