bios 6648: design & conduct of clinical...
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Date: 25 Nov 2013
5. Special topics anddesigns5.1 Biomarker validationstudies
Bios 6648- pg 1
Bios 6648: Design & conduct of clinical researchSection 4 - Documenting the study
5. Special topics and designs
5.1 Design and analysis of biomarker validation studies5.2 Design and analysis of crossover studies5.3 Design and evaluation of factorial studies
Date: 25 Nov 2013
5. Special topics anddesigns5.1 Biomarker validationstudies
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5.1 Design and analysis of biomarker validation studies
Context
I The search for new biomarkers has been accelerated bynew molecular and genetic technologies.
I Potential uses:I Improved diagnostic testI Elucidating disease biologyI Risk stratificationI Targeted therapies
I Example (see also lecture 1.2): Molecular guided care
Date: 25 Nov 2013
5. Special topics anddesigns5.1 Biomarker validationstudies
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5.1 Design and analysis of biomarker validation studies
Molecular guided therapy for heart failure
I Background: Patients who present with heart failure arestarted on beta-blocker therapy. If their ejection fractionhas not improved to greater than 35% after one month oftherapy, then are often given an implantable defibrillator.This procedure is used in about 90% of heart failurepatients. After 12 months most of those patients have notneeded the defibrillator, and most (80%) have ejectionfraction above 35%.
I Clinical question: Can we use molecular expressions topredict the patients who should and should not receiveimplantable defibrillators?
I Initial studies evaluate gene expression from heart biopsytissue:
I Cases: Heart failure patients given beta blockade who fail torespond by 30 days, but respond by 1 year.
I Controls: Heart failure patients given beta blockade who fail torespond by 30 days, but do not respond by 1 year.
* Response = Eject fraction > 35%.I Can molecular expression discriminate between cases and
controls?
Date: 25 Nov 2013
5. Special topics anddesigns5.1 Biomarker validationstudies
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5.1 Design and analysis of biomarker validation studies
Recall the Pepe phases
I Phase I: Preclinical explorationI Phase II: Clinical assay and validation (prevalent
case-control study)I Phase III: Retrospective longitudinal (incident case-control
study)I Phase IV: Prospective screening (extend and type of
disease detected; false referral rate estimated)I Phase V: Disease control (screening with the biomarker
reduces disease mortality).
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5. Special topics anddesigns5.1 Biomarker validationstudies
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Example: Molecular guided therapy for heart failure
Pepe phases I&II
I Thousands of molecular markers evaluated:I Computational biology tools:
I Thousands of t-tests on each marker separately (pick themost significant).
I Machine learning.I Leave-one-out cross-validation.I Considering documented pathways from other settings
(natural language processing).I Results are used to select ≈ 50 key molecular markers.I Custom chip constructed to measure expression of the 50
markers.I Analytic questions:
I How do we predict "cases" from molecular markers?I Does this predictor add anything to standard clinical
predictors?
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5. Special topics anddesigns5.1 Biomarker validationstudies
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Example: Molecular guided therapy for heart failure
Pepe phases I&II
I Logistic regression gives risk score:
logit(p) = β0 + β1M1 + β2M2 + ...+ β50M50
where p = Pr(case), and M1,M2, ...,M50 denoteexpression magnitudes for markers 1 through 50.
I Risk is often scored by the fitted value for theright-hand-side of the logistic regression model:
Score = β̂0 + β̂1M1 + β̂2M2 + ...+ β̂50M50
I Larger values for the Score indicate greater chance forbeing a case.
I Predictive ability of the Score is often summarized in aROC curve.
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Receiver Operator Characteristic Curves
ROC curve
I Steps to construct:1. Calculate the sensitivity and specificity for all possible
thresholds:I For threshold T :
If score > T , then diagnose “case".If score ≤ T , then diagnose “non-case".
I Repeat for all possible thresholds.
2. Plot sensitivity versus 1−specificity(i.e., True positive probability versus False positiveprobability).
I Accuracy of the diagnostic test is sometimes summarized bythe area under the ROC curve.
I Best possible test has area = 1.0.I Flipping a coin has area = 0.5.I There is a statistical test for whether 2 diagnostic tests have a
significant difference in the area under the ROC curve.
I Problem: depending on the clinical situation sensitivitymay be more important than specificity (or vice versa).
I Area under ROC curve does not consider relativeimportance of sensitivity and specificity.
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Example: Molecular guided therapy for heart failure
Pepe phase III
I Prospective cohort of heart failure patients with:I Initially presenting with ejection fraction < 35%.I Treated with beta-blockadeI Still had ejection faction < 35% after 1 month.I All patients receive heart biopsy so that molecular risk can
be calculated.I All patients followed for 2 years:
I Cases = patients with ejection fraction > 35% at 1 year (lateresponders).
I Controls = patients with EF < 35% at 1 year(non-responders).
I Upon study completion:I Threshold chosen to give 100% sensitivity for cases (late
responders).I Specificity estimated using this threshold.
Specificity = proportion of patients who could potentiallyforego implantable defibrillator.
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Example: Molecular guided therapy for heart failure
Pepe phase IV
I Patients with EF < 35% after 1 month of beta-blockaderandomized to:
I Molecular guided care:I Risk score larger than threshold then do not receive ICD
(implantable defibrillator).I Risk score less than threshold then receive ICD.I Patients without ICD get defibrillator vest for safety.
I Standard care: Use of ICD determined by physicianjudgement (no molecular risk score provided).
I Outcome: probability that EF > 35% at 12 months (orcardiac event).
I Hypothesize that patients with high score will haveEF > 35% at 12 months, so it was safe to forego the ICD.
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Example: Molecular guided therapy for heart failure
Pepe phase V
I RCT as in phase IVI Follow for long-term outcomes (mortality and morbidity).
Date: 25 Nov 2013
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Example: Molecular guided therapy for heart failure
Summary/overview
I Molecular markers identified from heart tissue biopsiesI Development of predictor (phase I/II):
I Logistic regression (in prevalent case/controls) givesmolecular risk score.
I Logistic regression evaluates whether molecular risk scoreadds to prediction based on Seattle Heart Failure RiskScore.
I ROC curves allow comparisons across all possible cut-offs.I Testing risk equation (Pepe phase III):
I Risk equation validated in prospective cohort of heart failurepatients.
I Diagnostic criteria:I Diagnostic threshold selected to give 100% sensitivity.I Specificity estimated (low specificity is more acceptable than
low sensitivity)I Sample size selected to give sufficient precision for estimated
sensitivity and specificity
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Example: Molecular guided therapy for heart failure
Summary/overview (con’t)
I Prospective screening (Pepe phase IV):I Randomize to standard care versus molecular guided careI Defibrillation vest assures safety
I Long-term outcomes (Pepe phase V):I Randomize to standard care versus molecular guided care
I Note: subsequent care and follow-up are likely to differdramatically between arms.
I Endpoint: all-cause mortality (my preference).