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Dimethylation of histone H3 lysine 9 (H3K9me2), a crucial modification for heterochromatin formation and transcriptional silencing, is essential for proper meiotic prophase progression in mammals. We analyzed meiotic defects and generated genome-wide profiles of H3K9me2 and transcriptomes for the mutants of H3K9 demethylases. Moreover, we also identified proteins interacting with H3K9 demethylases. H3K9me2 is usually found at transposable elements and repetitive sequences but is absent from the bodies of protein-coding genes. In this study, we show that the Arabidopsis thaliana H3K9 demethylases IBM1 and JMJ27 cooperatively regulate crossover formation and chromosome segregation. They protect thousands of protein-coding genes from ectopic H3K9me2, including genes essential for meiotic prophase progression. In addition to removing H3K9me2, IBM1 and JMJ27 interact with the Precocious Dissociation of Sisters 5 (PDS5) cohesin complex cofactors. The pds5 mutant shared similar transcriptional alterations with ibm1 jmj27, including meiosis-essential genes, yet without affecting H3K9me2 levels. Hence, PDS5s, together with IBM1 and JMJ27, regulate male meiosis and gene expression independently of H3K9 demethylation. These findings uncover a novel role of H3K9me2 removal in meiosis and a new function of H3K9 demethylases and cohesin cofactors in meiotic transcriptional regulation.

At the core of wireless devices, systems, networks, and ecosystems are their antennas and antenna arrays. To achieve the many stunning and amazing 5G and 6G promises, significant advances in antenna and antenna array technologies must be accomplished. Desirable integrated space and terrestrial network features pose a number of significant and interesting challenges for future 6G antennas and antenna arrays. There are several ways to form multiple beams from an array. Major schemes can be categorized into digital, analog, and crossover strategies. This chapter describes digital beamforming and a major crossover of much recent excitement, hybrid beamforming. High‐gain antenna arrays are even more necessary for anticipated 6G operations. Other important related THz technologies that must be developed to address 6G expectations are high power sources and highly sensitive receivers. The chapter discusses the concept of single input and multiple output and multiple input and multiple output multi‐beam antennas.

Gene‐treatment interactions, just like drug‐drug interactions, can have dramatic effects on a patient response and therefore influence the clinician decision at the patient’s bedside. Crossover designs, although they are known to decrease the number of subjects in drug‐interaction studies, are seldom used in pharmacogenetic studies. We propose to evaluate, via realistic clinical trial simulations, to what extent crossover designs can help quantifying the gene‐treatment interaction effect. We explored different scenarios of crossover and parallel design studies comparing two symptom‐modifying treatments in a chronic and stable disease accounting for the impact of a one gene and one gene‐treatment interaction. We varied the number of subjects, the between and within subject variabilities, the gene polymorphism frequency and the effect sizes of the treatment, gene, and gene‐treatment interaction. Each simulated dataset was analyzed using three models: (i) estimating only the treatment effect, (ii) estimating the treatment and the gene effects, and (iii) estimating the treatment, the gene, and the gene‐treatment interaction effects. We showed how ignoring the gene‐treatment interaction results in the wrong treatment effect estimates. We also highlighted how crossover studies are more powerful to detect a treatment effect in the presence of a gene‐treatment interaction and more often lead to correct treatment attribution.

The 2 × 2 crossover trial uses subjects as their own control to reduce the intersubject variability in the treatment comparison, and typically requires fewer subjects than a parallel design. The generalized estimating equations (GEE) methodology has been commonly used to analyze incomplete discrete outcomes from crossover trials. We propose a unified approach to the power and sample size determination for the Wald Z-test and t-test from GEE analysis of paired binary, ordinal and count outcomes in crossover trials. The proposed method allows misspecification of the variance and correlation of the outcomes, missing outcomes, and adjustment for the period effect. We demonstrate that misspecification of the working variance and correlation functions leads to no or minimal efficiency loss in GEE analysis of paired outcomes. In general, GEE requires the assumption of missing completely at random. For bivariate binary outcomes, we show by simulation that the GEE estimate is asymptotically unbiased or only minimally biased, and the proposed sample size method is suitable under missing at random (MAR) if the working correlation is correctly specified. The performance of the proposed method is illustrated with several numerical examples. Adaption of the method to other paired outcomes is discussed.

Coupling electrochemical CO2 reduction (CO2R) with a renewable energy source to create high‐value fuels and chemicals is a promising strategy in moving toward a sustainable global energy economy. CO2R liquid products, such as formate, acetate, ethanol, and propanol, offer high volumetric energy density and are more easily stored and transported than their gaseous counterparts. However, a significant amount (~30%) of liquid products from electrochemical CO2R in a flow cell reactor cross the ion exchange membrane, leading to the substantial loss of system‐level Faradaic efficiency. This severe crossover of the liquid product has—until now—received limited attention. Here, we review promising methods to suppress liquid product crossover, including the use of bipolar membranes, solid‐state electrolytes, and cation‐exchange membranes‐based acidic CO2R systems. We then outline the remaining challenges and future prospects for the production of concentrated liquid products from CO2.

The present preregistered meta-analysis aimed to assess the evidence regarding the work-family interactions, as dyadic studies report them. The introduction differentiated between spillover (or intra-individual) effects and crossover (or inter-individual) effects by using dyadic studies. Following an online and reference list search, out of the initial 339 studies, 36 eligible articles reported correlations between one partner's variables and the other partner's variables. Analyses included 1504 effect sizes and used meta-analytical calculations and structural equation modeling. Both partners' well-being measures had small proportions of shared variance (i.e. shared variance up to 13.69%). The following analyses focused on the potential effects that could explain the shared variance of family-related well-being. There was little evidence of a crossover effect from one's work-related variables toward the partners' family-related well-being. Furthermore, analyses using structural equation modeling did not yield any results to support a crossover effect from one's work-family interaction toward their partner's well-being. These findings suggest that the literature needs new research studies regarding how family-related demands and resources are related to well-being and personal resources in the crossover processes.

Objective Sacral neuromodulation (SNM) has been used to treat patients with lower urinary tract dysfunction and bowel dysfunction for many years. Success rates vary between 50% and 80%, indicating that there is much room for improvement. Altering stimulation parameters may result in improved outcome. This paper reports a systematic review of the clinical efficacy of nonconventional stimulation parameters on urinary tract and bowel dysfunction. Materials and Methods Three databases were used for the literature search: Ovid (Medline, Embase) and PubMed. Papers were screened by two independent reviewers, who also extracted data from these papers. Clinical papers studying SNM stimulation parameters, that is, intermittent stimulation, frequency, pulse width, and amplitude, in urinary tract and bowel dysfunction were included. Quality of included papers was assessed using standardized guidelines. Results Out of 5659 screened papers, 17 papers, studying various stimulation parameters, were included. Overall quality of these papers differed greatly, as some showed no risk of bias, whereas others showed high risk of bias. Stimulation parameters included intermittent stimulation, frequency, pulse width, amplitude, and unilateral vs. bilateral stimulation. Especially high frequency SNM and either a narrow or wide pulse width seem to improve efficacy in patients with bowel dysfunction. Additionally, implementation of short cycling intervals is promising to improve quality of life for patients with urinary tract or bowel dysfunction. Conclusion The results of our systematic review indicate that stimulation parameters may improve efficacy of SNM in treatment of both urinary tract dysfunction and bowel dysfunction.

Sustainable operation of energy-restrained wireless network services requires multiple objectives to be satisfied synchronously. Among these objectives, reduced spectrum outage, energy conservation, and minimal packet transmission failures considerably affect the energy harvesting operation of these networks. These three objectives are associated with disparate protocol layers incorporating the transport, medium access control, and physical layers of traditional networking architecture. The authors investigate energy harvesting wireless communications by formulating the multi-objective optimisation problem comprising these global networking criteria, which are simultaneously optimised with the heuristic design procedure. For this, they employ a Pareto-based evolutionary genetic algorithm technique built in the wireless network design and operation to find the optimal set of all non-dominated solutions traversing the entire design search space. Besides, iterative implementation of the presented genetic optimisation model with distinct feasible integrations of crossover and mutation operations is performed to evaluate the proficiency of the proposed scheme for evaluating the Pareto-optimal frontier set. The influence of different combinations of these operations is examined and adaptively applied with appropriate genetic parameters tuning for efficient meta-heuristic search through the candidate solution space. Simulation results demonstrate that the proposed hybrid genetic mechanism outperforms the existing methods in terms of throughput, energy efficiency, and loss rate.

This article addresses the analysis of crossover designs with nonignorable dropout. We study nonreplicated crossover designs and replicated designs separately. With a primary objective of comparing the treatment mean effects, we jointly model the longitudinal measures and discrete time to dropout. We propose shared-parameter models and mixed-effects selection models. We adapt a linear-mixed effects model as the conditional model for the longitudinal outcomes. We invoke a discrete-time hazards model with a complementary log-log link function for the conditional distribution of time to dropout. We apply maximum likelihood for parameter estimation. We perform simulation studies to investigate the robustness of our proposed approaches under various missing data mechanisms. We then apply the approaches to two examples with a continuous outcome and one example with a binary outcome using existing software. We also implement the controlled multiple imputation methods as a sensitivity analysis of the missing data assumption.

This is the peer reviewed version of the following article: Molins, E. [et al.]. An iterative method to protect the type I error rate in bioequivalence studies under two-stage adaptive 2×2 crossover designs. "Biometrical journal", 2021, vol. 63, núm. 1, p. 122-133., which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/bimj.201900388. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Bioequivalence studies are the pivotal clinical trials submitted to regulatory agen- cies to support the marketing applications of generic drug products. Average bioequivalence (ABE) is used to determine whether the mean values for the pharmacokinetic measures determined after administration of the test and refer- ence products are comparable. Two-stage 2×2 crossover adaptive designs (TSDs) are becoming increasingly popular because they allow making assumptions on the clinically meaningful treatment effect and a reliable guess for the unknown within-subject variability. At an interim look, if ABE is not declared with an ini- tial sample size, they allow to increase it depending on the estimated variability and to enroll additional subjects at a second stage, or to stop for futility in case of poor likelihood of bioequivalence. This is crucial because both parameters must clearly be prespecified in protocols, and the strategy agreed with regulatory agen- cies in advance with emphasis on controlling the overall type I error. We present an iterative method to adjust the significance levels at each stage which preserves the overall type I error for a wide set of scenarios which should include the true unknown variability value. Simulations showed adjusted signif- icance levels higher than 0.0300 in most cases with type I error always below 5%, and with a power of at least 80%. TSDs work particularly well for coefficients of variation below 0.3 which are especially useful due to the balance between the power and the percentage of studies proceeding to stage 2. Our approach might support discussions with regulatory agencies. This research is partially supported by the grant MTM2015-64465-C2-1-R (MINECO/FEDER) from the Ministerio de Economia y Competitividad (Spain) and by the grant 2014 SGR 464 from the Generalitat de Catalunya.

Semantic image segmentation treats the issues involved in the object recognition and image segmentation as a combined task. The chief notion of semantic segmentation is to partition the image into visually uniform regions and to discriminate the class of the partitioned regions. Pixel classification is done over the segmented regions by assigning semantic labels. In general, inference frameworks are fed with the combination of low‐level features and high‐level contextual cues to segment an image. Since these combinations are rarely object consistent, result with minimum classification accuracy because of choosing non‐influencing features and cues to track specific objects. To overcome this problem, a nature‐inspired meta‐heuristic optimization algorithm called Seed Picking Crossover Optimization (SPCO) is proposed to optimize i.e. train the CRF (Conditional Random Field) for choosing relevant feature to segment the object with high accuracy. To meritoriously recognize the objects, a semi‐segmentation process is initially performed using Simple Linear Iterative Clustering (SLIC) algorithm. For pixel transformation and pixel association, Dirichlet process mixture model and CRF are employed. Optimized CRFs are used where the parametric optimization is done using the proposed SPCO algorithm. The proposed work results with 84% on classification accuracy and the performance evaluations are done using MSRC‐21 dataset.

Meiotic recombination rates vary considerably between species, populations and individuals. The genetic exchange between homologous chromosomes plays a major role in evolution by breaking linkage between advantageous and deleterious alleles in the case of introgressions. Identifying recombination rate modifiers is thus of both fundamental and practical interest to understand and utilize variation in meiotic recombination rates. We investigated recombination rate variation in a large intraspecific hybrid population (named HEB-25) derived from a cross between domesticated barley and 25 wild barley accessions. We observed quantitative variation in total crossover number with a maximum of a 1.4-fold difference between subpopulations and increased recombination rates across pericentromeric regions. The meiosis-specific α-kleisin cohesin subunit REC8 was identified as a candidate gene influencing crossover number and patterning. Furthermore, we quantified wild barley introgression patterns and revealed how local and genome-wide recombination rate variation shapes patterns of introgression. The identification of allelic variation in REC8 in combination with the observed changes in crossover patterning suggest a difference in how chromatin loops are tethered to the chromosome axis, resulting in reduced crossover suppression across pericentromeric regions. Local and genome-wide recombination rate variation is shaping patterns of introgressions and thereby directly influences the consequences of linkage drag.

The purpose of this study was to explore the use of immersive and interactive virtual reality (VR) for analgesia, anxiety reduction, and overall satisfaction in patients undergoing outpatient postoperative debridements.Randomized crossover-controlled trial.Academic outpatient clinic.Adult patients who had functional endoscopic sinus surgery and skull base surgery and were undergoing office-based postoperative nasal endoscopy and debridement were recruited and followed for 2 consecutive office visits. Participants were randomized to receive either the control or experimental analgesia for the first postoperative visit (PO1) and crossed over into the opposite treatment arm during the second postoperative visit (PO2). Outcomes included procedural pain, anxiety and satisfaction scores, procedural time, and reflexive head movements per minute (RHM).Eighty-two participants were recruited. At PO1, 39 received standard analgesia, and 43 received an immersive VR experience. At PO1, the VR group experienced significantly less anxiety (VR technology holds promise as a nonpharmacologic analgesic and anxiolytic intervention for otolaryngology office-based procedures. Further study of VR use in other procedures is warranted.1, randomized controlled trial.

In controlled trials, "treatment switching" occurs when patients in one treatment group switch to alternative treatments during the trial, and poses challenges to treatment effect evaluation owing to crossover of the treatments groups. In this work, we assume that treatment switching can occur after some disease progression event and view the progression and death events as two semicompeting risks. The proposed model consists of a copula model for the joint distribution of time-to-progression (TTP) and overall survival (OS) up to the earlier of the two events, as well as a conditional hazard model for OS subsequent to progression. The copula model facilitates assessing the marginal distributions of TTP and OS separately from the association between the two events, and, in particular, the treatment effect on OS in the absence of treatment switching. The proposed conditional hazard model for death subsequent to progression allows us to assess the treatment switching (crossover) effect on OS given occurrence of progression and covariates. Semiparametric proportional hazards models are employed in the marginal models for TTP and OS. A nonparametric maximum likelihood procedure is developed for model inference, which is verified through asymptotic theory and simulation studies. The proposed analysis is applied to a lung cancer dataset to illustrate its real utility.

Recombination's omnipresence in nature is one of the most intriguing problems in evolutionary biology. The question of why recombination exhibits certain general features is no less interesting than that of why it exists at all. One such feature is recombination's fitness dependence (FD). The so far developed population genetics models have focused on the evolution of FD recombination mainly in haploids, although the empirical evidence for this phenomenon comes mostly from diploids. Using numerical analysis of modifier models for infinite panmictic populations, we show here that FD recombination can be evolutionarily advantageous in diploids subjected to purifying selection. We ascribe this advantage to the differential rate of disruption of lower‐ versus higher‐fitness genotypes, which can be manifested in selected systems with at least three loci. We also show that if the modifier is linked to such selected system, it can additionally benefit from modifying this linkage in a fitness‐dependent manner. The revealed evolutionary advantage of FD recombination appeared robust to crossover interference within the selected system, either positive or negative. Remarkably, FD recombination was often favored in situations where any constant nonzero recombination was evolutionarily disfavored, implying a relaxation of the rather strict constraints on major parameters (e.g., selection intensity and epistasis) required for the evolutionary advantage of nonzero recombination formulated by classical models., An amazing feature of recombination is fitness dependence (FD). The so far developed models have focused on the evolution of FD recombination mainly in haploids, although the empirical evidence for this phenomenon comes mostly from diploids. Using numerical analysis of modifier models for infinite panmictic populations, we show here that FD recombination can be evolutionarily advantageous in diploids subjected to purifying selection. We ascribe this advantage to the differential rate of disruption of lower‐ versus higher‐fitness genotypes, which can be manifested in selected systems with at least three loci.

Biosimilars of low molecular weight heparins (LMWHs) are more alike the originator than different branded LMWHs. The latter differ largely in molecular weight, anti-FXa/anti-FIIa ratio and antithrombin binding. The Food and Drug Administration and European Medicines Agency guidelines are sufficient for the clinical use of high quality LMWHs. However, the Food and Drug Administration guideline lacks the results of a phase I clinical trial in the approval process. Most information about biosimilars is available for enoxaparin given that many biosimilars of enoxaparin have received market access. The guidelines of many International Thrombosis Societies for LMWH biosimilars are too stringent, not updated and impractical for formulary uptake discussions. This review gives background information on critical factors for the formulary uptake process of LMWHs with special attention for the use of the System of Objectified Judgment Analysis/Infomatrix model.

Meiotic crossovers facilitate chromosome segregation and create new combinations of alleles in gametes. Crossover frequency varies along chromosomes and crossover interference limits the coincidence of closely spaced crossovers. Crossovers can be measured by observing the inheritance of linked transgenes expressing different colors of fluorescent protein in Arabidopsis pollen tetrads. Here we establish DeepTetrad, a deep learning-based image recognition package for pollen tetrad analysis that enables high-throughput measurements of crossover frequency and interference in individual plants. DeepTetrad will accelerate the genetic dissection of mechanisms that control meiotic recombination.

Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective therapy for selected Parkinson's disease patients with motor fluctuations, but can adversely affect speech and axial symptoms. The use of short pulse width (PW) has been shown to expand the therapeutic window acutely, but its utility in reducing side effects in chronic STN-DBS patients has not been evaluated.To compare the effect of short PW settings using 30-μs with conventional 60-μs settings on stimulation-induced dysarthria in Parkinson's disease patients with previously implanted STN-DBS systems.In this single-center, double-blind, randomized crossover trial, we assigned 16 Parkinson's disease patients who had been on STN-DBS for a mean of 6.5 years and exhibited moderate dysarthria to 30-μs or 60-μs settings for 4 weeks followed by the alternative PW setting for a further 4 weeks. The primary outcome was difference in dysarthric speech measured by the Sentence Intelligibility Test between study baseline and the 2 PW conditions. Secondary outcomes included motor, nonmotor, and quality of life measures.There was no difference in the Sentence Intelligibility Test scores between baseline and the 2 treatment conditions (P = 0.25). There were also no differences noted in motor, nonmotor, or quality of life scores. The 30-μs settings were well tolerated, and adverse event rates were similar to those at conventional PW settings. Post hoc analysis indicated that patients with dysarthria and a shorter duration of DBS may be improved by short PW stimulation.Short PW settings using 30 μs did not alter dysarthric speech in chronic STN-DBS patients. A future study should evaluate whether patients with shorter duration of DBS may be helped by short PW settings. © 2019 International Parkinson and Movement Disorder Society.

The article deals with the analysis of the fractional COVID‐19 epidemic model (FCEM) with a convex incidence rate. Keeping in view the fading memory and crossover behavior found in many biological phenomena, we study the coronavirus disease by using the noninteger Caputo derivative (CD). Under the Caputo operator (CO), existence and uniqueness for the solutions of the FCEM have been analyzed using fixed point theorems. We study all the basic properties and results including local and global stability. We show the global stability of disease‐free equilibrium using the method of Castillo‐Chavez, while for disease endemic, we use the method of geometrical approach. Sensitivity analysis is carried out to highlight the most sensitive parameters corresponding to basic reproduction number. Simulations are performed via first‐order convergent numerical technique to determine how changes in parameters affect the dynamical behavior of the system.

A requirement for calculating sample sizes for cluster randomized trials (CRTs) conducted over multiple periods of time is the specification of a form for the correlation between outcomes of subjects within the same cluster, encoded via the within-cluster correlation structure. Previously proposed within-cluster correlation structures have made strong assumptions; for example, the usual assumption is that correlations between the outcomes of all pairs of subjects are identical ("uniform correlation"). More recently, structures that allow for a decay in correlation between pairs of outcomes measured in different periods have been suggested. However, these structures are overly simple in settings with continuous recruitment and measurement. We propose a more realistic "continuous-time correlation decay" structure whereby correlations between subjects' outcomes decay as the time between these subjects' measurement times increases. We investigate the use of this structure on trial planning in the context of a primary care diabetes trial, where there is evidence of decaying correlation between pairs of patients' outcomes over time. In particular, for a range of different trial designs, we derive the variance of the treatment effect estimator under continuous-time correlation decay and compare this to the variance obtained under uniform correlation. For stepped wedge and cluster randomized crossover designs, incorrectly assuming uniform correlation will underestimate the required sample size under most trial configurations likely to occur in practice. Planning of CRTs requires consideration of the most appropriate within-cluster correlation structure to obtain a suitable sample size.

We propose a molecular simulation method using genetic algorithm (GA) for biomolecular systems to obtain ensemble averages efficiently. In this method, we incorporate the genetic crossover, which is one of the operations of GA, to any simulation method such as conventional molecular dynamics (MD), Monte Carlo, and other simulation methods. The genetic crossover proposes candidate conformations by exchanging parts of conformations of a target molecule between a pair of conformations during the simulation. If the candidate conformations are accepted, the simulation resumes from the accepted ones. While conventional simulations are based on local update of conformations, the genetic crossover introduces global update of conformations. As an example of the present approach, we incorporated genetic crossover to MD simulations. We tested the validity of the method by calculating ensemble averages and the sampling efficiency by using two kinds of peptides, ALA3 and (AAQAA)3 . The results show that for ALA3 system, the distribution probabilities of backbone dihedral angles are in good agreement with those of the conventional MD and replica-exchange MD simulations. In the case of (AAQAA)3 system, our method showed lower structural correlation of α-helix structures than the other two methods and more flexibility in the backbone ψ angles than the conventional MD simulation. These results suggest that our method gives more efficient conformational sampling than conventional simulation methods based on local update of conformations. © 2018 Wiley Periodicals, Inc.

The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel cluster randomized design, which often involves a limited number of clusters. In recent years, the cluster randomized crossover design has been increasingly used to evaluate the effectiveness of health care policy or programs, and the interest often lies in quantifying the population-averaged intervention effect. In this paper, we consider the two-treatment two-period crossover design, and develop sample size procedures for continuous and binary outcomes corresponding to a population-averaged model estimated by generalized estimating equations, accounting for both within-period and interperiod correlations. In particular, we show that the required sample size depends on the correlation parameters through an eigenvalue of the within-cluster correlation matrix for continuous outcomes and through two distinct eigenvalues of the correlation matrix for binary outcomes. We demonstrate that the empirical power corresponds well with the predicted power by the proposed formulae for as few as eight clusters, when outcomes are analyzed using the matrix-adjusted estimating equations for the correlation parameters concurrently with a suitable bias-corrected sandwich variance estimator.

This paper proposes an approach to design and monitor survival trials accounting for complex scenarios such as delayed treatment effect, treatment dilution, and treatment crossover. These scenarios often lead to non-proportional hazards, making study design and monitoring more difficult. We demonstrate that, with event times following piecewise exponential distributions, the log-rank statistic as well as its variance-covariance structure can be easily computed, which greatly simplifies study design and monitoring. As the number of pieces in the exponential distributions can be arbitrary, this approach can handle a wide range of scenarios. Three hypothetical examples are used to demonstrate its potential use.

The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these parameters at the design stage means that there is often a risk a trial may be under‐ or overpowered. For many study designs, this problem has been addressed by considering adaptive design methodology that allows for the re‐estimation of the required sample size during a trial. Here, we propose and compare several approaches for this in multitreatment crossover trials. Specifically, regulators favor reestimation procedures to maintain the blinding of the treatment allocations. We therefore develop blinded estimators for the within and between person variances, following simple or block randomization. We demonstrate that, provided an equal number of patients are allocated to sequences that are balanced for period, the proposed estimators following block randomization are unbiased. We further provide a formula for the bias of the estimators following simple randomization. The performance of these procedures, along with that of an unblinded approach, is then examined utilizing three motivating examples, including one based on a recently completed four‐treatment four‐period crossover trial. Simulation results show that the performance of the proposed blinded procedures is in many cases similar to that of the unblinded approach, and thus they are an attractive alternative.