The need to tailor the omission of axillary lymph node dissection to patients with good prognosis and sentinel node micro-metastases.

BACKGROUND: Results of IBCSG-23-01-trial which included breast cancer patients with involved sentinel nodes (SN) by isolated-tumor-cells or micro-metastases supported the non-inferiority of completion axillary-lymph-node-dissection (cALND) omission. However, current data are considered insufficient to avoid cALND for all patients with SN-micro-metastases. METHODS: To investigate the impact of cALND omission on disease-free-survival (DFS) and overall survival (OS), we analyzed a cohort of 1421 patients <75 years old with SN-micro-metastases who underwent breast conservative surgery (BCS). We used inverse probability of treatment weighting (IPTW) to obtain adjusted Kaplan-Meier estimators representing the experience in the analysis cohort, based on whether all or none had been subject to cALND omission. RESULTS: Weighted log-rank tests comparing adjusted Kaplan-Meier survival curves showed significant differences in OS (p-value = 0.002) and borderline significant differences in DFS (p-value = 0.090) between cALND omission versus cALND. Cox's regression using stabilized IPTW evidenced an average increase in the risk of death associated with cALND omission (HR = 2.77, CI95% = 1.36-5.66). Subgroup analyses suggest that the rates of recurrence and death associated with cALND omission increase substantially after a large period of time in the half sample of women less likely to miss cALND. CONCLUSIONS: Using IPTW to estimate the causal treatment effect of cALND in a large retrospective cohort, we concluded cALND omission is associated with an increased risk of recurrence and death in women of <75 years old treated by BCS in the absence of a large consensus in favor of omitting cALND. These results are particularly contributive for patients treated by BCS where cALND omission rates increase over time.

Adjusting for covariates in analysis based on restricted mean survival times.

We summarize extensions to the analysis of restricted mean survival time (RMST) in the context of time-to-event outcomes. The RMST estimate and its inference are based on the classical Kaplan-Meier curves. When covariate effects are considered, an adjusted RMST (ARMST) estimate can be derived analogously based on adjusted Kaplan-Meier curves. The adjusted Kaplan-Meier Estimator (AKME) was developed to reduce confounding by the method of inverse probability of treatment weighting. We will show how the ARMST method combines the concepts of the RMST and AKME to make inferences. Two regression based methods to adjust for potential covariate effect on the RMST estimates will be compared with the ARMST approach. Simulation studies are performed to compare the different methods with and without covariate adjustments. In addition, we will summarize the extension of RMST and ARMST to the setting with competing risks. The restricted mean time lost (RMTL) and adjusted RMTL (ARMTL) are estimates of interest from cumulative incidence curves. A phase 3 oncology clinical trial example is provided to demonstrate the applications of these methods.

Comparisons of the performance of different statistical tests for time-to-event analysis with confounding factors: practical illustrations in kidney transplantation.

Confounding factors are commonly encountered in observational studies. Several confounder-adjusted tests to compare survival between differently exposed subjects were proposed. However, only few studies have compared their performances regarding type I error rates, and no study exists evaluating their type II error rates. In this paper, we performed a comparative simulation study based on two different applications in kidney transplantation research. Our results showed that the propensity score-based inverse probability weighting (IPW) log-rank test proposed by Xie and Liu (2005) can be recommended as a first descriptive approach as it provides adjusted survival curves and has acceptable type I and II error rates. Even better performance was observed for the Wald test of the parameter corresponding to the exposure variable in a multivariable-adjusted Cox model. This last result is of primary interest regarding the exponentially increasing use of propensity score-based methods in the literature.