Treatment effects measured by restricted mean survival time in trials of immune checkpoint inhibitors for cancer

Treatment effects measured by restricted mean survival time in trials of immune checkpoint inhibitors for cancer

Annals of Oncology 29: 1320–1324, 2018 doi:10.1093/annonc/mdy075 Published online 23 February 2018 ORIGINAL ARTICLE F. Liang1, S. Zhang2*, Q. Wang3 ...

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Annals of Oncology 29: 1320–1324, 2018 doi:10.1093/annonc/mdy075 Published online 23 February 2018

ORIGINAL ARTICLE

F. Liang1, S. Zhang2*, Q. Wang3 & W. Li4 1 Clinical Statistical Center; 2Medical Oncology, Fudan University, Shanghai Cancer Center, Shanghai; 3Clinical Laboratory; 4Medical Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China

*Correspondence to: Dr Sheng Zhang, Medical Oncology, Shanghai Cancer Center, Fudan University, 270 Dongan Road, 200032 Shanghai, China. Tel: þ86-21-67822026; Fax: þ86-21-67820385; E-mail: [email protected]

Background: The hazard ratio (HR) is used routinely to quantify the treatment effect for time-to-event end points in oncology trials, but its use requires that there be proportional hazards in the treatment arms. Non-proportional hazards are observed frequently in cancer immunotherapy trials due to the long-term survival and delayed clinical effect. Although values of HR are quoted in such trials, they are not valid measures of outcome. Methods: Reports of parallel group randomized controlled trials (RCTs) evaluating immune checkpoint inhibitors with overall survival data were eligible. For each trial, the ratio of restricted mean survival time (RMST) between the arms was based on reconstructed individual patient data for overall survival. Results: Twenty-five RCTs totaling 12 870 patients were included in this study. Overall survival was used as primary or coprimary end point in 18 trials (72%). In all trials, there was agreement between the ratio of RMST or RMTL and the reported HR about the direction of treatment effect. Estimates of HR provided larger estimates of treatment effect than the ratio of RMST or RMTL in all these trials. The estimated HR and RMST-based measures were in agreement regarding the statistical significance of the effect in all but two trials. Conclusions: Ratio of RMST is a complementary technique that provides alternative method of summarizing treatment effects. Proportional hazards of the treatment effect should not be assumed in RCTs evaluating immune checkpoint inhibitors, and RMST analysis should be reported in such trials. Key words: treatment effects, immune checkpoint inhibitors, randomized controlled trials, hazard ratio

Introduction Over the past several years, randomized controlled trials (RCTs) have shown that immune checkpoint inhibitors can improve survival of people with a wide range of hard-to-treat advanced cancers previously considered intractable [1, 2]. The hazard ratio (HR), which requires the assumption of proportional hazards, is used routinely to quantify treatment effects in RCTs evaluating new types of cancer therapy, including immunotherapy. However, the results of RCTs comparing treatment with immune checkpoint inhibitors to previous standard therapy often show delayed separation of survival curves, a proportion of long-term survivals, and sometimes survival curves

that cross over, indicating violation of the assumption of proportional hazards. This concern has been discussed in the clinical and statistical literature [3–5]. An alternative and statistically valid method of analysis is to use the restricted mean survival time (RMST) or restricted mean time lost (RMTL) to quantify the treatment effect; these methods do not require assumptions such as proportional hazards. The restricted mean is a measure of average survival from time 0 to a specified time point, and may be estimated as the area under (RMST) or above (RMTL) the survival curve up to that point [5–8]. In this article, we estimated the treatment effects measured by the ratio of RMST or RMTL in RCTs evaluating effects of immune checkpoint inhibitors.

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Treatment effects measured by restricted mean survival time in trials of immune checkpoint inhibitors for cancer

Original article

Annals of Oncology Methods

Results A total of 25 trials met the inclusion criteria and were included in our analysis [1, 2, 12–34] (flow of trial screening and references are provided in supplementary Figure S1, available at Annals of Oncology online). Baseline characteristics of these trials are summarized in Table 1. A total of 12 870 patients (median, 425 patients; range, 168–2559 patients) were enrolled in the 25 trials. Overall survival was used as a primary or coprimary end point in 18 (72%) trials (Table 1). Evidence of nonproportional hazards was found in 7 (28%) trials but all of the trials reported a value of HR. The reported HRs ranged from 0.42 to 1.11 with a median of 0.73, and reconstructed data demonstrated estimates close to the HRs or median survival times reported in the original articles (supplementary Table S1, available at Annals of Oncology online). Ratios of RMST ranged from 0.7 to 1.07 with a median of 0.88; differences of RMST ranged from –1.89 to 6.40 months with a median of 1.7 months; the ratios of RMTL ranged from 0.52 to 1.09 with a median of 0.85 (supplementary Table S2, available at Annals of Oncology online).

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Table 1. Characteristics of trials included in the analysis Study characteristics

No. of trials (%)

Overall Journal New England Journal of Medicine Lancet Oncology Lancet Annals of Oncology Journal of Clinical Oncology JAMA Trial phase Phase II Phase III Sample size, median (range) Treatment setting Metastatic First line Second line or beyond Mixed Adjuvant Control arm Placebo Chemotherapy Targeted therapy Immune checkpoint inhibitor Others Treatment arm Pembrolizumab Nivolumab Atezolizumab Ipilimumab Tremelimumab Primary end point OS OS and PFS OS and ORR PFSa ORR RFS Treatment mode Monotherapy Combination therapy Both Trial met primary end point Yesb No

25 (100) 12 (48.0) 3 (12.0) 3 (12.0) 1 (4.0) 5 (20.0) 1 (4.0) 6 (24.0) 19 (76.0) 425 (123–1132) 24 (96.0) 11 (44.0) 12 (48.0) 1 (4.0) 1 (4.0) 3 (12.0) 17 (68.0) 1 (4.0) 3 (12.0) 1 (4.0) 5 (20.0) 8 (32.0) 2 (8.0) 9 (36.0) 1 (4.0) 14 (56.0) 3 (12.0) 1 (4.0) 4 (16.0) 2 (8.0) 1 (4.0) 17 (68.0) 7 (28.0) 1 (4.0) 20 (80.0) 5 (20.0)

a

Including two trials using immune-related PFS as primary end point. For trials with two primary end points, at least one was met. OS, overall survival; PFS, progression-free survival; ORR, overall response rate; RFS, recurrence-free survival.

b

In all trials, there was agreement between the ratio of RMST and the HR about the direction of treatment effect. In 23 trials, both HR and ratio of RMST favored the treatment arm, but HR provided larger estimates of treatment effect than the ratio of RMST in all of them. The HR and RMST-based measures were in

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We searched Medline, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) from inception to 15 July 2017. We combined both MeSH and free text words to identify relevant studies. The search strategy is based on two previously published systematic reviews [9, 10]. Eligibility required reports of parallel group randomized trials of the immune checkpoint inhibitors, which included a Kaplan–Meier curve for overall survival. Phase I, single-arm phase II, and dose-finding trials were excluded. News, editorials, letters or commentaries, retrospective studies, review articles, and secondary analyses of RCTs were also excluded. Multiple-arm trials were included, but only one comparison arm was selected. Two authors (SZ and FL) screened trials independently for eligibility, and extracted the following information from each included trial using standardized forms: first author’s name, journal name, phase of the trial, cancer type, treatment regimen in both arms, sample size, and primary end point. We used digital software (DigitizeIt) to determine the time-dependent probability of overall survival from the published Kaplan–Meier curves. We used information on numbers at risk, and total number of events, where available, to reconstruct the Kaplan–Meier data for each arm [6, 8]. We calculated the RMST and RMTL in the experimental and control groups at the time horizon t* which was defined as the maximum (rounded) time that was shorter than or equal to the lesser of the longest time of follow-up for each of the two groups. For example, if the longest follow-up times were 18.4 and 19.2 months for treatment and control arms, respectively, 18 was chosen as t*. We also conducted a sensitivity analysis by choosing t* as the last failure time in the study. We estimated the ratio of RMST, difference of RMST, as well as the ratio of RMTL. The ratio of RMST was transformed so that a ratio of RMST or RMTL less than 1 indicated superiority of the experimental treatment, as would be the case for an estimate of HR in a trial where proportional hazards were satisfied. We examined proportional-hazards assumptions by testing the period by-treatment interaction term in a time-dependent Cox model [6, 8, 11]. Analyses involved use of R with the survRM2 package to derive the RMST and RMTL and the survival package for proportional-hazards assumptions test. A two-tailed P < 0.05 was considered statistically significant for analyses of RMST and RMTL, and P < 0.10 for the proportional-hazards assumptions test.

Original article

Annals of Oncology

A 1.20 Consistent conclusion of ratio RMST and HR Ratio of RMST significant, HR not significant Ratio of RMST significant, HR significant Proportional hazards Nonproportional hazards

0.80

0.60

0.40 0.40

0.60

0.80 HR

1.00

1.20

1.00

1.20

B 1.20

Consistent conclusion of ratio RMST and HR Ratio of RMST significant, HR not significant Ratio of RMST not significant, HR significant Proportional hazards Nonproportional hazards

Ratio of RMST

1.00

0.80

0.60

0.40 0.40

0.60

0.80 HR

Figure 1. (A) Estimates of treatment effect according to hazard ratio (HR) and ratio of restricted mean survival times (RMSTs). (B) Estimates of treatment effect according to HR and ratio of restricted mean time lost (RMTL).

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Ratio of RMST

1.00

Original article

Annals of Oncology

Discussion There is empirical evidence from previous studies that proportional hazards cannot be always assumed in survival curves for RCTs [3, 4]. Furthermore, failure to demonstrate a violation of proportional hazards may reflect lack of power to detect such violation rather than confirm that hazards are proportional. In trials where the hazard function for two treatment groups is time-dependent during the study follow-up, a method of analysis such as RMST, which is appropriate for any time-to-event relationship, is valuable [7]. Results of our study revealed that estimates of HR and RMST-based measures were generally in agreement regarding the direction and statistical significance of treatment. Evidence of nonproportional hazards was identified in over one quarter of included trials. However, the treatment effect estimated by HR was always larger than that of the ratio of RMST or of RMTL, so that the latter may be more conservative measures for evaluating the benefit of immune checkpoint inhibitors. When the impact of immune checkpoint blockade manifests later in the survival follow-up, little or no difference in survival curves is seen for an initial period and then a late separation occurs. In this scenario, the PH assumption is clearly violated and the HR is difficult to interpretation. A ‘cumulative’ measure such as area between the curves (RMST) may reflect late differences. For example, the survival curves could cross at the median or at some other t* but still show a substantial difference in RMST at t*final. RMST-based measures depend on no distributional assumption. Furthermore, previous studies [4, 35] have showed that study power under proportional hazards reduced under a ‘late effect’, when little or no difference in survival curves is seen for an initial period and then a late separation occurs. Because RMST-based measures use all survival information before the prespecified t*, they are potentially more powerful. RMST or RMTL must be interpreted relative to a time horizon t* and the choice of t* to define the RMST is crucial. In our study, we used a rule that made full use of the available data to capture the long-term benefit of immune checkpoint inhibition, but other rules have been used in previous studies. The rule for choosing t* could be prespecified at the study design stage with respect to the clinical relevance and feasibility of conducting the study [7]. It is possible that ratio of RMTL may be close to the HR when the event rate is low. For example, previous studies [8, 36, 37] have revealed similar results that for trials in the adjuvant setting with low event rates, ratio of RMTL is closer to HR than ratio of RMST.

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Our study has limitations. We calculated RMST and RMTL using reconstructed individual patient data, since it is impractical to obtain them from all trials. But the methods we used were validated with excellent accuracy and reproducibility stated in previous studies [6–8]. A single measure is not sufficient to fully characterize the survival profile for immune checkpoint inhibitors, given the complexity of survival curves. Ratios of RMST or RMTL serve as important supplementary measures and should be considered in order to fully interpret the results. In summary, the ratios of RMST and RMTL are complementary techniques that provide valid methods of summarizing treatment effects when the proportional hazards assumption is violated, as may occur in RCTs evaluating immune checkpoint inhibitors.

Acknowledgement We thank Dr Ian Tannock, Princess Margaret Cancer Centre and University of Toronto, for his assistance in reading and editing the manuscript.

Funding None declared.

Disclosure The authors have declared no conflicts of interest.

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Annals of Oncology