The EKiTE network (epidemiology in kidney transplantation - a European validated database): an initiative epidemiological and translational European collaborative research.

BACKGROUND: Kidney transplantation is considered to be the treatment of choice for people with end-stage renal disease (ESRD). However, due to the shortage of available organs and the increase in the ESRD prevalence in Europe, it is essential to improve transplantation outcomes by studying the related prognostic factors. Today, there is no European registry collecting data to perform such clinical epidemiology studies. MAIN BODY: Entitled EKiTE, for European cohort for Kidney Transplantation Epidemiology, this prospective and multicentric cohort includes patients from Spanish (Barcelona), Belgian (Leuven), Norwegian (Oslo) and French (Paris Necker, Lyon, Nantes, Nancy, Montpellier, Nice and Paris Saint Louis) transplantation centers and currently contains 13,394 adult recipients of kidney (only) transplantation from 2005 and updated annually. A large set of parameters collected from transplantation until graft failure or death with numbers of post-transplantation outcomes. The long-term follow-up and the collected data enable a wide range of possible survival and longitudinal analyses. CONCLUSION: EKiTE is a multicentric cohort aiming to better assess the natural history of the ESRD in European kidney transplant recipients and perform benchmarking of clinical practices. The data are available for clinical epidemiology studies and open for external investigators upon request to the scientific council. Short-term perspectives are to extend EKITE network to other European countries and collect additional parameters in respect of the common thesaurus.

Sequential trials in the context of competing risks: Concepts and case study, with R and SAS code.

Sequential designs and competing risks methodology are both well established. Their combined use has recently received some attention from a theoretical perspective, but their joint application in practice has been discussed less. The aim of this paper is to provide the applied statistician with a basic understanding of both sequential design theory and competing risks methodology and how to combine them in practice. Relevant references to more detailed theoretical discussions are provided, and all discussions are illustrated using a real case study. Extensive R and SAS code is provided in the online Supplementary Material.

Factors of selection and failure of ambulatory incisional hernia repair: A cohort study of 1429 patients.

BACKGROUND: Ambulatory surgery for incisional hernia repair (IHR) is not a widespread practice and is mainly performed for small incisional hernias. AIM: To assess outpatient IHR practice in France and to identify predictive factors of selection and failure of ambulatory procedures. METHOD: Surgeons of the French "Club Hernie" prospectively gathered data concerning IHR over a period of almost 5years within a nationwide database. RESULTS: A number of patients (1429) were operated on during the period of the study. The mean age was 63.3 (22-97) years old. An ambulatory procedure was planned in 305 (21%) patients. Among these, 272 (89%) IHR were effectively performed as one-day procedures. Upon multivariate analysis, predictive factors influencing practitioners for not propose an ambulatory care were increasing age (OR 0.97, P<0.001), body mass index (OR 0.95, P<0.001), ASA grade≥III (OR 0.23, P<0.001), hernia width≥4cm (OR 0.44, P<0.001), recurrent hernia (OR 0.55, P=0.01) and a laparoscopic IHR (OR 0.54, P<0.001). A number of patients (1157) were not selected preoperatively for outpatient IHR mainly because of organizational issues or an ASA grade≥III. Medical or social reasons were the main causes of failure of initially planned ambulatory settings. CONCLUSION: Ambulatory IHR is a safe and feasible practice subject to a good preoperative selection of the patients. Increasing age, body mass index, ASA grade≥III, hernia width≥4cm, recurrent hernia and a laparoscopic IHR were identified to be preoperative factors for not proposing an ambulatory care. One-day surgery for IHR could be systematically proposed for IHR of small incisional hernias (<4cm) in young patients with few comorbidities.

Outpatient groin hernia repair: assessment of 9330 patients from the French "Club Hernie" database.

BACKGROUND: Groin hernia repair (GHR) is one of the most frequent surgical interventions practiced worldwide. Outpatient surgery for GHR is known to be safe and effective. AIM: To assess the outpatient practice for GHR in France and identify predictive factors of failure. METHOD: Forty one surgeons of the French "Club Hernie" prospectively gathered data concerning successive GHR over a period of 4 years within a multicenter database. RESULTS: A total of 9330 patients were operated on during the period of the study. Mean age was 61.8 (1-100) years old and 8245 patients (88.4%) were males. 6974 GHR (74.7%) were performed as outpatient procedures. In 262 patients (3.6%), the outpatient setting, previously selected, did not succeed. Upon multivariate analysis, predictive factors of ambulatory failure were ASA grade ≥ III (OR 0.42, p < 0.001), bilateral GHR (OR 0.47, p < 0.001), emergency surgery for incarcerated hernia (OR 0.10, p < 0.001), spinal anesthesia (OR 0.27, p < 0.001) and occurrence of an early post-operative complication (OR 0.07, p < 0.001). The more frequent complications were acute urinary retention and surgical site collections. 2094 patients (21.5%) were not selected preoperatively for 1-day surgery. CONCLUSION: More than 74% of the patients benefited from outpatient surgery for GHR with a poor failure rate. Predictive factors of outpatient GHR failure were ASA grade ≥ III, bilateral GHR, emergency surgery for incarcerated hernia, spinal anesthesia and occurrence of an early post-operative complication. Ambulatory failures were often related to social issues or medical complications. Outpatient surgery criteria could become less restrictive in the future.

Confidence bounds for nonlinear dose-response relationships.

An important aim of drug trials is to characterize the dose-response relationship of a new compound. Such a relationship can often be described by a parametric (nonlinear) function that is monotone in dose. If such a model is fitted, it is useful to know the uncertainty of the fitted curve. It is well known that Wald confidence intervals are based on linear approximations and are often unsatisfactory in nonlinear models. Apart from incorrect coverage rates, they can be unreasonable in the sense that the lower confidence limit of the difference to placebo can be negative, even when an overall test shows a significant positive effect. Bootstrap confidence intervals solve many of the problems of the Wald confidence intervals but are computationally intensive and prone to undercoverage for small sample sizes. In this work, we propose a profile likelihood approach to compute confidence intervals for the dose-response curve. These confidence bounds have better coverage than Wald intervals and are more precise and generally faster than bootstrap methods. Moreover, if monotonicity is assumed, the profile likelihood approach takes this automatically into account. The approach is illustrated using a public dataset and simulations based on the Emax and sigmoid Emax models.

Testing effect of a drug using multiple nested models for the dose-response.

During development of a drug, typically the choice of dose is based on a Phase II dose-finding trial, where selected doses are included with placebo. Two common statistical dose-finding methods to analyze such trials are separate comparisons of each dose to placebo (using a multiple comparison procedure) or a model-based strategy (where a dose-response model is fitted to all data). The first approach works best when patients are concentrated on few doses, but cannot conclude on doses not tested. Model-based methods allow for interpolation between doses, but the validity depends on the correctness of the assumed dose-response model. Bretz et al. (2005, Biometrics 61, 738-748) suggested a combined approach, which selects one or more suitable models from a set of candidate models using a multiple comparison procedure. The method initially requires a priori estimates of any non-linear parameters of the candidate models, such that there is still a degree of model misspecification possible and one can only evaluate one or a few special cases of a general model. We propose an alternative multiple testing procedure, which evaluates a candidate set of plausible dose-response models against each other to select one final model. The method does not require any a priori parameter estimates and controls the Type I error rate of selecting a too complex model.