1 what do these tell us? a higher percentage of people in hospitals die each day than do people not...

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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: bhross@uiuc.edu

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