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Personalized Medicine
• Detection
• Diagnosis
• Treatment
• Survival
Prediction is very difficult, especially about the future.
Niels Bohr
Danish physicist (1885 - 1962)
Biomarkers
Test Information Decision Outcome
1. Discrimination(sensitivity, specificity, predictive value, ROC analysis)
2. Utility
(disease free survival, recurrence rates, survival etc)
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
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%
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).
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
Clinical value of tests
Test
Information
Decision
Outcome
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
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).
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
Nested case control studiesScreening programs: NBSS, SMPBC, OBSP
case
case
case
6-8 years follow-up
case
control
control
control
control
Baseline mammogramRisk factors
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.
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 -
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
SUBSEQUENT EVENTSR }
PRINCIPAL AGENT
COMPARATIVE AGENT
{INITIAL STATE
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
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)
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
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
Analysis
P<0.05
What does this mean?
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.
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
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.
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
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!
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.
The “Winners Curse”
False positives more likely:Small studiesSmall effects
Early, hypothesis generating studiesFinancial interest
“Hot” field
Ioannidis PLos Medicine 2005
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.