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Design of Experiments and Its Deployment in SAS and R
Published in Tanya Kolosova, Samuel Berestizhevsky, Supervised Machine Learning, 2020
Tanya Kolosova, Samuel Berestizhevsky
Randomization is the process of assigning treatments to experimental units, so that each unit has the same chance to receive treatment. The assignment of treatments to experimental units at random distinguishes a designed (planned) experiment from an observational study or so-called “quasi-experiment.” There is an extensive body of statistical theory exploring the consequences of the allocation of units to treatments using some random number generator. Assigning units to treatments randomly tends to decrease confounding, which appears due to latent factors related to the treatment. It is only if the experimental units are a random sample from a population that the results of the experiment can be applied reliably from the experimental units to the larger statistical population The probable error of such an extrapolation depends on the sample size, among other things, and can be estimated.
Accuracy of Pressure and Shear Measurement
Published in J G Webster, Prevention of Pressure Sores, 2019
Replication and randomization are the two fundamental principles of experimental design (Montgomery 1976). Replication has two important properties. First, it allows the experimenter to obtain an estimate of the experimental error. This estimate is important in determining whether observed differences in the data are really statistically different or due to experimental error. Second, a sample mean or average is normally used to estimate the effect of a factor in the experiment. Replication allows the experimenter to obtain a more precise estimate of the effect. What do we mean by randomization? Randomization means that both the allocation of the experimental material and the order in which the individual runs or trials of the experiment are to be performed are randomly determined. Proper randomization will tend to minimize or average out the effect of extraneous factors that may be present.
Clinical Trials
Published in John M. Centanni, Michael J. Roy, Biotechnology Operations, 2016
John M. Centanni, Michael J. Roy
Comparative drug studies were first reported in the eighteenth century but used infrequently until the twentieth century when, as noted in Chapter 3, laws and regulations were developed to ensure that drugs and medical devices were adequately tested for safety and effectiveness. Indeed, these regulations have, in part, influenced clinical trial designs and are based on good scientific research practices that had been used in laboratories, notably the need to test a hypothesis in a formal manner. Another process called randomization or the blinding process, that is, assigning a patient to receive one or another drug, was first used in laboratory and agricultural field research and then was adopted as good scientific practice for clinical trials. Monitoring and auditing, now standard practices for clinical trials, were earlier used in nonclinical studies and were found to be an effective means of ensuring the quality of data. Indeed, as drug development became more complex and clinical studies grew larger, many scientific and quality practices were applied to clinical trials, which have now become the norm for biopharmaceutical clinical research.
Localized roughness effects in non-uniform hydraulic waterways
Published in Journal of Hydraulic Research, 2021
L. Robin Andersson, I. A. Sofia Larsson, J. Gunnar I. Hellström, Anton J. Burman, Patrik Andreasson
The overall measuring accuracy in PIV is a product of several features ranging from the recording process to the methods of evaluation (Raffel et al., 2013). A cornerstone of all experimental design is proper randomization of the measuring procedure. Accordingly, the effects of extraneous factors that may be present will have less impact on the results (Montgomery, 2009). The measurement uncertainties consist of those due to systematic biased errors and random precision errors (or due to erroneous measurements) (Coleman & Steele, 1999). The biased error associated with the scaling from pixels to metres is estimated to be 0.5%, this was derived from measuring over a known length-scale. The primary source of random error is introduced by the sub-pixel estimator in the cross-correlation, an error estimated to be 10% of the particle image diameter [pixels] as seen through the camera (Balakumar et al., 2009). Therefore the estimated random error of the measured velocity vector in each interrogation area is about 4% for the streamwise velocity component.
Approaches to Mobile Health Evaluation: A Comparative Study
Published in Information Systems Management, 2020
Samantha Dick, Yvonne O’Connor, Ciara Heavin
All of the methodologies outlined in Table 1 include a randomization process. Randomization is used to eliminate certain biases and confounding factors and therefore allows a high level of confidence to be placed on the results. The randomizations in SMART allow unbiased comparisons between treatment components at each decision stage in their development (Almirall, Nahum-Shani, Sherwood, & Murphy, 2014). As outlined, there is a difficulty in blinding recipients of a mHealth intervention due to the physical presence of the device but the SMART trial suggests the use of an independent evaluator who is blind to treatment assignment to eliminate any information bias which may result (Almirall et al., 2014). This is important because a lack of blinding in a study design could lead to an over-estimation of the effects of an intervention, as was illustrated by Colditz, Miller, and Mosteller (1989) who found that medical interventions evaluated within randomized trials that did not use a double-blind design reported a significantly greater likelihood of success on average than the studies that used double blinding.
Challenges and new methods for designing reliability experiments
Published in Quality Engineering, 2019
Laura J. Freeman, Rebecca M. Medlin, Thomas H. Johnson
Fisher (1937) identified randomization, replication, and blocking (local control of error) as core tenets of experimental design. Randomization is essential in that it breaks the link to potential confounding variables, allowing randomized experiments to provide statements about the cause of the experimental result. Common design strategies for dealing with restrictions in randomization include blocking and split-plot designs. Jones and Nachtsheim (2009) highlight the prevalence of split-plot designs in industrial applications provides a straightforward overview of their value. Goos (2002) provides a comprehensive review of designing optimal experiments for various analysis models with blocking and split-plot experimental structures.