2014 Bayesian Biostatistics and Bioinformatics Conference Keeping clinical risk calculators current Donna Pauler Ankerst

Transcription

1 04 Bayesian Biostatistics and Bioinformatics Conference Keeping clinical risk calculators current Donna Pauler Ankerst

2 ... Other experts said there has not been a real appreciation of the difficulties with this and other risk calculators. I don t think people have a good idea of what needs to be done Dr. Michael Blaha, JHU... the problem might have stemmed from the fact that the calculator uses as reference points data collected more than a decade ago...

3 US National Cancer Institute There are hundreds of unregulated calculators in cancer alone. You can make your own calculator and submit to the NCI or Cleveland Clinic has a calculator to make a calculator. What needs to be done in this field?

4 4 Risk prediction: the past One calculator built on one general cohort log X X X X X X X X p p β β β β β β β β β = X,, X 8 established risk factors measured in cohort Breast Cancer Risk Assessment Tool (BCRAT) (989): age, family history, menopause Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) (006): prostate-specific antigen (PSA), race, age

5 Risk prediction: the present One calculator built from many specialized cohorts. p log p = β + β X + β X + β X + β X + β X + β X + β X + β X Base cohort Serum biomarker cohort Genomic/genetic cohort The challenge is that not all cohorts measure the same predictors. 5

6 Risk prediction: the future A changing model based on specialized cohorts. p log p t t t t t t = β + β X + β X + β X + β X + β X + β X + β X + β X t 6 6 t 7 t + β 9 X 9 t Base cohort Serum biomarker cohort Genomic/genetic cohort Clinical and diagnostic practice changes with time t new markers X 9 are discovered 6

7 Risk prediction: the future A changing model based on specialized cohorts that can adapt to a specific institution. p log p i,t i,t i,t i,t i,t i,t = β + β X + β X + β X + β X + β X + β X + β X + β X i,t 6 6 i,t 7 i,t + β 9 X 9 i,t Base cohort Serum biomarker cohort Genomic/genetic cohort Institutions i from different countries or with different patient populations (screening versus clinically referred) have proven different risks that can t be adjusted for by covariates. 7

8 Outline Incorporating new risk factors based on different cohorts into an existing risk prediction tool. Automatic self-updating risk calculators. The solution is easy. 8

9 Experience from the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) The PCPT is the exclusive prostate cancer screening trial in world history that required all men at the end of seven years to undergo biopsy regardless of PSA and digital rectal exam. Ergo, we thought a calculator built on 6,000 men from the placebo arm in 006 would be foolproof (bulletproof as we roll in Texas). Thompson, Ankerst et al, NEJM 004; JAMA 005; JNCI 006a,b

10 PCPTRC.0 is based on nominal logistic regression using the standard risk factors 0

11 New markers found to be associated with prostate cancer risk Serum and urine markers, including percent free PSA and PCA3 from the Early Detection Research Network (EDRN) Multiple SNPs from genomewide association studies (GWAS) Extended family history from pedigrees, the Swedish Family-Cancer Database (SFCD) These markers were not measured in the original PCPTRC cohort and vice versa: the cohorts above did not measure most of the PCPTRC risk factors.

12 Bayes theorem to update an existing risk calculator with new markers X = PCPT Risk factors: PSA, DRE, family history, prior biopsy, race, age Y = New markers P(Cancer X,Y) P(No Cancer X,Y) = P(Y X, Cancer) P(Y X, No Cancer) P(Cancer X) P(No Cancer X) Posterior odds /7/04 Likelihood ratio, can be estimated from an external case control study Prior odds, from PCPTRC

13 Urine marker PCA3 X = PCPT Risk factors Y = log(pca3) P(Cancer X,Y) P(No Cancer X,Y) = P(Y X, Cancer) P(Y X, No Cancer) P(Cancer X) P(No Cancer X) Analysis from a exp cohort from ( Y µ ) Genprobe cancer σ σ cancer cancer exp ( Y µ ) no cancer σ σ no cancer no cancer µ cancer = log(psa) age +.055dre priorbiop µ log(psa) age dre +.60priorbiop σ σ no cancer cancer no cancer =.0366 =.09 /7/04 PCPTRC exp( β X ) β X = log(psa) famhist dre priorbiop Confidence,prediction intervals for posterior risk by delta rule. 3

14 The first validation online shortly after added to website; The impact of publishing algorithms. others since. 4

15 33 single nucleotide polymorphisms (SNPs) multiply validated for association with prostate cancer Old school: scouring supplementary appendices, handcalculating. 5

16 Multivariate random metaanalysis: 373 cases and 4798 controls from studies for this SNP Multivariate random-effects meta-analysis 6

17 Single nucleotide polymorphisms X = PCPT Risk factors: PSA, DRE, family history, prior biopsy; we believe that mutations are inherited or occur before X and so do not need to condition on X. Y = SNP with published genotype or allele frequencies (example T,C). P(Cancer X,Y) P(No Cancer X,Y) = P(Y X, Cancer) P(Y X, No Cancer) TT I ( Y = TT ) TC, CT I ( Y = TC, CT ) CC I ( Y ( π cancer ) ( π cancer ) ( π cancer ) TT I ( Y = TT ) TC, CT I ( Y = TC, CT ) CC ( π ) ( π ) ( π ) no cancer ˆ π ˆ π Z = no.of cancer no cancer I ( Z = ) no cancer ˆ π cancer ˆ π risk alleles ( T ). no cancer I ( Z = ) no cancer 0 ˆ π 0 ˆ π cancer no cancer = CC ) I ( Y = CC ) I ( Z = 0) Meta-analysis of GWAS, P(Cancer X) P(No Cancer X) PCPTRC exp( β X ) β X = log(psa) famhist dre priorbiop 7

18 Multiple SNPs Y = Y,Y,...,Y r : Multiple SNPs from different studies Hapmap not to be in linkage disequilibrium (LD) that are known from the P(Cancer X,Y ) P(No Cancer X,Y ) = = i= r i= r r P( Y P( Y i= i LR Cancer) No Cancer) i i P(Cancer X ) P(No Cancer X ) P(Cancer X ) P(No Cancer X ) LR s for different SNPs can come from different GWAS. If SNP s not in LD, the LD can be imported from HapMap. 8

19 Detailed family history The Swedish Family-Cancer Database (SFCD) records all cancers in all extended family members since 93. Since the model is compartmentalized they can build the likelihood ratios and ship to us. P(Cancer X,Y) P(No Cancer X,Y) = P(Y X, Cancer) P(Y X, No Cancer) P(Cancer X) P(No Cancer X) 9

20 SFCD: Detailed family history Extended tables for number of relatives and seconddegree relatives. 0

21 Updated risk curves based on detailed family history Posterior odds P(Cancer X,Y) = P(No Cancer X,Y) = P(Y X, Cancer) P(Y X, No Cancer) P(Cancer X) P(No Cancer X) = LR Prior odds Posterior risk Posterior odds = + Posterior odds

22 The need for change: what happens when the base cohort supporting your calculator becomes outdated? The PCPT cohort was collected from the late 990 s through 004. The PCPT protocol for the biopsy procedure was a 6-core sample, but modern practice collects - or even more cores. It has been documented that a higher number of cores increases the likelihood of detecting cancer and high-grade cancer.

23 Prostate Biopsy Collaborative Group (PBCG) Cheaper to build a new house if the foundation is too old? Canadian Consortium Hamburg Mayo Clinic Milan UCSF Cleveland Clinic Durham VA UTHSCSA Puerto Rico Biopsies are being performed in institutions across the world daily with the data flowing into Electronic Medical Records (EMR) nightly; nobody is using it. 3

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25 Can one risk calculator fit all? Our experience says no. Empirical risk curves according to PSA across cohorts in the Prostate Biopsy Collaborative Group (PBCG) Vickers et al., CCR, 00; Ankerst et al., WJUR, 03,04 5

26 Methods for updating a risk calculator vs. To yearly update a risk model: Steyerberg Build a new model from scratch Recalibration in the large: Use log PCPTRC risk as offset and estimate new intercept in logistic regression Recalibration: Logistic regression to estimate new intercepts and slopes for log PCPTRC risk as single covariate Revision: Same as recalibration but allow individual risk factors to enter separately as covariates Bayesian: Use prior to posterior updating on parameters Automate from electronic medical records 6

27 Dynamic Updating Methods: We used all years past and the next year as the validation year, so that the training sample got larger and larger as experience accumulated. We evaluated three strategies:.) Building a new calculator from scratch on the cohort..) Revision using PCPTRC risks as a single covariate in logistic regression. 3.) Just using the PCPTRC. Results: Either revising the PCPTRC calculator or building a new model each year performed on average better than just using the PCPTRC.

28 Acknowledgements PCPTRC 7 minute video Ian M. Thompson UTHSCSA CTRC Director Josef Hoefler,Sonja Grill, Andreas Strobl TUM PhD students Patients Everywhere 8