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Statistical Issues in the Evaluation of Predictive Biomarkers. Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute Kinds of Biomarkers. Surrogate endpoint Pre & post rx, early measure of clinical outcome Pharmacodynamic

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statistical issues in the evaluation of predictive biomarkers

Statistical Issues in the Evaluation of Predictive Biomarkers

Richard Simon, D.Sc.

Chief, Biometric Research Branch

National Cancer Institute

kinds of biomarkers
Kinds of Biomarkers
  • Surrogate endpoint
    • Pre & post rx, early measure of clinical outcome
  • Pharmacodynamic
    • Pre & post rx, measures an effect of rx on disease
  • Prognostic
    • Which patients need rx
  • Predictive
    • Which patients are likely to benefit from a specific rx
  • Product characterization
prognostic biomarkers can be therapeutically relevant
Prognostic Biomarkers Can be Therapeutically Relevant
  • 3-5% of node negative ER+ breast cancer patients require or benefit from systemic rx other than endocrine rx
  • Prognostic biomarker development should focus on specific therapeutic decision context
predictive biomarkers
Predictive Biomarkers
  • In the past often studied as un-focused post-hoc subset analyses of RCTs.
    • Numerous subsets examined
    • Same data used to define subsets for analysis and for comparing treatments within subsets
    • No control of type I error
  • Led to conventional wisdom
    • Only hypothesis generation
    • Only valid if overall treatment difference is significant
Cancers of a primary site are often a heterogeneous gouping of diverse molecular diseases
  • The molecular diseases vary enormously in their responsiveness to a given treatment
  • It is feasible (but difficult) to develop prognostic markers that identify which patients need systemic treatment and which have tumors likely to respond to a given treatment
    • e.g. breast cancer and ER/PR, Her2
“Hypertension is not one single entity, neither is schizophrenia. It is likely that we will find 10 if we are lucky, or 50, if we are not very lucky, different disorders masquerading under the umbrella of hypertension. I don’t see how once we have that knowledge, we are not going to use it to genotype individuals and try to tailor therapies, because if they are that different, then they’re likely fundamentally … different problems…”
    • George Poste
the standard approach to designing phase iii clinical trials is based on three assumptions
The standard approach to designing phase III clinical trials is based on three assumptions
  • Qualitative treatment by subset interactions are unlikely
  • “Costs” of over-treatment are less than “costs” of under-treatment
  • It is not feasible to reliably evaluate treatments for subsets
Qualitative treatment by subset interactions are unlikely
    • Biology has shown that this is often false
  • “Costs” of over-treatment are less than “costs” of under-treatment
    • With today’s drugs this is economically unsustainable
  • It is not feasible to reliably evaluate treatments for subsets
    • With molecularly targeted treatment, and prospectively defined candidate subsets, this is feasible
standard clinical trial approaches
Standard Clinical Trial Approaches
  • Have led to widespread over-treatment of patients with drugs to which few benefit
  • Possible failure to appreciate the effectiveness of some drugs in biologically restricted target populations
This is not a plea for acceptance of the typical unreliable post-hoc data dredging approach to subset analysis
  • Subset analysis does not have to be about post-hoc comparing treatments in numerous subsets with no control of overall type I error
the roadmap
The Roadmap
  • Develop a completely specified genomic classifier of the patients likely to benefit from a new drug
  • Establish analytical and pre-analytical validity of the classifier
  • Use the completely specified classifier to design and analyze a new clinical trial to evaluate effectiveness of the new treatment with a pre-defined analysis plan that preserves the overall type-I error of the study.
guiding principle
Guiding Principle
  • The data used to develop the classifier must be distinct from the data used to test hypotheses about treatment effect in subsets determined by the classifier
    • Developmental studies are exploratory
    • Studies on which treatment effectiveness claims are to be based should be definitive studies that test a treatment hypothesis in a patient population completely pre-specified by the classifier
new drug developmental strategy i
New Drug Developmental Strategy I
  • Restrict entry to the phase III trial based on the binary predictive classifier, i.e. targeted design

Develop Predictor of Response to New Drug

Using phase II data, develop predictor of response to new drug

Patient Predicted Responsive

Patient Predicted Non-Responsive

Off Study

New Drug


applicability of design i
Applicability of Design I
  • Primarily for settings where the classifier is based on a single gene whose protein product is the target of the drug
    • eg trastuzumab
  • With a strong biological basis for the classifier, it may be unacceptable to expose classifier negative patients to the new drug
  • Analytical validation, biological rationale and phase II data provide basis for regulatory approval of the test
  • Phase III study focused on test + patients to provide data for approving the drug
If a drug is found safe and effective in a defined patient population, approval should not depend on finding the drug ineffective in some other population
  • Consequently, if the drug is found safe and effective in biomarker classifier positive patients, approval of the drug should not be contingent on testing the drug in classifier negative patients
evaluating the efficiency of strategy i
Evaluating the Efficiency of Strategy (I)
  • Simon R and Maitnourim A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Research 10:6759-63, 2004; Correction and supplement 12:3229, 2006
  • Maitnourim A and Simon R. On the efficiency of targeted clinical trials. Statistics in Medicine 24:329-339, 2005.
  • reprints and interactive sample size calculations at
Relative efficiency of targeted design depends on
    • proportion of patients test positive
    • effectiveness of new drug (compared to control) for test negative patients
  • When less than half of patients are test positive and the drug has little or no benefit for test negative patients, the targeted design requires dramatically fewer randomized patients
  • The targeted design may require fewer or more screened patients than the standard design
trastuzumab herceptin
  • Metastatic breast cancer
  • 234 randomized patients per arm
  • 90% power for 13.5% improvement in 1-year survival over 67% baseline at 2-sided .05 level
  • If benefit were limited to the 25% assay + patients, overall improvement in survival would have been 3.375%
    • 4025 patients/arm would have been required
developmental strategy ii

Develop Predictor of

Response to New Rx

Predicted Responsive

To New Rx

Predicted Non-responsive to New Rx

New RX


New RX


Developmental Strategy (II)
developmental strategy ii23
Developmental Strategy (II)
  • Do not use the diagnostic to restrict eligibility, but to structure a prospective analysis plan
  • Having a prospective analysis plan is essential
  • “Stratifying” (balancing) the randomization is useful to ensure that all randomized patients have tissue available but is not a substitute for a prospective analysis plan
  • The purpose of the study is to evaluate the new treatment overall and for the pre-defined subsets; not to modify or refine the classifier
  • The purpose is not to demonstrate that repeating the classifier development process on independent data results in the same classifier
Validation of EGFR biomarkers for selection of EGFR-TK inhibitor therapy for previously treated NSCLC patients
  • PFS endpoint
    • 90% power to detect 50% PFS improvement in FISH+
    • 90% power to detect 30% PFS improvement in FISH−
  • Evaluate EGFR IHC and mutations as predictive markers
  • Evaluate the role of RAS mutation as a negative predictive marker



(~ 30%)


2nd line NSCLC with specimen

1° PFS

2° OS, ORR




1-2 years minimum additional follow-up


(~ 70%)



4 years accrual, 1196 patients

957 patients

analysis plan b limited confidence in test
Analysis Plan B(Limited confidence in test)
  • Compare the new drug to the control overall for all patients ignoring the classifier.
    • If poverall 0.03 claim effectiveness for the eligible population as a whole
  • Otherwise perform a single subset analysis evaluating the new drug in the classifier + patients
    • If psubset 0.02 claim effectiveness for the classifier + patients.
This analysis strategy is designed to not penalize sponsors for having developed a classifier
  • It provides sponsors with an incentive to develop genomic classifiers
analysis plan c adaptive
Analysis Plan C(adaptive)
  • Test for difference (interaction) between treatment effect in test positive patients and treatment effect in test negative patients
  • If interaction is significant at level int then compare treatments separately for test positive patients and test negative patients
  • Otherwise, compare treatments overall
sample size planning for analysis plan c
Sample Size Planning for Analysis Plan C
  • 88 events in test + patients needed to detect 50% reduction in hazard at 5% two-sided significance level with 90% power
  • If 25% of patients are positive, when there are 88 events in positive patients there will be about 264 events in negative patients
    • 264 events provides 90% power for detecting 33% reduction in hazard at 5% two-sided significance level
simulation results for analysis plan c
Simulation Results for Analysis Plan C
  • Using int=0.10, the interaction test has power 93.7% when there is a 50% reduction in hazard in test positive patients and no treatment effect in test negative patients
  • A significant interaction and significant treatment effect in test positive patients is obtained in 88% of cases under the above conditions
  • If the treatment reduces hazard by 33% uniformly, the interaction test is negative and the overall test is significant in 87% of cases
development of genomic classifiers
Development of Genomic Classifiers
  • Single gene or protein based on knowledge of therapeutic target
  • Empirically determined based on evaluation of a set of candidate genes
  • Empirically determined based on genome-wide correlating gene expression, copy number variation or genotype to patient outcome after treatment
development of genomic classifiers31
Development of Genomic Classifiers
  • During phase II development or
  • After failed phase III trial using archived specimens.
  • Adaptively during early portion of phase III trial.
  • Neither academic research, industry, NCI or FDA have adequately adapted to the fundamental discoveries of the heterogeneity of human cancers
  • There is great potential for developing treatments that are highly effective for the right patients using prognostic and predictive biomarkers
  • There is great potential for reducing the waste of economic resources from vast over-treatment of cancer patients
  • Critical path objectives are more likely to be achieved thru development of predictive biomarkers than thru development of surrogate endpoint biomarkers