Search

Background Recent evidence suggests an overestimation of the benefits associated with arteriovenous (AV) fistula versus graft in certain populations. We assessed hazards of all-cause and cause-specific hospitalization and death associated with AV access type in patients who started hemodialysis with a catheter in France, overall and by subgroups of age, sex and comorbidities. Methods We performed a target trial emulation including patients who initiated hemodialysis with a catheter from 2010 through 2018 and were followed by the REIN Registry. We identified first-created fistula or graft through the French national health-administrative database. We used joint frailty models to deal with recurrent hospitalizations and potential informative censoring by death, and inverse probability weighting to account for confounding. Results From the 18 800 patients included (mean age 68 ± 15 years, 35% women), 5% underwent AV graft creation first. The weighted hazard ratio (wHR) of all-cause hospitalization associated with graft was 1.08 [95% confidence interval (CI) 1.02 to 1.15], that of vascular access–related hospitalization was 1.43 (95% CI 1.32 to 1.55), and those of cardiovascular- and infection-related hospitalizations were 1.14 (95% CI 1.03 to 1.26) and 1.11 (95% CI 0.97 to 1.28), respectively. Results were consistent for most subgroups, except that the highest hazard of all-cause, cardiovascular- and infection-related hospitalizations with graft was blunted in patients with comorbidities (i.e. diabetes, wHR 1.01, 95% CI 0.93 to 1.10; 1.10, 95% CI 0.96 to 1.26; and 0.94, 95% CI 0.78 to 1.12, respectively). Conclusions In patients starting hemodialysis with a catheter, AV graft creation is associated with increased hazard of vascular access–related hospitalizations compared with fistula. This may not be the case for death or other causes of hospitalization. [ABSTRACT FROM AUTHOR]

Background While opportunistic infections are a frequent and challenging problem in kidney transplant recipients, their long-term epidemiology remains hardly known. Methods Opportunistic infections were recorded in 1144 recipients transplanted in our center between 2004 and 2015. Incidence rates and baseline risk factors were determined using joint frailty models. Results After a median follow-up of 5.6 years, 544 opportunistic infections occurred in 373/1144 (33%) patients, dominated by viral infections (396/544, 72%), especially cytomegalovirus (CMV) syndromes and diseases (213/544, 39%). One-third of the infected patients experienced at least two opportunistic infections. The incidence of opportunistic infections was 10 times higher during the first year post-transplantation than after that (34.7 infections for 100 patient-years vs 3.64). Opportunistic infections associated with the age of the donor (P  = .032), the age of the recipient (P  = .049), the CMV serostatus (P  < 10−6), a higher class II HLA mismatch (P  = .032) and an induction treatment including rabbit anti-thymocyte globulins (P  = .026). Repeated opportunistic infections associated with each other (P  < 10−6) and with renal death (P  < 10−6). Conclusion Opportunistic infections occur with a two-period incidence pattern and many susceptible patients suffer from repeated episodes. This knowledge may help tailor new prevention and follow-up strategies to reduce the burden of opportunistic infections and their impact on transplantation outcome. [ABSTRACT FROM AUTHOR]

With the ongoing development of treatments and the resulting increase in survival in oncology, clinical trials based on endpoints such as overall survival may require long follow-up periods to observe sufficient events and ensure adequate statistical power. This increase in follow-up time may compromise the feasibility of the study. The use of surrogate endpoints instead of final endpoints may be attractive for these studies. However, before a surrogate can be used in a clinical trial, it must be statistically validated. In this article, we propose an approach to validate surrogates when both the surrogate and final endpoints are censored event times. This approach is developed for meta-analytic data and uses a mediation analysis to decompose the total effect of the treatment on the final endpoint as a direct effect and an indirect effect through the surrogate. The meta-analytic nature of the data is accounted for in a joint model with random effects at the trial level. The proportion of the indirect effect over the total effect of the treatment on the final endpoint can be computed from the parameters of the model and used as a measure of surrogacy. We applied this method to investigate time-to-relapse as a surrogate endpoint for overall survival in resectable gastric cancer. [ABSTRACT FROM AUTHOR]

The sum of the longest diameter (SLD) of the target lesions is a longitudinal biomarker used to assess tumor response in cancer clinical trials, which can inform about early treatment effect. This biomarker is semicontinuous, often characterized by an excess of zeros and right skewness. Conditional two‐part joint models were introduced to account for the excess of zeros in the longitudinal biomarker distribution and link it to a time‐to‐event outcome. A limitation of the conditional two‐part model is that it only provides an effect of covariates, such as treatment, on the conditional mean of positive biomarker values, and not an overall effect on the biomarker, which is often of clinical relevance. As an alternative, we propose in this article, a marginalized two‐part joint model (M‐TPJM) for the repeated measurements of the SLD and a terminal event, where the covariates affect the overall mean of the biomarker. Our simulation studies assessed the good performance of the marginalized model in terms of estimation and coverage rates. Our application of the M‐TPJM to a randomized clinical trial of advanced head and neck cancer shows that the combination of panitumumab in addition with chemotherapy increases the odds of observing a disappearance of all target lesions compared to chemotherapy alone, leading to a possible indirect effect of the combined treatment on time to death. [ABSTRACT FROM AUTHOR]

A key issue when designing clinical trials is the estimation of the number of subjects required. Assuming for multicenter trials or biomarker‐stratified designs that the effect size between treatment arms is the same among the whole study population might be inappropriate. Limited work is available for properly determining the sample size for such trials. However, we need to account for both, the heterogeneity of the baseline hazards over clusters or strata but also the heterogeneity of the treatment effects, otherwise sample size estimates might be biased. Most existing methods account for either heterogeneous baseline hazards or treatment effects but they dot not allow to simultaneously account for both sources of variations. This article proposes an approach to calculate sample size formula for clustered or stratified survival data relying on frailty models. Both theoretical derivations and simulation results show the proposed approach can guarantee the desired power in worst case scenarios and is often much more efficient than existing approaches. Application to a real clinical trial designs is also illustrated. [ABSTRACT FROM AUTHOR]