An Introduction to Dynamic Treatment Regimes Marie Davidian Department of Statistics North Carolina State University http://www4.stat.ncsu.edu/davidian 1/64 Dynamic Treatment Regimes Webinar Outline • What is a dynamic treatment regime, and why study them? • Clinical trials to study dynamic treatment regimes • Thinking in terms of dynamic treatment regimes • Constructing dynamic treatment regimes • Discussion 2/64 Dynamic Treatment Regimes Webinar Hot topic Personalized Medicine Source of graphic: http://www.personalizedmedicine.com/ 3/64 Dynamic Treatment Regimes Webinar • “Personalize ” treatment to the patient That is: Treatment in practice involves sequential decision-making based on accruing information • Suggests thinking about and studying treatment from this perspective. A perspective on personalized medicine Clinical practice: Clinicians make (a series of) treatment decisions(s) over the course of a patient’s disease or disorder • Key decision points in the disease process • Fixed schedule , milestone in the disease process, event necessitating a decision • Several treatment options at each decision point • Accruing information on the patient 4/64 Dynamic Treatment Regimes Webinar That is: Treatment in practice involves sequential decision-making based on accruing information • Suggests thinking about and studying treatment from this perspective. A perspective on personalized medicine Clinical practice: Clinicians make (a series of) treatment decisions(s) over the course of a patient’s disease or disorder • Key decision points in the disease process • Fixed schedule , milestone in the disease process, event necessitating a decision • Several treatment options at each decision point • Accruing information on the patient • “Personalize ” treatment to the patient 4/64 Dynamic Treatment Regimes Webinar A perspective on personalized medicine Clinical practice: Clinicians make (a series of) treatment decisions(s) over the course of a patient’s disease or disorder • Key decision points in the disease process • Fixed schedule , milestone in the disease process, event necessitating a decision • Several treatment options at each decision point • Accruing information on the patient • “Personalize ” treatment to the patient That is: Treatment in practice involves sequential decision-making based on accruing information • Suggests thinking about and studying treatment from this perspective. 4/64 Dynamic Treatment Regimes Webinar Can clinical decision-making be formalized and made “evidence-based?” Clinical decision-making How are these decisions made? • Clinical judgment • Practice guidelines based on study results, expert opinion • Synthesize all information on a patient up to the point of the decision to determine the next treatment action 5/64 Dynamic Treatment Regimes Webinar Clinical decision-making How are these decisions made? • Clinical judgment • Practice guidelines based on study results, expert opinion • Synthesize all information on a patient up to the point of the decision to determine the next treatment action Can clinical decision-making be formalized and made “evidence-based?” 5/64 Dynamic Treatment Regimes Webinar Dynamic treatment regime Dynamic treatment regime: • A set of sequential decision rules, each corresponding to a key decision point • Each rule dictates the treatment to be given from among the available options based on the accrued information on the patient to that point • Taken together, the rules define an algorithm for making treatment decisions • Dynamic because the treatment action can vary depending on the accrued information • Ideally , provides an “evidence-based ” approach to personalized treatment 6/64 Dynamic Treatment Regimes Webinar Treatment regime Terminology/Convention: • Often, treatment regime is used to refer generally to any approach to deciding on treatment • And dynamic treatment regime is reserved for the case where patient information is used • We will use these terms interchangeably In fact: Many common situations can be cast as involving (dynamic) treatment regimes 7/64 Dynamic Treatment Regimes Webinar ADHD therapy Sequential (scheduled) decision points • Decision 1: Low dose therapy – 2 options: medication or behavior modification • Subsequent monthly decisions: I Responders – Continue initial therapy I Non-responders – 2 options: add the other therapy or increase dose of current therapy • Objective: Improved end-of-school-year performance Example from Susan Murphy, University of Michigan 8/64 Dynamic Treatment Regimes Webinar Cancer treatment Two (milestone) decision points: • Decision 1 : Induction chemotherapy (options C1,C2) • Decision 2 : I Maintenance treatment for patients who respond (options M1,M2) I Salvage chemotherapy for those who don’t respond (options S1,S2) • Objective : Maximize survival time 9/64 Dynamic Treatment Regimes Webinar • “If age < 50, progesterone receptor level < 10 fmol, RAD51 mutation, then give C1, else, give C2” • “If patient is a Libra, Scorpio, or Sagittarius, give C1, else, give C2” Possible rules at Decision 2: • “If patient responds, give maintenance M1; if does not respond, give salvage S1”(dynamic ) • “If patient responds, age < 60, CEA > 10 ng/mL, progesterone receptor level < 8 fmol, give M1, else, give M2; if does not respond, age > 65, P53 mutation, CA 15-3 > 25 units/mL, then give S1, else, give S2” Possible treatment regimes Possible rules at Decision 1: • “Give C1”(non-dynamic ) 10/64 Dynamic Treatment Regimes Webinar • “If patient is a Libra, Scorpio, or Sagittarius, give C1, else, give C2” Possible rules at Decision 2: • “If patient responds, give maintenance M1; if does not respond, give salvage S1”(dynamic ) • “If patient responds, age < 60, CEA > 10 ng/mL, progesterone receptor level < 8 fmol, give M1, else, give M2; if does not respond, age > 65, P53 mutation, CA 15-3 > 25 units/mL, then give S1, else, give S2” Possible treatment regimes Possible rules at Decision 1: • “Give C1”(non-dynamic ) • “If age < 50, progesterone receptor level < 10 fmol, RAD51 mutation, then give C1, else, give C2” 10/64 Dynamic Treatment Regimes Webinar Possible rules at Decision 2: • “If patient responds, give maintenance M1; if does not respond, give salvage S1”(dynamic ) • “If patient responds, age < 60, CEA > 10 ng/mL, progesterone receptor level < 8 fmol, give M1, else, give M2; if does not respond, age > 65, P53 mutation, CA 15-3 > 25 units/mL, then give S1, else, give S2” Possible treatment regimes Possible rules at Decision 1: • “Give C1”(non-dynamic ) • “If age < 50, progesterone receptor level < 10 fmol, RAD51 mutation, then give C1, else, give C2” • “If patient is a Libra, Scorpio, or Sagittarius, give C1, else, give C2” 10/64 Dynamic Treatment Regimes Webinar • “If patient responds, age < 60, CEA > 10 ng/mL, progesterone receptor level < 8 fmol, give M1, else, give M2; if does not respond, age > 65, P53 mutation, CA 15-3 > 25 units/mL, then give S1, else, give S2” Possible treatment regimes Possible rules at Decision 1: • “Give C1”(non-dynamic ) • “If age < 50, progesterone receptor level < 10 fmol, RAD51 mutation, then give C1, else, give C2” • “If patient is a Libra, Scorpio, or Sagittarius, give C1, else, give C2” Possible rules at Decision 2: • “If patient responds, give maintenance M1; if does not respond, give salvage S1”(dynamic ) 10/64 Dynamic Treatment Regimes Webinar Possible treatment regimes Possible rules at Decision 1: • “Give C1”(non-dynamic ) • “If age < 50, progesterone receptor level < 10 fmol, RAD51 mutation, then give C1, else, give C2” • “If patient is a Libra, Scorpio, or Sagittarius, give C1, else, give C2” Possible rules at Decision 2: • “If patient responds, give maintenance M1; if does not respond, give salvage S1”(dynamic ) • “If patient responds, age < 60, CEA > 10 ng/mL, progesterone receptor level < 8 fmol, give M1, else, give M2; if does not respond, age > 65, P53 mutation, CA 15-3 > 25 units/mL, then give S1, else, give S2” 10/64 Dynamic Treatment Regimes Webinar Possible treatment regimes Result: Rules, and thus regimes , can be simple or complex (or not realistic ) • More complex rules involve more “personalization ” and more closely mimic clinical practice • There is an infinitude of possible rules at each decision point, and thus an infinitude of possible regimes • Ultimate goal : Find the “best ” or “optimal ” regime Regimes of interest and “optimal” depend on the question • For definiteness, assume larger outcomes are preferred 11/64 Dynamic Treatment Regimes Webinar • Cancer example: Decision 1 • Two regimes of interest: “Give C1” vs. “Give C2” • Class of regimes of interest is D = f “Give C1” , “Give C2”g • Usual question : “If all patients in the population were to be given C1, would mean outcome (mean survival time ) be different from (better than ) that if all patients in the population were to be given C2?” • Optimal regime in D: The regime such that, if all patients in the population were to receive treatment according to it , mean outcome would be the largest among all regimes in D (here, “Give C1” or “Give C2”) Classes of treatment regimes 1. Classical treatment comparison: • Focus on a single decision point 12/64 Dynamic Treatment Regimes Webinar • Usual question : “If all patients in the population were to be given C1, would mean outcome (mean survival time ) be different from (better than ) that if all patients in the population were to be given C2?” • Optimal regime in D: The regime such that, if all patients in the population were to receive treatment according to it , mean outcome