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In clinical trials, crossover design is widely used to assess treatment effects of drugs. Due to many practical issues, each patient in the study may receive only a subset of treatments under comparison, which is called an incomplete block crossover design. Correspondingly, the associated challenges are limited information and small sample size. In this article, we propose a Bayesian approach to analyze the incomplete block crossover design. Markov chain sampling method is used to analyze the model. We use several approaches such as data augmentation, scaled mixture of normals representation, parameter expansion to improve efficiency. The approach is illustrated using a simulation study and a real data example. [ABSTRACT FROM AUTHOR]

Stopping for futility is a useful tool in a clinical trial. It is widely used in single-arm trials in oncology and in many two-arm trials. We review three stopping rules for futility. We give recommendations for the optimal timing of futility looks in two-stage trials in terms of the information fraction and the probability of stopping under the alternative hypothesis. We discuss futility stopping in trials with substantial uncertainty about the variability of the outcome and in crossover trials. [ABSTRACT FROM AUTHOR]

When patient responses are dichotomous under an AB/BA design, we commonly assume the normal random effects logistic regression model. This normality assumption for random effects is, however, unlikely to hold. Based on the maximum likelihood estimator, we apply Monte Carlo simulations to investigate the impact of misspecification on hypothesis testing and estimation because of the incorrect normal assumption for random effects. We find that Type I error is not affected by misspecifying the normal random effects distributions. We further find that the influence due to misspecifying distributions of random effects on power, bias and mean-squared-error, the coverage probability and the average length of the confidence interval is generally minimal when the variation of responses between patients is small and the number of patients per group is large. This influence can be substantial when the variation of responses between patients is large and the number of patients per group is small. Thus, the estimate of the required sample size or the accuracy of an interval estimator under the normal random effects assumption can be liberal. We use the data comparing a drug with placebo in treating cerebrovascular deficiency to illustrate the potential difference in inference between various random effects distributions considered here. [ABSTRACT FROM AUTHOR]

Using Prescott’s model-free approach, we develop an asymptotic procedure and an exact procedure for testing equality between treatments with binary responses under an incomplete block crossover design. We employ Monte Carlo simulation and note that these test procedures can not only perform well in small-sample cases but also outperform the corresponding test procedures accounting for only patients with discordant responses published elsewhere. We use the data taken as a part of the crossover trial comparing two different doses of an analgesic with placebo for the relief of primary dysmenorrhea to illustrate the use of test procedures discussed here. [ABSTRACT FROM AUTHOR]

Objective: Insulin injection aspects, such as fear of injection and pain, directly affect glycemic control, patient adherence and quality of life. Use of thinner and shorter needles could increase acceptance of injections. The aim of the study is to evaluate the non-inferiority of the new 34G × 3.5 mm needle compared to a 32G × 4 mm in patients with diabetes treated with insulin.Methods: This is an open, randomized, two-period crossover, non-inferiority trial. Every treatment period lasted 3 weeks. Patients with type 1 or type 2 diabetes, treated with multiple daily insulin injections, were randomly assigned to receive a 34G × 3.5 mm or a 32G × 4 mm pen needle. The primary endpoint was the non-inferiority of the 34G × 3.5 mm in comparison with the 32G × 4 mm pen needle in terms of percentage absolute change of blood fructosamine (% |ΔFru|), using a non-inferiority margin of 20%.Results: Overall 77 patients were randomized and 73 completed the study. Patients characteristics were: 52% male, 80.5% affected by type 1 diabetes, mean age 52 years (±14.6), mean BMI 24.5 kg/m2 (±5.6), HbA1c 8% (±1.1) and baseline fructosamine level 350 µmol/l (±84). Mean fructosamine levels increased by 0.56 µmol/l with the 34G needle, while a reduction of 7.29 μmol/l was documented with the 32G needle. The difference between the two groups (7.84 μmol/l) was not statistically significant (p = .27). The % |ΔFru| between the two groups was 7.55% (95% CI 5.67-9.44), meeting the non-inferiority criterion. Glycemic variability, expressed as standard deviation of fasting blood glucose and post-prandial glucose, was not different between the two treatment groups (p = .63 and p = .77, respectively).Conclusions: The 34G × 3.5 mm needle was non-inferior to the 32G × 4 mm needle regarding fructosamine levels and glycemic variability supporting the suitability of the 34G × 3.5 mm needle for insulin injection in patients with diabetes.Clinical Trial Registration: NCT02690467. [ABSTRACT FROM AUTHOR]

Objective: Alternative medicine and herbal drugs have been taken into account for managing cardiovascular risk factors. Sumac (Rhus coriaria L.) is rich in biologically active ingredients known to improve cardiovascular health. We investigated the effect of sumac on systolic (SBP) and diastolic (DBP) blood pressure, flow-mediated dilation (FMD), body mass index (BMI), and serum concentrations of lipids and fasting blood sugar (FBS) in participants with hyperlipidemia in a triple-blind randomized placebo- controlled crossover trial.Methods: Thirty adults with dyslipidemia (mild to moderate elevation of plasma total cholesterol and/or triglycerides [TG; total cholesterol ≥ 6.0 mmol/L or TG ≥ 1.7 mmol/L and TG ≤ 5.0 mmol/L]) were assigned randomly to a sumac or a placebo group. Participants in the sumac group received sumac capsules (500 mg/twice daily) for the first 4 weeks, followed by 2 weeks' washout period; the patients were then switched to a 4-week interval and received placebo for 4 weeks in the second period. The placebo group received these treatments in reverse order. FMD, BMI, SBP, DBP, lipids, and FBS were measured at baseline and after each period.Results: Differences between placebo group and sumac group (placebo-sumac) were significantly decreased for BMI (0.21 ± 0.075 kg/m2), SBP (1.87 ± 0.83 mm Hg), DBP (1.32 ± 0.46 mm Hg), and total cholesterol (14.42 ± 4.95 mmol/L) and significantly increased for FMD (-0.23% ± 0.065%). Plasma level of TG did not change significantly across the treatment.Conclusion: Sumac consumption may decrease cardiovascular risk factors in persons with mild to moderate hyperlipidemia. [ABSTRACT FROM AUTHOR]

Three test procedures accounting for patients with tied responses based on Prescott’s ideas are developed for comparing three treatments under a three-period crossover trial in binary data. Monte Carlo simulation is employed to evaluate the performance of these test procedures in a variety of situations. The test procedures proposed here are noted to have power larger than those procedures, which utilize only those patients with un-tied responses. The data taken from a three-period crossover trial comparing two different doses of an analgesic with placebo for the relief of primary dysmenorrhea are used to illustrate the use of the test procedures developed here. [ABSTRACT FROM AUTHOR]

The authors focus discussion on estimation of the proportion ratio (PR) in the presence of residual effects under the AB/BA design. Under a randomeffects multiplicative risk model,we develop three point estimators and three interval estimators accounting for residual effects for the PR. Using Monte Carlo simulations, we compare the performance of these point (or interval) estimators with point (or interval) estimators assuming no residual effects with respect to the bias and mean-squared-error (or the coverage probability and average length). The authors use the data taken from an AB/BA trial comparing two new inhalation devices to illustrate the use of these estimators. [ABSTRACT FROM AUTHOR]

Under a random effects linear additive risk model, we compare two experimental treatments with a placebo in continuous data under an incomplete block crossover trial. We develop three test procedures for simultaneously testing equality between two experimental treatments and a placebo, as well as interval estimators for the mean difference between treatments. We apply Monte Carlo simulations to evaluate the performance of these test procedures and interval estimators in a variety of situations. We note that the bivariate test procedure accounting for the dependence structure based on the F-test is preferable to the other two procedures when there is only one of the two experimental treatments has a non-zero effect vs. the placebo. We note further that when the effects of the two experimental treatments vs. a placebo are in the same relative directions and are approximately of equal magnitude, the summary test procedure based on a simple average of two weighted-least-squares (WLS) estimators can outperform the other two procedures with respect to power. When one of the two experimental treatments has a relatively large effect vs. the placebo, the univariate test procedure with using Bonferroni’s equality can be still of use. Finally, we use the data about the forced expiratory volume in 1 s (FEV1) readings taken from a double-blind crossover trial comparing two different doses of formoterol with a placebo to illustrate the use of test procedures and interval estimators proposed here. [ABSTRACT FROM AUTHOR]

Under the AB/BA crossover trial, we focus our attention on estimation of the intraclass correlation in normal data. We develop both point and interval estimators in closed form for the intraclass correlation. We employ Monte Carlo simulation to study the performance of these estimators in a variety of situations. We note that the estimators developed here for the intraclass correlation remain valid even when there are possibly unexpected carry-over effects. [ABSTRACT FROM PUBLISHER]

When comparing two doses of a new drug with a placebo, we may consider using a crossover design subject to the condition that the high dose cannot be administered before the low dose. Under a random-effects logistic regression model, we focus our attention on dichotomous responses when the high dose cannot be used first under a three-period crossover trial. We derive asymptotic test procedures for testing equality between treatments. We further derive interval estimators to assess the magnitude of the relative treatment effects. We employ Monte Carlo simulation to evaluate the performance of these test procedures and interval estimators in a variety of situations. We use the data taken as a part of trial comparing two different doses of an analgesic with a placebo for the relief of primary dysmenorrhea to illustrate the use of the proposed test procedures and estimators. [ABSTRACT FROM AUTHOR]

Pharmacokinetic studies of drug and metabolite concentrations in the blood are usually conducted as crossover trials, especially in Phases I and II. A longitudinal series of measurements is collected on each subject within each period. Dependence among such observations, within and between periods, will generally be fairly complex, requiring two levels of variance components, for the subjects and for the periods within subjects, and an autocorrelation within periods as well as a time-varying variance. Until now, the standard way in which this has been modeled is using a multivariate normal distribution. Here, we introduce procedures for simultaneously handling these various types of dependence in a wider class of distributions called the multivariate power exponential and Student t families. They can have the heavy tails required for handling the extreme observations that may occur in such contexts. We also consider various forms of serial dependence among the observations and find that they provide more improvement to our models than do the variance components. An integrated Ornstein–Uhlenbeck (IOU) stochastic process fits much better to our data set than the conventional continuous first-order autoregression, CAR(1). We apply these models to a Phase I study of the drug, flosequinan, and its metabolite. [ABSTRACT FROM AUTHOR]

Pharmacokinetic studies of drug and metabolite concentrations in the blood are usually conducted as crossover trials, especially in phases I and II. A longitudinal series of measurements is collected on each subject within each period. However, much of the dependence among such observations, within and between periods, is generally ignored in analyzing this type of data. Usually, only a random coefficient model is fitted for the parameters in the nonlinear mean function, along with allowing the variance to depend on the mean so that it changes over time. Here, we develop models to allow more fully for the structure of the crossover study. We introduce two levels of variance components, for the subjects and for the periods within subjects, and also an autocorrelation within periods. We also retain the time-varying variance, using a separate variance function for this, different from that for the mean. We apply this model to a phase I study of the drug flosequinan and its metabolite. This drug was developed for the treatment of heart failure. Because the metabolite also exhibits an active pharmacologic effect, study of both the parent drug and the metabolite is of interest. We find that the autocorrelation is the element in the covariance structure that most improves the fit of the model but that two levels of variance components can also be necessary. [ABSTRACT FROM AUTHOR]