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  1. Some Statistical Aspects of Predictive Medicine Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute

  2. Biometric Research Branch Website • Powerpoint presentations • Reprints • BRB-ArrayTools software • Web based tools for clinical trial design with predictive biomarkers

  3. Prediction for Informing Treatment Selection • Most cancer treatments benefit only a minority of patients to whom they are administered • Being able to predict which patients are (are not) likely to benefit from a treatment might • Save patients from unnecessary complications and enhance their chance of receiving a more appropriate treatment • Help control medical costs • Improve the success rate of clinical drug development

  4. Prognostic vs Predictive Biomarkers • Predictive biomarkers • Measured before treatment to identify who is likely or unlikely to benefit from a particular treatment • Prognostic biomarkers • Measured before treatment to indicate long-term outcome for patients untreated or receiving standard treatment

  5. In Oncology • Recognition of the heterogeneity of tumors of the same primary site • Availability of the tools of genomics for characterizing tumors • Focus on molecularly targeted drugs • Has resulted in • Increased interest in prediction problems • Need for new clinical trial designs

  6. p>n prediction problems in which number of variables is much greater than the number of cases • Many of the methods of statistics are based on inference problems • Standard model building and evaluation strategies are not effective for p>n prediction problems

  7. Model Evaluation for p>n Prediction Problems • Goodness of fit is not a proper measure of predictive accuracy • Importance of Separating Training Data from Testing Data for p>n Prediction Problems

  8. Separating Training Data from Testing Data • Split-sample method • Re-sampling methods • Leave one out cross validation • K-fold cross validation • Replicated split-sample • Bootstrap re-sampling

  9. “Prediction is very difficult; especially about the future.”

  10. SEARCH STRING: prediction future (name)875,000 Einstein584,000 Twain364,000 Bohr113,000 Berra

  11. SEARCH STRING: prediction "especially * the future" (name)31,200 Bohr18,500 Berra864 Einstein539 Twain

  12. Prediction on Simulated Null DataSimon et al. J Nat Cancer Inst 95:14, 2003 • Generation of Gene Expression Profiles • 20 specimens (Pi is the expression profile for specimen i) • Log-ratio measurements on 6000 genes • Pi ~ MVN(0, I6000) • Can we distinguish between the first 10 specimens (Class 1) and the last 10 (Class 2)? • Prediction Method • Compound covariate predictor built from the log-ratios of the 10 most differentially expressed genes.

  13. Cross Validation • With proper cross-validation, the model must be developed from scratch for each leave-one-out training set. This means that feature selection must be repeated for each leave-one-out training set. • The cross-validated estimate of misclassification error is an estimate of the prediction error for the model developed by applying the specified algorithm to the full dataset

  14. Permutation Distribution of Cross-validated Misclassification Rate of a Multivariate ClassifierRadmacher, McShane & SimonJ Comp Biol 9:505, 2002 • Randomly permute class labels and repeat the entire cross-validation • Re-do for all (or 1000) random permutations of class labels • Permutation p value is fraction of random permutations that gave as few misclassifications as e in the real data

  15. Model Evaluation for p>n Prediction Problems • Odds ratios and hazards ratios are not proper measures of prediction accuracy • Statistical significance of regression coefficients are not proper measures of predictive accuracy

  16. Evaluation of Prediction Accuracy • For binary outcome • Cross-validated prediction error • Cross-validated sensitivity & specificity • Cross-validated ROC curve • For survival outcome • Cross-validated Kaplan-Meier curves for predicted high and low risk groups • Cross-validated K-M curves within levels of standard prognostic staging system • Cross-validated time-dependent ROC curves

  17. LOOCV Error Estimates for Linear Classifiers

  18. Cross-validated Kaplan-Meier Curves for Predicted High and Low Risk Groups

  19. Cross-Validated Time Dependent ROC Curve

  20. Is Accurate Prediction Possible For p>>n? • Yes, in many cases, but standard statistical methods for model building and evaluation are often not effective • Standard methods may over-fit the data and lead to poor predictions • With p>n, unless data is inconsistent, a linear model can always be found that classifies the training data perfectly

  21. Is Accurate Prediction Possible For p>>n? • Some problems are easy; real problems are often difficult • Simple methods like DLDA, nearest neighbor classifiers and shrunken centroid classifiers are as effective or more effective than more complex methods for many datasets • Because of correlated variables, there are often many very distinct models that predict about equally well

  22. p>n prediction problems are not multiple testing problems • The objective of prediction problems is accurate prediction, not controlling the false discovery rate • Parameters that control feature selection in prediction problems are tuning parameters to be optimized for prediction accuracy • Optimizaton by cross-validation nested within the cross-validation used for evaluating prediction accuracy • Biological understanding is often a career objective; accurate prediction can sometimes be achieved in less time

  23. Traditional Approach to Oncology Clinical Drug Development • Phase III trials with broad eligibility to test the null hypothesis that a regimen containing the new drug is on average not better than the control treatment for all patients who might be treated by the new regimen • Perform exploratory subset analyses but regard results as hypotheses to be tested on independent data

  24. Traditional Clinical Trial Approaches • Have protected us from false claims resulting from post-hoc data dredging not based on pre-defined biologically based hypotheses • Have led to widespread over-treatment of patients with drugs to which many don’t need and from which many don’t benefit • Are less suitable for evaluation of new molecularly targeted drugs which are expected to benefit only the patients whose tumors are driven by de-regulation of the target of the drug

  25. Molecular Heterogeneity of Human Cancer • Cancers of a primary site in many cases appear to represent a heterogeneous group of diverse molecular diseases which vary fundamentally with regard to • their oncogenecis and pathogenesis • their responsiveness to specific drugs • The established molecular heterogeneity of human cancer requires the use new approaches to the development and evaluation of therapeutics

  26. How Can We Develop New Drugs in a Manner More Consistent With Modern Tumor Biology and ObtainReliable Information About What Regimens Work for What Kinds of Patients?

  27. 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 Control

  28. Evaluating the Efficiency of Enrichment and Stratification Clinical Trial Designs With Predictive Biomarkers • 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.

  29. Develop Predictor of Response to New Rx Predicted Responsive To New Rx Predicted Non-responsive to New Rx New RX Control New RX Control Developmental Strategy (II)

  30. 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

  31. R Simon. Using genomics in clinical trial design, Clinical Cancer Research 14:5984-93, 2008 • R Simon. Designs and adaptive analysis plans for pivotal clinical trials of therapeutics and companion diagnostics, Expert Opinion in Medical Diagnostics 2:721-29, 2008

  32. It can be difficult to identify a single completely defined classifier candidate prior to initiation of the phase III trial evaluating the new treatment

  33. Cross-Validated Adaptive Signature Design(In press) Wenyu Jiang, Boris Freidlin, Richard Simon

  34. Cross-Validated Adaptive Signature DesignEnd of Trial Analysis • Compare T to C for all patients at significance level overall (e.g. 0.03) • If overall H0 is rejected, then claim effectiveness of T for eligible patients • Otherwise

  35. Otherwise • Partition the full data set into K parts P1 ,…,PK • Form a training set by omitting one of the K parts, e.g. part k. • Trk={1,…,n}-Pk • The omitted part Pk is the test set • Using the training set, develop a predictive binary classifier B-k of the subset of patients who benefit preferentially from the new treatment compared to control • Classify the patients i in the test set as sensitive B-k(xi)=1 or insensitive B-k(xi)=0 • Let Sk={j in Pk : B-k(xi)=1}

  36. Repeat this procedure K times, leaving out a different part each time • After this is completed, all patients in the full dataset are classified as sensitive or insensitive • Scv= Sk

  37. For patients classified as sensitive, compare outcomes for patients who received new treatment T to those who received control treatment C. • Outcomes for patients in Scv  T vs outcomes for patients in Scv  C • Compute a test statistic Dsens • e.g. the difference in response proportions or log-rank statistic for survival • Generate the null distribution of Dsens by permuting the treatment labels and repeating the entire K-fold cross-validation procedure • Perform test at significance level 0.05 - overall

  38. If H0 is rejected, claim superiority of new treatment T for future patients with expression vector x for which B(x)=1 where B is the classifier of sensitive patients developed using the full dataset • The estimate of treatment effect for future sensitive patients is Dsens computed from the cross-validated sensitive subset Scv • The stability of the sensitive subset {x:B(x)=1} can be evaluated based on applying the classifier development algorithm to non-parametric bootstrap samples of the full dataset {1,...,n}

  39. 70% Response to T in Sensitive Patients25% Response to T Otherwise25% Response to C20% Patients Sensitive, n=400

  40. Prediction Based Analysis of Clinical Trials • Using cross-validation we can evaluate any classification algorithm for identifying the patients sensitive to the new treatment relative to the control using any set of covariates. • The algorithm and covariates should be pre-specified. • The algorithm A, when applied to a dataset D should provide a function B(x;A,D) that maps a covariate vector x to {0,1}, where 1 means that treatment T is prefered to treatment C for the patient. • The algorithm can be simple or complex, frequentist or Bayesian based. • Prediction effectiveness depends on the algorithm and the dataset • Complex algorithms may over-fit the data and provide poor results • Including Bayesian models with many parameters and non-informative priors • Prediction effectiveness for the given clinical trial dataset can be evaluated by cross-validation

  41. Standard Analysis Algorithm • Test the overall H0 at 5% significance level • If you reject H0 then treat all future patients with T • Expected survival KM(t;T) • Otherwise treat all future patients with C • Expected survival KM(t;C)