Primary Aims Using Data Arising from a SMART (Part I) Module 4Day 2 Getting SMART About Developing Individualized Adaptive Health Interventions Methods Work, Chicago, Illinois, June Daniel Almirall & Susan A. Murphy Primary Aims Part I, Outline Review the Adaptive Interventions for Children with ADHD Study design (a SMART design) Will learn how to analyze two typical primary research questions in a SMART design PI(a): Main effect of initial (first-stage) treatment? PI(b): Comparing second-stage tactics? Will prepare for a third primary aim analysis by PI(c): Learning to estimate the mean outcome under each of the embedded ATS (separately) using an easy-to-use weighting approach Before we beginSAS preparation 1. Download SAS files: You received a thumb-drive These files are also available for download from:2. Create a folder on your notebook computer and place all of the files in that folder. 3. Inside the folder SAS Code, open the file sas_code_modules_4_5_and_6_ADHD.doc 4. Copy-paste code up to Line 20 into SAS 5. Change path on Line 20 to new folder 6. Run code (by clicking on running man). Check. Primary Aims Part I, Outline Review the Adaptive Interventions for Children with ADHD Study design (a SMART design) Will learn how to analyze two typical primary research questions in a SMART design PI(a): Main effect of initial (first-stage) treatment? PI(b): Comparing second-stage tactics? treatments? Will prepare for a third primary aim analysis by PI(c): Learning to estimate the mean outcome under each of the embedded ATS (separately) using an easy-to-use weighting approach Review the ADHD SMART Design Continue Medication Responders Medication Increase Medication Dose Add Behavioral Intervention R Continue Behavioral Intervention Behavioral Intervention Increase Behavioral Intervention Add Medication Non-Responders R Responders Non-Responders R O1A1O2 / R StatusA2Y For the next 3 modules, keep handy the handout that describes this design. There are two stage 1 treatment options that are being compared Continue Medication Responders Medication Increase Medication Dose Add Behavioral Intervention R Continue Behavioral Intervention Behavioral Intervention Increase Behavioral Intervention Add Medication Non-Responders R Responders Non-Responders R O1A1O2 / R StatusA2Y Response/non-response until Month 8 is the primary tailoring variable Continue Medication Responders Medication Increase Medication Dose Add Behavioral Intervention R Continue Behavioral Intervention Behavioral Intervention Increase Behavioral Intervention Add Medication Non-Responders R Responders Non-Responders R O1A1O2 / R StatusA2Y There are a total of 6 stage 2 treatments that any one participant may receive Continue Medication Responders Medication Increase Medication Dose Add Behavioral Intervention R Continue Behavioral Intervention Behavioral Intervention Increase Behavioral Intervention Add Medication Non-Responders R Responders Non-Responders R O1A1O2 / R StatusA2Y There are two stage 2 treatment options being compared for non-responders Continue Medication Responders Medication Increase Medication Dose Add Behavioral Intervention R Continue Behavioral Intervention Behavioral Intervention Increase Behavioral Intervention Add Medication Non-Responders R Responders Non-Responders R O1A1O2 / R StatusA2Y Continue Medication Responders Medication Increase Medication Dose Add Behavioral Intervention R Continue Behavioral Intervention Behavioral Intervention Increase Behavioral Intervention Add Medication Non-Responders R Responders Non-Responders R There are 4 embedded adaptive treatment strategies in this SMART; Here is one O1A1O2 / R StatusA2Y Continue Medication Responders Medication Increase Medication Dose Add Behavioral Intervention R Continue Behavioral Intervention Behavioral Intervention Increase Behavioral Intervention Add Medication Non-Responders R Responders Non-Responders R There are 4 embedded adaptive treatment strategies in this SMART; Here is another O1A1O2 / R StatusA2Y Sequential randomizations ensure between treatment group balance Continue Medication Responders Medication Increase Medication Dose Add Behavioral Intervention R Continue Behavioral Intervention Behavioral Intervention Increase Behavioral Intervention Add Medication Non-Responders R Responders Non-Responders R O1A1O2 / R StatusA2Y A subset of the data arising from this SMART may look like this (part 1) ODD Dx Baseline ADHD Score Prior Med ? First Line Txt Resp /Non -resp Second Line Txt School Perfm IDO11O12O13A1RA2Y MED INTSFY BMOD0-1 ADD This is simulated data. A subset of the data arising from this SMART may look like this (part 2) Race First Line Txt Resp/ Non- resp Time until NR (mnths) Adher ence Second Line Txt School Perfm IDO14A1RO21O22A2Y 11-1 MED INTSFY4 301 BMOD011-1 ADD This is simulated data. Try it yourself in SAS Open SAS (you may already have this open) Open the file (you may already have this open): sas_code_modules_4_5_and_6_ADHD.doc Copy remaining SAS code starting on Line 21 to end of Page 1 (you already ran through Line 20) Paste into SAS Enhanced Editor window Press F8 or click the Submit button (the little running man) Primary Aims Part I, Outline Review the Adaptive Interventions for Children with ADHD Study design (a SMART design) Will learn how to analyze two typical primary research questions in a SMART design PI(a): Main effect of initial (first-stage) treatment? PI(b): Comparing second-stage tactics? treatments? Will prepare for a third primary aim analysis by PI(c): Learning to estimate the mean outcome under each of the embedded ATS (separately) using an easy-to-use weighting approach Typical Primary Aim 1: Main effect of first-line treatment? Stated 3 ways. What is the best first-line treatment in terms of long-term outcomes, controlling for future treatment by design? What is the effect in terms of end of study school performance of starting with MED vs starting with BMOD? Is it better on average, in terms of end of study mean school performance, to begin treatment with BMOD or with MED? Primary Question 1 is simply a comparison of two groups! Continue Medication Responders Medication Increase Medication Dose Add Behavioral Intervention R Continue Behavioral Intervention Behavioral Intervention Increase Behavioral Intervention Add Medication Non-Responders R Responders Non-Responders R O1A1O2 / R StatusA2Y Mean end of study outcome for all participants initially assigned to Medication Medication R Mean end of study outcome for all participants initially assigned to Behavioral Intervention Behavioral Intervention... Primary Question 1 is simply a comparison of two groups O1A1O2 / R StatusA2Y Before we show you how to do this in SAS, we review contrast coding Recall that A1 = 1 = behavioral modification = BMOD Whereas A1 = -1 = medication = MED The Regression and Contrast Coding Logic: Y = b0 + b1*A1 + e or you can fit Y = b0 + b1*A1 + b2*O11c + b3*O12c + b4*O13c + b5*O14c + e (O11c, O12c and O13c are mean-centered O11, O12, O13) Overall Mean Y under BMOD = b0 + b1*1 Overall Mean Y under MED = b0 + b1*(-1) Between groups diff = b0 + b1 (b0 b1) = 2*b1 As we go through the SAS code to analyze the simulated ADHD data set, we encourage you to follow along and actually run SAS code snippets (i.e., highlight the snippet we are discussing in the slides and hit F8). This will permit you to compare the output on your computer screen with the results shown on the slides. This will also help familiarize you with the SAS code and prepare you for the practicum using a separate data set (Autism SMART). SAS code for a 2-group mean comparison in end of study outcome * mean center covariates prior to regression; data dat1; set adhddat; o11c = o11 ; o12c = o ; o13c = o ; o14c = o ; run; * run regression to get between groups difference; proc genmod data = dat1; model y = a1 o11c o12c o13c o14c; estimate 'Mean Y under BMOD' intercept 1 a1 1; estimate 'Mean Y under MED' intercept 1 a1 -1; estimate 'Between groups difference' a1 2; run; This analysis is with simulated data. The SAS code corresponds to a simple regression model proc genmod data = dat1; model y = a1 o11c o12c o13c o14c; estimate 'Mean Y under BMOD' intercept 1 a1 1 o11c 0; estimate 'Mean Y under MED' intercept 1 a1 -1; estimate 'Between groups difference' a1 2; run; In SAS estimate statements, setting a coefficient to zero is just like leaving it blank. The Regression Logic: Y = b0 + b1*A1 + b2*O11c + b3*O12c + b4*O13c + b5*O14c + e Mean Y under BMOD = E( Y | A1=1 ) = b0 + b1*1 Mean Y under MED = E( Y | A1=-1 ) = b0 + b1*(-1) Between groups diff = E( Y | A1=1 ) - E( Y | A1=1 ) = b0 + b1 (b0 b1) = 2*b1 Primary Question 1 Results Contrast Estimate Results 95% Conf Limits Label Estimate Lower Upper P-value Mean Y under BMOD