The Crossover Experiment for Clinical Trials

Ambient air particles in the ultrafine size range (diameter < 100 nm) may contribute to the health effects of particulate matter. However, there are few data on ultrafine particle deposition during spontaneous breathing, and none in people with asthma. Sixteen subjects with mild to moderate asthma were exposed for 2 hr, by mouthpiece, to ultrafine carbon particles with a count median diameter (CMD) of 23 nm and a geometric standard deviation of 1.6. Deposition was measured during spontaneous breathing at rest (minute ventilation, 13.3 ± 2.0 L/min) and exercise (minute ventilation, 41.9 ± 9.0 L/min). The mean ± SD fractional deposition was 0.76 ± 0.05 by particle number and 0.69 ± 0.07 by particle mass concentration. The number deposition fraction increased as particle size decreased, reaching 0.84 ± 0.03 for the smallest particles (midpoint CMD = 8.7 nm). No differences between sexes were observed. The deposition fraction increased during exercise to 0.86 ± 0.04 and 0.79 ± 0.05 by particle number and mass concentration, respectively, and reached 0.93 ± 0.02 for the smallest particles. Experimental deposition data exceeded model predictions during exercise. The deposition at rest was greater in these subjects with asthma than in previously studied healthy subjects (0.76 ± 0.05 vs. 0.65 ± 0.10, p < 0.001). The efficient respiratory deposition of ultrafine particles increases further in subjects with asthma.

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“…Data means were compared using the two-tailed Student's t-test (Brown 1980), with p < 0.05 denoting significance.…”

Section: Methodsmentioning

confidence: 99%

Ultrafine particle deposition in subjects with asthma.

Chalupa

Morrow

Oberdörster

et al. 2004

Environ Health Perspect

299

159

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“…It is the correlation between pairs of measurements in Period 1 and 2, taken on randomly selected subjects (Brown, 1980). Brown (1980) stressed the efficiency of the simple crossover design relative to a completely randomized trial, in case the assumption of no carry-over is likely to be valid.…”

Section: The Crossover Designmentioning

confidence: 99%

“…Brown (1980) stressed the efficiency of the simple crossover design relative to a completely randomized trial, in case the assumption of no carry-over is likely to be valid. The ratio of the error variance of a crossover trial and a simple randomized trial with an equal number of subjects included in both trials, is (Brown, 1980):…”

Section: The Crossover Designmentioning

confidence: 99%

“…The attractive feature of this design, where each student is included in both treatment conditions subsequently, is elicited by each student serving as its own control. By studying the difference between the treatment conditions within students, between-subject variation is eliminated from the variance of the treatment effect estimator, which makes this design more powerful than a completely randomized trial (Brown, 1980). In a cluster randomized crossover trial complete clusters of subjects are randomly assigned to sequences of interventions.…”

Section: Introductionmentioning

confidence: 99%

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The Design of Cluster Randomized Crossover Trials

Rietbergen¹,

Moerbeek

2011

Journal of Educational and Behavioral Statistics

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The inefficiency induced by between-cluster variation in cluster randomized trials can be reduced by implementing a crossover design. In a simple crossover trial each subject receives each treatment in random order. A powerful characteristic of this design is that each subject serves as its own control. In a cluster randomized crossover trial clusters of subjects are randomly allocated to a sequence of interventions. Under this design each subject is either included in only one of the treatment periods (crossover at cluster level) or in both periods (crossover at subject level). In this study the efficiency of both cluster randomized crossover trials relative to the cluster randomized trial without crossover is demonstrated. Furthermore, the optimal allocation of clusters and subjects given a fixed budget or desired power level is discussed.

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“…effect increases or decreases the effectiveness of the later treatment, is known as carryover (Brown 1980). Carryover is an important risk in medical experiments, as drug residues can remain in the body for quite some time and interact with later treatments (Senn 2002).…”

Section: Designmentioning

confidence: 99%

Effectiveness for detecting faults within and outside the scope of testing techniques: an independent replication

et al. 2013

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The verification and validation activity plays a fundamental role in improving software quality. Determining which the most effective techniques for carrying out this activity are has been an aspiration of experimental software engineering researchers for years. This paper reports a controlled experiment evaluating the effectiveness of two unit testing techniques (the functional testing technique known as equivalence partitioning (EP) and the control-flow structural testing technique known as branch testing (BT)). This experiment is a literal replication of Juristo et al. (2013). Both experiments serve the purpose of determining whether the effectiveness of BT and EP varies depending on whether or not the faults are visible for the technique (InScope or OutScope, respectively). We have used the materials, design and procedures of the original experiment, but in order to adapt the experiment to the context we have: (1) reduced the number of studied techniques from 3 to 2; (2) assigned subjects to experimental groups by means of stratified randomization to balance the influence of programming experience; (3) EP is more effective than BT at detecting InScope faults. The session/program and group variables are found to have significant effects. BT is more effective than EP at detecting OutScope faults. The session/program and group variables have no effect in this case. The results of the replication and the original experiment are similar with respect to testing techniques. There are some inconsistencies with respect to the group factor. They can be explained by small sample effects. The results for the session/program factor are inconsistent for InScope faults. We believe that these differences are due to a combination of the fatigue effect and a technique x program interaction. Although we were able to reproduce the main effects, the changes to the design of the original experiment make it impossible to identify the causes of the discrepancies for sure. We believe that further replications closely resembling the original experiment should be conducted to improve our understanding of the phenomena under study.

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The Crossover Experiment for Clinical Trials

Cited by 349 publications

References 6 publications

Ultrafine particle deposition in subjects with asthma.

Ultrafine particle deposition in subjects with asthma.

The Design of Cluster Randomized Crossover Trials

Effectiveness for detecting faults within and outside the scope of testing techniques: an independent replication

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