Min–max crossover designs for two treatments binary and poisson crossover trials

Abstract Background Crossover designs are commonly utilised in randomised controlled trials investigating treatments for long-term chronic illnesses. One problem with this design is its inherent repeated measures necessitate the availability of an estimate of the within-person standard deviation (SD) to perform a sample size calculation, which may be rarely available at the design stage of a trial. Interim sample size re-estimation designs can be used to help alleviate this issue by adapting the sample size mid-way through the trial, using accrued information in a statistically robust way. Methods The AIM HY-INFORM study is part of the Informative Markers in Hypertension (AIM HY) Programme and comprises two crossover trials, each with a planned recruitment of 600 participants. The objective of the study is to test whether blood pressure response to first line antihypertensive treatment depends on ethnicity. An interim analysis is planned to reassess the assumptions of the planned sample size for the study. The aims of this paper are: (1) to provide a formula for sample size re-estimation in both crossover trials; and (2) to present a simulation study of the planned interim analysis to investigate alternative within-person SDs to that assumed. Results The AIM HY-INFORM protocol sample size calculation fixes the within-person SD to be 8 mmHg, giving > 90% power for a primary treatment effect of 4 mmHg. Using the method developed here and simulating the interim sample size reassessment, if we were to see a larger within-person SD of 9 mmHg at interim, 640 participants for 90% power 90% of the time in the three-period three-treatment design would be required. Similarly, in the four-period four-treatment crossover design, 602 participants would be required. Conclusions The formulas presented here provide a method for re-estimating the sample size in crossover trials. In the context of the AIM HY-INFORM study, simulating the interim analysis allows us to explore the results of a possible increase in the within-person SD from that assumed. Simulations show that without increasing the planned sample size of 600 participants, we can reasonably still expect to achieve 80% power with a small increase in the within-person SD from that assumed. Trial registration ClinicalTrials.gov, NCT02847338. Registered on 28 July 2016.

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Advantages, Disadvantages, and Future Trends on the Use of Design of Experiments in Cross-Over Trials in Nutritional Clinical Investigation

Design of Experiments for Chemical, Pharmaceutical, Food, and Industrial Applications - Advances in Chemical and Materials Engineering ◽

10.4018/978-1-7998-1518-1.ch007 ◽

2020 ◽

pp. 158-173

Author(s):

Jose Manuel Miranda ◽

Alicia del Carmen Mondragon Portocarrero ◽

Alexandre Lamas Freire ◽

Carlos Manuel Franco Abuin ◽

Alberto Cepeda Saez

Keyword(s):

Design Of Experiments ◽

Human Health ◽

Scientific Community ◽

Clinical Investigation ◽

Economic Cost ◽

Crossover Designs ◽

Advantages And Disadvantages ◽

Nutrition Research ◽

Human Nutrition Research ◽

Crossover Trials

The use of clinical trials to demonstrate effect of foods consumption on human health has increased significantly in recent years at the global level. As in other areas of human health, some authors choose to use parallel trial designs, while others prefer to use crossover designs for these trials. Because crossover trials have the advantage of reducing the number of subjects needed and the economic cost to be performed, they have many advocates within the scientific community. However, these types of tests also have numerous drawbacks, due to the difficulty of carrying out adequate statistical analyses, the lack of reliable standards adapted to them or confounding factors. In this chapter, the advantages and disadvantages of crossover designs and whether they are a recommended option for human nutrition research are shown. The usefulness of design of experiments coupled to crossover trials, especially when comparing various levels of the dependent variable, are also discussed.

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Within-Subject Mediation Analysis in AB/BA Crossover Designs

The International Journal of Biostatistics ◽

10.1515/ijb-2014-0057 ◽

2015 ◽

Vol 11 (1) ◽

pp. 1-22 ◽

Cited By ~ 7

Author(s):

Haeike Josephy ◽

Stijn Vansteelandt ◽

Marie-Anne Vanderhasselt ◽

Tom Loeys

Keyword(s):

Mediation Analysis ◽

Indirect Effects ◽

Direct And Indirect Effects ◽

Crossover Designs ◽

Intermediate Variable ◽

Crossover Trials ◽

Scant Attention ◽

Underlying Mechanisms ◽

Insight Into

AbstractCrossover trials are widely used to assess the effect of a reversible exposure on an outcome of interest. To gain further insight into the underlying mechanisms of this effect, researchers may be interested in exploring whether or not it runs through a specific intermediate variable: the mediator. Mediation analysis in crossover designs has received scant attention so far and is mostly confined to the traditional Baron and Kenny approach. We aim to tackle mediation analysis within the counterfactual framework and elucidate the assumptions under which the direct and indirect effects can be identified in

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Evaluating the Long-Term Efficacy of COVID-19 Vaccines

Clinical Infectious Diseases ◽

10.1093/cid/ciab226 ◽

2021 ◽

Cited By ~ 1

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.

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Dose Ranging Crossover Designs

Wiley Encyclopedia of Clinical Trials ◽

10.1002/9780471462422.eoct360 ◽

2008 ◽

Author(s):

Scott D. Patterson ◽

Byron Jones ◽

N��vine Zariffa

Keyword(s):

Crossover Designs ◽

Dose Ranging

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Crossover designs for comparing treatments with a control

Biometrika ◽

10.1093/biomet/74.2.321 ◽

1987 ◽

Vol 74 (2) ◽

pp. 321-328 ◽

Cited By ~ 21

Author(s):

J. G. PIGEON ◽

D. RAGHAVARAO

Keyword(s):

Crossover Designs

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Are Brand-Name and Generic Warfarin Interchangeable? Multiple N-of-1 Randomized, Crossover Trials

Annals of Pharmacotherapy ◽

10.1345/aph.1g003 ◽

2005 ◽

Vol 39 (7-8) ◽

pp. 1188-1193 ◽

Cited By ~ 19

Author(s):

Jennifer A Pereira ◽

Anne M Holbrook ◽

Lisa Dolovich ◽

Charles Goldsmith ◽

Lehana Thabane ◽

...

Keyword(s):

North America ◽

International Normalized Ratio ◽

Warfarin Interaction ◽

Study Patient ◽

Control Group ◽

Brand Name ◽

Double Blind ◽

Safety And Efficacy ◽

Crossover Trials

BACKGROUND Warfarin is a commonly used anticoagulant in North America. Several generic formulations have been approved, raising concern over the safety and efficacy of these products compared with brand-name Coumadin. OBJECTIVE To ensure that generic warfarin products can be safely interchanged with Coumadin. METHODS Multiple n-of-1 randomized, double-blind, crossover trials switched outpatients (N = 7) between a generic warfarin formulation (Apo-warfarin) and Coumadin over 30 weeks. Study patients took each drug for five 3-week periods, with international normalized ratio (INR) measurements taken twice per period. Inter- and intrapatient differences between generic warfarin and Coumadin were compared, and overall study patient results were compared with those of a Coumadin control group. RESULTS There were no differences between warfarin products in terms of mean INR results or number of dosage adjustments required. There also was no difference in INR variation based on warfarin formulation (p > 0.69), nor was a patient and warfarin interaction found (p > 0.81). The INR results were not influenced by whether patients were maintained on Coumadin only (control group) or interchanged between Coumadin and generic warfarin (p = 0.98). CONCLUSIONS It appears that patients can safely and effectively switch between generic warfarin and Coumadin.

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