CheckMate 9ER was the pivotal study that established the cabozantinib-nivolumab combination, often abbreviated CaboNivo, as a first-line option for patients with advanced renal cell carcinoma, a disease that arises in the kidneys and, once metastatic, carries a historically poor prognosis. The original trial demonstrated a significant overall survival benefit for the immunotherapy-targeted therapy duo compared with sunitinib, a tyrosine kinase inhibitor that had long served as a benchmark treatment. Patients who received CaboNivo lived longer, and the finding helped reshape treatment guidelines around the world. But the trial’s overall survival results, like those of many modern oncology studies, carried a hidden complication that statisticians have increasingly sought to address.

That complication is subsequent therapy. In advanced renal cell carcinoma, patients whose initial treatment stops working almost always move on to further lines of treatment, and the sequence of therapies a patient receives can profoundly influence how long they ultimately live. When patients in the control arm of a trial later cross over to newer, more effective treatments, or when imbalances arise in the quality and intensity of post-progression care between the two arms, the measured overall survival benefit of the experimental regimen can be diluted or distorted. The observed survival difference then reflects not only the effect of the initial treatment but also a tangled web of downstream decisions made after the trial’s assigned therapy ended.

To untangle this web, the research team, led by Marc-Oliver Grimm of Jena University Hospital in Germany together with colleagues from industry and health economics consultancies and the University of Bari in Italy, turned to two-stage estimation, a counterfactual modeling approach designed to isolate the effect of the initial randomized treatment from the effects of everything that follows. The method proceeds in two distinct analytical steps, each addressing a different piece of the puzzle. It is a technique that has gained traction in oncology health technology assessment precisely because it offers a way to model what survival would have been in a hypothetical world where subsequent therapy exerted no influence.

In the first stage, the investigators used multivariate modeling to estimate how much subsequent therapy extended overall survival among patients who discontinued their original assigned study treatment. This step required careful characterization of the patients who moved on to later lines of therapy, including the types of treatments they received and the clinical characteristics that predicted both the receipt of subsequent therapy and the survival benefit it conferred. By quantifying this extension, the model could then estimate what those patients’ survival would have been in the absence of the additional treatments, constructing the counterfactual scenario that lies at the heart of the analysis.

In the second stage, the researchers computed an adjusted hazard ratio for overall survival comparing CaboNivo with sunitinib, now freed from the confounding effects of subsequent treatment. The hazard ratio is the workhorse statistic of survival analysis, expressing the relative risk of death over time between two groups, and an adjusted value below one indicates a survival advantage for the experimental arm. By feeding the stage-one counterfactual estimates into this calculation, the team produced a picture of the true relative efficacy of the two first-line regimens, unclouded by the downstream therapeutic landscape.

The results were revealing. Under the two-stage estimation, the adjusted counterfactual median overall survival was 42.7 months for patients treated with cabozantinib plus nivolumab, compared with just 21.3 months for those treated with sunitinib, a difference of more than twenty-one months. The corresponding adjusted hazard ratio was 0.53, with a 95 percent confidence interval of 0.43 to 0.65 and a p value below 0.001, indicating that the combination cut the hazard of death nearly in half in this counterfactual scenario. By contrast, the unadjusted estimates told a more modest story: median overall survival of 46.5 months for CaboNivo versus 35.5 months for sunitinib, with a hazard ratio of 0.79 and a p value of 0.0196.

The comparison between the adjusted and unadjusted figures is where the analytical insight becomes vivid. Counterintuitively, the counterfactual median survival for the CaboNivo arm was actually lower than the observed median, 42.7 months versus 46.5 months, which reflects the removal of the survival extension that subsequent therapies contributed to patients in that arm. Yet the counterfactual median for the sunitinib arm fell even more sharply, from 35.5 months to 21.3 months, indicating that patients in the control arm derived a larger relative boost from the therapies they received after discontinuing their assigned treatment. When those downstream effects were removed from both arms, the underlying advantage of the initial CaboNivo regimen emerged as considerably larger than the raw trial comparison had shown.

For clinicians, the finding offers reassurance that the survival benefit of first-line cabozantinib plus nivolumab is not an artifact of favorable downstream care patterns but a genuine property of the regimen itself. For patients with advanced renal cell carcinoma, a disease in which treatment sequences have grown increasingly complex with the arrival of immune checkpoint inhibitors and multiple targeted agents, the implication is that starting with the combination may confer a deeper and more durable survival advantage than conventional analysis suggests. The authors conclude that removing the effect of subsequent therapy increased the estimated overall survival benefit of CaboNivo versus sunitinib, reinforcing the combination as a first-line standard of care in advanced renal cell carcinoma.

The analysis also carries broader methodological significance for the field of oncology evidence generation. Two-stage estimation is increasingly relevant to health technology assessment bodies, which must judge the value of new cancer therapies using trial data that are inevitably contaminated by subsequent treatment effects, and the approach offers a structured, transparent way to model counterfactual scenarios that no randomized trial can directly observe. As a post hoc analysis, however, the study relies on modeling assumptions rather than prospective randomization to the counterfactual condition, and its findings should be interpreted alongside the original trial results rather than as a replacement for them. The CheckMate 9ER trial is registered as NCT03141177, and the original research underlying this summary was published in Oncologic Therapy in November 2025, with the summary article appearing in Advances in Therapy in October 2026 under open access, funded by Ipsen, which was involved in the design, execution, analysis, and writing of the summary.

Subject of Research: Two-stage estimation of overall survival adjusting for subsequent therapy in the phase 3 CheckMate 9ER trial of cabozantinib plus nivolumab versus sunitinib in advanced renal cell carcinoma

Article Title: Summary of Research: Two-Stage Estimation of Overall Survival in the Phase 3 CheckMate 9ER Trial, Adjusting for the Impact of Subsequent Therapy

Article References: Grimm, M.-O., Karim, E., Kapetanakis, V., Lothgren, M., Ogareva, A., Shukla, P., Truscott, J., & Porta, C. (2026). Summary of Research: Two-Stage Estimation of Overall Survival in the Phase 3 CheckMate 9ER Trial, Adjusting for the Impact of Subsequent Therapy. Advances in Therapy. https://doi.org/10.1007/s12325-026-03773-3

Image Credits: AI Generated

DOI: 10.1007/s12325-026-03773-3

Keywords: advanced renal cell carcinoma, cabozantinib, nivolumab, sunitinib, CheckMate 9ER, overall survival, two-stage estimation, subsequent therapy, hazard ratio, phase 3 trial, immunotherapy, post hoc analysis