Sizing a Trial for the Development of Adaptive Treatment Strategies Alena I. Oetting The Society for Clinical Trials, 29th Annual Meeting St. Louis, MO May 2008 joint work with Janet Levy, Roger Weiss, and Susan Murphy

Multi-stage decision making Problem: Making multiple decisions across time in order to optimize outcome of interest –diseases which require many treatment decisions heterogeneity of patient response variable nature of disease: symptoms wax & wane high relapse rate Research: Sample size formulae for answering questions about treatment decision rules (adaptive treatment strategies) using new trial design (SMART) –current work: binary final outcome

Adaptive Treatment Strategy (ATS) Set of decision rules that tailor a sequence of treatments to an individual patient –input: patient characteristics, response to treatment –output: how/when to change treatment Questions to address in constructing ATS –Best sequencing of treatments? –Best timings of treatment transitions? (more to less; switch; augment) –What information to use to make decisions?

Example of ATS: Prescription Opiod Dependence Nonresponse: Any opiod use during first 4 wks Response: No opiod use during first 4 wks Buprenorphine/Naloxone (Bup/Nx) + Medical Management (MM) + Individual Drug Counseling (IDC) for 4 wks Relapse Prevention Therapy (RPT) Bup/Nx + MM + Cognitive- Behavioral Therapy (CBT) Rules adapt to previous response * Example modeled after a trial currently in progress within Clinical Trials Network of NIDA Final response (binary): abstinence

A SMART Design Sequential Multiple Assignment Randomized Trial A multi-stage trial; at each stage, subject randomized to treatment decision SMART- precursors already conducted: –CATIE (2001): psychosis in Alzheimer’s patients –STAR*D (2003): depression Focus: Sample sizes for SMART design w/ –two decision points –arbitrary number of options at each decision point

A SMART Design: Example nonresponse Bup/Nx + MM + IDC Bup/Nx + MM + CBT Bup/Nx + MM Bup/Nx + MM + IDC RPT R R = randomization R Bup/Nx + MM + CBT Bup/Nx + MM + IDC R response nonresponse

Sample Size Formulae Many potential primary research questions COMPONENTS 1.Is there effect on final outcome btwn initial txts? 2.Is there effect on final outcome btwn txts for nonresponders? WHOLE STRATEGIES 1.Is there effect on final outcome btwn two (prespecified) txt strategies? 2.What is best (highest response rate) txt strategy?

Used normal approximation to binomial, with & without continuity correction Sample sizes with continuity correction are larger than those without. Simulations show both versions (with and without continuity correction) give conservative sample sizes; power estimates not significantly different Sample Size Formulae: Is continuity correction needed?

Sample Sizes for SMART Example 2 initial txt options; 2 options for nonresponders, 1 for responders Powered to detect difference in proportions of 0.2 w/ probability 0.8; for hypothesis tests, α = 0.1 response rate after initial treatment testing: main effect of 1st stage txt testing: 2nd stage txt effect (non- responders) testing: difference between two strategies choosing best strategy (not hypo- thesis test)

Summary Sample size formulae & corresponding test statistics developed for using SMART to construct adaptive treatment strategies. –continuous & binary final outcomes; two decision points, arbitrary number of treatment options at each decision –survival time as final outcome (Wahed and Feng) Binary final outcome: no advantage to using continuity correction in sizing trial Sample size calculators available on web (already available for continuous; binary in June 2008)

Questions? This talk is based on joint work with Janet Levy (Duke School of Nursing), Roger Weiss (Harvard Medical School), Susan Murphy (University of Michigan) me with questions or if you would like a copy of our paper (continuous), technical report (binary):