Hope for Motherhood: Pregnancy After Allogeneic Hematopoietic Cell Transplantation - a National Multicenter Study.

Improved long-term survival rates after allogeneic hematopoietic cell transplantation (alloHCT) make family planning for young adult cancer survivors an important topic. However, treatment-related infertility risk poses challenges. To assess pregnancy and birth rates in a contemporary cohort, we conducted a national multicenter study using data from the German Transplant Registry, focusing on adult women aged 18-40 who underwent alloHCT between 2003 and 2018. Out of 2,654 transplanted women, 50 women experienced 74 pregnancies, occurring at a median of 4.7 years post-transplant. Fifty-seven of these resulted in live births (77%). The annual first birth rate among HCT recipients was 0.45% (95%CI: 0.31 - 0.59%), which is more than six times lower than in the general population. The probability of a live birth 10 years after HCT was 3.4 % (95%CI: 2.3- 4.5%). Factors associated with an increased likelihood of pregnancy were younger age at alloHCT, non-malignant transplant indications, no total-body-irradiation (TBI) or a cumulative dose of <8 Gray, and non-myeloablative/reduced-intensity conditioning. 72% of pregnancies occurred spontaneously, with assisted reproductive technologies (ART) used in the remaining cases. Preterm delivery and low birth weight were more common than in the general population. This study represents the largest dataset reporting pregnancies in a cohort of adult female alloHCT recipients. Our findings underscore a meaningful chance of pregnancy in alloHCT recipients. ART techniques are important and funding should be made available. However, the potential for spontaneous pregnancies should not be underestimated, and patients should be informed of the possibility of unexpected pregnancy despite reduced fertility. Further research is warranted to understand the impact of conditioning decisions on fertility preservation.

Comparison of nonparametric estimators of the expected number of recurrent events.

We compare the performance of nonparametric estimators for the mean number of recurrent events and provide a systematic overview for different recurrent event settings. The mean number of recurrent events is an easily interpreted marginal feature often used for treatment comparisons in clinical trials. Incomplete observations, dependencies between successive events, terminating events acting as competing risk, or gaps between at risk periods complicate the estimation. We use survival multistate models to represent different complex recurrent event situations, profiting from recent advances in nonparametric estimation for non-Markov multistate models, and explain several estimators by using multistate intensity processes, including the common Nelson-Aalen-type estimators with and without competing mortality. In addition to building on estimation of state occupation probabilities in non-Markov models, we consider a simple extension of the Nelson-Aalen estimator by allowing for dependence on the number of prior recurrent events. We pay particular attention to the assumptions required for the censoring mechanism, one issue being that some settings require the censoring process to be entirely unrelated while others allow for state-dependent or event-driven censoring. We conducted extensive simulation studies to compare the estimators in various complex situations with recurrent events. Our practical example deals with recurrent chronic obstructive pulmonary disease exacerbations in a clinical study, which will also be used to illustrate two-sample-inference using resampling.

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): summary of findings and assessment of existing guidelines.

BACKGROUND: The SAVVY project aims to improve the analyses of adverse events (AEs) in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events (CEs). This paper summarizes key features and conclusions from the various SAVVY papers. METHODS: Summarizing several papers reporting theoretical investigations using simulations and an empirical study including randomized clinical trials from several sponsor organizations, biases from ignoring varying follow-up times or CEs are investigated. The bias of commonly used estimators of the absolute (incidence proportion and one minus Kaplan-Meier) and relative (risk and hazard ratio) AE risk is quantified. Furthermore, we provide a cursory assessment of how pertinent guidelines for the analysis of safety data deal with the features of varying follow-up time and CEs. RESULTS: SAVVY finds that for both, avoiding bias and categorization of evidence with respect to treatment effect on AE risk into categories, the choice of the estimator is key and more important than features of the underlying data such as percentage of censoring, CEs, amount of follow-up, or value of the gold-standard. CONCLUSIONS: The choice of the estimator of the cumulative AE probability and the definition of CEs are crucial. Whenever varying follow-up times and/or CEs are present in the assessment of AEs, SAVVY recommends using the Aalen-Johansen estimator (AJE) with an appropriate definition of CEs to quantify AE risk. There is an urgent need to improve pertinent clinical trial reporting guidelines for reporting AEs so that incidence proportions or one minus Kaplan-Meier estimators are finally replaced by the AJE with appropriate definition of CEs.