Download presentation

Presentation is loading. Please wait.

Published byZoey Jarrard Modified about 1 year ago

1
Evaluating Induction-Salvage Treatment Regimes in Therapy of AML/MDS Wahed and Thall, “Evaluating joint effects of induction- salvage treatment regimes on overall survival in acute leukemia.” J. Royal Statistical Society, Series C. In press.

2
Example 1: Treating Severe Infection Each “stage” of therapy is approximately one week. Stage 1 Based on the patient’s type of infection and characteristics, choose an antibiotic and a dose. a) “Success” = Infection is resolved within 1 week b) “Failure” = The patient dies c) If the patient is alive with infection Go to Stage 2 Stage 2 Based on the patient’s updated characteristics, choose a second, different antibiotic and dose. a) “Success” = Infection is resolved within 1 week b) “Failure” = The patient dies c) If the patient is alive with infection Go to Stage 3 And so on

3
Baseline Prognostic Covariates Alive, Infection Alive, No Infection Antibiotic # 1 Death Antibiotic # 2 The choice of Antibiotic # 2 may be informed by updated patient covariates, called “tailoring variables” The Basic Algorithm :Try something. If it doesn’t work, try something else. Repeat as needed. Repeat, until “Death” or “Alive, No Infection”

4
Example 2: A Trial of 12 Dynamic Treatment Regimes for Advanced Prostate Cancer (Thall, Millikan, et al., Stat in Med, 2000; JNCI 2007, 2008; Wang, et al., J. American Statistical Assoc, In press.) The Basic Idea: Try a chemo combination. If it works, repeat it. If it fails, try something else. Four chemo combinations (Chosen by R. Millikan, PI) TEE = Taxol / Estramustine / VP-16 (Etoposide) KAVE = Ketoconazole / Adriamycin / Vinblastin / VP-16 CVD = Cyclophosphamide / VCR / Dexamethasone TEC = Taxol / Estramustine / Carboplatin

5
First Success >40% drop in PSA from baseline Regression of any measurable disease Improvement in cancer-related symptoms No new lesions or cancer-related symptoms Second Success ( “Raise the bar” ) >80% drop in PSA from baseline Resolution of all cancer-related symptoms Tumor regression of > 50% No new lesions or cancer-related symptoms Per-Course (8 week) Treatment Success ( Also defined by R. Millikan )

6
Randy Millikan’s Dynamic Treatment Regime 1. Randomize each patient among the 4 treatments 2. Repeat a successful treatment, otherwise re-randomize the patient among the other 3 treatments 3. Stop therapy if overall success or overall failure occurs Overall Success = {2 consecutive successful courses} Overall Failure = {2 unsuccessful courses} The first randomization was stratified by disease volume. Each patient received 2, 3, or 4 courses of chemotherapy. 4 Treatments 4x3 = 12 possible two-stage strategies Goals: Estimate frontline agent effects, salvage agent effects, and the effect of each two-stage regime

7
( TEE, TEC ) ( TEE, CVD ) ( TEE, KAVE ) TEE KAVE CVD TEC KAVE CVD TEE KAVE TEC TEE CVD TEC TEE TEC CVD KAVE ( TEC, TEE ) ( TEC, CVD ) ( TEC, KAVE ) ( CVD, TEE ) ( CVD, TEC ) ( CVD, KAVE ) ( KAVE, TEE ) ( KAVE, TEC ) ( KAVE, CVD ) “The Scramble”

8
S a Repeat S a F a S b Repeat S a F a S b F b S a F a Switch S a F a F b F a F b F a Switch F a S b Repeat F a S b F b S a F a S b S b F a S b F b S a F a F b SaFaSbSbSaFaSbSb SaFaSbFbSaFaSbFb F a F b Possible Outcomes with Regime (a,b)

9
Armstrong et al. (letter to JNCI, 2008): “Because there was no docetaxel single-agent comparator arm in the study by Thall et al., it is difficult to judge the merits of this aggressive and toxic approach.” Note: Their Docetaxel + Prednisone study was published in NEJM in years after “The Scramble” was started. We used the fitted survival model of Armstrong et al., with our patients’ covariates, to estimate how long patients in “The Scramble” would have lived if they had been given Docetaxel + Prednisone. Then we wrote our own letter. Love Letters

10
How long would the patients in “The Scramble” have lived if they had been treated with Docetaxel + Prednisone ? Estimated Survival with Docetaxel + Prednisone Kaplan-Meier Plot of Actual Survival Data of the Patients in The Scramble

11
Possible Reasons Why Patients in The Scramble Survived Longer Compared to Their Estimated Survival with Docetaxel + Prednisone 1.The medical oncologists in the MDACC Genitourinary Oncology Dept are better physicians 2. The 12 two-stage regimes in The Scramble are better than Docetaxel + Prednisone 3. Using the “switch-away” rule is better than just giving the same treatment combination without switching.

12
Example 3: An ATRA Trial (Estey, et al. Blood, 1999) 210 newly diagnosed poor prognosis AML/MDS patients were randomized among 4 induction treatment arms, balancing dynamically on patient prognostic covariates: FAI (Fludarabine + ara-C + Idarubicin) FAI + ATRA (all-trans retinoic acid, vitamin A) FAI + G-CSF FAI + ATRA + G-CSF A 2x2 Factorial Design FAIFAI + ATRA FAI + G-CSF FAI + ATRA + G-CSF G-CSF Effect ATRA Effect

13
Goals and Results of the ATRA Trial in 1999 Goals: Assess the effects of adding ATRA, or G-CSF, or both, to FAI on Prob[Alive & in CR at 6 months] and on overall survival time Results: Based on logistic and Cox model regressions... After accounting for prognostic covariates (age, platelets, treated in protected environment, performance status, cytogenetics), there was no difference among the 4 treatment arms in terms of CR, EFS, or EFS following CR (p-values =.18 to.99 for the various tests)

14
What Do These Examples Have in Common? A “Dynamic Treatment Regime” (DTR) is a mathematical model for what physicians do in everyday practice. A DTR is a set of rules for choosing a patient’s treatment at each stage of therapy, with each choice based on the patient’s entire history up to that stage - - including all previous treatments and outcomes. Get baseline patient information Make 1 st treatment decision Observe the outcome Make 2 nd treatment decision Repeat, until some “final” event is observed A “decision” may be complex - - because it is medical practice. Choose a treatment or combination of treatments Modify a treatment dose or schedule Suspend or stop therapy due to severe toxicity

15
DTR as a Multi-Stage Process A DTR has three components: Patient baseline prognostic covariates Treatments given over time Outcomes observed over time Basic Form of a Dynamic Treatment Regime Observe Treat Observe Treat Observe … Final Event Some Mathematical Notation: For Outcomes (Y 0, Y 1, Y 2, Y 3,..) and Treatments/Decisions, DTR = (T 1, T 2, T 3,..) The sequence is Y 0 T 1 Y 1 T 2 Y 2 T 3 Y 3... The Essential Complication: Each treatment decision depends on all previous outcomes and decisions For example, T 3 depends on (Y 0, T 1, Y 1, T 2, Y 2 )

16
Evaluating Dynamic Treatment Regimes Data Analysis Based on available data, estimate the effect of each DTR, i.e. each sequence of decision rules (T 1, T 2, T 3,...), on some final outcome, such as overall survival time. Trial Design Randomize wherever possible and ethical Evaluate and compare two or more competing DTRs, in terms of their effects on the final outcome Goal : Figure out which DTR (T 1, T 2, T 3,...) is best.

17
Even A Two-Stage DTR May Be Non-Intuitive Example 1: Obtaining a response in 1 or 2 stages Response Probabilities TreatmentFrontlineSalvage with C if Frontline Fails Overall (In 1 or 2 stages) A60%10%64% B50%40%70% 1) If salvage is ignored, then A > B as frontline therapy. 2) If the goal is to get a response in either 1 or 2 stages of therapy, then (B,C) > (A,C) as a two-stage strategy. E.g. if A is highly immunosuppressive

18
Example 2: Maximizing time to failure in 2 stages Mean Time to Failure, in Months TreatmentFrontlineSalvage with C if Frontline Fails Overall, if Pr(salvage is possible) =.80 A B If 80% of frontline failures allow subsequent salvage therapy (frontline failure is PD, not death, dropout, or severe toxicity) A > B as frontline (B,C) > (A,C) as a 2-stage strategy This may occur, e.g., if C acts synergistically if given after B Even A Two-Stage DTR May Be Non-Intuitive

19
“Treatment” at each stage may be complex. The # stages of therapy varies from patient to patient, depending on the patient’s outcomes. Physicians typically use patient covariates, at each stage, as “tailoring variables” to choose a treatment. But this introduces “selection bias” if it is ignored in the statistical analysis! Adverse outcomes (progressive disease, severe toxicity) often cause physicians to stop therapy. Such decisions are part of the regime. Some Complications with DTRs

20
A Closer Look at the 1999 AML/MDS Trial Data: Accounting for Both Induction and Salvage Therapies

21
Survival Time = T D if death during induction T R + T RD if death after salvage for resistant disease T C + T CP + T PD if death after salvage for progression after CR T C + T CD if death in CR Keeping Track of Survival Time

22
Re-Analysis of the ATRA Trial Data Because there were many salvage treatments We classified each salvage as either containing high dose ara-C (HDAC) or not. We distinguished between − Salvage for resistant disease during induction chemo − Salvage at disease progression following CR There were 16 regimes, of the form (frontline, salvage after resistance, salvage after progression) FAI FAI + ATRA FAI + G-CSF FAI + ATRA + G-CSF HDAC or Other

23
The 16 Actual Dynamic Treatment Regimes in the AML/MDS Trial InductionSalvage for Resistant Disease Salvage after Progression FAIHDAC FAIHDACOther FAIOtherHDAC FAIOther FAI + ATRAHDAC FAI + ATRAHDACOther FAI + ATRAOtherHDAC FAI + ATRAOther FAI + G-CSFHDAC FAI + G-CSFHDACOther FAI + G-CSFOtherHDAC FAI + G-CSFOther FAI + G-CSF + ATRAHDAC FAI + G-CSF + ATRAHDACOther FAI + G-CSF + ATRAOtherHDAC FAI + G-CSF + ATRAOther

24
The 16 Actual Dynamic Treatment Regimes in the AML/MDS Trial InductionSalvage for Resistant Disease Salvage after Progression FAIHDAC FAIHDACOther FAIOtherHDAC FAIOther FAI + ATRAHDAC FAI + ATRAHDACOther FAI + ATRAOtherHDAC FAI + ATRAOther FAI + G-CSFHDAC FAI + G-CSFHDACOther FAI + G-CSFOtherHDAC FAI + G-CSFOther FAI + G-CSF + ATRAHDAC FAI + G-CSF + ATRAHDACOther FAI + G-CSF + ATRAOtherHDAC FAI + G-CSF + ATRAOther

25
The 16 Actual Dynamic Treatment Regimes in the AML/MDS Trial InductionSalvage for Resistant Disease Salvage after Progression FAIHDAC FAIHDACOther FAIOtherHDAC FAIOther FAI + ATRAHDAC FAI + ATRAHDACOther FAI + ATRAOtherHDAC FAI + ATRAOther FAI + G-CSFHDAC FAI + G-CSFHDACOther FAI + G-CSFOtherHDAC FAI + G-CSFOther FAI + G-CSF + ATRAHDAC FAI + G-CSF + ATRAHDACOther FAI + G-CSF + ATRAOtherHDAC FAI + G-CSF + ATRAOther

26
The 16 Actual Dynamic Treatment Regimes in the AML/MDS Trial InductionSalvage for Resistant Disease Salvage after Progression FAIHDAC FAIHDACOther FAIOtherHDAC FAIOther FAI + ATRAHDAC FAI + ATRAHDACOther FAI + ATRAOtherHDAC FAI + ATRAOther FAI + G-CSFHDAC FAI + G-CSFHDACOther FAI + G-CSFOtherHDAC FAI + G-CSFOther FAI + G-CSF + ATRAHDAC FAI + G-CSF + ATRAHDACOther FAI + G-CSF + ATRAOtherHDAC FAI + G-CSF + ATRAOther

27
The 16 Actual Dynamic Treatment Regimes in the AML/MDS Trial InductionSalvage for Resistant Disease Salvage after Progression FAIHDAC FAIHDACOther FAIOtherHDAC FAIOther FAI + ATRAHDAC FAI + ATRAHDACOther FAI + ATRAOtherHDAC FAI + ATRAOther FAI + G-CSFHDAC FAI + G-CSFHDACOther FAI + G-CSFOtherHDAC FAI + G-CSFOther FAI + G-CSF + ATRAHDAC FAI + G-CSF + ATRAHDACOther FAI + G-CSF + ATRAOtherHDAC FAI + G-CSF + ATRAOther

28
Ignoring salvage treatment may lead to biased estimation of induction treatment effects on survival The goal of the analysis to estimate overall survival for the induction-salvage treatment regimes, not just the 4 frontline combinations. Main Question: Which induction-salvage treatment regimes (if any) led to longer overall survival time? This requires modern statistical methods for analyzing Dynamic Treatment Regimes Why We Evaluated DTRs

29
patients in one induction arm had a higher rate of resistant disease than the other arms? patients in the 4 induction arms received salvage treatments at disproportionate rates? one particular salvage treatment led to longer (or shorter) overall survival than the others? one particular salvage treatment led to longer overall survival than the others, but only among patients who were resistant to induction? What if...

30
1. For each transition time, in each possible pathway to death, we accounted for : − Four possible distributional forms (“Goodness of Fit”) − Effects of Age and Cytogenetic Category − Effects of previous transition times − Effects of frontline and salvage treatments (the DTR) 2. Mean survival time was − modeled as a weighted average of all 4 possible mean survival times, and − estimated for each of the 16 possible regimes 3. We analyzed the data two different ways : − Likelihood Based − Inverse-Probability-of-Treatment-Weighted (IPTW) The two methods gave the same conclusions. Re-Analysis of the ATRA Trial Data

31
Likelihood Based Estimation of Overall Mean Survival =

32
Inverse Probability of Treatment Weighted (IPTW) Estimation of Overall Mean Survival IPTW is used to correct for selection bias

33
Final Estimates of Overall Mean Survival ?

34

35
Some Conclusions from the ATRA Trial Data 1) 1)FAI + ATRA followed by HDAC at progression after CR seems promising, versus the other regimes 2) 2)FAI + ATRA followed by either HDAC or not for resistant disease also seems promising, versus the other regimes 3) 3)If we had done this analysis in 1999, ATRA might have been studied further : − ATRA dose and schedule were never optimized − Many combinations and sequences are possible 4) Tailoring variables probably were used to choose salvage regimens Can we do a more refined analysis? 5) There are many possible molecular mechanisms of interactions between ATRA and the cytotoxic agents, and possible sequential effects

36
Current Events Schlenk et al. ( Blood 118, 2011; ASH 2011 Abstract 80 ) reported a large randomized study of 1112 AML patients, age < 60 years ( conducted 2004 – 2009 ) : Induction: 2 cycles of ICE(Idarubicin + Cytarabine + Etoposide) +/- ATRA Consolidation or Salvage : High risk patients, or induction failures, received allosct, if a matched donor was available, others received HDAC (“tailoring variables” were used) 1) 1)Survival benefit in the ATRA arm (p=.02) 2) 2)Patients with the NPM1 mutation had significantly higher CR rate and better EFS Remark: This data set might reveal more, with a DTR analysis accounting for consolidation and salvage.

37
Some General Conclusions 1) 1)Actual oncology practice is a DTR. Most cancer patients receive two or more treatments in sequence, chosen adaptively at each stage. 2) 2)Conventional clinical trials are of limited use to practicing physicians – because they focus on only one stage of therapy, and so do not reflect actual medical practice. 3) 3)Studying actual treatment regimes provides more clinically useful data, and more informative conclusions. 4) 4)With many regimes, # pats/regime is small, but one may borrow strength between regimes for estimation. 5) 5) Conducting trials of DTRs actually is not difficult. 6) 6) Designing trials of DTRs is very difficult: It is a complex process, involving physicians, nurses, administrators, statisticians, and programmers.

Similar presentations

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google