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The Mean Doesn’t Mean As Much Anymore Stephen J. Ruberg in conjunction with Lei Chen, Yanping Wang, Doug Haney Eli Lilly & Company The University of Pennsylvania.

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Presentation on theme: "The Mean Doesn’t Mean As Much Anymore Stephen J. Ruberg in conjunction with Lei Chen, Yanping Wang, Doug Haney Eli Lilly & Company The University of Pennsylvania."— Presentation transcript:

1 The Mean Doesn’t Mean As Much Anymore Stephen J. Ruberg in conjunction with Lei Chen, Yanping Wang, Doug Haney Eli Lilly & Company The University of Pennsylvania Annual Conference on Statistical Issues in Clinical Trials - Targeted Therapies 29 April 2009 Company Confidential Copyright © 2000 Eli Lilly and Company

2 Disclosure I am a full time employee of Eli Lilly I own stock in Eli Lilly I will be using examples involving 2 Eli Lilly compounds The examples represent ongoing analysis and interpretation by Eli Lilly and represent off-label information Company Confidential Copyright © 2000 Eli Lilly and Company

3 Problem Statement “Doctors are men who prescribe medicines of which they know little, to cure diseases of which they know less, in human beings of whom they know nothing.” Voltaire (1694 – 1778) French writer and philosopher Company Confidential Copyright © 2000 Eli Lilly and Company

4 Spear et al. TRENDS in Molecular Medicine Vol. 7 No. 5 May 2001 Therapeutic Area Effective Rate (%) 25% On average only about 50% of patients respond to prescribed drugs Average drug efficacy is low Problem Statement Company Confidential Copyright © 2000 Eli Lilly and Company

5 Average Effects Active Drug vs. Placebo * ** ** * *p<0.001 Problem Statement Company Confidential Copyright © 2000 Eli Lilly and Company

6 The Individual and Group Profile Problem Statement Company Confidential Copyright © 2000 Eli Lilly and Company

7 Conclusion It is not enough to show the mean effect of a new treatment is statistically significantly better than control. Patients, physicians, payers want (are demanding) more. Problem Statement Company Confidential Copyright © 2000 Eli Lilly and Company

8 Dimensions of Tailored Therapeutics Perspectives One size fits all GOAL: Improve individual patient outcomes and health outcome predictability through tailoring drug, dose, timing of treatment, and relevant information. Tailoring ( e.g. oncology products comprising drug and companion diagnostic) Targeted Therapy Targeted Therapy Prospective Retrospective The Continuum Company Confidential Copyright © 2000 Eli Lilly and Company

9 Prospective Tailoring Define target population on a molecular basis (e.g. gene, biomarker) Engineer molecules to target such specific populations (and companion diagnostics as needed) –Many oncology examples –Drug metabolism examples –Not so much in other areas (psychiatry) Company Confidential Copyright © 2000 Eli Lilly and Company

10 Retrospective Tailoring Sub-group analyses and data mining Examples of non-biomarkers –Marriage and smoking cessation –Insurance and emergency room –Child abuse and depression –Obesity is affected by those around you –Alimta and non-squamous histology Company Confidential Copyright © 2000 Eli Lilly and Company

11 Tailoring to the Whole Patient Co-Morbidities Obesity Vascular Comp. HTN Hi LDL-C Hi Triglyceride Disease Parameters Pre-Diabetes Type II – Exer/Wgt Type II – 1 Oral Type II – 2 Oral Type II – 2 Oral + Ins Type II – 2 Oral + GlpType I Patient Factors Genetics Compliance Diet / Exercise Personal History Demographics Positive Benefit-Risk Negative Benefit-Risk Diabetes Illustration Each box represents a phenotype The calculus of benefit risk may change for each phenotype Source: Paul, S. Tailoring Therapies for Better Patient Outcomes: Drug Development Meets Evidence-Based Medicine. IOM 37th Annual Meeting presentation – Oct 8, 2007. Company Confidential Copyright © 2000 Eli Lilly and Company

12 Tailored Therapeutics Analysis (From Sub-group Analysis To Variable Selection) Traditional ApproachProposed Approach Efficacy Model Y = f (TRT, x i ) Assess well-known sub-groups Age, Gender, Race, Baseline Sub-group Analysis (one at a time) Y = f (TRT, x i ) + Age + TRT*Age heterogeneity test Define Responders / Non-responders Efficacy: Y 1, Y 2 Safety: S 1, S 2, S 3 Possible Predictors (100’s) Baseline, Early Response, PK, Genomic, Environmental? Social? Data Mining – Classification Trees, … Company Confidential Copyright © 2000 Eli Lilly and Company

13 A decision tree model consists of a set of rules for dividing a large heterogeneous population into smaller, more homogeneous groups with respect to a particular target variable (e.g., adverse event). Very useful for finding complex interactions. Tailored Therapeutics Analysis (From Sub-group Analysis To Variable Selection) Company Confidential Copyright © 2000 Eli Lilly and Company

14 Retrospective Tailoring Examples First Example –Identify baseline information that helps us decide who should get a treatment –Tailoring on phenotypic/clinical measures Second Example –For those who get a drug, how do we decide quickly whether they are on the right drug or not –Tailoring on timing of treatment Company Confidential Copyright © 2000 Eli Lilly and Company

15 Example 1 Disease outcome can be assessed as a dichotomous response –Many covariates analyzed one at a time –Stepwise logistic regression to select multiple covariates in one functional prediction Exploratory analysis of 60+ potential covariates/predictors –Other studies/analyses needed to confirm Company Confidential Copyright © 2000 Eli Lilly and Company

16 Example 1 - Objective What marker(s) can be used to predict the largest population of patients that are most responsive to Treatment? If the belief is such that Treatment works best in the highest risk patients, what marker(s) are the best predictors of high risk? What are the simplest marker(s)? Easiest to measure, least expensive, available Measurable / responsive over time Could a ‘complex’ marker be made simpler thru a new diagnostic? What is the sub-group size associated with marker(s)? Company Confidential Copyright © 2000 Eli Lilly and Company

17 Outcome Based on Placebo Data Variable X22 > A P: 90/476=0.19 P: 80/223=0.36P: 18/22=0.82 P: 51/69=0.74 P: 99/250=0.40 P: 163/349=0.47 Variable X37 > B Variable X4 < C 825 Pbo Patients YES NO Company Confidential Copyright © 2000 Eli Lilly and Company

18 Variable X22 > A Variable X37 > B Variable X4 < C 825 Pbo Patients 823 Treatment Patients YES NO Treatment vs. Pbo in Subgroups Based on CART P: 90/476=0.19 T: 86/468=0.18 P-value=0.87 RR=0.05 P: 163/349=0.47 T: 118/355=0.33 P-value<0.0001 RR=0.30 P: 51/69=0.74 T: 29/75=0.39 P-value<0.0001 RR=0.47 P: 99/250=0.40 T: 79/259=0.31 P-value=0.03 RR=0.23 P: 80/223=0.36 T: 69/236=0.29 P-value=0.14 RR=0.19 P: 18/22=0.82 T: 8/20=0.40 P-value=0.01 RR=0.51 Company Confidential Copyright © 2000 Eli Lilly and Company

19 Example 2 - Schizophrenia Help practicing physicians decide what to do in treating schizophrenics Inadequate ResponseMinimum Number of Weeks to Wait Maximum Number of Weeks to Wait Initial Antipsychotic Little or no response36 Partial response410 Second Antipsychotic Little or no response36 Partial response511 Adapted from Expert Consensus Panel for Optimizing Pharmacologic Treatment of Psychotic Disorders. J Clin Psychiatry 2003;64 (suppl 12): 2-97. Company Confidential Copyright © 2000 Eli Lilly and Company

20 Early Response Assessment GOAL: Identify what amount of change … in which of the fewest symptoms/measures … at the earliest time in treatment … predicts both responders and non-responders. Has to be “implementable” for the typical clinician on a routine basis (i.e. not a research tool as part of research studies) Company Confidential Copyright © 2000 Eli Lilly and Company

21 Example 2 – Zyprexa & Atypicals Predicting Efficacy Responders Response = 30% reduction in PANSS Total Symptom Score at 8 weeks Predictors are the change in individual symptom ratings from PANSS at week 1 and week 2 of treatment –30 individual symptoms = 60 predictors Integrated data from 6 studies (1494 patients) –Moderately to severely ill patients –All patients on active atypical antipsychotics PANSS = Positive and Negative Symptom Scale Company Confidential Copyright © 2000 Eli Lilly and Company

22 Generic Classification Tree R:% NR:% Nnumber Symptom Criteria #1 R:% NR:% Nnumber R:% NR:% Nnumber R:Mis% NR:NPV% N Symptom Criteria #2N PPV NPV Mixed Miscls YESNO YES R:% NR:% Nmixed R:PPV% NR:Mis% N Symptom Criteria #2Y NOYES R:% NR:% Nmixed Example 2 – Zyprexa & Atypicals Company Confidential Copyright © 2000 Eli Lilly and Company

23 Initial Findings Early Response CART – 2 Week Assessment R:43% NR:57% N1494 At least 2 unit drop in Item Unusual Thought Content? R:33% NR:67% N1205 R:79% NR:21% N289 YESNO PPV79% NPV67% Mixed0% Miscls31% Example 2 – Zyprexa & Atypicals Company Confidential Copyright © 2000 Eli Lilly and Company

24 Initial Findings Early Response CART – 2 Week Assessment R:43% NR:57% N1494 At least 2 unit drop in Delusions? R:33% NR:67% N1178 R:76% NR:24% N316 YES NO PPV76% NPV67% Mixed0% Miscls31% Example 2 – Zyprexa & Atypicals Company Confidential Copyright © 2000 Eli Lilly and Company

25 Final Model Early Response CART – 2 Week Assessment R:43% NR:57% N1494 At least 2 unit drop in at least 2 psychotic items? R:28% NR:72% N1049 R:79% NR:21% N445 R:25% NR:75% N929 R:53% NR:47% N120 At least 2 unit drop in excitement? PPV79% NPV75% Mixed8% Miscls24% YESNO YES Psychotic items = Unusual Thought Content, Delusions, Hallucinatory Behavior, Conceptual Disorganization, Suspiciousness Company Confidential Copyright © 2000 Eli Lilly and Company

26 Model Evaluation Yes P(response) =PPV No/No P(non-response) =NPV No/Yes Proportion of mix- response Proportion of misclassification 6 pooled studies79%75%8%24% Study A70%77%7%25% Study B76%72%7%29% Study C*63%75%5%29% * acute illness study Example 2 – Zyprexa & Atypicals Company Confidential Copyright © 2000 Eli Lilly and Company

27 YES NO/YES NO/NO Average Total Symptom Scores Over 8 Weeks of Study Company Confidential Copyright © 2000 Eli Lilly and Company

28 Conclusions Company Confidential Copyright © 2000 Eli Lilly and Company

29 “What's required is a revolution called ‘evidence-based medicine,’ says Eddy, a heart surgeon turned mathematician and health-care economist. “The human brain, Eddy explains, needs help to make sense of patients who have combinations of diseases, and of the complex probabilities involved in each.” BusinessWeek 29 May 2006 Medical Guesswork From heart surgery to prostate care, the medical industry knows little about which treatments really work Company Confidential Copyright © 2000 Eli Lilly and Company

30 Conclusions (1) Tailor to the whole patient There is prospective and retrospective tailoring approaches Physicians like decision trees –Understandable and implementable Move from sub-group analysis mindset to variable selection mindset –CART is a useful omnibus tool Company Confidential Copyright © 2000 Eli Lilly and Company

31 Conclusions (2) More and more, there is less and less interest in the overall mean response in a broad population of patients. There is a shift to greater interest in smaller, more responsive populations. The key questions emerging seem to be: Company Confidential Copyright © 2000 Eli Lilly and Company

32 Conclusions (3) 1.“What is the largest population that has a very high probability of showing a clinically meaningful benefit?” a.A really large benefit in a really small population may be useful but will have less medical or public health impact. b.The exceptions are rare diseases. Company Confidential Copyright © 2000 Eli Lilly and Company

33 Conclusions (4) 2.“What measurable/observable characteristics define that population?” a.What are the easiest and cheapest characteristics to measure? b.They may not be genetic or biochemical? c.It doesn’t have to be perfect, just better than what we do now. Company Confidential Copyright © 2000 Eli Lilly and Company

34 Conclusion (5) Pharmaceutical research will continue to refine our understanding of who is likely to respond to drugs. Personalized medicine as a general rule has a long way to go, and it may never be achieved in some disease states. Tailored medicine is happening today and refinements in treatment paradigms are being studied at the present time. This area of medicine is ripe with statistical problems, and much more research is needed. Company Confidential Copyright © 2000 Eli Lilly and Company

35 Thank you. 謝謝。 Gracias. Вы. Obrigado. ありがとう。 Grazie. Σας ευχαριστούμε. Danke. Merci. Dank u. 너를 감사하십시요. Company Confidential Copyright © 2000 Eli Lilly and Company


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