Incomplete Block Crossover Design Research Articles

Clinical trial design and analysis for comparing three treatments with intra-individual right- and left-hand data.

Chemotherapy-induced peripheral neuropathy can occur in the right and left hand. Studies on prevention treatments for chemotherapy-induced peripheral neuropathy have largely adopted either self-controlled designs or parallel designs to compare two preventive treatments. When three treatment options (two experimental treatments and a control treatment) are available, both designs can be extended. However, no clinical trials have adopted a self-controlled design to compare three prevention treatments for chemotherapy-induced peripheral neuropathy. The incomplete block crossover design for more than two treatments can be extended to compare three treatments in the self-controlled design. In simple extension, some of the participants receive two experimental treatments in both hands; however, it may be difficult to administer different experimental treatments in both hands for practical reasons, such as a concern for the different types of unexpected adverse events. This study proposes a design and analysis method appropriate for the situation where only one experimental treatment is provided to each participant. We assume clinical trials to compare each of the two experimental treatments (E1 and E2) with the control treatment (C) and between two experimental treatments only when both experimental treatments are superior to the control treatment. We propose a self-controlled design, which equally randomizes to four arms to adjust for the dominant hand effect: Arm 1: E1 for right hand, C for left hand; Arm 2: C for right hand, E1 for left hand; Arm 3: E2 for right hand, C for left hand; and Arm 4: C for right hand, E2 for left hand. We compare operating characteristics of the proposed design with the three-arm parallel design in which the same treatment is performed in both hands by participants. We also assess three proposed analysis methods for comparisons between experimental treatments in the self-controlled design under several conditions of correlations between right and left hands using simulation studies. The simulation studies showed that the proposed design was more powerful than the three-arm parallel design when correlation was 0.3 or higher. For comparisons between experimental treatments, the methods based on the regression model, including the outcome of hands with C as a covariate, had the highest power under modest to high correlation among the analysis methods in the self-controlled design. The proposed design can improve the power for comparing between two experimental treatments and the control treatment. Our design is useful in situations where it is undesirable for participants to receive different experimental treatments in both hands for practical reasons.

A Bayesian analysis of the incomplete block crossover design

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.

Notes on the two-stage procedure under an incomplete block two-period crossover design

Under an incomplete block crossover design with two periods, we derive the least-squares estimators for the period effect, treatment effects and carry-over effects in explicit formulae based on within-patient differences. Using the commonly-used strategy of searching a base model for making inferences in regression analysis, we define a two-stage test procedure in studying treatment effects. On the basis of Monte Carlo simulation, we evaluate the performance of the two-stage procedure for hypothesis testing, point and interval estimation of treatment effects in a variety of situations. We note that use of the two-stage procedure can be potentially misleading and hence one should not apply a test procedure to exclusively determine whether he/she needs to account for the carry-over effect in studying treatment effects. We use the double-blind crossover trial comparing two different doses of formoterol with placebo on the forced expiratory volume in 1 second (FEV1) readings to illustrate the use of the two-stage procedure, as well as the distinction between use of two-stage procedure and the approach with assuming no carry-over effects based on one's subjective knowledge.

Model-free tests of equality in binary data under an incomplete block design

ABSTRACTUsing 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.

Sample size determination for testing equality in frequency data under an incomplete block crossover design

ABSTRACTWhen there are more than two treatments under comparison, we may consider the use of the incomplete block crossover design (IBCD) to save the number of patients needed for a parallel groups design and reduce the duration of a crossover trial. We develop an asymptotic procedure for simultaneously testing equality of two treatments versus a control treatment (or placebo) in frequency data under the IBCD with two periods. We derive a sample size calculation procedure for the desired power of detecting the given treatment effects at a nominal-level and suggest a simple ad hoc adjustment procedure to improve the accuracy of the sample size determination when the resulting minimum required number of patients is not large. We employ Monte Carlo simulation to evaluate the finite-sample performance of the proposed test, the accuracy of the sample size calculation procedure, and that with the simple ad hoc adjustment suggested here. We use the data taken as a part of a crossover trial comparing the number of exacerbations between using salbutamol or salmeterol and a placebo in asthma patients to illustrate the sample size calculation procedure.

Notes on testing carry-over effects in continuous data under an incomplete block crossover design

ABSTRACTWe consider hypothesis testing and estimation of carry-over effects in continuous data under an incomplete block crossover design when comparing two experimental treatments with a placebo. We develop procedures for testing differential carry-over effects based on the weighted-least-squares (WLS) method. We apply Monte Carlo simulations to evaluate the performance of these test procedures in a variety of situations. We use the data regarding the forced expiratory volume in one second (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 proposed here.

Testing Equality of Treatments under an Incomplete Block Crossover Design with Ordinal Responses.

The generalized odds ratio (GOR) for paired sample is considered to measure the relative treatment effect on patient responses in ordinal data. Under a three-treatment two-period incomplete block crossover design, both asymptotic and exact procedures are developed for testing equality between treatments with ordinal responses. Monte Carlo simulation is employed to evaluate and compare the finite-sample performance of these test procedures. A discussion on advantages and disadvantages of the proposed test procedures based on the GOR versus those based on Wald's tests under the normal random effects proportional odds model is provided. The data taken as a part of a crossover trial studying the effects of low and high doses of an analgesic versus a placebo for the relief of pain in primary dysmenorrhea over the first two periods are applied to illustrate the use of these test procedures.

Exact tests in binary data under an incomplete block crossover design.

To improve the power of a parallel groups design and reduce the time length of a crossover trial, we may consider an incomplete block crossover design. Under a distribution-free random effects logistic regression model, we derive an exact test and a Mantel-Haenszel Type of summary test procedure for testing non-equality in binary data when comparing three treatments. We employ Monte Carlo simulation to evaluate the performance of these test procedures. We find that both test procedures developed here can perform well in a variety of situations. We use the data taken as a part of the crossover trial comparing the low and high doses of an analgesic with a placebo for the relief of pain in primary dysmenorrhea to illustrate the use of the proposed test procedures.

Notes on estimation in Poisson frequency data under an incomplete block crossover design

For comparison of two experimental treatments with a placebo under an incomplete block crossover design, we develop the weighted-least-squares estimator (WLSE) and the conditional maximum likelihood estimator (CMLE) of the relative treatment effects in Poisson frequency data. We further develop the interval estimator based on the WLSE, the interval estimator based on the CMLE, the interval estimator based on the conditional-likelihood-ratio test and the interval estimator based on the exact conditional distribution. Using Monte Carlo simulations, we find that all interval estimators developed here can perform well in a variety of situations. The exact interval estimator derived here can be especially of use when both the number of patients and the mean number of event occurrences are small in a trial. We use the data taken as part of a double-blind randomized crossover trial comparing salbutamol and salmeterol with a placebo with respect to the number of exacerbations in asthma patients to illustrate the use of these estimators.

Estimation of the treatment effect under an incomplete block crossover design in binary data - A conditional likelihood approach.

A random effects logistic regression model is proposed for an incomplete block crossover trial comparing three treatments when the underlying patient response is dichotomous. On the basis of the conditional distributions, the conditional maximum likelihood estimator for the relative effect between treatments and its estimated asymptotic standard error are derived. Asymptotic interval estimator and exact interval estimator are also developed. Monte Carlo simulation is used to evaluate the performance of these estimators. Both asymptotic and exact interval estimators are found to perform well in a variety of situations. When the number of patients is small, the exact interval estimator with assuring the coverage probability larger than or equal to the desired confidence level can be especially of use. The data taken from a crossover trial comparing the low and high doses of an analgesic with a placebo for the relief of pain in primary dysmenorrhea are used to illustrate the use of estimators and the potential usefulness of the incomplete block crossover design.

Test and estimation in binary data analysis under an incomplete block crossover design

Under a random effects logistic regression model, we compare two experimental treatments with a placebo in dichotomous data under an incomplete block crossover trial. We develop procedures for testing non-equality of treatments, as well as interval estimators for the relative treatment effects. We employ Monte Carlo simulations to evaluate the performance of these test procedures and interval estimators in a variety of situations. Finally, we use the data taken as a part of the crossover trial that compared the low and high doses of an analgesic with a placebo for the relief of pain in primary dysmenorrhea to illustrate the use of these test procedures and estimators developed here.

Test Equality between Three Treatments under an Incomplete Block Crossover Design

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.

P153 The impact of TRPV1 Antagonism on the Treatment of Seasonal Allergic Rhinitis

BackgroundTopical intranasal steroids are widely considered to be the most efficacious pharmacotherapy for the treatment of allergic rhinitis, and yet, for many, symptoms still remain troublesome. We hypothesise that the...

Efficacy and Safety of Indacaterol, a New 24-hour β 2-Agonist, in Patients with Asthma: A Dose-Ranging Study

Background. Indacaterol is a new once-daily inhaled β2-agonist in clinical development for asthma as a component of a fixed-dose combination with an inhaled corticosteroid. Objectives. To investigate the efficacy and safety of indacaterol in patients with chronic persistent asthma. Methods. A total of 115 patients were randomized in a double-blind, incomplete-block cross-over design to sequences of four 7-day treatment periods (separated by 7-day washouts) with indacaterol 100, 200, 300, 400, or 600 μ g or placebo, once daily, via single-dose dry-powder inhaler. After the fourth washout, patients received 1 day of open-label formoterol 12 μ g twice daily. Forced expiratory volume in 1 second (FEV1) was measured for 24 hours post-dose on days 1 and 7. Results. For standardized (with respect to time) FEV1 area under the curve at 22 to 24 hours (AUC22–24h) on day 1, indacaterol doses ≥200 μ g were superior to placebo (p < 0.05) and similar or greater than formoterol 12 μg twice daily. By day 7, mean differences from placebo in FEV1 standardized AUC22–24h were 0.08, 0.16, 0.15, 0.11, and 0.16 L for indacaterol 100, 200, 300, 400, and 600 μg, respectively (all p < 0.05 vs. placebo). Mean FEV1 for indacaterol doses ≥ 200 μg on day 7 was higher than placebo (p < 0.05) pre-dose and at all post-dose time points. AEs were generally mild in severity; no serious AEs occurred. No clinically meaningful differences were observed between treatments in any safety assessments. Conclusions. Once-daily indacaterol demonstrated sustained 24-hour bronchodilator efficacy, with similar efficacy on days 1 and 7, and was generally well tolerated.

Effects of different wavelengths in seasonal affective disorder

The aim of this study was to compare the relative therapeutic efficacies of three different light sources for treating winter depression. A balanced incomplete block crossover design was employed, whereby all patients ( n = 18) were randomly assigned to two out of the three treatment conditions: white, red and blue light. The degree of depression was assessed by the 21-item Hamilton Depression Rating Scale. The data suggest that at a photon density of 2.3 × 10 15 photons/ s/ cm 2, white light has greater therapeutic benefit than red or blue light. It is clear that a larger sample population should be tested to confirm this result. This preliminary finding indicates that light sources currently in use for phototherapy could not be improved by narrowing the wavelengths provided and shifting them towards either end of the visible spectrum.

Absorption and Excretion of Radioactively Tagged Dextroamphetamine Sulfate From a Sustained-Release Preparation

This study was undertaken to test the reliability of a sustained-release dosage form of dextroamphetamine sulfate. We tagged the drug with the radioactive carbon atom and gave four dosage regimens to 16 subjects following an incomplete block crossover design. The plasma and urine data obtained showed that the 15-mg sustained-release dosage form tested performed in a way comparable to 5 mg given three times each day, that it released its drug over a prolonged period, and that it was equivalent to other 15-mg regimens in the amount of drug made available for absorption. On the basis of these findings, we can say that the sustainedrelease dosage form tested is a reliable way to administer dextroamphetamine sulfate. The tests described herein, and tests of similar complexity, are becoming a more and more essential part of the evaluation of such dosage forms.