Deviation from the Proportional Hazards Assumption in Randomized Phase 3 Clinical Trials in Oncology: Prevalence, Associated Factors, and Implications

There Is More to Length of Survival Than Survival: Endpoints in Oncology Trials

Progression-free survival as a primary endpoint for comparative trials does not fully capture the therapeutic risk/benefit ratio. Additionally, summarization of the treatment effect via a hazard ratio is problematic when the proportional hazards assumption is violated. Restricted mean survival time metrics may address these challenges but have other limitations. See related article by Chang et al., p. 3282.

Comparative Outcomes of Surgical and Transcatheter Aortic Valve Replacement: A Meta-analysis and Parametric Extrapolation of Clinical Trials

INTRODUCTION

We aimed to pool randomized clinical trials (RCTs) comparing surgical aortic valve replacement (SAVR) with transcatheter aortic valve replacement (TAVR) and extrapolate pooled time-to-event data to compare long-term outcomes.

METHODS

An electronic database search was performed for RCTs comparing SAVR with TAVR. The most current longest follow-up data for each RCT were included. Data were pooled using a random-effects model. Survival data were pooled for Kaplan-Meier analysis as well as parametric modeling with extrapolation.

RESULTS

Seven RCTs comprising 7774 patients were included. Mean valve gradient at 5 y was comparable between SAVR [11 mmHg (3.7; 18.3)] and TAVR [8.1 mmHg (1.9; 14.3)] (P = 0.38). TAVR had a higher mean valve area at 30 d, 1 y, and 2 y [1.68 cm2 (1.22; 2.13) versus 1.8 cm2 (1.35; 2.25), P = 0.02]. SAVR had a higher freedom from any paravalvular leak at 30 d and 1 y [86% (81; 90) versus 39% (36; 41), P < 0.01]. All-cause death was lower in the SAVR group at 5 y [39% (29; 50) versus 43% (31; 57), P < 0.01]. Although no differences were seen between SAVR and TAVR in the pooled Kaplan-Meier analysis of all-cause mortality and composite of all-cause mortality or stroke, parametric modeling with extrapolation showed significant divergence for both outcomes.

CONCLUSIONS

Pooled all-cause mortality as well as pooled composite of all-cause mortality or stroke indicated better survival with SAVR at 5 y. Long-term parametric extrapolation also indicated superior survival with SAVR.

Evaluation of Overall Survival by Restricted Mean Survival Time of Advanced Biliary Tract Cancer treated with Immunotherapy: A Systematic Review and Meta-Analysis

BACKGROUND

For biliary tract cancer (BTC), the addition of immunotherapy (durvalumab or pembrolizumab) to gemcitabine and cisplatin (GemCis) significantly improved overall survival (OS) in phase 3 clinical trials (RCTs). However, the interpretation and magnitude of the treatment effect is challenging because OS Kaplan-Meier curves violate the proportional hazards (PH) assumption. Analysis using restricted mean survival time (RMST) allows quantification of the benefits in the absence of PH. This systematic review and meta-analysis aims to assess the benefit of immunotherapy-based regimens for OS at 24 months using RMST analysis.

METHODS

A systematic review was conducted using studies published up to 8 November 2023. Only phase 3 RCTs evaluating the use of anti-PD-1/PD-L1 combined with GemCis and reporting OS were included. KM curves for OS were digitized, and the data were reconstructed. A meta-analysis for OS by RMST at 24 months was performed.

RESULTS

A total of 1754 participants from the TOPAZ-1 and KEYNOTE-966 trials were included. In TOPAZ-1, RMSTs at 24 months were 13.52 (7.92) and 12.21 (7.22) months with GemCis plus durvalumab and GemCis alone, respectively. In KEYNOTE-966, RMSTs at 24 months were 13.60 (7.76) and 12.45 (7.73) months with GemCis plus pembrolizumab and GemCis alone, respectively. Immunotherapy-based regimens showed a mean OS difference at 24 months by an RMST of 1.21 months [(95% CI: 0.49-1.93), p < 0.001, I2 = 0%].

CONCLUSIONS

Immunotherapy-based regimens improve OS in advanced BTC. Given this magnitude of benefit, it is essential to weigh up individual patient factors, preferences, and potential risks. RMST analysis provides valuable information to patients and physicians, facilitating decision-making in a value-based medical environment.

Association between sex and efficacy of immune checkpoint inhibitors: a systematic review and meta-analysis

Aim: To explore the association between sex and immune checkpoint inhibitors (ICIs). Materials & methods: We assessed the difference in survival outcomes from ICIs between sexes using an interaction test. Results: 108 studies representing 70,243 patients were included. In the first-line setting, the pooled interaction HR was 0.97 (95% CI: 0.91-1.04). In the subsequent-line setting, the pooled interaction HR was 0.85 (95% CI: 0.77-0.95). When ICIs were given as perioperative therapy or as systemic therapy in patients with positive PD-L1 expression, both men and women obtained equal survival benefits. Conclusion: Both sex, line of therapy, cancer (sub)type and PD-L1 status should be taken into account in the assessment of risk versus benefit when deciding to offer ICIs to patients.

Assessing Long-term Treatment Benefits Using Complementary Statistical Approaches: An In Silico Analysis of the Phase III Keynote-045 and Checkmate-214 Immune Checkpoint Inhibitor Trials

BACKGROUND

The Keynote-045 trial illustrates that the long-term benefit (LTB) of treatment does not always translate to improved progression-free survival (PFS). Milestone survival and flexible parametric survival model with cure (FPCM) have been proposed as complementary statistical approaches to more comprehensively evaluate LTBs of treatments.

OBJECTIVE

The current study compares milestone survival and FPCM analyses to evaluate treatment effects of immune checkpoint inhibitor (ICI) phase III trials.

DESIGN, SETTING, AND PARTICIPANTS

Individual patient data, from initial and follow-up analyses of Keynote-045 (urothelial cancer) and Checkmate-214 (advanced renal cell carcinoma), were reconstructed for PFS.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Each trial was reanalyzed using the Cox proportional hazard regression and two complementary methods (milestone survival and FPCM) to estimate treatment impact on the LTB.

RESULTS AND LIMITATIONS

For each trial, there was evidence of nonproportional hazards. For the long-term analysis of the Keynote-045 trial, FPCM identified a time-dependent effect on PFS, but the Cox model found no statistical difference in PFS (hazard ratio, 0.90; 95% confidence interval, 0.75-1.08). Milestone survival and FPCM identified improvements in the LTB fractions. This was consistent with the results from the reanalysis of Keynote-045, based on the shorter follow-up, although the LTB fraction was not retained. The increase in PFS in Checkmate-214 was identified by both Cox model and FPCM. Experimental treatment-dependent improvement in the LTB fraction was demonstrated using milestone survival and FPCM. The LTB fraction estimated with FPCM was consistent with the results from the reanalysis of the shorter follow-up period.

CONCLUSIONS

Although ICIs show substantial shifts toward LTBs in terms of PFS, based on a conventional Kaplan-Meier or Cox model analysis, our approach provides an alternative assessment of benefit-risk ratios for new therapeutics and facilitates communicating risk to patients. Kidney patients treated with ICIs can be counseled that they are potentially cured, but future work will need to definitively validate this conclusion.

PATIENT SUMMARY

Although immune checkpoint inhibitor treatments show substantial shifts toward long-term benefits in terms of progression-free survival, a more rigorous attempt to quantify this shift, rather than simply using a Kaplan-Meier estimate or comparing progression-free survival curves using the classic Cox model, is warranted. Our results suggest that advanced renal cell carcinoma patients who had not received a previous treatment are functionally cured by nivolumab and ipilimumab, which is not the case for second-line urothelial carcinoma.

Analysis of survival data with nonproportional hazards: A comparison of propensity-score-weighted methods

One of the most common ways researchers compare cancer survival outcomes across treatments from observational data is using Cox regression. This model depends on its underlying assumption of proportional hazards, but in some real-world cases, such as when comparing different classes of cancer therapies, substantial violations may occur. In this situation, researchers have several alternative methods to choose from, including Cox models with time-varying hazard ratios; parametric accelerated failure time models; Kaplan-Meier curves; and pseudo-observations. It is unclear which of these models are likely to perform best in practice. To fill this gap in the literature, we perform a neutral comparison study of candidate approaches. We examine clinically meaningful outcome measures that can be computed and directly compared across each method, namely, survival probability at time T, median survival, and restricted mean survival. To adjust for differences between treatment groups, we use inverse probability of treatment weighting based on the propensity score. We conduct simulation studies under a range of scenarios, and determine the biases, coverages, and standard errors of the average treatment effects for each method. We then demonstrate the use of these approaches using two published observational studies of survival after cancer treatment. The first examines chemotherapy in sarcoma, which has a late treatment effect (i.e., similar survival initially, but after 2 years the chemotherapy group shows a benefit). The other study is a comparison of surgical techniques for kidney cancer, where survival differences are attenuated over time.

Challenges, Complexities, and Considerations in the Design and Interpretation of Late-Phase Oncology Trials

Optimal management of cancer patients relies heavily on late-phase oncology randomized controlled trials. A comprehensive understanding of the key considerations in designing and interpreting late-phase trials is crucial for improving subsequent trial design, execution, and clinical decision-making. In this review, we explore important aspects of late-phase oncology trial design. We begin by examining the selection of primary endpoints, including the advantages and disadvantages of using surrogate endpoints. We address the challenges involved in assessing tumor progression and discuss strategies to mitigate bias. We define informative censoring bias and its impact on trial results, including illustrative examples of scenarios that may lead to informative censoring. We highlight the traditional roles of the log-rank test and hazard ratio in survival analyses, along with their limitations in the presence of nonproportional hazards as well as an introduction to alternative survival estimands, such as restricted mean survival time or MaxCombo. We emphasize the distinctions between the design and interpretation of superiority and noninferiority trials, and compare Bayesian and frequentist statistical approaches. Finally, we discuss appropriate utilization of phase II and phase III trial results in shaping clinical management recommendations and evaluate the inherent risks and benefits associated with relying on phase II data for treatment decisions.

Trial Design for Cancer Immunotherapy: A Methodological Toolkit

Immunotherapy with checkpoint inhibitors (CPIs) and cell-based products has revolutionized the treatment of various solid tumors and hematologic malignancies. These agents have shown unprecedented response rates and long-term benefits in various settings. These clinical advances have also pointed to the need for new or adapted approaches to trial design and assessment of efficacy and safety, both in the early and late phases of drug development. Some of the conventional statistical methods and endpoints used in other areas of oncology appear to be less appropriate in immuno-oncology. Conversely, other methods and endpoints have emerged as alternatives. In this article, we discuss issues related to trial design in the early and late phases of drug development in immuno-oncology, with a focus on CPIs. For early trials, we review the most salient issues related to dose escalation, use and limitations of tumor response and progression criteria for immunotherapy, the role of duration of response as an endpoint in and of itself, and the need to conduct randomized trials as early as possible in the development of new therapies. For late phases, we discuss the choice of primary endpoints for randomized trials, review the current status of surrogate endpoints, and discuss specific statistical issues related to immunotherapy, including non-proportional hazards in the assessment of time-to-event endpoints, alternatives to the Cox model in these settings, and the method of generalized pairwise comparisons, which can provide a patient-centric assessment of clinical benefit and be used to design randomized trials.

Robotic Versus Conventional Minimally Invasive Esophagectomy for Esophageal Cancer: A Meta-analysis

OBJECTIVES

To give a comprehensive review of the literature comparing perioperative outcomes and long-term survival with robotic-assisted minimally invasive esophagectomy (RAMIE) versus minimally invasive esophagectomy (MIE) for esophageal cancer.

BACKGROUND

Curative minimally invasive surgical treatment for esophageal cancer includes RAMIE and conventional MIE. It remains controversial whether RAMIE is comparable to MIE.

METHODS

This review was registered at the International Prospective Register of Systematic Reviews (CRD42021260963). A systematic search of databases was conducted. Perioperative outcomes and long-term survival were analyzed and subgroup analysis was conducted. Cumulative meta-analysis was performed to track therapeutic effectiveness.

RESULTS

Eighteen studies were included and a total of 2932 patients (92.88% squamous cell carcinoma, 29.83% neoadjuvant therapy, and 38.93% stage III-IV), 1418 underwent RAMIE and 1514 underwent MIE, were analyzed. The number of total lymph nodes (LNs) [23.35 (95% CI: 21.41-25.29) vs 21.98 (95% CI: 20.31-23.65); mean difference (MD) = 1.18; 95% CI: 0.06-2.30; P =0.04], abdominal LNs [9.05 (95% CI: 8.16-9.94) vs 7.75 (95% CI: 6.62-8.88); MD = 1.04; 95% CI: 0.19-1.89; P =0.02] and LNs along the left recurrent laryngeal nerve [1.74 (95% CI: 1.04-2.43) vs 1.34 (95% CI: 0.32-2.35); MD = 0.22; 95% CI: 0.09-0.35; P <0.001] were significantly higher in the RAMIE group. RAMIE is associated with a lower incidence of pneumonia [9.61% (95% CI: 7.38%-11.84%) vs 14.74% (95% CI: 11.62%-18.15%); odds ratio = 0.73; 95% CI: 0.58-0.93; P =0.01]. Meanwhile, other perioperative outcomes, such as operative time, blood loss, length of hospital stay, 30/90-day mortality, and R0 resection, showed no significant difference between the two groups. Regarding long-term survival, the 3-year overall survival was similar in the two groups, whereas patients undergoing RAMIE had a higher rate of 3-year disease-free survival compared with the MIE group [77.98% (95% CI: 72.77%-82.43%) vs 70.65% (95% CI: 63.87%-77.00%); odds ratio = 1.42; 95% CI: 1.11-1.83; P =0.006]. A cumulative meta-analysis conducted for each outcome demonstrated relatively stable effects in the two groups. Analyses of each subgroup showed similar overall outcomes.

CONCLUSIONS

RAMIE is a safe and feasible alternative to MIE in the treatment of resectable esophageal cancer with comparable perioperative outcomes and seems to indicate a possible superiority in LNs dissection in the abdominal cavity, and LNs dissected along the left recurrent laryngeal nerve and 3-year disease-free survival in particular in esophageal squamous cell carcinoma. Further randomized studies are needed to better evaluate the long-term benefits of RAMIE compared with MIE.

In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to anticipate such effects in advance and adjust the design accordingly. Here, we use in silico cancer immunotherapy trials - simulated trials based on three different mathematical models - to assemble virtual patient cohorts undergoing late-stage immunotherapy, chemotherapy, or combination therapies. We find that all three simulation models predict the distinctive survival curve shapes commonly associated with immunotherapies. Considering four aspects of clinical trial design - sample size, endpoint, randomization rate, and interim analyses - we demonstrate how, by simulating various possible scenarios, the robustness of trial design choices can be scrutinized, and possible pitfalls can be identified in advance. We provide readily usable, web-based implementations of our three trial simulation models to facilitate their use by biomedical researchers, doctors, and trialists.

Time-varying prognostic value of primary tumor sidedness in metastatic colorectal cancer: A population-based study and meta-analysis

We studied the prognostic value of primary tumor sidedness in metastatic colorectal cancer over time and across treatment lines. Population data on synchronous metastatic colorectal cancer patients were extracted from the Netherlands Cancer Registry and SEER database. Pubmed, EMBASE and Cochrane library were searched for prospective studies on metastatic colorectal cancer to conduct a meta-analysis. Inclusion criteria consisted of metastatic disease, systemic treatment with palliative intent and specification of primary tumor location. Data were pooled using a random-effects model. For the population-based data, multivariable Cox models were constructed. The Grambsch-Therneau test was conducted to evaluate the potential time-varying nature of sidedness. Meta-regression incorporating treatment-line as variable was conducted to test the pre-specified hypothesis that the prognostic value of sidedness varies over time. Analysis of 12 885 and 16 160 synchronous metastatic colorectal cancer patients registered in the Netherlands Cancer Registry and SEER database, respectively, indicated a time-varying prognostic value of sidedness (P < .01). Thirty-one studies were selected for the meta-analysis (9558 patients for overall survival analysis). Pooled univariable hazard ratio_{left-sided/right-sided} for overall survival was 0.71 (95% CI: 0.65-0.76) in 1st-line, 0.76 (0.54-1.06) in 2nd-line and 1.01 (0.86-1.19) in 3rd-line studies. Hazard ratios were significantly influenced by treatment line (P = .035). The prognostic value of sidedness of the primary tumor in metastatic colorectal cancer patients treated with palliative systemic therapy decreases over time since diagnosis, suggesting that sidedness may not be a useful stratification factor in late-line trials. This decrease in prognostic value should be taken into account when providing prognostic information to patients.

Stratified modestly weighted log-rank tests in settings with an anticipated delayed separation of survival curves

Delayed separation of survival curves is a common occurrence in confirmatory studies in immuno-oncology. Many novel statistical methods that aim to efficiently capture potential long-term survival improvements have been proposed in recent years. However, the vast majority do not consider stratification, which is a major limitation considering that most large confirmatory studies currently employ a stratified primary analysis. In this article, we combine recently proposed weighted log-rank tests that have been designed to work well under a delayed separation of survival curves, with stratification by a baseline variable. The aim is to increase the efficiency of the test when the stratifying variable is highly prognostic for survival. As there are many potential ways to combine the two techniques, we compare several possibilities in an extensive simulation study. We also apply the techniques retrospectively to two recent randomized clinical trials.

Virtual Trials: Causally-validated treatment effects efficiently learned from an observational cancer registry

Randomized controlled trials (RCTs) offer a clear causal interpretation of treatment effects, but are inefficient in terms of information gain per patient. Moreover, because they are intended to test cohort-level effects, RCTs rarely provide information to support precision medicine, which strives to choose the best treatment for an individual patient. If causal information could be efficiently extracted from widely available real-world data, the rapidity of treatment validation could be increased, and its costs reduced. Moreover, inferences could be made across larger, more diverse patient populations. We created a "virtual trial" by fitting a multilevel Bayesian survival model to treatment and outcome records self-reported by 451 brain cancer patients. The model recovers group-level treatment effects comparable to RCTs representing over 3200 patients. The model additionally discovers the feature-treatment interactions needed to make individual-level predictions for precision medicine. By learning from heterogeneous real-world data, virtual trials can generate more causal estimates with fewer patients than RCTs, and they can do so without artificially limiting the patient population. This demonstrates the value of virtual trials as a complement to large randomized controlled trials, especially in highly heterogeneous or rare diseases.

The design and evaluation of hybrid controlled trials that leverage external data and randomization

Patient-level data from completed clinical studies or electronic health records can be used in the design and analysis of clinical trials. However, these external data can bias the evaluation of the experimental treatment when the statistical design does not appropriately account for potential confounders. In this work, we introduce a hybrid clinical trial design that combines the use of external control datasets and randomization to experimental and control arms, with the aim of producing efficient inference on the experimental treatment effects. Our analysis of the hybrid trial design includes scenarios where the distributions of measured and unmeasured prognostic patient characteristics differ across studies. Using simulations and datasets from clinical studies in extensive-stage small cell lung cancer and glioblastoma, we illustrate the potential advantages of hybrid trial designs compared to externally controlled trials and randomized trial designs.

Patient-level external control data from prior clinical studies or electronic health records can be used in the design and analysis of clinical trials. Here the authors report a hybrid trial design combining the use of external control data and randomization to test experimental treatments, using small cell lung cancer and glioblastoma datasets as examples.

A Causal Framework for Making Individualized Treatment Decisions in Oncology

Simple Summary

Physicians routinely make individualized treatment decisions by accounting for the joint effects of patient prognostic covariates and treatments on clinical outcomes. Ideally, this is performed using historical randomized clinical trial (RCT) data. Randomization ensures that unbiased estimates of causal treatment effect parameters can be obtained from the historical RCT data and used to predict each new patient’s outcome based on the joint effect of their baseline covariates and each treatment being considered. However, this process becomes problematic if a patient seen in the clinic is very different from the patients who were enrolled in the RCT. That is, if a new patient does not satisfy the entry criteria of the RCT, then the patient does not belong to the population represented by the patients who were studied in the RCT. In such settings, it still may be possible to utilize the RCT data to help choose a new patient’s treatment. This may be achieved by combining the RCT data with data from other clinical trials, or possibly preclinical experiments, and using the combined dataset to predict the patient’s expected outcome for each treatment being considered. In such settings, combining data from multiple sources in a way that is statistically reliable is not entirely straightforward, and correctly identifying and estimating the effects of treatments and patient covariates on clinical outcomes can be complex. Causal diagrams provide a rational basis to guide this process. The first step is to construct a causal diagram that reflects the plausible relationships between treatment variables, patient covariates, and clinical outcomes. If the diagram is correct, it can be used to determine what additional data may be needed, how to combine data from multiple sources, how to formulate a statistical model for clinical outcomes as a function of treatment and covariates, and how to compute an unbiased treatment effect estimate for each new patient. We use adjuvant therapy of renal cell carcinoma to illustrate how causal diagrams may be used to guide these steps.

Abstract

We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT’s entry criteria, or a treatment’s effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment–covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.

Clinical research in ovarian cancer: consensus recommendations from the Gynecologic Cancer InterGroup

The Gynecologic Cancer InterGroup (GCIG) sixth Ovarian Cancer Conference on Clinical Research was held virtually in October, 2021, following published consensus guidelines. The goal of the consensus meeting was to achieve harmonisation on the design elements of upcoming trials in ovarian cancer, to select important questions for future study, and to identify unmet needs. All 33 GCIG member groups participated in the development, refinement, and adoption of 20 statements within four topic groups on clinical research in ovarian cancer including first line treatment, recurrent disease, disease subgroups, and future trials. Unanimous consensus was obtained for 14 of 20 statements, with greater than 90% concordance in the remaining six statements. The high acceptance rate following active deliberation among the GCIG groups confirmed that a consensus process could be applied in a virtual setting. Together with detailed categorisation of unmet needs, these consensus statements will promote the harmonisation of international clinical research in ovarian cancer.

Network meta-analysis of immune-oncology monotherapy as first-line treatment for advanced non-small-cell lung cancer in patients with PD-L1 expression ⩾50

Background:

For patients with advanced non-small-cell lung cancer (NSCLC) and high (⩾50%) programmed cell death-ligand 1 (PD-L1) expression, effective first-line immune-oncology monotherapies with significant survival benefits are approved, cemiplimab being the most recent. In a phase III trial, cemiplimab demonstrated significantly improved overall survival (OS) and progression-free survival (PFS) versus chemotherapy in patients with advanced NSCLC and PD-L1 ⩾50%. A systematic literature review and network meta-analysis (NMA) was conducted to identify/compare the efficacy/safety of cemiplimab versus pembrolizumab or other immune-oncology monotherapies from randomized-controlled trials (RCTs) published in November 2010–2020.

Methods:

Relevant RCTs were identified by searching databases and conference proceedings as per ISPOR, NICE, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. NMA with time-varying hazard ratios (HRs) was performed for OS and PFS. Analyses were conducted for objective response rate (ORR) and safety/tolerability. Fixed-effect models were used due to limited evidence. Various sensitivity analyses were conducted to validate the base case analyses.

Results:

The feasibility assessment determined that EMPOWER-Lung 1, KEYNOTE-024, and KEYNOTE-042 trials were eligible. IMpower110 was excluded because an incompatible PD-L1 assay (SP142) was used for patient selection. For first-line advanced NSCLC with PD-L1 ⩾50%, cemiplimab was associated with statistically significant improvements in PFS [HR (95% credible interval [CrI]): 0.65 (0.50–0.86), 1–12 months] and ORR [odds ratio (OR) (95% CrI): 1.64 (1.04–2.62)], and comparable OS [HR (95% CrI): 0.77 (0.54–1.10), 1–12 months] versus pembrolizumab. There was no evidence of differences between cemiplimab and pembrolizumab for Grade 3–5 adverse events (AEs) [OR (95% CrI): 1.47 (0.83–2.60)], immune-mediated AEs [1.75 (0.33–7.49)], and all-cause discontinuation due to AEs [1.21 (0.58–2.61)].

Conclusions:

Considering the limitations of indirect treatment comparisons, in patients with advanced NSCLC and PD-L1 ⩾50%, cemiplimab monotherapy demonstrated significant improvements in PFS and ORR, comparable OS, and no evidence of differences in safety/tolerability versus pembrolizumab.

Design and analysis of group-sequential clinical trials based on a modestly weighted log-rank test in anticipation of a delayed separation of survival curves: A practical guidance

BACKGROUND

A common feature of many recent trials evaluating the effects of immunotherapy on survival is that non-proportional hazards can be anticipated at the design stage. This raises the possibility to use a statistical method tailored towards testing the purported long-term benefit, rather than applying the more standard log-rank test and/or Cox model. Many such proposals have been made in recent years, but there remains a lack of practical guidance on implementation, particularly in the context of group-sequential designs. In this article, we aim to fill this gap.

METHODS

We illustrate how the POPLAR trial, which compared immunotherapy versus chemotherapy in non-small-cell lung cancer, might have been re-designed to be more robust to the presence of a delayed effect using the modestly-weighted log-rank test in a group-sequential setting.

CONCLUSION

We provide step-by-step instructions on how to analyse a hypothetical realization of the trial, based on this new design. Basic theory on weighted log-rank tests and group-sequential methods is covered, and an accompanying R package (including vignette) is provided.

Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success.

Analysis of more than 150 Phase 3 oncology clinical trials supports parametric statistical analysis, significantly increasing the precision of small early-phase trials and relating deviations from the Cox proportional hazards model to trial duration.

Bayesian interpretation of immunotherapy trials with dynamic treatment effects

INTRODUCTION

The mechanism of action of immune checkpoints inhibitors hinders the writing of rational statistical analysis plans for phase III randomised clinical trials (RCTs) because of their unpredictable dynamic effects. The purpose is to illustrate the advantages of Bayesian reporting of treatment efficacy analysis in immunotherapy RCTs, in contrast to frequentist reporting.

METHOD

Fourteen RCTs (one with two pairwise comparisons) that failed to achieve their primary objective (overall survival, OS) were selected. These RCTs were reanalysed using Bayesian Cox models with dynamic covariate coefficients and time-invariant models.

RESULTS

The RCTs that met inclusion criteria were 7 lung cancer trials, various other tumours, with antiPD1, antiPDL1 or antiCTLA4 therapies. The minimum detectable effect (δ_S) was superior to the true benefit observed in all cases, in conditions of non-proportional hazards. Schoenfeld tests indicated the existence of PH assumption violations (p<0.05) in 6/15 cases. The Bayesian Cox models revealed a probability of benefit >79% in all the RCTs, with the therapeutic equivalence hypothesis unlikely. The OS curves diverged after a median of 9.1 months. Since the divergency, no non-proportionality was evinced in 13/15, while the Wald tests achieved p<0.05 in 12/15 datasets. In all cases, the Bayesian Cox models with dynamic coefficients detected fluctuations of the hazard ratio, and increased 2-year OS was the most likely hypothesis.

CONCLUSION

We recommend progressively implementing Bayesian and dynamic analyses in all RCTs of immunotherapy to interpret and assess the credibility of frequentist results.

Bayesian multivariate network meta-analysis model for the difference in restricted mean survival times

Network meta-analysis (NMA) is essential for clinical decision-making. NMA enables inference for all pair-wise comparisons between interventions available for the same indication, by using both direct evidence and indirect evidence. In randomized trials with time-to event outcome data, such as lung cancer data, conventional NMA methods rely on the hazard ratio and the proportional hazards assumption, and ignore the varying follow-up durations across trials. We introduce a novel multivariate NMA model for the difference in restricted mean survival times (RMST). Our model synthesizes all the available evidence from multiple time points simultaneously and borrows information across time points through within-study covariance and between-study covariance for the differences in RMST. We propose an estimator of the within-study covariance and we then assume it to be known. We estimate the model under the Bayesian framework. We evaluated our model by conducting a simulation study. Our multiple-time-point model yields lower mean squared error over the conventional single-time-point model at all time points, especially when the availability of evidence decreases. We illustrated the model on a network of randomized trials of second-line treatments of advanced non-small-cell lung cancer. Our multiple-time-point model yielded increased precision and detected evidence of benefit at earlier time points as compared to the single-time-point model. Our model has the advantage of providing clinically interpretable measures of treatment effects.

A modified weighted log-rank test for confirmatory trials with a high proportion of treatment switching

In confirmatory cancer clinical trials, overall survival (OS) is normally a primary endpoint in the intention-to-treat (ITT) analysis under regulatory standards. After the tumor progresses, it is common that patients allocated to the control group switch to the experimental treatment, or another drug in the same class. Such treatment switching may dilute the relative efficacy of the new drug compared to the control group, leading to lower statistical power. It would be possible to decrease the estimation bias by shortening the follow-up period but this may lead to a loss of information and power. Instead we propose a modified weighted log-rank test (mWLR) that aims at balancing these factors by down-weighting events occurring when many patients have switched treatment. As the weighting should be pre-specified and the impact of treatment switching is unknown, we predict the hazard ratio function and use it to compute the weights of the mWLR. The method may incorporate information from previous trials regarding the potential hazard ratio function over time. We are motivated by the RECORD-1 trial of everolimus against placebo in patients with metastatic renal-cell carcinoma where almost 80% of the patients in the placebo group received everolimus after disease progression. Extensive simulations show that the new test gives considerably higher efficiency than the standard log-rank test in realistic scenarios.

Semi-parametric accelerated failure-time model: A useful alternative to the proportional-hazards model in cancer clinical trials

The accelerated failure-time (AFT) model has been long recognized as a useful alternative to the proportional-hazards (PH) model. Semi-parametric AFT model has been known since 1981. Its use has been hampered by the difficulty in solving the estimating equations for the model's coefficients. In recent years, however, important developments have taken place regarding the methods of solving the equations. In this article, we briefly review the developments, focusing mainly on rank-based estimation. We conduct a simulation study that directly focuses on the applicability of the model in the context of (cancer) clinical trials. We also investigate the robustness of the AFT model to the omission of covariates. Finally, we conduct a meta-analysis of multiple clinical trials in gastric cancer to illustrate the benefits of the use of the model in practice.

Impact of a non-constant baseline hazard on detection of time-dependent treatment effects: a simulation study

Background

Non-proportional hazards are common with time-to-event data but the majority of randomised clinical trials (RCTs) are designed and analysed using approaches which assume the treatment effect follows proportional hazards (PH). Recent advances in oncology treatments have identified two forms of non-PH of particular importance - a time lag until treatment becomes effective, and an early effect of treatment that ceases after a period of time. In sample size calculations for treatment effects on time-to-event outcomes where information is based on the number of events rather than the number of participants, there is crucial importance in correct specification of the baseline hazard rate amongst other considerations. Under PH, the shape of the baseline hazard has no effect on the resultant power and magnitude of treatment effects using standard analytical approaches. However, in a non-PH context the appropriateness of analytical approaches can depend on the shape of the underlying hazard.

Methods

A simulation study was undertaken to assess the impact of clinically plausible non-constant baseline hazard rates on the power, magnitude and coverage of commonly utilized regression-based measures of treatment effect and tests of survival curve difference for these two forms of non-PH used in RCTs with time-to-event outcomes.

Results

In the presence of even mild departures from PH, the power, average treatment effect size and coverage were adversely affected. Depending on the nature of the non-proportionality, non-constant event rates could further exacerbate or somewhat ameliorate the losses in power, treatment effect magnitude and coverage observed. No single summary measure of treatment effect was able to adequately describe the full extent of a potentially time-limited treatment benefit whilst maintaining power at nominal levels.

Conclusions

Our results show the increased importance of considering plausible potentially non-constant event rates when non-proportionality of treatment effects could be anticipated. In planning clinical trials with the potential for non-PH, even modest departures from an assumed constant baseline hazard could appreciably impact the power to detect treatment effects depending on the nature of the non-PH. Comprehensive analysis plans may be required to accommodate the description of time-dependent treatment effects.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01372-0.

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.

Interaction Between Primary Tumor Resection, Primary Tumor Location, and Survival in Synchronous Metastatic Colorectal Cancer: A Population-Based Study

OBJECTIVES

Location of the primary tumor has prognostic value and predicts the effect of certain therapeutics in synchronous metastatic colorectal cancer. We investigated whether the association between primary tumor resection (PTR) and overall survival (OS) also depends on tumor location.

METHODS

Data on synchronous metastatic colorectal cancer patients from the Netherlands Cancer Registry (n=16,106) and Surveillance, Epidemiology, and End Results (SEER) registry (n=19,584) were extracted. Cox models using time-varying covariates were implemented. Median OS for right-sided colon cancer (RCC), left-sided colon cancer, and rectal cancer was calculated using inverse probability weighting and a landmark point of 6 months after diagnosis as reference.

RESULTS

The association between PTR and OS was dependent on tumor location (P<0.05), with a higher median OS of upfront PTR versus upfront systemic therapy in Netherlands Cancer Registry (NCR) of 1.9 (95% confidence interval: 0.9-2.8), 4.3 (3.3-5.6), and 3.4 (0.6-7.6) months in RCC, left-sided colon cancer and rectal cancer, respectively. In SEER data, the difference was 6.0 (4.0-8.0), 8.0 (5.0-10.0), and 10.0 (7.0-13.0) months, respectively. Hazard plots indicate a higher hazard of death 2 to 3 months after PTR in RCC.

CONCLUSION

Upfront PTR is associated with improved survival regardless of primary tumor location. Patients with RCC appear to have less benefit because of higher mortality during 2 to 3 months after PTR.

KMDATA: a curated database of reconstructed individual patient-level data from 153 oncology clinical trials

We created a database of reconstructed patient-level data from published clinical trials that includes multiple time-to-event outcomes such as overall survival and progression-free survival. Outcomes were extracted from Kaplan–Meier (KM) curves reported in 153 oncology Phase III clinical trial publications identified through a PubMed search of clinical trials in breast, lung, prostate and colorectal cancer, published between 2014 and 2016. For each trial that met our search criteria, we curated study-level information and digitized all reported KM curves with the software Digitizelt. We then used the digitized KM survival curves to estimate (possibly censored) patient-level time-to-event outcomes. Collections of time-to-event datasets from completed trials can be used to support the choice of appropriate trial designs for future clinical studies. Patient-level data allow investigators to tailor clinical trial designs to diseases and classes of treatments. Patient-level data also allow investigators to estimate the operating characteristics (e.g. power and type I error rate) of candidate statistical designs and methods.

Database URL: https://10.6084/m9.figshare.14642247.v1

Clinical challenges in neoadjuvant immunotherapy for non-small cell lung cancer

Immune checkpoint inhibitors (ICIs), a type of immunotherapy, have become one of the most important therapeutic options for first- and second-line treatment of advanced non-small cell lung cancer (NSCLC). Recent clinical studies have shown that immunotherapy can offer substantial survival benefits to patients with early-stage or resectable advanced NSCLC. However, considering the importance of timing when using ICIs and their associated adverse events (AEs), the advantages and disadvantages of using these agents need to be weighed carefully when deciding the use of a combined treatment. In addition, the inconsistency between imaging assessment and pathological results poses further challenges to the evaluation of efficacy of neoadjuvant immunotherapy. It is also important to develop new methodologies and discover suitable biomarkers that can be used to evaluate survival outcomes of immunotherapy and identify patients who would benefit the most from this treatment. In this review, we aimed to summarize previous results of ongoing clinical trials on neoadjuvant immunotherapy for lung cancer and discuss the challenges and future perspectives of this therapeutic approach in the treatment of resectable NSCLC.

Assessment of Simulated SARS-CoV-2 Infection and Mortality Risk Associated With Radiation Therapy Among Patients in 8 Randomized Clinical Trials

IMPORTANCE

During the COVID-19 pandemic, cancer therapy may put patients at risk of SARS-CoV-2 infection and mortality. The impacts of proposed alternatives on reducing infection risk are unknown.

OBJECTIVE

To investigate how the COVID-19 pandemic is associated with the risks and benefits of standard radiation therapy (RT).

DESIGN, SETTING, AND PARTICIPANTS

This comparative effectiveness study used estimated individual patient-level data extracted from published Kaplan-Meier survival figures from 8 randomized clinical trials across oncology from 1993 to 2014 that evaluated the inclusion of RT or compared different RT fractionation regimens. Included trials were Dutch TME and TROG 01.04 examining rectal cancer; CALGB 9343, OCOG hypofractionation trial, FAST-Forward, and NSABP B-39 examining early stage breast cancer, and CHHiP and HYPO-RT-PC examining prostate cancer. Risk of SARS-CoV-2 infection and mortality associated with receipt of RT in the treatment arms were simulated and trials were reanalyzed. Data were analyzed between April 1, 2020, and June 30, 2020.

EXPOSURES

COVID-19 risk associated with treatment was simulated across different pandemic scenarios, varying infection risk per fractions (IRFs) and case fatality rates (CFRs).

MAIN OUTCOMES AND MEASURES

Overall survival was evaluated using Cox proportional hazards modeling under different pandemic scenarios.

RESULTS

Estimated IPLD from a total of 14 170 patients were included in the simulations. In scenarios with low COVID-19-associated risks (IRF, 0.5%; CFR, 5%), fractionation was not significantly associated with outcomes. In locally advanced rectal cancer, short-course RT was associated with better outcomes than long-course chemoradiation (TROG 01.04) and was associated with similar outcomes as RT omission (Dutch TME) in most settings (eg, TROG 01.04 median HR, 0.66 [95% CI, 0.46-0.96]; Dutch TME median HR, 0.91 [95% CI, 0.80-1.03] in a scenario with IRF 5% and CFR 20%). Moderate hypofractionation in early stage breast cancer (OCOG hypofractionation trial) and prostate cancer (CHHiP) was not associated with survival benefits in the setting of COVID-19 (eg, OCOG hypofractionation trial median HR, 0.89 [95% CI, 0.74-1.06]; CHHiP median HR, 0.87 [95% CI, 0.75-1.01] under high-risk scenario with IRF 10% and CFR 30%). More aggressive hypofractionation (FAST-Forward, HYPO-RT-PC) and accelerated partial breast irradiation (NSABP B-39) were associated with improved survival in higher risk scenarios (eg, FAST-Forward median HR, 0.58 [95% CI, 0.49-0.68]; HYPO-RT-PC median HR, 0.60 [95% CI, 0.48-0.75] under scenario with IRF 10% and CFR 30%).

CONCLUSIONS AND RELEVANCE

In this comparative effectiveness study of data from 8 clinical trials of patients receiving radiation therapy to simulate COVID-19 risk and mortality rates, treatment modification was not associated with altered risk from COVID-19 in lower-risk scenarios and was only associated with decreased mortality in very high COVID-19-risk scenarios. This model, which can be adapted to dynamic changes in COVID-19 risk, provides a flexible, quantitative approach to assess the potential impact of treatment modifications and supports the continued delivery of standard evidence-based care with appropriate precautions against COVID-19.

Survival analysis using a 5-step stratified testing and amalgamation routine (5-STAR) in randomized clinical trials

Randomized clinical trials are often designed to assess whether a test treatment prolongs survival relative to a control treatment. Increased patient heterogeneity, while desirable for generalizability of results, can weaken the ability of common statistical approaches to detect treatment differences, potentially hampering the regulatory approval of safe and efficacious therapies. A novel solution to this problem is proposed. A list of baseline covariates that have the potential to be prognostic for survival under either treatment is pre-specified in the analysis plan. At the analysis stage, using all observed survival times but blinded to patient-level treatment assignment, "noise" covariates are removed with elastic net Cox regression. The shortened covariate list is used by a conditional inference tree algorithm to segment the heterogeneous trial population into subpopulations of prognostically homogeneous patients (risk strata). After patient-level treatment unblinding, a treatment comparison is done within each formed risk stratum and stratum-level results are combined for overall statistical inference. The impressive power-boosting performance of our proposed 5-step stratified testing and amalgamation routine (5-STAR), relative to that of the logrank test and other common approaches that do not leverage inherently structured patient heterogeneity, is illustrated using a hypothetical and two real datasets along with simulation results. Furthermore, the importance of reporting stratum-level comparative treatment effects (time ratios from accelerated failure time model fits in conjunction with model averaging and, as needed, hazard ratios from Cox proportional hazard model fits) is highlighted as a potential enabler of personalized medicine. An R package is available at https://github.com/rmarceauwest/fiveSTAR.

Non-proportional hazards in immuno-oncology: Is an old perspective needed?

A fundamental concept in two-arm non-parametric survival analysis is the comparison of observed versus expected numbers of events on one of the treatment arms (the choice of which arm is arbitrary), where the expectation is taken assuming that the true survival curves in the two arms are identical. This concept is at the heart of the counting-process theory that provides a rigorous basis for methods such as the log-rank test. It is natural, therefore, to maintain this perspective when extending the log-rank test to deal with non-proportional hazards, for example, by considering a weighted sum of the "observed - expected" terms, where larger weights are given to time periods where the hazard ratio is expected to favor the experimental treatment. In doing so, however, one may stumble across some rather subtle issues, related to difficulties in the interpretation of hazard ratios, that may lead to strange conclusions. An alternative approach is to view non-parametric survival comparisons as permutation tests. With this perspective, one can easily improve on the efficiency of the log-rank test, while thoroughly controlling the false positive rate. In particular, for the field of immuno-oncology, where researchers often anticipate a delayed treatment effect, sample sizes could be substantially reduced without loss of power.

Clinical Challenges of Immune Checkpoint Inhibitors

Even though the immuno-oncology (IO) era has achieved many successes, some signs of research development deceleration are arising. Recently, the number of FDA immunotherapy approvals has decreased concurrently with a decline in the relative number of patients recruited to these trials. Identifying the unique features of IO treatments and taking them into consideration on clinical research will lead to a better evaluation of these agents and patient outcomes. In this review, we discuss current challenges and new potential approaches to implement rationally designed clinical trials of IO drugs, particularly those targeting immune checkpoints.

Immunotherapy for Early Stage Colorectal Cancer: A Glance into the Future

Immune checkpoint inhibitors (ICI) have reshaped therapeutic strategies for cancer patients. The development of ICI for early stage colorectal cancer is accompanied by specific challenges: (i) the selection of patients who are likely to benefit from these treatments, i.e., patients with tumors harboring predictive factors of efficacy of ICI, such as microsatellite instability and/or mismatch repair deficiency (MSI/dMMR), or other potential parameters (increased T cell infiltration using Immunoscore® or others, high tumor mutational burden, POLE mutation), (ii) the selection of patients at risk of disease recurrence (poor prognostic features), and (iii) the choice of an accurate clinical trial methodological framework. In this review, we will discuss the ins and outs of clinical research of ICI for early stage MSI/dMMR CC patients in adjuvant and neoadjuvant settings. We will then summarize data that might support the development of ICI in localized colorectal cancer beyond MSI/dMMR.

Optimality of testing procedures for survival data in the nonproportional hazards setting

Most statistical tests for treatment effects used in randomized clinical trials with survival outcomes are based on the proportional hazards assumption, which often fails in practice. Data from early exploratory studies may provide evidence of nonproportional hazards, which can guide the choice of alternative tests in the design of practice-changing confirmatory trials. We developed a test to detect treatment effects in a late-stage trial, which accounts for the deviations from proportional hazards suggested by early-stage data. Conditional on early-stage data, among all tests that control the frequentist Type I error rate at a fixed α level, our testing procedure maximizes the Bayesian predictive probability that the study will demonstrate the efficacy of the experimental treatment. Hence, the proposed test provides a useful benchmark for other tests commonly used in the presence of nonproportional hazards, for example, weighted log-rank tests. We illustrate this approach in simulations based on data from a published cancer immunotherapy phase III trial.

A Quantitative Framework for Modeling COVID-19 Risk During Adjuvant Therapy Using Published Randomized Trials of Glioblastoma in the Elderly

BACKGROUND

During the ongoing COVID-19 pandemic, contact with the healthcare system for cancer treatment can increase risk of infection and associated mortality. Treatment recommendations must consider this risk for elderly and vulnerable cancer patients. We re-analyzed trials in elderly glioblastoma (GBM) patients, incorporating COVID-19 risk, in order to provide a quantitative framework for comparing different radiation (RT) fractionation schedules on patient outcomes.

METHODS

We extracted individual patient-level data (IPLD) for 1,321 patients from Kaplan-Meier curves from five randomized trials on treatment of elderly GBM patients including available subanalyses based on MGMT methylation status. We simulated trial data with incorporation of COVID-19 associated mortality risk in several scenarios (low, medium, and high infection and mortality risks). Median overall survival and hazard ratios were calculated for each simulation replicate.

RESULTS

Our simulations reveal how COVID-19-associated risks affect survival under different treatment regimens. Hypofractionated RT with concurrent and adjuvant temozolomide (TMZ) demonstrated the best outcomes in low and medium risk scenarios. In frail elderly patients, shorter courses of RT are preferable. In patients with methylated MGMT receiving single modality treatment, TMZ-alone treatment approaches may be an option in settings with high COVID-19-associated risk.

CONCLUSIONS

Incorporation of COVID-19-associated risk models into analysis of randomized trials can help guide clinical decisions during this pandemic. In elderly GBM patients, our results support prioritization of hypofractionated RT and highlight the utility of MGMT methylation status in decision-making in pandemic scenarios. Our quantitative framework can serve as a model for assessing COVID-19 risk associated with treatment across neuro-oncology.

The Lung Immune Prognostic Index Discriminates Survival Outcomes in Patients with Solid Tumors Treated with Immune Checkpoint Inhibitors

Immune checkpoint inhibitors (ICI) have revolutionized the treatment landscape of several solid tumor types. However, as patient outcomes are heterogeneous, clinical tools to aid in prognostication are needed. The Lung Immune Prognostic Index (LIPI) correlates with outcomes in patients with non-small cell lung cancer (NSCLC) treated with ICI, but its applicability beyond NSCLC is poorly defined. We sought to determine whether LIPI is associated with overall survival (OS), progression-free survival (PFS) and objective response rate (ORR) in a pooled, real-world, retrospective cohort of patients with solid tumors treated with ICI. Of the total pooled cohort (N = 578), 47.2%, 38.2% and 14.5% of patients were stratified into good, intermediate and poor LIPI group, respectively. Median OS were 22.8 (95% CI 17.4-29.5), 7.8 (95% CI 6.6-9.6), and 2.5 months (95% CI 1.4-3.4) (p < 0.0001). Median PFS were 9.9 (95% CI 7.2-11.5), 3.6 (95% CI 2.7-4.3), and 1.4 months (95% CI 1.2-2.2) (p < 0.0001). ORR was also associated with LIPI group (p < 0.001). Intermediate and poor LIPI were independently prognostic of OS compared to good LIPI, with hazard ratios (HR) of 1.8 (95% CI 1.4-2.3, p < 0.001) and 3.6 (95% CI 2.5-5.1, p < 0.001), respectively. These data are the first to suggest that in a real-world setting, the prognostic value of LIPI may be tumor agnostic.