One-way layouts, i.e., a single factor with several levels and multiple observations at each level, frequently arise in various fields. Usually not only a global hypothesis is of interest but also multiple comparisons between the different treatment levels. In most practical situations, the distribution of observed data is unknown and there may exist a number of atypical measurements and outliers. Hence, use of parametric and semiparametric procedures that impose restrictive distributional assumptions on observed samples becomes questionable. This, in turn, emphasizes the demand on statistical procedures that enable us to accurately and reliably analyze one-way layouts with minimal conditions on available data. Nonparametric methods offer such a possibility and thus become of particular practical importance. In this article, we introduce a new R package nparcomp which provides an easy and user-friendly access to rank-based methods for the analysis of unbalanced one-way layouts. It provides procedures performing multiple comparisons and computing simultaneous confidence intervals for the estimated effects which can be easily visualized. The special case of two samples, the nonparametric Behrens-Fisher problem, is included. We illustrate the implemented procedures by examples from biology and medicine.

The importance of subgroup analyses has been increasing due to a growing interest in personalized medicine and targeted therapies. Considering designs with multiple nested subgroups and a continuous endpoint, we develop methods for the analysis and sample size determination. First, we consider the joint distribution of standardized test statistics that correspond to each (sub)population. We derive multivariate exact distributions where possible, providing approximations otherwise. Based on these results, we present sample size calculation procedures. Uncertainties about nuisance parameters which are needed for sample size calculations make the study prone to misspecifications. We discuss how a sample size review can be performed in order to make the study more robust. To this end, we implement an internal pilot study design where the variances and prevalences of the subgroups are reestimated in a blinded fashion and the sample size is recalculated accordingly. Simulations show that the procedures presented here do not inflate the type I error significantly and maintain the prespecified power as long as the sample size of the smallest subgroup is not too small. We pay special attention to the case of small sample sizes and attain a lower boundary for the size of the internal pilot study.

Adaptive enrichment designs offer an efficient and flexible way to demonstrate the efficacy of a treatment in a clinically defined full population or in, eg, biomarker-defined subpopulations while controlling the family-wise Type I error rate in the strong sense. Frequently used testing strategies in designs with two or more stages include the combination test and the conditional error function approach. Here, we focus on the latter and present some extensions.In contrast to previous work, we allow for multiple subgroups rather than one subgroup only. For nested as well as nonoverlapping subgroups with normally distributed endpoints, we explore the effect of estimating the variances in the subpopulations. Instead of using a normal approximation, we derive new t-distribution-based methods for two different scenarios. First, in the case of equal variances across the subpopulations, we present exact results using a multivariate t-distribution. Second, in the case of potentially varying variances across subgroups, we provide some improved approximations compared to the normal approximation. The performance of the proposed conditional error function approaches is assessed and compared to the combination test in a simulation study. The proposed methods are motivated by an example in pulmonary arterial hypertension. KEYWORDSadaptive design, enrichment, interim analysis, multiple testing, subgroup analysis Statistics in Medicine. 2019;38:3105-3122. wileyonlinelibrary.com/journal/sim

We present new inference methods for the analysis of low- and high-dimensional repeated measures data from two-sample designs that may be unbalanced, the number of repeated measures per subject may be larger than the number of subjects, covariance matrices are not assumed to be spherical, and they can differ between the two samples. In comparison, we demonstrate how crucial it is for the popular Huynh-Feldt (HF) method to make the restrictive and often unrealistic or unjustifiable assumption of equal covariance matrices. The new method is shown to maintain desired α-levels better than the well-known HF correction, as demonstrated in several simulation studies. The proposed test gains power when the number of repeated measures is increased in a manner that is consistent with the alternative. Thus, even increasing the number of measurements on the same subject may lead to an increase in power. Application of the new method is illustrated in detail, using two different real data sets. In one of them, the number of repeated measures per subject is smaller than the sample size, while in the other one, it is larger.

BackgroundRenal injury is a serious complication after cardiac surgery and therefore, early detection and much more prediction of postoperative kidney injury is desirable. Neutrophil gelatinase-associated lipocalin (NGAL) is a predictive biomarker of acute kidney injury and may increase after cardiopulmonary bypass (CPB). However, time correlation of NGAL expression and severity of renal injury is still unclear. The aim of our study was to investigate CPB-related urine NGAL (uNGAL) secretion in correlation to postoperative renal function.MethodsData of NGAL expression along with clinical data of 81 patients (52 male and 29 female) were included in this study. Mean age of the patients was 66.8 ± 12.8 years. Urine NGAL was measured at seven time points (T0: baseline; T1: start CPB, T2: 40 min on CPB; T3: 80 min on CPB; T4: 120 min on CPB; Tp1: 15 min after CPB; Tp2: 4 h after admission to the intensive care unit) and renal function in the postoperative period was classified daily according to Acute Kidney Injury Network (Ronco et al, Int J Artif Organs 30(5): 373–6) criteria (AKIN).ResultsExpression of uNGAL increased at T4 (120 min on CPB) and post-CPB (Tp1 and Tp2; p < 0.01 vs. baseline) but there was no correlation between uNGAL level and duration of CPB nor between uNGAL expression and occurrence of postoperative kidney injury. The renal function over 10 days after surgery remained normal in 50 patients (AKIN level 0), 18 patients (22%) developed mild and insignificant renal injury (AKIN level 1), eight patients (10%) developed moderate renal failure (AKIN level 2), and five patients (6%) severe kidney failure (AKIN level 3). Twenty-four out of 31 patients developed renal failure within the first 48 h after surgery. However, there was no correlation between uNGAL expression and severity of acute renal failure.ConclusionAlthough uNGAL expression increased after CPB, the peak values neither predict acute postoperative kidney injury, nor severity of the injury.