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Getting the help of SAS in Clinical Trial setting: Monitoring and Simulations Presented By: Mehmet Kocak.

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Presentation on theme: "Getting the help of SAS in Clinical Trial setting: Monitoring and Simulations Presented By: Mehmet Kocak."— Presentation transcript:

1 Getting the help of SAS in Clinical Trial setting: Monitoring and Simulations Presented By: Mehmet Kocak

2 Phase I Clinical Trials Objective is to find a maximum tolerated dose (MTD) of a new cytotoxic drug –MTD is not really the “maximum” tolerated dose but rather the highest dose that yields manageable side effects. –This dose is called the “target” dose. –Think of MTD as the target dose which is the dose that yields a specified probability of toxicity, e.g. 25%.

3 Continual Reassesment Method(CRM) Bayesian dose-finding method developed by O’Quigley et al (Biometrics, 1990) Statistical model is used to estimate the relationship between dose and probability of toxicity (dose-toxicity) After study opens, the model is fit to the actual data and used to estimate the target dose.

4 Continual Reassesment Method(CRM) Go to Next Dose Add More Go to Previous Dose Decision Start with the first Dose First Dose is too toxic.

5 Continual Reassesment Method(CRM) ObsDosenrMTDMidDoseDecision 1120201216.67180GOTONEXT 2240201165.46360GOTONEXT 3480301243.45600GOTONEXT 472032539.85960GOTOPREV 548010560.85600ADDMORE 648010578.19600ADDMORE 748010592.62600ADDMORE 848010604.67600GOTONEXT 972010654.01960ADDMORE 1072010703.34960ADDMORE 1172010753.23960ADDMORE 1272010803.51960ADDMORE 1372011679.89960ADDMORE

6 CRM – Statistical Model Logistic function is used frequently to model the dose-toxicity relationship. Don’t know the true relationship between dose and the probability of toxicity. Here are three sample logistic curves:

7 CRM – Statistical Model Dose Prob. Tox. Curve 1 Prob. Tox. Curve 2 Prob. Tox. Curve 3 1002%25%1% 2356%95%3% 47244%100%16% 62878%100%35%

8 If you don’t know the true relationship between dose and toxicity, how do you estimate the MTD? –Use the actual data from the study to estimate the dose-toxicity curve –Borrow data from other experiences What is the target dose of interest? –Dose that has 25% toxicity What is the proposed dose-toxicity relationship? –Don’t have actual data when the study opens –Need idea about the relationship between dose and toxicity to initiate the model fitting (priors) CRM – Statistical Model

9 CRM – Priors Other Studies –Adult study –Study in different population Guess –Quantify clinical intuition about drug behavior at high and low doses What dose would you guess has 90% toxicity? What dose would you guess has 10% toxicity?

10 CRM - Example Investigator wants to open a phase I study with 4 dose levels –100 mg/m2, 235 mg/m2, 472 mg/m2, and 628 mg/m2 Need priors to initiate model –Prior studies Has there been a previous phase I study using this drug? –Investigator’s clinical intuition about high and low doses What dose would you expect 90% toxicity? What dose would you expect 10% toxicity? –Reduce the lowest dose by half for the low prior and increase the highest dose by half for the high prior 50 for low prior and about 950 (628 + 314) for high prior

11 Modified Continual Reassesment (CRM) Software Programmed by Dr. Steve Piantadosi –Nice interface –Has problems Required data for the model to run: –Dose –N (number of patients treated) –r (number of responses (DLTs)) Probability of toxicity –Weight

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13 Depending on the priors, our initial curve changes tremendously.

14 Actual Patient Data PatientDose Date on Treatment End of Dose Finding Period DLT? 11002/14/033/14/03No 21002/23/033/23/03No First two patients at Dose 100 mg/m 2 did not have DLTs.

15 DECISION: ESCALATE TO THE NEXT DOSE LEVEL

16 Sample Patient Data (Cont.) PatientDose Date on Treatment End of Dose Finding Period DLT? 11002/14/033/14/03No 21002/23/033/23/03No 32353/19/034/19/03No 42354/05/035/05/03No Note: Next two patients treated at Dose 235 mg/m 2 did not have DLTs, either.

17 DECISION: ESCALATE TO THE NEXT DOSE LEVEL

18 Sample Patient Data (Cont.) PatientDose Date on Treatment End of Dose Finding Period DLT? 11002/14/033/14/03No 21002/23/033/23/03No 32353/19/034/19/03No 42354/05/035/05/03No 54724/21/035/12/03Yes Note: Patient-5 had a DLT. We will immediately re- estimate the MTD based on the current toxicity information.

19 DECISION: GO BACK TO Dose Level 235.

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21 History of CRM Decision

22 Two Step Simulation Remember that we decided to de-escalate from Dose 472 mg/m 2 to 235 mg/m 2. What can we say about the future decisions? Not the actual doses under investigation!

23 Two Step Simulation with SAS Thanks to SAS ANNOTATE Facility Function= “move” Function= “draw”

24 Simulation Study with SAS: Does CRM really Works? Go to Next Dose Add More Go to Previous Dose Decision Start with the first Dose First Dose is too toxic.

25 Simulation Study in SAS Various Dose-toxicity relationships Iterative Procedure, which is most likely different for each simulation run; You cannot sample the whole data at once; 10,000 simulations in each setting Preserving all necessary components of runs for summarization Huge data sets, complicated algorithm.

26 The Brain of the Simulation in SAS If the current dose is safe %next: If you need more data %addmore: If the current dose is not safe, %prev: %Decision: Processes… %goto … Start with the first Dose If First Dose is too toxic Or you find the MTD, %exit:

27 Simulation Study in SAS %decision: --- DATA STEPS --- --- SEVERAL %IF AND % GOTO STATEMENTS--- %if &maxcount>=6 and &decision=GOTONEXT and &dose<&nofdl %then %goto next; %else %if &maxcount>=6 and &decision=GOTOPREV and &dose^=1 %then %goto prev; %else %if &decision=GOTONEXT and &dose=&nofdl %then %goto addmore; %else %if &decision=GOTOPREV and &dose=1 %then %goto exit; %else %if &decision=GOTONEXT %then %goto next; %else %if &decision=ADDMORE %then %goto addmore; %else %if &decision=GOTOPREV %then %goto prev; %next: %let dose=%sysevalf(&dose+1); %let ctr=%sysevalf(&ctr+1); %goto decision; %addmore: %let ctr=%sysevalf(&ctr+1); %goto decision; %prev: %let dose=%sysevalf(&dose-1); %let ctr=%sysevalf(&ctr+1); %goto decision; %exit:

28 A paper submitted for publication Modified Continual Reassessment Method versus the Traditional Empirically-Based Design for Phase I Trials in Pediatric Oncology: Experiences of the Pediatric Brain Tumor Consortium Arzu Onar*, Mehmet Kocak, James M. Boyett* Biostatistics Department, St. Jude Children’s Research Hospital, 332 North Lauderdale St. Mail Stop 768 Memphis TN 38105 ** Corresponding author: Arzu Onar Biostatistics Department, St. Jude Children’s Research Hospital, 332 North Lauderdale St. Mail Stop 768 Memphis TN 38105. Email: arzu.onar@stjude.org. Tel: 901 495 5499. Fax: 901 544 8843. arzu.onar@stjude.org

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30 References Piantadosi S, Fisher JD, Grossman S. Practical implementation of a modified continual reassessment method. Cancer Chemother Pharmacol, 41:29-436, 1998. Goodman SN, Zahurak ML, Piantadosi, S. Some practical improvements in the continual reassessment method for phase I studies. Statistics In Medicine, 14:1149-1161, 1995.


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