Introduction to Crossover Designs

What is/are Crossover Designs?

Crossover Designs - In this article min–max crossover designs for binary and Poisson crossover trials with two treatments are proposed. ^[1] In addition, we compare the performance of blinded and unblinded crossover designs in estimating long-term vaccine efficacy. ^[2] Both experiments use sham- and polarity-controlled, randomised, double-blind, crossover designs. ^[3] Case-crossover designs have become widespread in biomedical investigations of transient associations. ^[4] The Bayesian method is desirable in the analysis of crossover designs to eliminate errors associated with carryover effects. ^[5] Although the 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria is already underway as a parallel trial, applying the simulation parameters to a crossover design yielded improved power, suggesting that crossover designs may be valuable in settings where the number of available clusters is limited. ^[6] Based on these data and our experimental results, we determined that sample sizes commonly used in pain studies using CSD estimations are suitable to detect medium and large effect sizes in crossover designs and only large effects in parallel designs. ^[7] The linear trajectories of these surrogates make it possible to identify an acceleration in healing resulting from an intervention, and allows self-controlled or crossover designs with attendant advantages of statistical power and speed. ^[8] Interrupted time series (ITS) designs borrow from case-crossover designs and serve as quasi-experimental methodology able to retrospectively assess the impact of an intervention while accounting for temporal correlation. ^[9] Crossover designs involve two types of treatment effects, a direct effect and a carryover effect, and several optimality results are available for inferring on these two effects separately. ^[10] Although the linear mixed-effects model is one of the commonly used methods for crossover designs, sometimes it suffers from convergence problems. ^[11] Results The parallel and crossover designs (with analysis of change from baseline to the off treatment value) maintained the target type 1 error rate in all scenarios. ^[12] The optimal number of distinct treatments in a sequence is around 2k for crossover designs and k/0. ^[13] In the analysis plan, consideration must be given to comparing the characteristics of the subjects, taking account of differences in these characteristics, intention-to-treat analysis, interim analyses and stopping rules, mortality comparisons, composite outcomes, research design including run-in periods, factorial, stratified and crossover designs, number needed to treat, power issues, multivariate modeling, subgroup analysis, competing risks, and hypothesis-generating analyses. ^[14] Both studies are random, single‐center, 2‐period, open‐label, 2‐way crossover designs. ^[15] Interrupted time series (ITS) designs borrow from case-crossover designs and serve as quasi-experimental methodology able to retrospectively assess the impact of an intervention while accounting for temporal correlation. ^[16] Crossover designs, although they are known to decrease the number of subjects in drug‐interaction studies, are seldom used in pharmacogenetic studies. ^[17] All included studies used either randomized control trial or crossover designs with a sham control group. ^[18] Meanwhile, in crossover designs, both sparse sampling AUC estimation and the correlation between treatments should be determined. ^[19] Replicated crossover designs are critical when an IBE approach is used to allow separate estimation of within-subject variances for test and reference products and the subject-by-formulation interaction variance component. ^[20] Objective To assess the methodological advantages and disadvantages of parallel and crossover designs in randomised clinical trials on methylphenidate for children and adolescents with attention deficit hyperactivity disorder (ADHD). ^[21] 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. ^[22] Study 1 was a prospective cohort study conducted during the common cold season whereas studies 2 and 3 used repeated-measures crossover designs. ^[23] Parallel group design and 4-period crossover designs with sequential or interleaving cohorts were evaluated. ^[24] The discussion covers various issues including the advantages and disadvantages of repeated dose designs and within-group drug testing, including incremental dosing schedules, and crossover designs. ^[25] We used an eye tracking task to investigate the effects of anticipation of gain (experiment 1) or loss (experiment 2) of alcohol and chocolate on attentional bias for alcohol and chocolate pictures using full crossover designs; the effects of uncertain outcomes were investigated in both experiments. ^[26] BackgroundCrossover designs are commonly utilised in randomised controlled trials investigating treatments for long-term chronic illnesses. ^[27] ABSTRACT The paper describes nonparametric approaches for comparing three-period, two-treatment, four-sequence crossover designs through testing the hypothesis that the treatments are interchangeable. ^[28] When planning a clinical trial including a search for candidate biomarker, the frequency of the candidate biomarker helps design the sample size, and the intra‐subject correlation of the outcome should be taken into account for choosing between parallel‐group and crossover designs. ^[29] All included studies used either a randomized control trial or crossover designs with a sham control group. ^[30] Introduction : Crossover designs have applications in a wide range of sciences. ^[31] Context Previous studies have raised concerns about the analysis and meta-analysis of crossover experiments and we were aware of several families of experiments that used crossover designs and meta-analysis. ^[32] ABSTRACT The additional benefits in the analysis of crossover designs with two active treatments and a placebo motivated us to study these kinds of designs. ^[33] METHODS The power functions of the Fieller-type confidence interval and the asymptotic confidence interval in crossover designs with serial-sampling data are here derived. ^[34] All studies had open‐label, randomized, 2‐period, 2‐sequence, single‐dose crossover designs. ^[35] Interventions used both parallel and crossover designs and varied from single product substitutions to fully controlled diets with WG exposures of 3-12 weeks. ^[36] However, crossover designs have advantages because each participant works as his/her own control so that more power is obtained with the same number of participants when compared to parallel group studies [6]. ^[37] Results: Aggregated N-of-1 trials outperformed both traditional parallel RCT and crossover designs when these trial designs were simulated in terms of power and required sample size to obtain a given power. ^[38] In this consensus statement, we (1) briefly review the literature on exercise response variability and the various sources of variations in CRF response to an exercise programme, (2) introduce the key research designs and corresponding statistical models with an emphasis on randomised controlled designs with or without multiple pretests and post-tests, crossover designs and repeated measures designs, (3) discuss advantages and disadvantages of multiple methods of categorising exercise response levels—a topic that is of particular interest for personalised exercise medicine and (4) outline approaches that may identify determinants and modifiers of CRF exercise response. ^[39] The CONSORT 2010 checklist is revised for crossover designs, and introduces a modified flowchart and baseline table to enhance transparency. ^[40] In the paper ‘Eliminating systematic bias from case-crossover designs’, Wang et al. ^[41]