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1 Building and Using Disease Prediction Models in the Real World Building and Using Disease Prediction Models in the Real World Discussion leader: Heejung.

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Presentation on theme: "1 Building and Using Disease Prediction Models in the Real World Building and Using Disease Prediction Models in the Real World Discussion leader: Heejung."— Presentation transcript:

1 1 Building and Using Disease Prediction Models in the Real World Building and Using Disease Prediction Models in the Real World Discussion leader: Heejung Bang, Ph.D. Weill Medical College of Cornell University At Joint Statistical Meetings Salt Lake Cit, UT, 2007

2 2 What are risk score/prediction models? A “prediction” is a statement or claim that a particular event will occur in the future (current or past event is also sensible). A “prediction” is a statement or claim that a particular event will occur in the future (current or past event is also sensible). Response is often binary (event/non-event) or censored. Response is often binary (event/non-event) or censored. Mathematical equation can be used to model the rate (or probability or likelihood) of event. Mathematical equation can be used to model the rate (or probability or likelihood) of event. Scoring system (e.g., integer) can be derived to grade the risk, often by simplifying the mathematical model (e.g., regression coefficients). Scoring system (e.g., integer) can be derived to grade the risk, often by simplifying the mathematical model (e.g., regression coefficients). Mathematical equation and/or scoring system can be used to stratify subjects (e.g., high vs. low risk ) Mathematical equation and/or scoring system can be used to stratify subjects (e.g., high vs. low risk )

3 3 Why important? Evidence-based medicine = Science (theory) + Data + Statistics Evidence-based medicine = Science (theory) + Data + Statistics Risk score = Statistics + Art + Reality Risk score = Statistics + Art + Reality One of real practical solutions to reduce the burden/incidence of some diseases. One of real practical solutions to reduce the burden/incidence of some diseases. People use it in real world (esp., lay and underserved people) People use it in real world (esp., lay and underserved people) -- used in clinical setting, community setting, or self-use for pre-screening, screening or risk assessment/prediction.

4 4 But Prediction is very hard, especially about the future - Yogi Berra

5 5 Prediction via multiple regression (Stat 101) Two general applications for multiple regression: explanation & prediction, two differing goals in research Two general applications for multiple regression: explanation & prediction, two differing goals in research -- attempting to understand a phenomenon by examining a variable's correlates on a group level (explanation) -- being able to make valid projections concerning an outcome for a particular individual (prediction) Refs: 1. Osborne (2000). Prediction in multiple regression. Pract Assessment, Res & Eval. 2. Neter, Kutner, Nachtsheim & Wasserman (2003). Applied Linear Statistical Models. 2. Neter, Kutner, Nachtsheim & Wasserman (2003). Applied Linear Statistical Models.

6 6 Regression vs. Prediction/risk score what make these two tasks different?

7 7 1.Simple and easy: Don’t let the perfect be the enemy of the good! User-friendliness and easy use are important! User-friendliness and easy use are important! -- a perfect model can be a jewel in your closet or a journal. -- if biostatisticians can not use it, how lay persons can? Interactions or nonlinear function may make prediction model/risk score more complex. Interactions or nonlinear function may make prediction model/risk score more complex. -- worth it? -- proper detection and modeling require larger N (more later).

8 8 2. Variable categorization Most statisticians agree with Royston et al. (2005). “Dichotomizing continuous predictors in multiple regression: a bad idea”. Most statisticians agree with Royston et al. (2005). “Dichotomizing continuous predictors in multiple regression: a bad idea”. However, filling in continuous information (e.g., blood pressure (BP), BMI, CRP) can be hard for many people. However, filling in continuous information (e.g., blood pressure (BP), BMI, CRP) can be hard for many people. -- Q: do you know your BP? Which BP? In what unit? -- Q: do you know your BP? Which BP? In what unit? A wrong unit is can be worse than nothing! (pound vs. kg, mg vs. g) Unit is more complex than you ever imagine. A wrong unit is can be worse than nothing! (pound vs. kg, mg vs. g) Unit is more complex than you ever imagine. Prediction model that includes continuous variables may not be converted to a simple questionnaire. Prediction model that includes continuous variables may not be converted to a simple questionnaire. Prediction model solely based on categorical variables is still usable with a few missing inputs. Prediction model solely based on categorical variables is still usable with a few missing inputs. It may be safe, informative, and instructive to develop “continuous models” and “categorical models” together and present both and let users decide. It may be safe, informative, and instructive to develop “continuous models” and “categorical models” together and present both and let users decide. Intuitive cutpoints (optimal cutpoint may or may not help) Intuitive cutpoints (optimal cutpoint may or may not help)

9 9 3. Variable selection >5-10 variables can be too many. >5-10 variables can be too many. -- hard to believe but it is true. Everyone is busy and lazy! -- there are too many risk scores (or medical calculators) in the world. Not all significant predictors should be included in the final model: this is not a sin or cheating! Not all significant predictors should be included in the final model: this is not a sin or cheating! There are difficult variables and easy variables. There are difficult variables and easy variables. Statistical techniques (e.g., backward elimination, data mining) can guide variable search but subjectivity can come into play (e.g., some variables such as SES may be intentionally excluded. Race may be excluded not to limit generalizability). Statistical techniques (e.g., backward elimination, data mining) can guide variable search but subjectivity can come into play (e.g., some variables such as SES may be intentionally excluded. Race may be excluded not to limit generalizability). >1 model can be developed to accommodate different data availabilities, say, with or without CRP. >1 model can be developed to accommodate different data availabilities, say, with or without CRP. Even clinicians don’t agree on variables (e.g., internist vs. nephrologist, serum vs. urine). Even clinicians don’t agree on variables (e.g., internist vs. nephrologist, serum vs. urine).

10 10 4. Sample size (N) and data quality Power/N calculation is less relevant (no specific test involved) or can be complicated because it is multivariables-setting. Power/N calculation is less relevant (no specific test involved) or can be complicated because it is multivariables-setting. No absolute consensus on N requirement. As the goal is a stable regression equation, more is better. No absolute consensus on N requirement. As the goal is a stable regression equation, more is better. Creating a prediction equation involves gathering relevant data from a “large, representative” sample from the population (if not, less reproducible risk score!) Creating a prediction equation involves gathering relevant data from a “large, representative” sample from the population (if not, less reproducible risk score!) We may need to save some N for internal validation (e.g., split-sample method). We may need to save some N for internal validation (e.g., split-sample method). Therefore, a large database is often used, e.g., NHANES, NHS, SEER, Framingham, ARIC, etc. Therefore, a large database is often used, e.g., NHANES, NHS, SEER, Framingham, ARIC, etc. “No fancy statistical analysis is better than the quality of the data. Garbage in, garbage out, as they say.” (Robins) “No fancy statistical analysis is better than the quality of the data. Garbage in, garbage out, as they say.” (Robins)

11 11 5. Population characteristics Universal model may not exist. Universal model may not exist. Separate models may be needed: Separate models may be needed: -- by sex -- by race or country (e.g., many countries have their own diabetes risk score) -- by age -- high risk (e.g., clinical setting) vs. general population -- first vs. recurrent event. Before vs. after surgery Often, not under investigator’s control (e.g., ≥ 65 old subjects only in Medicare database, no minority in Framingham study). Often, not under investigator’s control (e.g., ≥ 65 old subjects only in Medicare database, no minority in Framingham study). May need to be mentioned in the title of your paper. May need to be mentioned in the title of your paper. Remark: More chances for publications! Not just a repeat but each effort can be meaningful. (good news!)

12 12 6. Databases Administrative data Generally HUGE Generally HUGE No lab data, not many variables No lab data, not many variables Represent the target population well Represent the target population well Generally well maintained by reliable organization Generally well maintained by reliable organization Data check well done Data check well done Clinical or Epi data Small or mid-size Small or mid-size Lab and clinical data and many more variables Lab and clinical data and many more variables Reduced generalizability or representiveness Reduced generalizability or representiveness Data quality not always guaranteed. Data quality not always guaranteed.

13 13 Statistical tools for model development Standard regression: Logistic and Cox Standard regression: Logistic and Cox -- most popular -- most popular -- explicit mathematical formula and numeric scoring system can be derived (e.g., by converting regression coefficients) -- explicit mathematical formula and numeric scoring system can be derived (e.g., by converting regression coefficients) Advanced regression: Fractional Polynomials regression (Royston & Altman 1999; Sauerbrei et al. 2006). Advanced regression: Fractional Polynomials regression (Royston & Altman 1999; Sauerbrei et al. 2006). -- combining variable selection with determination of functional relationships for predictors -- combining variable selection with determination of functional relationships for predictors Tree-based methods: CART (by Breiman), Recursive Partitioning (by Hawkins & Kass), Optimal Discriminant Analysis/Classification Tree Analysis (by Yarnold), Logical Analysis of Data (by Hammer), Bayesian CART Tree-based methods: CART (by Breiman), Recursive Partitioning (by Hawkins & Kass), Optimal Discriminant Analysis/Classification Tree Analysis (by Yarnold), Logical Analysis of Data (by Hammer), Bayesian CART -- complex interactions can be revealed. -- complex interactions can be revealed. -- cutpoints identified -- cutpoints identified Neural network Neural network Data mining techniques Data mining techniques

14 14 Statistical measures for model evaluation/diagnostics Sensitivity & Specificity — most popular Sensitivity & Specificity — most popular Discrimination (ROC/AUC) – most popular Discrimination (ROC/AUC) – most popular Predictive values, positive or negative (PPV, NPV) Predictive values, positive or negative (PPV, NPV) Likelihood ratio (LR) Likelihood ratio (LR) Accuracy (e.g., Youden index, Brier score) Accuracy (e.g., Youden index, Brier score) Yield, number needed to treat (NNT), number needed to screen (NNS) Yield, number needed to treat (NNT), number needed to screen (NNS) Model fit (e.g., AIC, BIC) Model fit (e.g., AIC, BIC) Lack of fit (e.g., Hosmer-Lemeshow test) Lack of fit (e.g., Hosmer-Lemeshow test) R 2 (coefficient of determination) R 2 (coefficient of determination) P-value (significance of association) P-value (significance of association) Predictiveness curve (based on R 2, by Pepe) Predictiveness curve (based on R 2, by Pepe) Calibration/re-calibration Calibration/re-calibration Decision curve analysis (Vickers 2006) Decision curve analysis (Vickers 2006) Remark: LR, Youden index and ROC/AUC are functions of sensitivity and specificity.

15 15 Noted limitations of some methods P-value: significant association is often not enough for good prediction. P-value: significant association is often not enough for good prediction. -- can have small p-value but poor values for everything else (e.g., low p and low R 2 is a well-known phenomenon). -- can have small p-value but poor values for everything else (e.g., low p and low R 2 is a well-known phenomenon). -- some adopt strict p-value thresholds (e.g., p<0.001) or multiple testing adjustment. -- some adopt strict p-value thresholds (e.g., p<0.001) or multiple testing adjustment. AUC: triply robust. Once it is high, it is extremely difficult to increase. Odds ratio alone can be problematic (Pepe et al. 2004; Cook 2007) AUC: triply robust. Once it is high, it is extremely difficult to increase. Odds ratio alone can be problematic (Pepe et al. 2004; Cook 2007) R 2: oftentimes, hard to increase. R 2: oftentimes, hard to increase. Sensitivity/Specificity: do not address the problems of the prevalence of disease in different populations, e.g., if prevalence=0.01, sensitivity =0.95, specificity=0.95 then PPV=0.16. Sensitivity/Specificity: do not address the problems of the prevalence of disease in different populations, e.g., if prevalence=0.01, sensitivity =0.95, specificity=0.95 then PPV=0.16. Hosmer-Lemeshow test: different software can produce different test statistics/p-values. Hosmer-Lemeshow test: different software can produce different test statistics/p-values. Remark: For novel markers, relying on one statistical measure may not be wise.

16 16

17 17 Good references Ridgeway G. (2003). Strategies and Methods for Prediction” In The Handbook of Data Mining (N. Ye, ed.). Ridgeway G. (2003). Strategies and Methods for Prediction” In The Handbook of Data Mining (N. Ye, ed.). Harrell FE Jr, Lee KL, Mark DB. (1996). Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine. 15(4): 361-87. Harrell FE Jr, Lee KL, Mark DB. (1996). Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine. 15(4): 361-87.

18 18 Prevalent vs. Incident disease Prevalent/concurrent disease: Prevalent/concurrent disease: --cross-sectional study is needed. --useful for asymptomatic disease for detecting undiagnosed cases (e.g., diabetes mellitus (DM), kidney disease), not for all diseases. --simplicity in prediction model/risk score is important. Incident disease: Incident disease: --prospective study of event-free cohort is needed. --simplicity is less important because prediction of new cases is not as urgent as diagnosis of (hidden) concurrent cases.

19 19 Models evolve Gail et al (1989, 1999, 2001)’s original and improved prediction models for breast cancer, called Gail et al. model1, model2, etc. Gail et al (1989, 1999, 2001)’s original and improved prediction models for breast cancer, called Gail et al. model1, model2, etc. & Barlow et al. and Chen et al. (2006) improved Gail et al. with novel predictors. Similarly, Stroke Prognosis Instrument (SPI) I & II, Similarly, Stroke Prognosis Instrument (SPI) I & II, Acute Physiology and Chronic Health Evaluation (APACHE) I, II, III. Acute Physiology and Chronic Health Evaluation (APACHE) I, II, III. Multiple efforts to simplify and improve the Framingham risk score. Multiple efforts to simplify and improve the Framingham risk score. Many risk scores exist for incident and prevalent DM. Many risk scores exist for incident and prevalent DM. Remark: act on the best available evidence, as opposed to waiting for the best possible evidence (Institute of Medicine)

20 20 Other highly cited scores/equations Not necessarily for specific disease prediction Charlson’s comorbidity index: to give a 10 year survival estimate for a patient (Charlson et al. 1987): Charlson’s comorbidity index: to give a 10 year survival estimate for a patient (Charlson et al. 1987): MDRD-GFR: kidney function measure (Levey et al. 2000) MDRD-GFR: kidney function measure (Levey et al. 2000) APACHE: a severity of disease classification system at intensive care unit (Knaus et al., 1981). APACHE: a severity of disease classification system at intensive care unit (Knaus et al., 1981). --most were developed by clinicians (not statisticians) and more use for clinicians

21 21 Individual vs. population-level risk Still unsolved issues Still unsolved issues Started from Rose’s legendary paper, “Sick individuals and sick population” (1985, republished in 2001). Started from Rose’s legendary paper, “Sick individuals and sick population” (1985, republished in 2001). Population-based model is a poor model in individual level. Population-based model is a poor model in individual level. -- think about Winston Churchill! -- a large number of people at a small risk may give rise to more cases of disease than the small number who are at a high risk. -- a preventive measure that brings large benefits to the community offers little to each individual. -- advantages/disadvantages of “high-risk strategy” vs. “population strategy”. Not competing. Both are necessary. Individual prediction ever possible? Individual prediction ever possible? -- Genetic studies may be helpful. -- LAD answers better but a more complex algorithm is needed—nothing is free!

22 22 Copyright restrictions may apply. Rose, G. Int. J. Epidemiol. 2001 30:427-432; doi:10.1093/ije/30.3.427 Percentage distribution of serum cholesterol levels (mg/dl) in men aged 50-62 who did or did not subsequently develop coronary heart disease (Framingham Study5)

23 23 Copyright restrictions may apply. Elmore, J. G. et al. J. Natl. Cancer Inst. 2006 98:1673-1675; doi:10.1093/jnci/djj501 Ability of the Gail et al. breast cancer risk prediction model to discriminate between women who were diagnosed with breast cancer and women who were not diagnosed in the Nurses' Health Study

24 24 What diseases can be predicted? Breast cancer Breast cancer --Gail et al. (1989), Rosner and Colditz (1996, 2000), Tyrer et al. (2004), Barlow et al. (2006) Other cancers: cervical (2006), ovarian (2006), prostate (2005), lung (2007), colorectal (2007) ---all are recent! Other cancers: cervical (2006), ovarian (2006), prostate (2005), lung (2007), colorectal (2007) ---all are recent! Coronary heart disease Coronary heart disease ---Framingham, ARIC, SCORE (Europe), Reynolds score Stroke Stroke ---SPI, Stroke-Thrombolytic Predictve Instrument, ARIC Diabetes Diabetes ---Herman et al., San Antonio (Stern et al.), ARIC (Schmidt et al.) Kidney disease Kidney disease ---SCORED (Bang et al. 2007) Numerous other specific diseases/events Numerous other specific diseases/events

25 25 Not all diseases/events are well predicted Some mental disorders (screening is more common than prediction) Some mental disorders (screening is more common than prediction) HIV HIV How about car accident, divorce, bankruptcy, suicide, lay-off? How about car accident, divorce, bankruptcy, suicide, lay-off? Cancer again (AUC can be as low as 0.56) Cancer again (AUC can be as low as 0.56) Too many “Don’t Do” (i.e., risk factors) are not more helpful than “Do Nothing”. Too many “Don’t Do” (i.e., risk factors) are not more helpful than “Do Nothing”. Poor PPV Poor PPV A good reading: Begg (2001). The search for cancer risk factors: when can we stop looking? AJPH.

26 26 Can we predict low risk or health? It is hard for a risk model based on clinical factors to identify a group at very low risk that does need to worry. In other words, a group with very low BMI, high exercise levels, good genes, usually is not well captured by screening questions. It is hard for a risk model based on clinical factors to identify a group at very low risk that does need to worry. In other words, a group with very low BMI, high exercise levels, good genes, usually is not well captured by screening questions. This is true virtually in all disease prediction problems. Discrimination at the low end may not be good but can be good at the high end of the risk spectrum. This is true virtually in all disease prediction problems. Discrimination at the low end may not be good but can be good at the high end of the risk spectrum. ‘We know exactly why certain people commit suicide. We don’t know, within the ordinary concepts of causality, why certain others don't commit suicide............... We know a great deal more about the causes of physical disease than we do about the causes of physical health.’ "The Road Less Travelled" by Peck, 1978) ‘We know exactly why certain people commit suicide. We don’t know, within the ordinary concepts of causality, why certain others don't commit suicide............... We know a great deal more about the causes of physical disease than we do about the causes of physical health.’’ ("The Road Less Travelled" by Peck, 1978)

27 27 Sample risk scores 1. Framingham score

28 28

29 29 2. Reynolds score

30 30 Copyright restrictions may apply. Bang, H. et al. Arch Intern Med 2007;167:374-381. 3. SCreening Occult REnal Disease (SCORED)

31 31

32 32 4. Prostate cancer nomogram

33 33 Issues to consider: before/during development Predictable and meaningful disease? Predictable and meaningful disease? 1 st model: always thrilled! No need to compare with other models. 1 st model: always thrilled! No need to compare with other models. Best model: also great. But one should show a new model improves the existing models/guidelines in important aspects. Best model: also great. But one should show a new model improves the existing models/guidelines in important aspects. External validation using independent dataset within the same publication is a great strength (editors seem to give brownie points). External validation using independent dataset within the same publication is a great strength (editors seem to give brownie points). -- advanced validation techniques (e.g., cross-validation, bootstrap) are not popularly used in clinical publications. Split-Sample method is widely used.). However, it still utilizes the sample data (“internal validation”). Ask yourself “Will this model be reproducible?” Ask yourself “Will this model be reproducible?”

34 34 After development VALIDATE or Perish! VALIDATE or Perish! Validations will be done by you and others (be careful and prepared to write Response.) Validations will be done by you and others (be careful and prepared to write Response.) People will compare your model and others’ once they are developed (not always in a fair manner. Usually, you don’t have a chance to review and give comments on others’ publications that criticize your method) People will compare your model and others’ once they are developed (not always in a fair manner. Usually, you don’t have a chance to review and give comments on others’ publications that criticize your method) Do not publish “model not to be replicated” or “type-I-error” and run! Do not publish “model not to be replicated” or “type-I-error” and run! Everybody loves external validation. Esp. Editors Everybody loves external validation. Esp. Editors

35 35 After publication, how to disseminate? Power of marketing: good method deserves good marketing. Power of marketing: good method deserves good marketing. Computer-system (e.g., web-based or handheld) vs. paper-pencil method. Computer-system (e.g., web-based or handheld) vs. paper-pencil method. Work with Public Affair department in your institute or contact Media directly. Work with Public Affair department in your institute or contact Media directly. -- authors may need to write Press Release (PR). -- no one reads/understands your paper as well as you do. Highlight the main findings clearly. -- no p-value in PR! -- ready for interviews (esp., for 1 st study) Work with authority and practitioners to implement/distribute your method (preferably after Validation). Work with authority and practitioners to implement/distribute your method (preferably after Validation).

36 36 Statistician’s role in risk prediction/score Statistician’s involvement is absolutely necessary. Statistician’s involvement is absolutely necessary. Statisticians (or epidemiologists) can be a leader/first author of clinical research/publication. Statisticians (or epidemiologists) can be a leader/first author of clinical research/publication. Statisticians who develop risk score should be highly familiar with the current literature of the relevant disease. Statisticians who develop risk score should be highly familiar with the current literature of the relevant disease. Your title beyond “statistician” or “faculty of (bio)statistics” may be helpful for PR and interview purposes (sadly true! some reporters search for MD authors). Your title beyond “statistician” or “faculty of (bio)statistics” may be helpful for PR and interview purposes (sadly true! some reporters search for MD authors). At times, clinical communities call for the development of a (new or improved) risk score. For example, At times, clinical communities call for the development of a (new or improved) risk score. For example, -- Multiple editorials in 2006 called for renal score. -- Aitkins (1994) wrote “It would be a shame if Spiegelman et al. were to stop short of presenting a new model based on their substantially more powerful tool, the cohort study." -- Beyond Framingham.

37 37 Screen or not screen? Not all prediction models/pre-screening/screening are beneficial. Not all prediction models/pre-screening/screening are beneficial. e.g., ADA recommended “Do screen” for DM (in one year) and “Do not screen” (in the following year), in the same journal. e.g., a lot of controversies in breast cancer screening Freedman DA, Petitti DB, and Robins JM. (2004). On the efficacy of screening for breast cancer. International Journal of Epidemiology, 33:43-55. Comment by Gotzsche, P.C. On the benefits and harms of screening for breast cancer,pp. 56-64. Comment by Miller, A.B. Commentary: A defence of the Health Insurance Plan (HIP) study and the Canadian National Breast Screening Study (CNBSS) pp. 64-65. Comment by Baum, M. Commentary: False premises, false promises and false positives - the case against mammographic screening for breast cancer. pp. 66-67. Comment by Berry, D. Commentary: Screening mammography: a decision analysis. pp. 68. Rejoinder by Freedman, D.A.,Petitti, D.B., and Robins, J.M.Rejoinder. pp. 69-73.On the efficacy of screening for breast cancer On the benefits and harms of screening for breast cancer,pp. 56-64. Comment by Miller, A.B. Commentary: A defence of the Health Insurance Plan (HIP) study and the Canadian National Breast Screening Study (CNBSS) pp. 64-65. Comment by Baum, M. Commentary: False premises, false promises and false positives - the case against mammographic screening for breast cancer. pp. 66-67. Comment by Berry, D. Commentary: Screening mammography: a decision analysis. pp. 68. Rejoinder by Freedman, D.A.,Petitti, D.B., and Robins, J.M.Rejoinder. pp. 69-73. WHO announced 10 principles for national screening programs (1968). WHO announced 10 principles for national screening programs (1968). False negatives can cause a serious problem. False negatives can cause a serious problem. False positives can create too much anxiety and scare people. False positives can create too much anxiety and scare people. Problem of high vs. low risk. What does “low” means? Problem of high vs. low risk. What does “low” means? Effectiveness of screening should ultimately be tested in RCTs. Effectiveness of screening should ultimately be tested in RCTs. Cost-effectiveness should also be evaluated. Cost-effectiveness should also be evaluated.

38 38 US Preventive Services Task Force The entity with the most rigorous evidence-based approach The entity with the most rigorous evidence-based approach An independent panel of experts in primary care and prevention that systematically reviews the evidence of effectiveness and develops recommendations for clinical preventive services. An independent panel of experts in primary care and prevention that systematically reviews the evidence of effectiveness and develops recommendations for clinical preventive services. http://www.ahrq.gov/clinic/uspstf/uspstopics.htm http://www.ahrq.gov/clinic/uspstf/uspstopics.htmhttp://www.ahrq.gov/clinic/uspstf/uspstopics.htm Many specific disease authorities (e.g., CDC, ADA, NKF) have their own screening recommendations. Often considerably different. Many specific disease authorities (e.g., CDC, ADA, NKF) have their own screening recommendations. Often considerably different. Some agencies (e.g., NCI, NHLBI) hold a workshop to review various risk models. Some agencies (e.g., NCI, NHLBI) hold a workshop to review various risk models. A recent ref: Campos-Outcalt (2007). Screening: New guidance on what and what not to do. J of Family Practice.

39 39 Risk score primer Simplicity, user-friendliness and accuracy are key issues in success. Simplicity, user-friendliness and accuracy are key issues in success. Can have real impacts on people’s lives (esp., for underserved) Can have real impacts on people’s lives (esp., for underserved) Useful for educating people about risk factors and increasing low-awareness of some diseases. Useful for educating people about risk factors and increasing low-awareness of some diseases. Great collaboration area for clinicians and statisticians. Great collaboration area for clinicians and statisticians. Name can be important (e.g., ABCD, APACHE, SCORED, Framingham, Reynolds, Gail et al., Take the test and know your score, Indian diabetes score), Googlable? Name can be important (e.g., ABCD, APACHE, SCORED, Framingham, Reynolds, Gail et al., Take the test and know your score, Indian diabetes score), Googlable? Nothing causal, all about association or correlation! Nothing causal, all about association or correlation! -- If causes can be removed, susceptibility ceases to matter (Rose 1985)

40 40 Sample risk scores on internet Cancer: http://riskfactor.cancer.gov/cancer_risk_prediction/ Cancer: http://riskfactor.cancer.gov/cancer_risk_prediction/http://riskfactor.cancer.gov/cancer_risk_prediction/ http://www.mskcc.org/mskcc/html/5794.cfm http://www.mskcc.org/mskcc/html/5794.cfmhttp://www.mskcc.org/mskcc/html/5794.cfm http://www4.utsouthwestern.edu/breasthealth/cagene/ http://www4.utsouthwestern.edu/breasthealth/cagene/ APACHE: http://www.sfar.org/scores2/apache22.html APACHE: http://www.sfar.org/scores2/apache22.htmlhttp://www.sfar.org/scores2/apache22.html http://www.apache-web.com/public/pub_main.html http://www.apache-web.com/public/pub_main.htmlhttp://www.apache-web.com/public/pub_main.html Charlson comorbity index: http://www.medalreg.com/qhc/medal/ch1/1_13/01-13-01- ver9.php3 Charlson comorbity index: http://www.medalreg.com/qhc/medal/ch1/1_13/01-13-01- ver9.php3http://www.medalreg.com/qhc/medal/ch1/1_13/01-13-01- ver9.php3http://www.medalreg.com/qhc/medal/ch1/1_13/01-13-01- ver9.php3 Framingham score: http://hp2010.nhlbihin.net/atpiii/calculator.asp?usertype=prof Framingham score: http://hp2010.nhlbihin.net/atpiii/calculator.asp?usertype=profhttp://hp2010.nhlbihin.net/atpiii/calculator.asp?usertype=prof & http://www.nhlbi.nih.gov/about/framingham/riskabs.htm & http://www.nhlbi.nih.gov/about/framingham/riskabs.htmhttp://www.nhlbi.nih.gov/about/framingham/riskabs.htm UK CVD score: http://www.riskscore.org.uk/ UK CVD score: http://www.riskscore.org.uk/http://www.riskscore.org.uk/ PROCAM score: http://www.chd-taskforce.de/ PROCAM score: http://www.chd-taskforce.de/http://www.chd-taskforce.de/ Reynolds score: http://www.reynoldsriskscore.org/ Reynolds score: http://www.reynoldsriskscore.org/http://www.reynoldsriskscore.org/ Herman et al.’s diabetes risk score: http://www.diabetes.org/risk-test.jsp Herman et al.’s diabetes risk score: http://www.diabetes.org/risk-test.jsphttp://www.diabetes.org/risk-test.jsp German diabetes risk score: http://www.dife.de/ German diabetes risk score: http://www.dife.de/http://www.dife.de/ Angina score: http://www.anginarisk.org/ Angina score: http://www.anginarisk.org/http://www.anginarisk.org/ Pneumonia score: http://www.ahrq.gov/clinic/pneuclin.htm#head1 Pneumonia score: http://www.ahrq.gov/clinic/pneuclin.htm#head1http://www.ahrq.gov/clinic/pneuclin.htm#head1 SCORED: http://kidneydiseases.about.com/od/diagnostictests/a/scored.htm SCORED: http://kidneydiseases.about.com/od/diagnostictests/a/scored.htmhttp://kidneydiseases.about.com/od/diagnostictests/a/scored.htm Depression: http://www.psycom.net/depression.central.screening.html Depression: http://www.psycom.net/depression.central.screening.html Medical calculator: http://medcalc3000.com/ (some are commercial) Medical calculator: http://medcalc3000.com/ (some are commercial)http://medcalc3000.com/ In general, google can find these.


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