Carle Hospital and Physician Group - WordPress.com · - Lewis Carroll . Title: PowerPoint...
Transcript of Carle Hospital and Physician Group - WordPress.com · - Lewis Carroll . Title: PowerPoint...
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Daniel Bronson-Lowe, PhD, CIC Carle Hospital and Physician Group
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Null hypothesis: values are equal.
Alternative hypothesis: values differ.
These statements are mutually exclusive. ◦ They cover all possible outcomes. ◦ In the end, only one can be selected.
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Null hypothesis: ◦ The average levels of hand hygiene compliance at Hospital
A and Hospital B are the same.
Alternative hypothesis: ◦ The average levels of hand hygiene compliance at Hospital
A and Hospital B are different.
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Hand Hygiene Compliance
Hand Hygiene Compliance
Hospital A
Hospital B
54%
86%
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Hand Hygiene Compliance
Hand Hygiene Compliance
Hospital A
Hospital B
54%
86%
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Hand Hygiene Compliance
Hand Hygiene Compliance
Hospital A
Hospital B
54%
86%
69%
69%
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Reality Same (null) Different (alt)
Same (null) Correct False Positive
Different (alt) False Negative Correct
Your Conclusion
Your Conclusion
There is a difference.
(Alternative Hypothesis)
Reality
There is NO difference.
(Null Hypothesis)
The False Positive Scenario (Type I Error)
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The probability of concluding a difference is real when it is actually just random variation (a false positive).
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0% 10%
1 in 10
Chance of False Positive
5%
1 in 100 1 in 20
1%
α = 0.01 α = 0.05 α = 0.10
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Data
Statistical Test • t-test • ANOVA • Chi-square • etc.
Test Statistic (e.g. t, F, X2)
More Math p-value
p-value: the probability that this difference (or a more extreme one) was caused by random chance if the null hypothesis is true.
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The amount of risk you are willing to accept.
The amount of risk present.
Alpha vs. p-Value
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0% 5% 10%
α = 0.05
p = 0.08
Significant Chance of False Positive
p-value > α
The risk of a false positive is too high.
Conclude there is no difference.
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0% 5% 10%
α = 0.05
p = 0.02
Significant Chance of False Positive
p-value < α
The risk of a false positive is acceptable.
Conclude a difference exists!
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SIR = Observed infections
Expected infections
SIR
0 1 2
Same
Better Worse
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Null hypothesis: ◦ Your infection rate and the benchmark infection rate are the same.
Alternative hypothesis: ◦ Your infection rate and the benchmark infection rate are different.
SIR
0 1 2
Same
Better Worse
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Facility
Number of
Procedures
Performed
Number of
Infections
Expected
Number of
Infections SIR
SIR
p-value
95% Confidence
Interval for SIR
A 290 1 5.8 0.2 0.03 0.0, 0.9
B 80 4 1.5 2.6 0.65 0.7, 6.7
C 1500 75 28.5 2.6 0.01 2.1, 3.3
CABG-Related Surgical Site Infections
SIR
0 1 2
Same
Better Worse
α = 0.05
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SIR
SIR
p-value
0.2 0.03
2.6 0.65
2.6 0.01
SIR
SIR
p-value
95% Confidence
Interval for SIR
0.2 0.03 0.0, 0.9
2.6 0.65 0.7, 6.7
2.6 0.01 2.1, 3.3
0 1 5 2 3 4 6 7
SIR
α = 0.05
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Sample Size
Small
Large
All
CI
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Data Variability
Large Variability
Small Variability
CI
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Hand Hygiene Compliance
Hand Hygiene Compliance
Hospital A
Hospital B
54%
86%
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Hand Hygiene Compliance
Hand Hygiene Compliance
Hospital A
Hospital B
54%
86%
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Alpha (α)
0.01
0.05
0.10
CI 99% CI
95% CI
90% CI
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Reality Same (null) Different (alt)
Same (null) Correct False Positive
Different (alt) False Negative Correct
Your Conclusion
Your Conclusion
There is NO difference.
(Null Hypothesis)
Reality
There is a difference.
(Alternative Hypothesis)
The False Negative Scenario (Type II Error)
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False Positive
False Negative
Less Likely
More Likely
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Difference of interest
Sample size
Data variability
Level of significance (α)
Test used
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Statistical significance ≠ Clinical relevance
0 1 5 2 3 4 6 7 8 9 10 11 12 13 14 15
SIR
p = 0.34
p = 0.01
13.5
1.01
Facility A
Facility B
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Vaccine,
# Doses
Relative
Risk
95% Confidence
Interval for RR
DTaP, 3 1.34 1.32 - 1.35
IPV, any 1.01 1.01 - 1.01
IPV, 3 1.27 1.25 - 1.29
PCV, any 1.04 1.03 - 1.04
PCV, 3 1.37 1.35 - 1.39
Bronson-Lowe D, Anderson S. Effects of a Minimum Interval Immunization Schedule for Diphtheria and Tetanus Toxoids and Acellular Pertussis Vaccination During a Pertussis Outbreak. Arch Pediatr Adolesc Med. 2009; 163(5):417-421.
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Bad Data
Wrong Statistical Test • t-test • ANOVA • Chi-square • etc.
Test Statistic (e.g. t, F, X2)
More Math p-value
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Statistically significant result? ◦ Alpha = 0.05
◦ Wrong 1 time in 20
No significant finding? ◦ Power = 80%
◦ Wrong 1 time in 5
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A Couple of Examples…
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Implemented use of chlorhexidine gluconate (CHG) bathing to reduce CLABSI rates in NICU
Compared mild soap to CHG: ◦ CHG bathing regimens varied by birth weight and
chronological age.
Compared Incidence Rate Ratios (IRR) ◦ IRR = 1.0 means the two groups are the same
Quach C, Milstone AM, Perpete C, Bonenfant M, Moore DL, Perereault T. Chlorhexidine Bathing in a Tertiary Care Neonatal Intensive Care Unit: Impact on Central Line-Associated Bloodstream Infections. Infect Control Hosp Epidemiol 2014;35(2):158-163.
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“CLABSI rates decreased during the intervention period for CHG-bathed neonates.” ◦ IRR = 0.33
◦ 95% Confidence Interval = 0.15-0.73
Is this a statistically significant finding?
A) Yes
B) No
IRR
0 1 2
Same
Better Worse
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“CLABSI rates decreased during the intervention period for CHG-bathed neonates.” ◦ IRR = 0.33
◦ 95% Confidence Interval = 0.15-0.73
What was the value of alpha for this study?
A) 0.01
B) 0.05
C) 0.10
D) 0.50
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“CLABSI rates decreased during the intervention period for CHG-bathed neonates.” ◦ IRR = 0.33 ◦ 95% Confidence Interval = 0.15-0.73
What was the likelihood of a false positive result? A) 1%
B) 5%
C) 20%
D) 95%
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“CLABSI rates decreased during the intervention period for CHG-bathed neonates.” ◦ IRR = 0.33 ◦ 95% Confidence Interval = 0.15-0.73
Which of these is a believable p-value for this comparison? A) 0.03
B) 0.06
C) 0.12
D) 0.83
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Study 1
Vaccination Rate
50% 95%
p-value = 0.120
Which study had a statistically significant result?
Study 2
Vaccination Rate
50% 95%
p-value = 0.001 α = 0.05
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Study 1
Vaccination Rate
50% 95%
p-value = 0.120
What might be making that one significant? A) The researchers used a more expensive statistician.
B) The researchers were more willing to risk a false positive.
C) It’s more clinically relevant.
D) It had a larger sample size.
Study 2
Vaccination Rate
50% 95%
p-value = 0.001 α = 0.05
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Study Design
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Observational Studies
Experimental Studies
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Observational Studies
Cross-sectional Surveys
Case-Control Studies
Cohort Studies
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Survey a random sample at a single point in time
Time
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Pros ◦ Quick and cheap (relatively)
◦ Can cover an entire population
Cons ◦ Based primarily on self-report
◦ Lack of a time sequence
◦ Getting a suitable sample can be difficult
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Watch people over a period of time and compare outcomes
Population
Sample: People
who lack the
outcome
Lack the risk
factor
Have the risk
factor
Have outcome
Lack outcome
Have outcome
Lack outcome
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Watch CABG patients for 1 year to see if those with BMI ≥30 are at higher risk for surgical site infection.
People who had CABG
Sample: People without
SSIs
BMI <30
BMI ≥30
SSI
No SSI
SSI
No SSI
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Pros ◦ Easier and cheaper than randomized controlled trial
◦ Can establish a temporal relationship
◦ Can watch for multiple outcomes
Cons ◦ Expensive
◦ Require a large sample size
◦ Inefficient for rare outcomes
◦ Can take a long time for outcome to occur
◦ No randomization
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Compare people who have a certain condition to
those who do not.
Not Exposed
Exposed Cases
(have outcome)
Controls (lack outcome)
Not Exposed
Exposed
Population
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Determine risk factors for CABG surgical site infections occurring over last 6 months.
Not Diabetic
Diabetic
SSI
No SSI
Not Diabetic
Diabetic
Population
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Pros ◦ Good for studying rare outcomes
◦ Quicker than cohort: the outcome has already occurred
◦ Can look at multiple risk factors
◦ Useful as an initial study to establish a possible association
Cons ◦ Reliance on historical data / memory (risk of recall bias)
◦ Examining single outcome
◦ Selection of a control group can be difficult
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Randomized Controlled Trial
Quasi-Experimental Studies
Experimental Studies
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Participants are randomly assigned to an experimental/intervention group or a control group.
Often used for testing of new drugs and treatments.
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Participants
Intervention Group
Control Group
Random allocation
Follow-up
Follow-up
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Participants
Skin Prep A
Skin Prep B
Random allocation
SSI
No SSI
SSI
No SSI
Is surgical skin prep A better at preventing SSIs than surgical skin prep B?
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Pros ◦ Randomization addresses many types of bias
◦ Easier to incorporate blinding
Cons ◦ Ethical concerns
◦ Results may not be generalizable
◦ Expensive
◦ Volunteer bias
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Only studying one population ◦ Group allocation should not be based on two
populations
Only difference between the groups should be the variables being studied ◦ Additional differences may be confounders.
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An intervention occurs, but an element of RCT is missing: Randomization.
Also may be missing: ◦ Pre-post test design
◦ Control groups
Often used to assess the impact of a program or intervention (e.g. process improvement projects)
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Intervention Group
Intervention Group
Intervention
Control Group
Control Group
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Prep A Prep B
Intervention
Is surgical skin prep A better at preventing SSIs than surgical skin prep B?
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Pros ◦ More practical than RCT
◦ Can use “natural experiments”
Cons ◦ Lack of randomization can lead to bias
◦ Difficulty controlling for confounding variables
◦ Regression to the mean
◦ Maturation effects
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If you want to inspire confidence, give plenty of statistics.
It does not matter that they should be accurate, or even intelligible, as long as there is enough of them.
- Lewis Carroll