The Right Treatment for the Right Patient (at the Right Time) Marie Davidian Department of Statistics North Carolina State University

Transcription

1 The Right Treatment for the Right Patient (at the Right Time) Marie Davidian Department of Statistics North Carolina State University

2 Ubiquitous Personalized Medicine DIA 1

3 Personalized Medicine One size does not fit all: Multiple treatment options Patient heterogeneity Basic premise: A patient s characteristics are implicated in which treatment option s/he should receive Genetic/genomic, other omic,... Demographic, physiologic/clinical measures, medical history,... DIA 2

4 Popular Perspective Subgroup identification/targeted treatment: Are there subgroups of patients who are likely to benefit from a particular treatment? Can biomarkers be developed to identify such patients? Can a treatment be developed that targets a subgroup that is very likely to benefit from that treatment? DIA 3

5 Popular Perspective Subgroup identification/targeted treatment: Are there subgroups of patients who are likely to benefit from a particular treatment? Can biomarkers be developed to identify such patients? Can a treatment be developed that targets a subgroup that is very likely to benefit from that treatment? Focus: Targeting treatment to subgroups of the population Right patient for the treatment DIA 3

6 Another Perspective Clinical practice: Clinicians make (a series of) treatment decision(s) over the course of a patient s disease or disorder Key decision points in the disease process Multiple treatment options at each Accrued information on the patient DIA 4

7 Another Perspective Clinical practice: Clinicians make (a series of) treatment decision(s) over the course of a patient s disease or disorder Key decision points in the disease process Multiple treatment options at each Accrued information on the patient Focus: Given information on patient s characteristics, can we determine the treatment(s) from among the available options most likely to benefit him/her? Right treatment for the patient DIA 4

8 Another Perspective Clinical practice: Clinicians make (a series of) treatment decision(s) over the course of a patient s disease or disorder Key decision points in the disease process Multiple treatment options at each Accrued information on the patient Focus: Given information on patient s characteristics, can we determine the treatment(s) from among the available options most likely to benefit him/her? Right treatment for the patient This is the perspective I will discuss DIA 4

9 Clinical Decision-Making How are these decisions made? Clinical judgment Practice guidelines based on combining study results and expert opinion DIA 5

10 Clinical Decision-Making How are these decisions made? Clinical judgment Practice guidelines based on combining study results and expert opinion How can statistics and statistical thinking be used to formalize clinical decision-making? DIA 5

11 Dynamic Treatment Regime Formalizing clinical decision-making: At any decision Need a rule that takes as input the accrued information on the patient to that point and dictates the next treatment from among the possible options Rule(s) must be developed based on evidence ; i.e., data Dynamic treatment regime: A set of formal such rules, each corresponding to a decision point Dynamic because the treatment action varies depending on the accrued information DIA 6

12 Cancer Treatment Simple example: Which treatment to give to patients who present with primary operable breast cancer? Treatment options : c 1 = L-phenylalanine mustard and 5-fluorouracil or c 2 = c 1 + tamoxifen Information : age, progesterone receptor level (PR) DIA 7

13 Cancer Treatment Simple example: Which treatment to give to patients who present with primary operable breast cancer? Treatment options : c 1 = L-phenylalanine mustard and 5-fluorouracil or c 2 = c 1 + tamoxifen Information : age, progesterone receptor level (PR) Example rule: If age < 50 years and PR < 10 fmol, give c 1 (coded as 1); otherwise, give c 2 (coded as 0) Mathematically, the rule d is Alternatively d(age, PR) = I(age < 50 and PR < 10) d(age, PR) = I{age log(pr) 60 > 0} DIA 7

14 Multiple Decision Points Two decision points: Decision 1 : Induction chemotherapy (options c 1, c 2 ) Decision 2 : Maintenance treatment for patients who respond (options m 1, m 2 ) Salvage chemotherapy for those who don t (options s 1, s 2 ) DIA 8

15 Multiple Decision Points At baseline: Information x 1 (accrued information h 1 = x 1 ) Decision 1: Two options {c 1, c 2 }; rule 1: d 1 (x 1 ) x 1 {c 1, c 2 } Between decisions 1 and 2: Collect additional information x 2, including responder status Accrued information h 2 = {x 1, chemotherapy at decision 1, x 2 } Decision 2: Four options {m 1, m 2, s 1, s 2 }; rule 2: d 2 (h 2 ) h 2 {m 1, m 2 } (responder), h 2 {s 1, s 2 } (nonresponder) Regime : d = (d 1, d 2 ) DIA 9

16 Dynamic Treatment Regime K key decision points: Decision 1: Baseline information x 1 Intermediate information x k between decisions k 1 and k, k = 2,..., K Set of treatment options at decision k, a k A k Accrued information h 1 = x 1, h k = {x 1, a 1, x 2, a 2,..., x k 1, a k 1, x k }, k = 2,..., K Decision rules d 1 (h 1 ), d 2 (h 2 ),..., d K (h K ), d k (h k ) : h k A k Dynamic treatment regime: d = (d 1, d 2,..., d K ) DIA 10

17 Best Treatment Regime? Infinitude of possible regimes d: D = class of all possible dynamic treatment regimes Can we find the best set of rules; i.e., the best dynamic treatment regime in D? How do we define best? DIA 11

18 Clinical Outcome Outcome: There is a clinical outcome by which treatment benefit can be assessed Survival time, CD4 count, indicator of no myocardial infarction within 30 days,... Larger outcomes are better DIA 12

19 Clinical Outcome Outcome: There is a clinical outcome by which treatment benefit can be assessed Survival time, CD4 count, indicator of no myocardial infarction within 30 days,... Larger outcomes are better Here s where statistical thinking and statistics come in DIA 12

20 Potential Outcome Potential outcome for a regime: For any regime d D Y (d) = the outcome a patient would potentially achieve if s/he received treatment according to d E{Y (d)} = expected outcome for the patient population if all patients received treatment according to d Formalizing best : Find d opt that maximizes E{Y (d)} among all d D Optimal treatment regime DIA 13

21 Estimating d opt Goal: Under suitable assumptions, estimate d opt = (d opt 1,..., d opt ) based on data K Required data: (X 1i, A 1i, X 2i, A 2i,..., X (K 1)i, A (K 1)i, X Ki, A Ki, Y i ), i = 1,..., n X 1i = Baseline information observed on subject i X ki, k = 2,..., K = intermediate information between decisions k 1 and k on subject i A ki, k = 1,..., K = observed treatment actually received by subject i at decision k Y i = observed outcome for subject i; can be ascertained after decision K or can be a function of X 2i,..., X Ki DIA 14

22 Estimating d opt Single decision, K = 1, 2 treatment options: (X 1i, A 1i, Y i ), i = 1,..., n, A 1 = {0, 1} Key assumption: No unmeasured confounders Y (0), Y (1) are potential outcomes under treatments 0, 1 With d(x 1 ) = 0, 1 Y (d) = Y (1)d(X 1 ) + Y (0){1 d(x 1 )} No unmeasured confounders Y (0), Y (1) A 1 X 1 Automatically satisfied in a clinical trial Unverifiable but necessary in an observational study DIA 15

23 Estimating d opt May be shown: With Q(x 1, a 1 ) = E(Y X 1 = x 1, A 1 = a 1 ) Under no unmeasured confounders d opt (x 1 ) = I{Q(x 1, 1) > Q(x 1, 0)} Posit and fit a regression model Q(x 1, a 1 ; β) d opt (x 1 ) = I{Q(x 1, 1; β) > Q(x 1, 0; β)} Intuitively: d opt selects the treatment option in A 1 that maximizes the expected outcome for the patient given the information available DIA 16

24 Interpretation Philosophical point: Distinguish between The best treatment for a patient The best treatment decision for a patient given the information available Extension to multiple decisions, K > 1: Q-learning, A-learning (backward induction ) Value search DIA 17

25 Data Cannot: Piece together data from different studies Delayed effects E.g., induction therapy with the highest proportion of responders might have lingering effects that render subsequent treatments less effective for survival DIA 18

26 Data Cannot: Piece together data from different studies Delayed effects E.g., induction therapy with the highest proportion of responders might have lingering effects that render subsequent treatments less effective for survival Data: Same subjects over the entire sequence of decisions Existing data from (longitudinal ) observational studies (e.g., registries), previously conducted clinical trials Prospectively collected data from clinical trials conducted specifically for this purpose SMART: Sequential Multiple Assignment Randomized Trial DIA 18

27 SMART Cancer example: Randomization at s M 1 Response M 2 C 1 No Response S 1 S 2 Cancer Response M 1 C 2 M 2 No Response S 1 S 2 DIA 19

28 Issues Observational studies vs. SMARTs: Confounding in longitudinal observational studies is exacerbated with sequential treatment decisions Sequential randomization obviates this problem SMARTs with rich information collected throughout are the gold standard for estimating optimal regimes DIA 20

29 Issues Observational studies vs. SMARTs: Confounding in longitudinal observational studies is exacerbated with sequential treatment decisions Sequential randomization obviates this problem SMARTs with rich information collected throughout are the gold standard for estimating optimal regimes Challenges: Wide open research area Design considerations for SMARTs? Measures of uncertainty for estimated regimes?... DIA 20

30 Crazy Ideas Right patient for the treatment vs. Right treatment for the patient Is there a role for dynamic treatment regimes in a regulatory context? Criteria for an effective regime? Design/conception of SMARTs in a regulatory context? Implications for a treatment that fails effectiveness in conventional testing but is shown to be an integral component of an overall regime? Implications for multiple sponsors whose products are components of an overall regime? DIA 21

31 Thought Leaders 2013 MacArthur Fellow Susan Murphy and Jamie Robins DIA 22