Treatment-Switching Adjustment of Overall Survival in CheckMate 227 Part 1 Evaluating First-Line Nivolumab Plus Ipilimumab Versus Chemotherapy for Metastatic Nonsmall Cell Lung Cancer.

OBJECTIVES: CheckMate 227 (NCT02477826) evaluated first-line nivolumab-plus-ipilimumab versus chemotherapy in patients with metastatic nonsmall cell lung cancer (NSCLC) with programmed death ligand 1 (PD-L1) expression ≥ 1% or < 1% and no EGFR/ALK alterations. However, many patients randomized to chemotherapy received subsequent immunotherapy. Here, overall survival (OS) and relative OS benefit of nivolumab-plus-ipilimumab were adjusted for potential bias introduced by treatment switching. MATERIALS AND METHODS: Treatment-switching adjustment analyses were conducted following the NICE Decision Support Unit Technical Support Document 16, for CheckMate 227 Part 1 OS data from treated patients (database lock, July 2, 2019). Inverse probability of censoring weighting (IPCW) was used in the base-case analysis; other methods were explored as sensitivity analyses. RESULTS: Of 1166 randomized patients, 391 (PD-L1 ≥ 1%) and 185 (PD-L1 < 1%) patients received nivolumab-plus-ipilimumab; 387 (PD-L1 ≥ 1%) and 183 (PD-L1 < 1%) patients received chemotherapy, with 29.3-month minimum follow-up. Among chemotherapy-treated patients, 169/387 (43.7%; PD-L1 ≥ 1%) and 66/183 (36.1%; PD-L1 < 1%) switched to immunotherapy poststudy. Among treated patients, median OS was 17.4 months with nivolumab-plus-ipilimumab versus 14.9 months with chemotherapy (hazard ratio [HR], 0.80; 95% confidence interval [CI], 0.68-0.95) in the PD-L1 ≥ 1% subgroup and 17.1 versus 12.4 months (HR, 0.62; 95% CI, 0.49-0.80) in the PD-L1 < 1% subgroup. After treatment-switching adjustment using IPCW, the HR (95% CI) for OS for nivolumab-plus-ipilimumab versus chemotherapy was reduced to 0.68 (0.56-0.83; PD-L1 ≥ 1%) and 0.53 (0.40-0.69; PD-L1 < 1%). Sensitivity analyses supported the robustness of the results. CONCLUSION: Treatment-switching adjustments resulted in a greater estimated relative OS benefit with first-line nivolumab-plus-ipilimumab versus chemotherapy in patients with metastatic NSCLC.

Estimating individualized treatment rules by optimizing the adjusted probability of a longer survival.

Individualized treatment rules inform tailored treatment decisions based on the patient's information, where the goal is to optimize clinical benefit for the population. When the clinical outcome of interest is survival time, most of current approaches typically aim to maximize the expected time of survival. We propose a new criterion for constructing Individualized treatment rules that optimize the clinical benefit with survival outcomes, termed as the adjusted probability of a longer survival. This objective captures the likelihood of living longer with being on treatment, compared to the alternative, which provides an alternative and often straightforward interpretation to communicate with clinicians and patients. We view it as an alternative to the survival analysis standard of the hazard ratio and the increasingly used restricted mean survival time. We develop a new method to construct the optimal Individualized treatment rule by maximizing a nonparametric estimator of the adjusted probability of a longer survival for a decision rule. Simulation studies demonstrate the reliability of the proposed method across a range of different scenarios. We further perform data analysis using data collected from a randomized Phase III clinical trial (SWOG S0819).

Transporting randomized trial results to estimate counterfactual survival functions in target populations.

When the distributions of treatment effect modifiers differ between a randomized trial and an external target population, the sample average treatment effect in the trial may be substantially different from the target population average treatment, and accurate estimation of the latter requires adjusting for the differential distribution of effect modifiers. Despite the increasingly rich literature on transportability, little attention has been devoted to methods for transporting trial results to estimate counterfactual survival functions in target populations, when the primary outcome is time to event and subject to right censoring. In this article, we study inverse probability weighting and doubly robust estimators to estimate counterfactual survival functions and the target average survival treatment effect in the target population, and provide their respective approximate variance estimators. We focus on a common scenario where the target population information is observed only through a complex survey, and elucidate how the survey weights can be incorporated into each estimator we considered. Simulation studies are conducted to examine the finite-sample performances of the proposed estimators in terms of bias, efficiency and coverage, under both correct and incorrect model specifications. Finally, we apply the proposed method to assess transportability of the results in the Action to Control Cardiovascular Risk in Diabetes-Blood Pressure (ACCORD-BP) trial to all adults with Diabetes in the United States.

Comparison of baseline covariate adjustment methods for restricted mean survival time.

The restricted mean survival time provides a straightforward clinical measure that dispenses with the need for proportional hazards assumptions. We focus on two strategies to directly model the survival time and adjust covariates. Firstly, pseudo-survival time is calculated for each subject using a leave-one-out approach, followed by a model analysis that adjusts for covariates using all pseudo-values. This method is used to reflect information of censored subjects in the model analysis. The second approach adjusts for covariates for those subjects with observed time-to-event while incorporating censored subjects using inverse probability of censoring weighting (IPCW). This paper evaluates these methods' power to detect group differences through computer simulations. We find the interpretation of pseudo-values challenging with the pseudo-survival time method and confirm that pseudo-survival times deviate from actual data in a primary biliary cholangitis clinical trial, mainly due to extensive censoring. Simulations reveal that the IPCW method is more robust, unaffected by the balance of censors, whereas pseudo-survival time is influenced by this balance. The IPCW method retains a nominal significance level for the type-1 error rate, even amidst group differences concerning censor incidence rates and covariates. Our study concludes that IPCW and pseudo-survival time methods differ significantly in handling censored data, impacting parameter estimations. Our findings suggest that the IPCW method provides more robust results than pseudo-survival time and is recommended, even when censor probabilities vary between treatment groups. However, pseudo-survival time remains a suitable choice when censoring probabilities are balanced.

Dealing with time-dependent exposures and confounding when defining and estimating attributable fractions-Revisiting estimands and estimators.

The population-attributable fraction (PAF) is commonly interpreted as the proportion of events that can be ascribed to a certain exposure in a certain population. Its estimation is sensitive to common forms of time-dependent bias in the face of a time-dependent exposure. Predominant estimation approaches based on multistate modeling fail to fully eliminate such bias and, as a result, do not permit a causal interpretation, even in the absence of confounding. While recently proposed multistate modeling approaches can successfully eliminate residual time-dependent bias, and moreover succeed to adjust for time-dependent confounding by means of inverse probability of censoring weighting, inadequate application, and misinterpretation prevails in the medical literature. In this paper, we therefore revisit recent work on previously proposed PAF estimands and estimators in settings with time-dependent exposures and competing events and extend this work in several ways. First, we critically revisit the interpretation and applied terminology of these estimands. Second, we further formalize the assumptions under which a causally interpretable PAF estimand can be identified and provide analogous weighting-based representations of the identifying functionals of other proposed estimands. This representation aims to enhance the applied statistician's understanding of different sources of bias that may arise when the aim is to obtain a valid estimate of a causally interpretable PAF. To illustrate and compare these representations, we present a real-life application to observational data from the Ghent University Hospital ICUs to estimate the fraction of ICU deaths attributable to hospital-acquired infections.