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Personalized Medicine
• Detection
• Diagnosis
• Treatment
• Survival
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Prediction is very difficult, especially about the future.
Niels Bohr
Danish physicist (1885 - 1962)
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Biomarkers
Test Information Decision Outcome
1. Discrimination(sensitivity, specificity, predictive value, ROC analysis)
2. Utility
(disease free survival, recurrence rates, survival etc)
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Diagnostic tests
Describing test performance
Test Result
Disease No disease
Total
Positive a b a+b
Negative c d c+d
Total a+c c+d a+b+c+d
Properties of a test• Sensitivity:
– a/a+c
• Specificity: – d/c+d
• Positive predictive value:– a/a+b
• Negative predictive value:– d/c+d
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The importance of disease prevalence
Test result
Breast cancer
No breast cancer
Total
Positive 360 4,980 5,340
Negative 40 94,620 94,660
Total 400 99,600 100,000
• Screening mammography • Properties of the test
Sensitivity: 90%
a/a+c = 360/400
Specificity: 95%
d/c+d = 94,620/99,600
Positive predictive value:
a/a+b = 360/5340 = 7%
Negative predictive value:
d/c+d =94,620/94,660 = 100%
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Desiderata for studies of diagnostic tests.
• “Gold” standard• Test result before outcome known• “Blind” reading• Pre-determined cut-off• Sensitivity and specificity.• Predictive value.• Receiver operating. characteristic
curves (ROC).
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Diagnostic tests and the spectrum of disease.
• Spectrum of patients.
• Clinical spectrum• Co-morbid spectrum• Pathologic spectrum
• Potential biases in test evaluation.
• Exclusion of equivocal cases• Work up bias• Test review bias• Incorporation bias
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Clinical value of tests
Test
Information
Decision
Outcome
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PRINCIPAL AGENT
COMPARATIVE AGENT
INITIAL STATE, RECIPIENTS OF
PRINCIPAL AGENT
INITIAL STATE, RECIPIENTS OF
COMPARATIVE AGENT
SUBSEQUENT EVENTS,
RECIPIENTS OF PRINCIPAL AGENT
SUBSEQUENT EVENTS,
RECIPIENTS OF COMPARATIVE
AGENT
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Research Designs-General Structure
• Purpose of research(initial states)
• Prevention.
• Prediction of risk in healthy.
• Treatment response or toxicity
in those with disease.
• Identify factors that influence
outcome (prognosis).
• Types of manoeuver
• Inherited (eg genetic variant).
• Acquired– Self selected (smoking,
alcohol)– Other (treatment).
• Imposed (atomic irradiation).
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Principal research designs
Disease
Present Absent
Present a bExposure
Absent c d
Passage of time
Relative risk = a/a+b ÷ c/c+d
Cohort study
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Nested case control studiesScreening programs: NBSS, SMPBC, OBSP
case
case
case
6-8 years follow-up
case
control
control
control
control
Baseline mammogramRisk factors
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How many subjects (or samples) do you need?
• Number of events (eg deaths).
• Willingness to risk a false positive (Type I) error.
• Willingness to risk a false negative result (Type II) error.
• Magnitude of difference worthwhile to detect.
• Time for accrual and follow-up.
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Sample size to detect an improvement in survival (alpha=0.05; 1-beta=0.90)
P2-P1
P1 0.10 0.30 0.50
0.10 395 76 41
0.30 879 118 51
0.50 1020 116 -
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Sample size for genetic studies
Odds ratio Allele %
5% 20% 30%1.2 12,217 3730 2896
1.3 5702 1763 1380
1.5 2249 712 566
2.0 687 377 188
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SUBSEQUENT EVENTSR }
PRINCIPAL AGENT
COMPARATIVE AGENT
{INITIAL STATE
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A trial to change diet
• Vancouver + Surrey• Windsor• London + Sarnia• Hamilton + KW• Toronto
• Funding: Ontario Ministry of Health, Medical Research Council, Canadian Breast Cancer Research Alliance, National Institutes of Health, American Institute for Cancer Research
Screening
Randomization 4,693
Low-fatdiet
Usual diet
>8 years counselingand follow-up
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200 2 4 6 8 10 12 14 16 18
Cu
mu
lativ
e h
aza
rdAll invasive breast cancer
HRa = 1.05 (95% CIb : 0.83 - 1.33)
Year
# eventsc I:
C: 16
16
9
25
24
20
18
18
18
23
20
16
20
10
4
6
10
6
1
3
# at riskd I:
C: 2349
2341
2323
2312
2300
2269
2258
2228
2221
2190
2181
2148
1878
1858
1194
1194
742
740
329
324
0.150
0.125
0.100
0.075
0.050
0.025
0.000
Intervention
Comparison
(A)
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Association or causation?
• Not all associations are causal
• All causal factors show association
• May be due to bias or confounding
• Genetic associations– Causal– In linkage
disequilibrium with the causal variant
– Population stratification
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Population stratification
• Type of confounding• Ethnicity
– associated with disease– associated with genotype– gives spurious association between genotype
and disease
• Can be controlled in analysis (if recognized)• Dispute about importance
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Analysis
P<0.05
What does this mean?
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The meaning of p-values.
If the TRUE difference between the compared
groups is zero (the null hypothesis), the
PROBABILITY of obtaining a difference as large
or larger than the one observed by CHANCE is p.
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Multiple comparisons
• The problem.• If alpha = 0.05• 20 comparisons can be
expected to generate one p<0.05.
• (1-(1-alpha)k, where alpha is the level for significance and k=number of tests.
• What protection?• Few, a priori hypotheses
• Correction for number of tests eg Bonferroni– Alpha/number of tests
• Stringent alpha eg E 10-8
• Replication/validation
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Francis Galton’s ox and the “Winner’s curse”.
• Country fair in 1906 - 800 bought tickets and predicted the weight of an ox.
• Actual weight was 1,198 lbs.
• None were close to the actual weight.
• Mean predicted weight (N=787) was 1,197 lbs.
• At auction, most bids cluster around the “true” value of the object.
• The winning bid is always higher than the “true” value.
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Replication -validation
• “leave one out”– Applied to “learning set”– Not an independent sample– May help avoid overfitting
• Independent data set– Preferably also an independent investigator
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How to get a statistically significant result.
• Count or ignore differences in follow-up time.
• Censor at different time points.• Exclude specific causes of death.• Exploit sub-group analysis.• Use different cut-offs for gene
expression (or other test result).• Note: all of the above increases the
number of statistical tests you can do!
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Can you believe the literature?
• Publication bias (author and editor bias).
• Multiple statistical testing.• The “Winner’s curse”.• Bias in the sampling,
measurement or analysis of the data.
• Most published reports are never replicated.
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The “Winners Curse”
False positives more likely:Small studiesSmall effects
Early, hypothesis generating studiesFinancial interest
“Hot” field
Ioannidis PLos Medicine 2005
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How to stay out of trouble
• Define target population.• Standardize sample collection.• Collect samples at zero time.• Define outcomes at the outset.• Random selection of cases and controls.• Analyze samples without knowledge of
case/control status.• Replicate.