David M Kent, MD, MS
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1 David M Kent, MD, MS Director, Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center Professor of Medicine, Neurology and Clinical and Translational Science, Tufts University
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6 ACTUAL OUTCOME COUNTER FACTUAL OUTCOME = alive = dead Individual Treatment Effects in a Deterministic Framework: Four possibilities Without Treatment With Treatment NO EFFECT HARM BENEFIT NO EFFECT Subject Name Without Treatment With Treatment SAM MARY BOB BEN CHRISTINE NEIL MOHAMED JENNIFER PAUL NISHA MIGUEL LAYLA PAUL EMANUEL CHERYL PATRICK OSCAR JULIANNE THOMAS GEORGE
7 ACTUAL OUTCOME COUNTER FACTUAL OUTCOME = alive = dead Individual Treatment Effects in a Deterministic Framework: Four possibilities Without Treatment With Treatment NO EFFECT HARM BENEFIT NO EFFECT Subject Name Without Treatment With Treatment SAM MARY BOB BEN CHRISTINE NEIL MOHAMED JENNIFER PAUL NISHA MIGUEL LAYLA PAUL EMANUEL CHERYL PATRICK OSCAR JULIANNE THOMAS GEORGE
8 ACTUAL OUTCOME COUNTER FACTUAL OUTCOME = alive = dead Individual Treatment Effects in a Deterministic Framework: Four possibilities Without Treatment With Treatment NO EFFECT HARM BENEFIT NO EFFECT Subject Name Without Treatment With Treatment SAM MARY BOB BEN CHRISTINE NEIL MOHAMED JENNIFER PAUL NISHA MIGUEL LAYLA PAUL EMANUEL CHERYL PATRICK OSCAR JULIANNE THOMAS GEORGE HARM HARM
9 ACTUAL OUTCOME COUNTER FACTUAL OUTCOME = alive = dead Individual Treatment Effects in a Deterministic Framework: Four possibilities Without Treatment With Treatment NO EFFECT HARM BENEFIT NO EFFECT Subject Name Without Treatment With Treatment SAM MARY BOB BEN CHRISTINE NEIL MOHAMED JENNIFER PAUL NISHA MIGUEL LAYLA PAUL EMANUEL CHERYL PATRICK OSCAR JULIANNE THOMAS GEORGE HARM BENEFIT BENEFIT HARM BENEFIT BENEFIT
10 = alive = dead Without Treatment With Treatment
11 = alive = dead Without Treatment With Treatment
12 = alive = dead Without Treatment With Treatment Proportion /2 9/2 Dead 55%) 45%)
13 = alive = dead Without Treatment With Treatment Proportion /2 9/2 Dead 55%) 45%) BENEFIT
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15 Patients have too many attributes.
16 Patients have too many attributes. One variable-at-a-time subgroup analyses:
17 Patients have too many attributes. One variable-at-a-time subgroup analyses: lead to multiplicity and spurious false positive results.
18 Patients have too many attributes. One variable-at-a-time subgroup analyses: lead to multiplicity and spurious false positive results. under-represent the degree of heterogeneity across patients.
19 Patients have too many attributes. One variable-at-a-time subgroup analyses: lead to multiplicity and spurious false positive results. under-represent the degree of heterogeneity across patients. not congruent with the goals of HTE analysis for patient-centered decisions (reference class problem).
20 Figure from Kent DM et al. (manuscript in submission)
21 Person-level HTE is ubiquitous. Yet in clinical trials subgroups rarely detect group-level HTE.
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24 Risk is a known mathematical determinant of treatment effect.
25 Risk Reduction (RR) Definition Absolute RR EER-CER Relative RR - EER CER Odds Ratio EER/(-EER) CER/(-CER) CER=control event rate EER=experimental event rate
26 Risk Reduction (RR) Definition Absolute RR EER-CER Relative RR - EER CER Odds Ratio EER/(-EER) CER/(-CER) CER=control event rate EER=experimental event rate
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29 Risk is a known mathematical determinant of treatment effect.
30 Risk is a known mathematical determinant of treatment effect. When baseline risk heterogeneity is present (and the treatment effect is non-zero), there is always HTE.
31 Kent DM, et al. J Gen Intern Med 22; 7:
32 Kent DM, et al. J Gen Intern Med 22; 7:
33 6.3%.% Kent DM, et al. J Gen Intern Med 22; 7:
34 Califf RM et al. Am Heart J. 997;33(6):63-9.
35 Califf RM et al. Am Heart J. 997;33(6):63-9.
36 Califf RM et al. Am Heart J. 997;33(6):63-9.
37 Califf RM et al. Am Heart J. 997;33(6):63-9.
38 High Risk Low Risk Thune JJ, et al. Circulation 25,2:27-22.
39 The DPP study was conducted by the DPP Investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).
40 Participants: 36 nondiabetic persons with evidence of impaired glucose metabolism. The DPP study was conducted by the DPP Investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).
41 Participants: 36 nondiabetic persons with evidence of impaired glucose metabolism. Intervention: Intervention groups received metformin or a lifestyle-modification program. The DPP study was conducted by the DPP Investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).
42 Participants: 36 nondiabetic persons with evidence of impaired glucose metabolism. Intervention: Intervention groups received metformin or a lifestyle-modification program. Main Outcome Measure: Development of diabetes The DPP study was conducted by the DPP Investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).
43 Kovalchik SA et al. N Engl J Med 23; 369:
44 Participants: Smokers between the ages of 55 and 74 years with a minimum of 3 packyears of smoking and no more than 5 years since quitting Kovalchik SA et al. N Engl J Med 23; 369:
45 Participants: Smokers between the ages of 55 and 74 years with a minimum of 3 packyears of smoking and no more than 5 years since quitting Intervention: Low-dose CT screening or chest radiography Kovalchik SA et al. N Engl J Med 23; 369:
46 Participants: Smokers between the ages of 55 and 74 years with a minimum of 3 packyears of smoking and no more than 5 years since quitting Intervention: Low-dose CT screening or chest radiography Main Outcome Measure: Lung-cancer deaths Kovalchik SA et al. N Engl J Med 23; 369:
47 Kovalchik SA et al. N Engl J Med 23; 369:
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49 Overall effectiveness results may be driven by a relatively small group of influential (typically high risk) patients;
50 Overall effectiveness results may be driven by a relatively small group of influential (typically high risk) patients; The typical (median) risk patient is frequently at considerably lower risk than the overall average;
51 Overall effectiveness results may be driven by a relatively small group of influential (typically high risk) patients; The typical (median) risk patient is frequently at considerably lower risk than the overall average; The average benefit seen in the summary result often over estimates the benefit (on the RD scale) in most patients (and may obscure harm in many).
52 CLINICAL CONDITION Symptomatic carotid stenosis Non-valvular atrial fibrillation Coronary artery disease INTERVENTION Carotid endarterectomy Anticoagulation for primary prevention of stroke Coronary artery bypass grafting Primary prevention of coronary artery disease Acute coronary syndromes ST-Elevation acute myocardial infarction Blood pressure lowering Aspirin Lipid lowering Early invasive strategy (versus conservative) Clopidogrel (versus placebo) Enaxparin (versus unfractionated heparin) tpa (versus streptokinase) Percutaneous coronary intervention (versus thrombolytic therapy) Severe sepsis Drotrecogin alfa (activated protein C) Pre-diabetes Tobacco smoking Lifestyle intervention Metformin Lung cancer screening Kent DM, et al. Trials 2;:85.
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54 Heterogeneity of outcome risk is ubiquitous.
55 Heterogeneity of outcome risk is ubiquitous. Heterogeneity of outcome risk inevitably gives rise to heterogeneity of treatment effect.
56 Heterogeneity of outcome risk is ubiquitous. Heterogeneity of outcome risk inevitably gives rise to heterogeneity of treatment effect. One variable at a time subgroup analyses are inadequate (and prone to spurious false positive results).
57 Heterogeneity of outcome risk is ubiquitous. Heterogeneity of outcome risk inevitably gives rise to heterogeneity of treatment effect. One variable at a time subgroup analyses are inadequate (and prone to spurious false positive results). Risk based subgroup analyses can do better.
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David M. Kent, MD, MSc Professor of Medicine, Neurology, Clinical and Translational Science, Director, Predictive Analytics and Comparative
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