One and Two Restrictions on Randomization
Daryl S. Paulson in Applied Statistical Designs for the Researcher, 2003
Generally, the randomized block design is used in order to reduce the error term value by reducing inherent variability that cannot be traced to the treatment effect. The variability is the sum of random error and innate differences between the experimental units when no treatment is applied. This variability can often be reduced by blocking, but not always. And if blocking does not significantly reduce the error term’s magnitude, it is counterproductive because of a significant loss of degrees of freedom [(a − 1)(b − 1)] as opposed to N − a. It is usually worthwhile to compute the relative efficiency of the randomized block design versus the completely randomized design. That relative efficiency equation is: where dfCB = degrees of freedom for the complete block design for MSERRORdfCR = degrees of freedom for the randomized block design for MSERROR = MSERROR term for the complete block of design = MSERROR term for the completely randomized design
R
Filomena Pereira-Maxwell in Medical Statistics, 2018
resulting in treatment groups with equal sample sizes. A randomized block design is a parallel trial in which restricted randomization, rather than simple randomization, is employed. Biased coin allocation is used to the same aim as block randomization. See also complete block design, incomplete block design. Further details are given by ALTMAN (1991), POCOCK (1983) and FLEISS (1999), and also under randomized block design.
Unchanged pulmonary toxicity of ZnO nanoparticles formulated in a liquid matrix for glass coating
Published in Nanotoxicology, 2022
Anne Thoustrup Saber, Niels Hadrup, Andrew Williams, Alicja Mortensen, Jozef Szarek, Zdenka Kyjovska, Alexander Kurz, Nicklas Raun Jacobsen, Håkan Wallin, Sabina Halappanavar, Ulla Vogel
Microarray data were analyzed as described previously (Husain et al. 2013; Labib et al. 2013; Halappanavar et al. 2015). In brief, a reference randomized block design was employed. Data were normalized using the LOcally WEighted Scatterplot Smoothing (LOWESS) regression modeling method and statistical significance of differentially expressed genes was evaluated using MicroArray ANalysis Of VAriance (MAANOVA) (Rahman et al. 2017) in R statistical software (R Core Team 2012). The Fs statistic (Rahman et al. 2017) was employed to test the treatment effects compared to the control vehicle, and p values were estimated by the permutation method using residual shuffling. In order to avoid false positives, false discovery rate (FDR) multiple testing correction (Rahman et al. 2017) was applied. The fold changes of gene expression were determined considering the least-square means. Genes with FDR p values of≤ 0.05 were considered significantly differentially expressed, and were included in all downstream analyses. Since the gene list was small, the fold-change-based filtering was not conducted.
Effects of vitamin E supplementation on the risk and progression of AD: a systematic review and meta-analysis
Published in Nutritional Neuroscience, 2021
Wanyu Wang, Jiao Li, Huizhen Zhang, Xiaokai Wang, Xiaofeng Zhang
Figure 3 shows a forest plot presenting the association between vitamin E supplements and risk of AD. The pooled RR of AD for the vitamin E supplements vs. the placebo was 0.81 (95% CI: 0.50–1.33). Statistically significant heterogeneity was observed among the individual results (I2 = 69.2%, P-heterogeneity <0.05). The random-effect model was used as the high heterogeneity existed in our study. The random-effect model just is analogous to the linear mixed model for incomplete block designs with a random block effect. This means information on treatment effects both within and between studies could be covered respectively through the use of random-effect model. This is generally considered highly desirable in single randomized experiments with blocking, and it can be justified based on the randomization of an incomplete block design.
Testing mediational processes of substance use relapse among youth who participated in a mobile texting aftercare project
Published in Substance Abuse, 2022
Rachel Gonzales-Castaneda, James R. McKay, Jane Steinberg, Ken C. Winters, Chong Ho (Alex) Yu, Irene C. Valdovinos, Janna M. Casillas, Kyle C. McCarthy
Caution should be taken when interpreting the results of this exploratory study for several reasons: (1) the findings are based on a small pilot study; (2) the youth sample is limited to those who completed treatment and self-selected to participate; and (3) although participants were randomly assigned to each condition, there was a higher proportion of participants in the target condition reporting methamphetamine as the primary substance used (37%) whereas a majority (48.8%) reporting marijuana use in the control condition. Future studies may utilize a randomized block design to account for important differences that may affect outcomes, such as substance use type or substance use severity. Additionally, the single item measure for extracurricular participation did not assess duration or intensity and therefore a threshold which begins impacting primary outcomes cannot be established. In addition, this study did not look at specific extracurricular activities that are important for youth in recovery, but a generic emphasis on positive pro-social activities that are commonly referenced in the larger literature among youth. Lastly, the study included a methodological limitation with a lack of a mobile control condition which limited our ability to examine effects of exposure to alternative mobile texting interventions; however, it should be noted that this was a pilot study that sought the initial efficacy and feasibility of mobile texting for aftercare compared to standard aftercare as usual.
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