Investigation of Dual-Balanced Crossover Designs for Two Treatments

Biometrics ◽  
1992 ◽  
Vol 48 (4) ◽  
pp. 1157 ◽  
Author(s):  
Keumhee Chough Carriere ◽  
Gregory C. Reinsel
Keyword(s):  
Crossover Designs

scholarly journals Evaluating the Long-Term Efficacy of COVID-19 Vaccines

2021 ◽  
Author(s):  
Dan-Yu Lin ◽  
Donglin Zeng ◽  
Peter B Gilbert
Keyword(s):  
Large Scale ◽  
Crossover Designs ◽  
Phase 3 ◽  
Term Efficacy ◽  
Timely Access ◽  
Efficacious Vaccine ◽  
Trial Participants ◽  
Ongoing Phase

Abstract Large-scale deployment of safe and durably effective vaccines can curtail the COVID-19 pandemic.1−3 However, the high vaccine efficacy (VE) reported by ongoing phase 3 placebo-controlled clinical trials is based on a median follow-up time of only about two months4−5 and thus does not pertain to long-term efficacy. To evaluate the duration of pro- tection while allowing trial participants timely access to efficacious vaccine, investigators can sequentially cross participants over from the placebo arm to the vaccine arm according to priority groups. Here, we show how to estimate potentially time-varying placebo-controlled VE in this type of staggered vaccination of participants. In addition, we compare the per- formance of blinded and unblinded crossover designs in estimating long-term VE.

Dose Ranging Crossover Designs

2008 ◽  
Author(s):  
Scott D. Patterson ◽  
Byron Jones ◽  
N��vine Zariffa
Keyword(s):  
Crossover Designs ◽  
Dose Ranging

Crossover designs for comparing treatments with a control

Biometrika ◽  
1987 ◽  
Vol 74 (2) ◽  
pp. 321-328 ◽  
Author(s):  
J. G. PIGEON ◽  
D. RAGHAVARAO
Keyword(s):  
Crossover Designs

scholarly journals Maximin Efficiencies under Treatment-Dependent Costs and Outcome Variances for Parallel, AA/BB, and AB/BA Designs

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Math J. J. M. Candel
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Optimal Allocation ◽  
Power Level ◽  
Equal Treatment ◽  
Small Samples ◽  
Asymptotic Results ◽  
Crossover Designs ◽  
Mixed Model Analysis ◽  
Intraclass Correlations

If there are no carryover effects, AB/BA crossover designs are more efficient than parallel (A/B) and extended parallel (AA/BB) group designs. This study extends these results in that (a) optimal instead of equal treatment allocation is examined, (b) allowance for treatment-dependent outcome variances is made, and (c) next to treatment effects, also treatment by period interaction effects are examined. Starting from a linear mixed model analysis, the optimal allocation requires knowledge on intraclass correlations in A and B, which typically is rather vague. To solve this, maximin versions of the designs are derived, which guarantee a power level across plausible ranges of the intraclass correlations at the lowest research costs. For the treatment effect, an extensive numerical evaluation shows that if the treatment costs of A and B are equal, or if the sum of the costs of one treatment and measurement per person is less than the remaining subject-specific costs (e.g., recruitment costs), the maximin crossover design is most efficient for ranges of intraclass correlations starting at 0.15 or higher. For other cost scenarios, the maximin parallel or extended parallel design can also become most efficient. For the treatment by period interaction, the maximin AA/BB design can be proven to be the most efficient. A simulation study supports these asymptotic results for small samples.

scholarly journals On MSE-Optimal Circular Crossover Designs

2021 ◽  
Vol 15 (4) ◽  
Author(s):  
C. Neumann ◽  
J. Kunert
Keyword(s):  
Optimal Design ◽  
Mean Square Error ◽  
Current Treatment ◽  
Mean Square ◽  
Crossover Designs ◽  
Carryover Effects ◽  
Circular Structure ◽  
Balanced Designs ◽  
Preceding Period ◽  
The Mean

AbstractIn crossover designs, each subject receives a series of treatments, one after the other in p consecutive periods. There is concern that the measurement of a subject at a given period might be influenced not only by the direct effect of the current treatment but also by a carryover effect of the treatment applied in the preceding period. Sometimes, the periods of a crossover design are arranged in a circular structure. Before the first period of the experiment itself, there is a run-in period, in which each subject receives the treatment it will receive again in the last period. No measurements are taken during the run-in period. We consider the estimate for direct effects of treatments which is not corrected for carryover effects. If there are carryover effects, this uncorrected estimate will be biased. In that situation, the quality of the estimate can be measured by the mean square error, the sum of the squared bias and the variance. We determine MSE-optimal designs, that is, designs for which the mean square error is as small as possible. Since the optimal design will in general depend on the size of the carryover effects, we also determine the efficiency of some designs compared to the locally optimal design. It turns out that circular neighbour-balanced designs are highly efficient.

36. Crossover designs for evaluating panel test procedures

1996 ◽  
Vol 7 (3-4) ◽  
pp. 317
Author(s):  
John C. Sourby ◽  
Cathy Walchak ◽  
Carol Buzzuffi ◽  
Donna Riggi
Keyword(s):  
Test Procedures ◽  
Crossover Designs ◽  
Panel Test

scholarly journals Comparison of Aggregated N-of-1 Trials with Parallel and Crossover Randomized Controlled Trials Using Simulation Studies

Healthcare ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 137 ◽  
Author(s):  
J. Blackston ◽  
Andrew Chapple ◽  
James McGree ◽  
Suzanne McDonald ◽  
Jane Nikles
Keyword(s):  
Randomized Controlled Trials ◽  
Sample Size ◽  
Selection Bias ◽  
Clinical Care ◽  
Controlled Trials ◽  
Type I ◽  
Operating Characteristics ◽  
Crossover Designs ◽  
Carryover Effects ◽  
Randomized Controlled

Background: N-of-1 trials offer an innovative approach to delivering personalized clinical care together with population-level research. While increasingly used, these methods have raised some statistical concerns in the healthcare community. Methods: We discuss concerns of selection bias, carryover effects from treatment, and trial data analysis conceptually, then rigorously evaluate concerns of effect sizes, power and sample size through simulation study. Four variance structures for patient heterogeneity and model error are considered in a series of 5000 simulated trials with 3 cycles, which compare aggregated N-of-1 trials to parallel randomized controlled trials (RCTs) and crossover trials. 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. N-of-1 designs resulted in a higher type-I error probability than parallel RCT and cross over designs when moderate-to-strong carryover effects were not considered or in the presence of modeled selection bias. However, N-of-1 designs allowed better estimation of patient-level random effects. These results reinforce the need to account for these factors when planning N-of-1 trials. Conclusion: N-of-1 trial designs offer a rigorous method for advancing personalized medicine and healthcare with the potential to minimize costs and resources. Interventions can be tested with adequate power with far fewer patients than traditional RCT and crossover designs. Operating characteristics compare favorably to both traditional RCT and crossover designs.

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