SUPPLEMENTARY MATERIAL
Exploring the Impact of Treatment Switching on Overall Survival from the PROfound Study in Homologous Recombination Repair (HRR)-Mutated Metastatic Castration-Resistant Prostate Cancer (mCRPC)
Rachel Evans1; Neil Hawkins1; Pascale Dequen-O’Byrne1; Charles McCrea2; Dominic Muston3; Christopher Gresty2; Sameer R. Ghate3; Lin Fan3; Robert Hettle2; Keith R. Abrams1; Johann de Bono4; Maha Hussain5; Neeraj Agarwal6
1Visible Analytics, Oxford, UK; 2AstraZeneca, Cambridge, UK; 3Center for Observational and Real-World Evidence, Merck & Co., Inc., Kenilworth, NJ; 4Institute of Cancer Research and Royal Marsden, London, UK; 5The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL; 6Oncology/Internal Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
Table of Contents
1 Switching in PROfound ... 2
2 Additional Methodology ... 2
2.1 Naïve ... 2
2.2 Complex ... 3
2.2.1 Rank Preserving Structural Failure Time Models (RPSFTM) ... 3
Acceleration Factor Calculation ... 3
2.2.2 Inverse Probability of Censoring Weights (IPCW) ... 3
2.2.3 Two-Stage Estimation (TSE) ... 4
3 Additional Results for Cohort A ... 4
3.1 RPSFTM ... 4
3.1.1 Acceleration Factors and Hazard Ratios ... 4
3.1.2 Testing the Randomization Assumption ... 5
3.1.3 Plots of Counterfactual Survival Curves ... 6
3.1.4 Kaplan Meier Plots for Weibull and Log Rank (Cohort A) ... 7
3.1.5 Sensitivity Analysis – Common Treatment Effect Assumption ... 8
3.1.6 IPCW ... 8
3.1.7 Naïve approaches ... 9
3.2 Cohort A+B (Minus PPP2R2A) ... 10
3.2.1 Methodology ... 10
3.2.2 Results... 10
1 Switching in PROfound
Supplementary Fig. 1 provides an overview of why the switching of patients from comparator to experimental regimen may lead to an underestimate of the overall survival (OS) benefit for the experimental treatment. A comparison between ‘investigator’s choice of NHA [NHA]
excluding subsequent olaparib’ (3) and ‘olaparib’ (1) represents a trial where no treatment switching has occurred. However, the PROfound trial investigator’s choice of NHA arm is represented by ‘investigator’s choice of NHA excluding subsequent olaparib’ (2), in which PPS is extended compared with (2) as some patients benefit from olaparib after disease progression. The difference between the intention-to-treat (ITT) OS difference and the adjusted OS difference represents the possible bias caused by treatment switching.
Supplementary Fig. 1 Potential impact of treatment switching (Cohort A)
*Per blinded independent central review; values refer to median radiological progression-free survival (rPFS) and post-progression survival (PPS). Figure adapted from National Institute of Health and Care Excellence Decision Support Unit (NICE DSU) document 16 [1].NHA new hormonal agent, OS overall survival
2 Additional Methodology
2.1 NaïveTwo naïve methods are considered in this analysis: excluding switchers and censoring switchers.
Patients randomized to the investigator’s choice arm who subsequently receive olaparib are identified and removed from the analysis (excluding); or censored from the analysis at point of switching (censoring).
The exclusion method results in a substantial reduction in the number of patients in the control arm, while the censoring method substantially increases in the number of patients censored in the control arm.
These methods are prone to selection bias if switching is associated with patient characteristics, which breaks the randomization balance. Both methods assume that patients in the control arm who switch treatments and those who do not switch treatments have the same prognosis, i.e., that there are no confounders that influence both outcome (survival) and the propensity to switch treatments. This is unlikely to hold: the analysis will include patients both with a ‘good’ prognosis, who are yet to progress and therefore do not need to switch, and those with a ‘poor’ prognosis, who do not get the chance to switch because they die before progression or are considered unsuitable for switching to olaparib after progression,
2.2 Complex
2.2.1 Rank Preserving Structural Failure Time Models (RPSFTM) Acceleration Factor Calculation
The acceleration factor is the degree to which being on olaparib increases survival, and is estimated through g-estimation. With g-estimation, a value for the acceleration factor is selected from a range of values, and input into the counterfactual survival model for every patient in the trial (both control and experimental arms) to work out an untreated survival time for every patient. The average untreated survival times between the randomized groups are then compared. The final value of the acceleration factor is that for which the untreated survival times between the randomized groups are equal. This counterfactual survival data are then used for the treatment switchers in the control arm. For g-estimation, the R package ‘RPSFTM’
was used [2,3].
2.2.1.1 Recensoring
Another consideration when estimating acceleration factors within the RPSFTM framework is the application of recensoring: for patients in the control arm who switch to olaparib, the counterfactual survival model involves shrinking both survival and censoring times and these censoring times may be prone to informative censoring if treatment switching decisions are related to prognostic factors and/or duration of treatment is related to prognostic factors. This possible bias may be avoided by breaking the dependence between censoring time and treatment by ‘recensoring’, which is where a trial participant is recensored at the minimum possible censoring time.
2.2.2 Inverse Probability of Censoring Weights (IPCW)
The Inverse Probability of Censoring Weights (IPCW) method aims to remove the selection bias introduced by censoring switchers by reweighting non-switchers according to an estimated probability based on covariables that they would have switched.
Individuals in the control arm who have not switched treatments but have similar characteristics to those that have switched treatments are weighted more highly, to account for their outcomes and also the outcomes of patients with similar characteristics to them but who switched treatments and were therefore censored from the dataset.
Weights were calculated using a generalized linear model, which includes both time- dependent and time-independent covariates. The following baseline time-independent covariates were available to predict switching: previous taxane use, measurable disease at baseline, homologous recombination repair (HRR) mutations, Eastern Cooperative Oncology Group (ECOG) status at baseline, metastases at baseline, race, gender, study region, and age. The following time-dependent covariates were also available: progression status, pain progression event, skeletal-related event, ECOG status over time, adverse events, hematological-related adverse events, and self-rated health-related quality of life (HRQoL) according to the EQ-5D visual analog scale over time.
In the base case, the switching prediction model should only include variables that both influence the probability of switching and are prognostic of survival. The selection of variables that predict switching was done through an automated selection process based on Akaike information criteria, and it was confirmed that these characteristics were also prognostic of survival. As a sensitivity analysis, the weights were calculated using a switching model containing all available variables; however, adding in variables that only affect the probability of switching or only affect the probability of survival (or neither) is inefficient and may result in additional bias. Weights were recalculated for the primary (Cohort A) and secondary (BRCAm [BRCA1 and/or BRCA2 mutation]) analysis.
The IPCW analysis relies on the ‘no unmeasured confounders’ assumption. This assumption may not hold when there are relatively few prognostic data collected post-randomization, limiting the scope of time-varying covariables that can be included in an analysis. An IPCW analysis can also be prone to error when there is a small sample size with large switching proportions.
2.2.3 Two-Stage Estimation (TSE)
Like the RPSFTM, the Two-Stage Estimation (TSE) method uses a counterfactual framework, where counterfactual survival times are those that would have been observed if treatment switching had not occurred.
The TSE method should only be used to adjust for switching that occurs after a specific disease-related time point, which is labelled as the ‘secondary baseline’.
For disease progression to be a suitable secondary baseline, it is assumed that all patients in the control arm are at a similar stage of disease at the point of disease progression, and the effect of the new treatment on survival can be estimated by comparing survival within the investigator’s choice of NHA arm, from the secondary baseline onwards, between those who switch and do not switch. Treatment can be included as a time-dependent variable to account for any lag between the secondary baseline and treatment switching.
Once a secondary baseline is established, post-secondary baseline survival in switchers and non-switchers is compared, and a treatment effect is calculated. Counterfactual survival times are then estimated using the calculated treatment effect: the treatment effect, as measured from this secondary baseline, is then used to adjust the survival times for those patients who switched to estimate their counterfactual survival had they not switched.
The key assumptions are no unmeasured confounders and no time-dependent confounding on treatment switching after disease progression.
3 Additional Results for Cohort A
3.1 RPSFTM3.1.1 Acceleration Factors and Hazard Ratios
The acceleration factors applied in the analysis are presented in Supplementary Table 1.
Supplementary Table 1 Range of acceleration factors calculated for the Rank Preserving Structural Failure Time Models (RPSFTM) (Cohort A)
Acceleration factor without
recensoring Acceleration factor with recensoring
Using log rank test 0.61 0.60
Using Cox proportional
hazards 0.61 0.59
Using Weibull 0.66 0.61
The central hazard ratio (HR) for olaparib compared with control ranged between 0.42 and 0.52 depending on model selected (i.e., log rank, Cox, Weibull, with or without recensoring).
There were wide confidence intervals around the central estimates because the method retains the P-value from the unadjusted analysis by design; thus, in situations where the point
estimate is reduced, the confidence intervals widen. Confidence intervals were also calculated using bootstrapping and produced similar results.
Kaplan–Meier curves using the Weibull and Log rank models are provided in Supplementary Fig. 3.
3.1.2 Testing the Randomization Assumption
To test the randomization assumption, the Kaplan–Meier plots of counterfactual survival times were analyzed, and these are provided in Supplementary Fig. 2. A comparison of the experimental and comparator arms confirm that their distributions were the same, so the randomization assumption holds.
3.1.3 Plots of Counterfactual Survival Curves
Supplementary Fig. 2 Counterfactual survival times for each Rank Preserving Structural Failure Time Models (RPSFTM) approach
3.1.4 Kaplan Meier Plots for Weibull and Log Rank (Cohort A)
Supplementary Fig. 3: Kaplan-Meier curves for Cohort A, adjusted for treatment switching using Rank Preserving Structural Failure Time Models (RPSFTM): Weibull and Log rank approach: A) Log rank without recensoring; B) Log rank with recensoring; C) Weibull without recensoring; D) Weibull with recensoring.
bid, twice daily; tx, treatment.
3.1.5 Sensitivity Analysis – Common Treatment Effect Assumption
To test the common treatment effect assumption, a sensitivity analysis was included that investigates applying a reduction to the treatment effect in patients who switch from control to olaparib compared with the treatment effect which patients receive if they are randomized to olaparib. A sensitivity analysis is provided in Supplementary Table 2, which investigates what would occur if the treatment effect in patients who switch from control to olaparib was reduced compared with the treatment effect that patients receive if they are treated with olaparib from randomization. When the treatment effect is reduced for the switchers, the central HR estimate increases slightly. This increase is not proportional to the reduction in treatment effect, however, and the analysis is relatively robust to changes in treatment effect over time.
Supplementary Table 2 Rank Preserving Structural Failure Time Models (RPSFTM) sensitivity analysis with reduced treatment effect for switchers (Cohort A)
Analysis Olaparib treatment effect applied to the
switchers in control arm, % Hazard ratio (95% CI) olaparib vs. control
Cox proportional hazards
100 (equivalent to base case) 0.50 (0.27–0.93)
90 0.51 (0.27–0.93)
80 0.51 (0.28–0.93)
70 0.52 (0.29–0.94)
60 0.53 (0.30–0.94)
50 0.54 (0.31–0.94)
Cox proportional hazards, with recensoring
100 (equivalent to base case) 0.42 (0.19–0.91)
90 0.45 (0.22–0.92)
80 0.48 (0.25–0.93)
70 0.47 (0.24–0.93)
60 0.47 (0.24–0.93)
50 0.48 (0.25–0.93)
3.1.6 IPCW
3.1.6.1 Switching prediction model
The following characteristics were included in the switching prediction model for Cohort A:
radiological progression status, HRR group (ATM vs. BRCA1 and/or BRCA2), and site of metastases at baseline.
The switching prediction model should contain variables that predict the probability of switching and are prognostic of survival. A Cox model run on the PROfound trial data identified radiological progression as a statistically significant predictor of switching. Although site of metastases at baseline were not identified in this model, it has been identified in published literature as being prognostic of survival [4,5]. Similarly, an HRR group was not identified in this Cox model, but it has been identified in published literature as being prognostic of survival [6]. No extreme weights were used in the analysis (0.07–5.12).
Kaplan–Meier curves for Cohort A after adjustment for treatment switching using IPCW are provided in Supplementary Fig. 4.
3.1.6.2 Kaplan–Meier Plot
Supplementary Fig. 4 Kaplan–Meier curves for Cohort A, adjusted for treatment switching using Inverse Probability of Censoring Weights (IPCW)
bid twice daily, OS overall survival, tx treatment
3.1.7 Naïve approaches
For the approach excluding switchers, the sample size in the control arm reduced dramatically, from 83 to 27. For the approach censoring switchers, the level of censoring is relatively high, and by 10 months only 22 patients remained in the control arm. The Kaplan-Meier plots for these approaches are shown in Supplementary Figure 5.
For the approach excluding switchers, median survival was not reached in the adjusted control arm, and for the approach censoring switchers, adjusted median survival in the control arm was 14.27 months.
Supplementary Fig. 5 Kaplan-Meier curves for Cohort A, adjusted for treatment switching by excluding (left) or censoring (right) switchers
bid twice daily
3.2 Cohort A+B (Minus PPP2R2A) 3.2.1 Methodology
For Cohort A+B (minus PPP2R2A), only the RPSFTM methodology was considered, using a Cox proportional hazards model both with and without recensoring.
3.2.2 Results
The baseline characteristics for Cohort A+B (minus PPP2R2A) are provided in Supplementary Table 3. For reference, characteristics for Cohort A+B have also been included.
Supplementary Table 3 Cohort A+B and Cohort A+B (minus PPP2R2A) baseline characteristics for the control arm
Cohort A + B Cohort A + B
(minus PPP2R2A) Control,
non-switchers Control,
switchers Control,
non-switchers Control, switchers
N 45 86 45 82
Mean age in years
(standard deviation) 69.1 (8.67) 68.8 (6.99) 69.1 (8.67) 68.8 (7.15) Number of patients with
race = white (%) 31 (69) 54 (63) 31 (69) 50 (61)
HRR mutation ATM (%) 9 (20) 15 (17) 9 (20) 15 (18)
BRCA1 (%) 3 (7) 2 (2) 3 (7) 2 (2)
BRCA2 (%) 13 (29) 34 (40) 13 (29) 34 (42)
Co-mutation
(%) 15 (33) 29 (34) 15 (33) 25 (31)
Number of patients with
previous taxane use (%) 5 (11) 6 (7) 5 (11) 6 (7)
Number of patients without
previous taxane use (%) 28 (62) 56 (65) 28 (62) 54 (66) Number of patients with
measurable disease at baseline (%)
17 (38) 30 (35) 17 (38) 28 (34)
Number of patients without measurable disease at baseline (%)
23 (51) 49 (57) 23 (51) 48 (59)
Number of patients with metastases at baseline (visceral) (%)
22 (49) 37 (43) 22 (49) 34 (42)
Number of patients with ECOG at baseline= 1 or 2 (%)
13 (29) 31 (36) 13 (29) 31 (38)
ECOG Eastern Cooperative Oncology Group, HRR homologous recombination repair
The HR without adjusting for treatment switching in the control arm is 0.76 (95% CI 0.58–1.00). After adjusting for switching, the HR estimate for olaparib compared with control including recensoring was 0.51 (95% CI 0.26–0.98), and excluding recensoring was 0.59 (95% CI 0.35–0.99) (Supplementary Fig. 6).
Supplementary Fig. 6 Overall survival hazard ratios for all methods, Cohort A+B (minus PPP2R2A).
Dashed line represents unadjusted for switching hazard ratio (HR) for Cohort A+B (minus PPP2R2A);
dotted line represents HR = 1.0
RPSFTM Rank Preserving Structural Failure Time Models
The adjusted Kaplan-Meier curves are provided in Supplementary Figures 7 and 8.
Supplementary Fig. 7 Kaplan-Meier curves for Cohort A+B (minus PPP2R2A), adjusted for treatment switching using Rank Preserving Structural Failure Time Models (RPSFTM) and a Cox proportional hazard model with recensoring
bid twice daily, NHA new hormonal agent, OS overall survival
Supplementary Fig. 8 Kaplan-Meier curves for Cohort A+B (minus PPP2R2A), adjusted for treatment switching using Rank Preserving Structural Failure Time Models (RPSFTM) and a Cox proportional hazard model without recensoring
bid twice daily, NHA new hormonal agent, OS overall survival
4 References
1. Latimer N, Abrams K. NICE DSU Technical Support Document 16: adjusting survival time estimates in the presence of treatment switching. NICE Decision Support Unit, ScHARR; 2014. http://www.nicedsu.org.uk.
2. Allison A, White IR, Bond S. rpsftm: An R package for rank preserving structural failure time models. R J 2017;9(2):342-53.
3. Latimer NR, White IR, Abrams KR, Siebert U. Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times? Stat Methods Med Res 2019;28(8):2475-93.
https://doi.org/10.1177/0962280218780856
4. Cui PF, Cong XF, Gao F, Yin JX, Niu ZR, Zhao SC et al. Prognostic factors for overall survival in prostate cancer patients with different site-specific visceral metastases: A study of 1358 patients. World J Clin Cases 2020;8(1):54-67.
https://doi.org/10.12998/wjcc.v8.i1.54
5. Gandaglia G, Karakiewicz P, Briganti A, Passoni N, Schiffmann J, Trudeau V et al.
Impact of the site of metastases on survival in patients with metastatic prostate cancer.
Eur Urol 2015;68(2):325-34. https://doi.org/10.1016/j.eururo.2014.07.020
6. Oh M, Alkhushaym N, Fallatah S, Althagafi A, Aljadeed R, Alsowaida Y et al. The association of BRCA1 and BRCA2 mutations with prostate cancer risk, frequency, and mortality: A meta-analysis. Prostate 2019;79(8):880-95.
https://doi.org/10.1002/pros.23795
