Development, validation and application of risk prediction models

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1 Development, validation and application of risk prediction models G. Colditz, E. Liu, M. Olsen, & others (Ying Liu, TA) 3/28/2012 Risk Prediction Models 1

2 Goals Through examples, class discussion, and homework, students will become familiar with the methods for development validation and implementation of prediction models. You will prepare an analysis plan for design and implementation of a prediction model to improve health outcomes 3/28/2012 Risk Prediction Models 2

3 Competencies Develop the knowledge and skills to design, implement, and evaluate epidemiologyrelated, health services or clinical research projects of clinical or public health significance including: Develop the knowledge and skills with the definitions in basic issues involved in the clinical prediction rules including, design, development, validation, implementation, and interpretation of results for their application in clinical or public health settings. Apply the principles of dissemination and implementation science to the evaluation of evidence for use of risk prediction models in clinical and public health programs. Design and implement strategies with appropriate integration of evaluation to inform the refinement of clinical and public health programs that will lead to improved health and wellness of the population. To achieve this competency, students will: Understand the development, implementation, evaluation and refinement of guidelines as they relate to risk prediction models. Apply principles of study design and evaluation to T2 research and implementation projects. 3/28/2012 Risk Prediction Models 3

4 Assignments Up to 3 key papers identified for each class Be prepared to lead discussion on these Supplementary papers listed Homework aims to replicate analysis and explore application of methods to refine approaches to prediction model building Guest speakers, national leaders, capitalize on their presence 3/28/2012 Risk Prediction Models 4

5 Class topics Overview of methods and issues Statistical approaches and applications in ID (methicillin-resistant Staph aureus) ROC analysis methods issues Sample size for comparing models Propensity scores Instrumental variables Application Urology Application CHD Application pharmaco-epidemiology Application breast Machine learning fmri predicting brain maturity Implementation 3/28/2012 Risk Prediction Models 5

6 Today Overview of issues Introduce examples we will revisit through class Next: Use ID examples (methicillin-resistant staph aureus) to discuss the sequence: develop, validate, implement pathway 3/28/2012 Risk Prediction Models 6

7 Phases in multivariable prediction research Development Validation Impact assessment (implementation research) Moons et al Prognosis and prognostic research. BMJ 2009; 338:b375 Royston P, et al ---developing a prognostic model. BMJ 2009; 338: b604 Altman DG, et al --- validating a prognostic model. BMJ 2009; 338: b605 Moons et al. --- application and impact of prognostic models in clinical practice. BMJ 2009; 338: b605 3/28/2012 Risk Prediction Models 7

8 Issues Predicting disease incidence or outcomes is not the same as explaining causes. Epidemiology predicts total burden of disease, but does not usually identify who will get the specific condition, or when it will occur. Cohort / observational data key to development and validation of most models. 3/28/2012 Risk Prediction Models 8

9 Applications of risk models Planning intervention trials (e.g, P1 eligibility) Identifying individuals at high risk of disease E.g., Framingham or other CVD score and treatment? Many others in this class OBGYN, ID, etc Assisting in creating benefit risk indices Risk stratification for improved clinical practice: breast health mammography services Identify women for MRI +/- genetic counseling Mammography + lifestyle or chemoprevention Reassurance Designing population prevention strategies

10 Validation issues Independent data set Calibration and discrimination Other issues? Next example of the Gail model Rockhill et al JNCI 3/28/2012 Risk Prediction Models 10

11 3/28/2012 Risk Prediction Models 11

12 Gail et al. model of breast cancer risk ("Risk Disk") Developed in 1989; used to estimated expected incidence in Breast Cancer Prevention Trial Risk factors in model: age at menarche, age at first birth/nulliparity, # of affected first-degree relatives, history of benign breast biopsy/hyperplasia, age Risk Disk widely distributed now to clinicians, members of lay public--for use in clinical decisionmaking about tamoxifen

13 Goodness-of-fit and discriminatory accuracy Goodness of fit--ability to predict incidence in groups defined by risk factor profile (χ 2 tests) Discriminatory accuracy--ability to separate individuals with different outcomes (concordance statistic)

14 Gail et al. model validation in Nurses' Health Study Cohort of 82,109 white women aged years in 1992 Observed 1,354 cases of breast cancer over five-year period Expected/Observed ratios: total sample: 0.94 ( ) high-risk subsample: 1.03 ( )

15 Estimated five-year risk of breast cancer, according to breast cancer status at end of follow-up Proportion of sample Estimated 5-year risk Did not develop breast cancer Developed breast cancer

16 Risk/benefit tradeoff Some groups of women are expected to have significant net benefit from tamoxifen: Women with a uterus: ages years regardless of five-year risk; ages years with five-year risk >=6.0% Women without a uterus: ages years regardless of five-year risk; ages years with five-year risk >=3.0%; ages years with five-year risk >=5.5%.

17 Findings on net benefit groups 2.3% of NHS sample fell into "significant net benefit" strata 3.3% of cases arose from these strata Estimate that 1.7% of cases could have been prevented in this five-year period if only women in "benefit" strata had been given tamoxifen treatment

18 Conclusions 1.67% cutpoint performs poorly at segregating women according to true risk status More information than just Gail risk estimate needed to determine eligibility for tamoxifen Based on best available data on risks and benefits, conclude only small proportion of breast cancer cases will be prevented, if tamoxifen chemoprevention limited to groups expected to have net benefit

19 Questions What is risk? How do I compare two ROC values? When is a risk factor worth adding to a prediction model? How will I know? Are there guidelines I should be aware of or follow? 3/28/2012 Risk Prediction Models 19

20 Framingham validation See D Agostino et al JAMA 2001, 286:180-7 Multiple independent data sets Model: age, age 2, BP (categories); Total cholesterol (Categories) HDL-C, Diabetes, current smoker CHD: coronary heart disease Are there limitations of Framingham risk prediction scores? 3/28/2012 Risk Prediction Models 20

21 3/28/2012 Risk Prediction Models 21

22 Framingham and CVD raise questions 3/28/2012 Risk Prediction Models 22

23 Cook ROC limitations Based on risk factor ROC issues Cook analyzes Women's Health Study a prospective cohort of 26,902 initially healthy women followed for future vascular events over 10 years. 3/28/2012 Risk Prediction Models 23

24 Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction. Cook, Nancy Circulation. 115(7): , February 20, DOI: /CIRCULATIONAHA American Heart Association, Inc. Published by American Heart Association. 2

25 Figure 1. Plot of probability density functions for noncases [f(x Non-case) = dashed line] and cases [f(x case) = dotted line] with an assumption of normal distributions for hypothetical predictor X, with an OR of 3.0 per 2 SD units. Also shown on the same scale is the probability of disease given the value of X [P(D X)], with an assumption of an overall prevalence of disease in the population of 10%. Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction. Cook, Nancy Circulation. 115(7): , February 20, DOI: /CIRCULATIONAHA American Heart Association, Inc. Published by American Heart Association. 3

26 Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction. Cook, Nancy Circulation. 115(7): , February 20, DOI: /CIRCULATIONAHA American Heart Association, Inc. Published by American Heart Association. 4

27 Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction. Cook, Nancy Circulation. 115(7): , February 20, DOI: /CIRCULATIONAHA American Heart Association, Inc. Published by American Heart Association. 7

28 Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction. Cook, Nancy Circulation. 115(7): , February 20, DOI: /CIRCULATIONAHA American Heart Association, Inc. Published by American Heart Association. 9

29 Considerations for application Simpler model may aid clinic use Simpler model may aid use in general public No consensus on model building approaches Royston, BMJ 2009 as noted earlier What will you measure for assessment of implementation? 3/28/2012 Risk Prediction Models 29

30 Not all models come from multivariable equations See your disease risk ( History Development Validation Application 3/28/2012 Risk Prediction Models 30

31 Guidelines for prediction Wasson et al NEJM 1985;313:793-9 Set forth 7 methodologic standards Definition of outcome (blind assessment) Definition of predictive finding Patient age and sex stated Study site described Test of misclassification rate Effects of clinical use prospectively measured Mathematical technique described

32 Performance to 94 Laupacis et al JAMA 1997;277: Review performance based on 29 studies Apply Wasson et al criteria Substantial variation in reporting 100% report mathematical technique to derive rule Outcomes (83%) and predictive variables (59%) clearly defined

33 Expanded criteria Describe results of clinical prediction rule Sn, Sp, PPV, etc Prospective validation Go beyond patients on whom rule developed Reproducibility Interobserver variability Sensibility Making clinical sense - content validity

34 Individualized Risk Prediction Classification problem Performance depends on success of classification Clinical vs statistical significance of risk factors How well do the models perform? Varies Can we do better? Probably Population strata may be more accurate Do people get this concept?

35 Current Cancer Crystal Balls Target for Prediction Breast Cancer Risk Mutation Status Resp. to Breast Cancer Therapy Example Model/Index Gail Model NHS Rosner model Cuzick BRCAPRO Model Estrogen receptor Her-2/neu Need for Breast Cancer Therapy Array-based Model

36 How Do These Models Compare? Diagnostic testing Predict events or disease state Common measure of performance Sensitivity Specificity ROC curve Area under the curve

37 Tyrer and Cuzick BRCA1 and BRCA2 estimation and a hypothetical low penetrance gene Personal risk factors: age at menarche, age at first birth, height, BMI, and age at menopause. Omitted details of type of menopause, use of postmenopausal hormones, and maintained a fixed adverse effect of age at first birth 30 or older, ignored number of children.» Stat Med 2004;23:

38 Expanding beyond Gail et al. Breast cancer incidence model (Rosner and Colditz) Validation test of goodness of fit and concordance statistic for prediction of risk (in separate set of cases ) Model includes age, family history, age at menarche, age at each birth, benign breast disease, alcohol intake, weight and height, age at menopause and type of menopause, use of postmenopausal hormones Variables such as weight and use of postmenopausal hormones allowed to vary over time Am J Epidemiol 2000;152:950-64

39 Pike model Factors associated with reduced risk of breast cancer were considered to lower the rate of breast tissue aging Pike et. al., Nature 1983;303: We translated this to mean the rate of cell division and accumulation of molecular damage on the pathway to breast cancer

40 One Birth Model Rate of tissue aging Rosner, Colditz, Willett, Am J Epidemiology 1994;139:824

41 One Birth Model Cumulative Tissue age Rosner, Colditz, Willett, Am J Epidemiology 1994;139:824

42 Rosner, Colditz, Willett, Am J Epidemiology 1994;139:826 Multiple Birth Model Rate of tissue aging

43 Multiple Birth Model Rosner, Colditz, Willett, Am J Epidemiology 1994;139:826

44 Model validation cont Good prediction overall. Classified cohort into deciles of risk and observed a relative risk of 5 comparing highest vs lowest deciles consistent across age strata Over predicting cases seen only at oldest age group where least data are available for model development (i.e. 70 years of age or more) Concordance statistic = 0.64 ( ) Rockhill et al J Clin Epi 2003;56:856-61

45 Breast cancer subtypes 1. Virtually all risk prediction for breast cancer assumes that breast cancer is a homogeneous disease, i.e. all types of breast cancer have the same risk profiles 2. Recent work (Colditz et al, JNCI 2004) indicates that risk factor profiles vary according to both ER status and PR status for some risk factors, but are the same for other risk factors 3. Apply such variation to development of model for ER+/PR+ and ER-/PR-

46 Extended follow-up of NHS Followed participants through 2002 Include 75,000 women with complete risk factor information Compared incidence vs. US SEER rates for white women O vs. E = 1.00 in overall cohort O vs. E = 1.06 in analysis subset

47 RR top vs. bottom decile RR Goodness-of-fit ER+/PR+ O = 6.6 E = 6.1 ER-/PR- O = 3.9 E = 4.0 Χ 2 9 = 9.95, p=0.35 Χ 2 9 = 8.09, p=0.53

48 ROC analysis Total Breast Ca. ER+/PR+ (3669 cases) (1728 cases) Gail Colditz, Rosner et al p<0.001 p<0.001 Q: how to compare these ROC values?

49 What does this mean? For ER+/PR+ breast cancer we can estimate the proportion of cases that arise from the top decile Within each 5 year age stratum, 20 to 25 % of all cases arise from the top decile Can we identify these women and offer them prevention strategies?

50 Meaning: Number needed to prevent 1 case If Raloxifene reduces risk of total cancer by 50% (CORE-JNCI; 66%, Ruth NEJM; 44%) Identify and treat women in the top decile for 5 years (Current model) We need to treat 79 women ages or just 43 women ages to prevent 1 case of breast cancer

51 Rosner hormones added to model 3/28/2012 Risk Prediction Models Rosner Breast Ca Res

52 Reclassification in breast model Within most model A risk deciles, there are important differences in estimated incidence according to model B risk decile (often two-fold). For a given model A risk decile, observed number of cases was higher than expected when the model B decile was high and lower than expected when model B decile as low. Overall slope beta=0.511 (p<0.001) Substantial increase in predictive power adding log e estradiol to model B 3/28/2012 Risk Prediction Models 52

53 Comparing ROC values Comparing model A (without estradiol) to Model B (with estradiol) we have Model A: C = Model B: C = P value comparing models < /28/2012 Risk Prediction Models 53

54 3/28/2012 Risk Prediction Models 54

55 3/28/2012 Risk Prediction Models 55

56 Conclusions Multiple methods issues for us to address Common themes of develop, test, validate Issues in clinical application beyond the methods for development Master understanding and some application experience through class and the associated homework 3/28/2012 Risk Prediction Models 56

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