Journal articles: 'Randomized block design'

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Karaagaoglu, Ergun. "An Experimental Design Technique: Randomized Block Design." Turkish Journal of Biochemistry 38, no. 1 (2013): 1–4. http://dx.doi.org/10.5505/tjb.2013.04796.

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Kushwaha, Ujjawal Kumar Singh. "Characterization and Evaluation of Rice (Oryza sativa L.) Genotypes in Randomized Complete Block Design." Journal of Environmental Science and Allied Research 2, no. 1 (April 1, 2019): 39–49. http://dx.doi.org/10.29199/2637-7063/esar-201020.

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Shieh, Gwowen, and Show-Li Jan. "The effectiveness of randomized complete block design." Statistica Neerlandica 58, no. 1 (February 2004): 111–24. http://dx.doi.org/10.1046/j.0039-0402.2003.00109.x.

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Gezan, Salvador A., Dudley A. Huber, and Timothy L. White. "Post hoc blocking to improve heritability and precision of best linear unbiased genetic predictions." Canadian Journal of Forest Research 36, no. 9 (September 1, 2006): 2141–47. http://dx.doi.org/10.1139/x06-112.

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Single-site clonal trials were simulated with a total of 256 clones "planted" in single-tree plots with three different environmental patterns: only patches (PATCH), only gradients (GRAD), and both components (ALL). Several simulated experimental designs were analyzed (a randomized complete block design; incomplete block designs with 4, 8, 16, and 32 incomplete blocks; and a row-column design) and compared with post hoc blocking of the same designs over a randomized complete block. Additionally, two more incomplete block designs (64 and 128 blocks) were superimposed after the fact to examine extremely small blocks. To select the best fit, the performance of the log-likelihood and mean standard error of the difference (SED) were studied and compared with mean individual broad-sense heritability. Improvement in statistical efficiency (or precision) were obtained with little effort using post hoc blocking. The results from post hoc blocking were promising with negligible differences compared with predesigned local control. The post hoc best designs were row-column (for ALL and PATCH) and incomplete block with eight blocks (for GRAD). Also, mean correlation between the true and predicted values (CORR) showed a reduction in efficiency for extremely small blocks, but no reduction in the genetic variance was noted as the size of the block decreased. Both of the criteria for model selection (log-likelihood and SED) showed similar trend to mean CORR, and their use is recommended.

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Shiraishi, Taka-Aki. "Studentized robust statistics in multivariate randomized block design." Journal of Nonparametric Statistics 10, no. 1 (January 1998): 95–110. http://dx.doi.org/10.1080/10485259808832755.

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Basford, K. E., and G. J. McLachlan. "Cluster analysis in a randomized complete block design." Communications in Statistics - Theory and Methods 14, no. 2 (January 1985): 451–63. http://dx.doi.org/10.1080/03610928508828924.

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Das, Rabindra Nath, and Jeong-Soo Park. "A Reinforced Randomized Block Design with Correlated Errors." Communications in Statistics - Theory and Methods 43, no. 1 (November 25, 2013): 191–209. http://dx.doi.org/10.1080/03610926.2011.650272.

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Gumpertz, M. L., and C. Brownie. "Repeated measures in randomized block and split-plot experiments." Canadian Journal of Forest Research 23, no. 4 (April 1, 1993): 625–39. http://dx.doi.org/10.1139/x93-083.

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Randomized block and split-plot designs are among the most commonly used experimental designs in forest research. Measurements for plots in a block (or subplots in a whole plot) are correlated with each other, and these correlations must be taken into account when analyzing repeated-measures data from blocked designs. The analysis is similar to repeated-measures analysis for a completely randomized design, but test statistics must allow for random block × time effects, and standard errors for treatment means must also incorporate block to block variation and variation among plots within a block. Two types of statistical analysis are often recommended for repeated-measures data: analysis of contrasts of the repeated factor and multivariate analysis of variance. A complete analysis of repeated measures should usually contain both of these components, just as in univariate analysis of variance it is often necessary to decompose the main effects into single degree of freedom contrasts to answer the research objectives. We demonstrate the multivariate analysis of variance and the analysis of contrasts in detail for two experiments. In addition, estimation of coefficients assuming a polynomial growth curve is discussed in detail for one of these experiments. The first experiment, a randomized complete block design, is a forest nutrition study of the long-term effects of midrotation nitrogen and phosphorus fertilization on loblolly pine (Pinustaeda L.); the second experiment, a split-plot design, is an air-pollution study of the effects of ozone and acid precipitation on loblolly pine growth.

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Kashif, M., M. I. Khan, M. Arif, M. Anwer, and M. Ijaz. "Efficiency of Alpha Lattice Design in Rice Field Trials in Pakistan." Journal of Scientific Research 3, no. 1 (December 19, 2010): 91. http://dx.doi.org/10.3329/jsr.v3i1.4773.

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Two rice trials were conducted from 2005 to 2006 in rice research institute, Kala Shah Kako Pakistan to evaluate the efficiency of alpha lattice design in field experiments. The average standard error of difference between genotypes mean is used to calculate relative efficiency of alpha lattice design. Both experiments clearly identified the advantages of small blocks. The average gain in efficiency was 119% with maximum 128%. Mean ranks comparison for both randomized complete block and alpha lattice design were performed. It was observed that the ranks were not constant across the experiments. The results emphasize that the traditional randomized complete block designs (RCBD) should be replaced by alpha lattice in the agricultural field experiments when number of varieties to be tested in an experiment increases to more than five or ten. In such a situation finding a homogeneous block is quite difficult in field experiments.Keywords: Rice; Alpha lattice design; RCBD; Pakistan.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.4773 J. Sci. Res. 3 (1), 91-95 (2011)

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Kang, Yuhyang, and Dongjae Kim. "Nonparametric Method for Ordered Alternative in Randomized Block Design." Korean Journal of Applied Statistics 27, no. 1 (February 28, 2014): 61–70. http://dx.doi.org/10.5351/kjas.2014.27.1.061.

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Möttönen, J., J. Hüsler, and H. Oja. "Multivariate nonparametric tests in a randomized complete block design." Journal of Multivariate Analysis 85, no. 1 (April 2003): 106–29. http://dx.doi.org/10.1016/s0047-259x(02)00068-4.

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García Ben, Marta, and Víctor J. Yohai. "Robust analysis of variance for a randomized block design." Communications in Statistics - Theory and Methods 21, no. 6 (January 1992): 1779–98. http://dx.doi.org/10.1080/03610929208830878.

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Carvalho, Agnaldo DF, and Giovani O. Silva. "Comparison of analyses types in carrot experiment: square lattice versus randomized blocks design." Horticultura Brasileira 36, no. 2 (June 2018): 217–22. http://dx.doi.org/10.1590/s0102-053620180212.

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ABSTRACT The square lattice 4x4 design was compared to a randomized block design, for carrot F1 hybrids. Sixteen experimental carrot hybrids were evaluated in Brasília, in 2014/15 and 2015/16 agricultural years. A square lattice 4x4 design with three replications was used. The experimental plot consisted of 1.5 m2 useful area. The experimental area was installed on the second half of November 2014 and 2015. Cultural practices were the usually given to the carrot crop in Brazilian savannah region. The incidence of leaf blight was evaluated 90 days after sowing date and, after 100 days, roots were harvested and yield components evaluated. We evaluated the three possible analyses in lattice: 1) as randomized block design; 2) intrablock analysis with adjusted treatments and blocks within unadjusted repetitions; 3) analysis with recovery of interblock information with adjusted treatments. The analysis in lattice permitted to reduce the mean squares of error and coefficients of variation; moreover, these were more efficient than the randomized block design experiments for most evaluated characters. Thus, use of the analysis in lattice is preferred in experiments with carrot when evaluating large number of treatments.

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C. Eze, Francis. "Choice of Confounding in the 2k Factorial Design in 2b Blocks." Academic Journal of Applied Mathematical Sciences, no. 55 (May 15, 2019): 50–56. http://dx.doi.org/10.32861/ajams.55.50.56.

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In 2k complete factorial experiment, the experiment must be carried out in a completely randomized design. When the numbers of factors increase, the number of treatment combinations increase and it is not possible to accommodate all these treatment combinations in one homogeneous block. In this case, confounding in more than one incomplete block becomes necessary. In this paper, we considered the choice of confounding when k > 2. Our findings show that the choice of confounding depends on the number of factors, the number of blocks and their sizes. When two more interactions are to be confounded, their product module 2 should be considered and thereafter, a linear combination equation should be used in allocating the treatment effects in the principal block. Other contents in other blocks are generated by multiplication module 2 of the effects not in the principal block. Partial confounding is recommended for the interactions that cannot be confounded.

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Fu, Yong-Bi, Alvin D. Yanchuk, and Gene Namkoong. "Incomplete block designs for genetic testing: some practical considerations." Canadian Journal of Forest Research 29, no. 12 (December 15, 1999): 1871–78. http://dx.doi.org/10.1139/x99-176.

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Full-sib progeny and clonal trials to evaluate family and clone performance were simulated on a test site with various patterns of environmental variation to examine the effectiveness of randomized incomplete block designs (ICBs) under different design parameters (constant block size, variable block size, block shape and orientation, and family size). Simulations showed that blocks of fixed size 5-20 were effective in removing most site variation in a test with 120 full-sib families of three seedlings per family. Slight variations in block size did not affect the precision of mean estimations much when blocks were considered random. Square blocks were more effective than either column or row blocks in removing both patchy variation and gradients in one direction. With blocks of fixed size 10, increasing family size from 3 to 12 (at the expense of family number, which dropped from 120 to 30) reduced the average variance of family mean contrasts by 75%; most of this reduction (>50%) was achieved at 6 seedlings per family. The differences in precision of mean estimation between full-sib progeny and clonal trials were small with respect to the design parameters and patterns of site variation examined.

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Sirikasemsuk, Kittiwat. "Measure of overall regression sum of squares of symmetric randomized complete block design with a lost observation." International Journal of Engineering & Technology 7, no. 2.3 (March 8, 2018): 50. http://dx.doi.org/10.14419/ijet.v7i2.3.9967.

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A randomized complete block design (RCBD) is useful for analyzing a treatment variable and one block variable under the condition where experimental units are limited. The RCBD is assumed that there is no interaction between the treatment variable and the block variable. This paper considered the symmetric randomized complete block design (SRCBD) with t treatments and t blocks, when a lost value occurs in the experiments. For the analysis of variance for the unbalanced data, the ready-made formulae were not provided in the past. The SRCBD with a lost value was analyzed by means of the fundamental underlying linear regression model in order to determine the reliable mathematical formulae for the fitted parameters and the overall regression sum of squares of experimental data. It is noted that all possible parameters are considered in the overall regression sum of squares which will be helpful for the analysis of variance through the exact approach (the model comparison approach) at a later stage.

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Konstantopoulos, Spyros. "Optimal Design in Three-Level Block Randomized Designs With Two Levels of Nesting." Educational and Psychological Measurement 73, no. 5 (April 25, 2013): 784–802. http://dx.doi.org/10.1177/0013164413485752.

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Sim, Sujin, and Dongjae Kim. "Nonparametric method using placement in a randomized complete block design." Journal of the Korean Data and Information Science Society 24, no. 6 (November 30, 2013): 1401–8. http://dx.doi.org/10.7465/jkdi.2013.24.6.1401.

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19

Klockars, Alan J., and S. Natasha Beretvas. "Analysis of Covariance and Randomized Block Design with Heterogeneous Slopes." Journal of Experimental Education 69, no. 4 (January 2001): 393–410. http://dx.doi.org/10.1080/00220970109599494.

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20

Piepho, Hans‐Peter. "Analysis of a Randomized Block Design with Unequal Subclass Numbers." Agronomy Journal 89, no. 5 (September 1997): 718–23. http://dx.doi.org/10.2134/agronj1997.00021962008900050002x.

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21

Esmailzadeh, Nabaz, and Reza Nikbakht. "Comparison of Variances Homogeneity Tests in Randomized Complete Block Design." Journal of Statistical Sciences 12, no. 2 (March 1, 2019): 323–40. http://dx.doi.org/10.29252/jss.12.2.323.

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22

Gregory, Gavin G. "Cumulative Damage Survival Models for the Randomized Complete Block Design." Biometrical Journal 45, no. 2 (March 2003): 153–64. http://dx.doi.org/10.1002/bimj.200390002.

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Shah, Arvind K., and P. L. Claypool. "Analysis of binary data in the randomized complete block design." Communications in Statistics - Theory and Methods 14, no. 5 (January 1985): 1175–79. http://dx.doi.org/10.1080/03610928508828969.

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Adetola Sanuade, Oluseun, Joel Olayide Amosun, Kehinde David Oyeyemi, Tokunbo Sanmi Fagbemigun, and Jane Idowu Faloyo. "Statistical comparison of Schlumberger arrays using randomized complete block design." Journal of Physics: Conference Series 1299 (August 2019): 012066. http://dx.doi.org/10.1088/1742-6596/1299/1/012066.

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25

Mejza, Iwona, Katarzyna Ambroży-Deręgowska, Kazuhiro Ozawa, Stanisław Mejza, and Shinji Kuriki. "Constructing non-orthogonal split-split-plot designs using some resolvable block designs." Biometrical Letters 57, no. 2 (December 1, 2020): 177–94. http://dx.doi.org/10.2478/bile-2020-0012.

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Summary We consider a new method of constructing non-orthogonal (incomplete) split-split-plot designs (SSPDs) for three (A, B, C) factor experiments. The final design is generated by some resolvable incomplete block design (for the factor A) and by square lattice designs for factors B and C using a modified Kronecker product of those designs (incidence matrices). Statistical properties of the constructed designs are investigated under a randomized-derived linear model. This model is strictly connected with a four-step randomization of units (blocks, whole plots, subplots, sub-subplots inside each block). The final SSPD has orthogonal block structure (OBS) and satisfies the general balance (GB) property. The statistical analysis of experiments performed in the SSPD is based on the analysis of variance often used for multistratum experiments. We characterize the SSPD with respect to the stratum efficiency factors for the basic estimable treatment contrasts. The structures of the vectors defining treatment contrasts are also given.

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Schochet, Peter Z. "Design-Based Estimators for Average Treatment Effects for Multi-Armed RCTs." Journal of Educational and Behavioral Statistics 43, no. 5 (July 19, 2018): 568–93. http://dx.doi.org/10.3102/1076998618786968.

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Design-based methods have recently been developed as a way to analyze randomized controlled trial (RCT) data for designs with a single treatment and control group. This article builds on this framework to develop design-based estimators for evaluations with multiple research groups. Results are provided for a wide range of designs used in education research, including clustered and blocked designs. Because analysis in the multi-armed setting involves pairwise contrasts across the research groups, the key methodological question addressed is: How do the estimators for the two-group design need to be adjusted for multi-armed trials? The critical insight is that in multi-armed trials where the goal is to identify the most effective treatments, the samples for each pairwise contrast are representative of the full set of randomized units, not just of themselves. The implication is that variance terms need to be adjusted slightly under the finite-population framework that can reduce precision, and blocks need to be weighted to reflect the full randomized sample in the block or biases can result. An empirical example using data from a multi-armed education RCT demonstrates the issues.

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Murray, Leigh W. "Missing-Cell Estimates in the Randomized Block Design with Random Effects." American Statistician 42, no. 2 (May 1988): 134. http://dx.doi.org/10.2307/2684487.

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House, Dennis E. "A Nonparametric Version of Williams' Test for a Randomized Block Design." Biometrics 42, no. 1 (March 1986): 187. http://dx.doi.org/10.2307/2531255.

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Shiraishi, Taka-Aki. "Multiple Comparisons Based on StudentizedM-Statistics in a Randomized Block Design." Communications in Statistics - Theory and Methods 39, no. 8-9 (April 21, 2010): 1563–73. http://dx.doi.org/10.1080/03610920802398243.

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Murray, Leigh W. "Missing-Cell Estimates in the Randomized Block Design with Random Effects." American Statistician 42, no. 2 (May 1988): 134–36. http://dx.doi.org/10.1080/00031305.1988.10475544.

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Hee Kim, Dong, and Young Cheol Kim. "Distribution-free tests for umbrella alternatives in a randomized block design." Journal of Nonparametric Statistics 1, no. 4 (January 1992): 277–85. http://dx.doi.org/10.1080/10485259208832530.

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Sim, Songyong. "A test based on quantile score for -sample randomized block design." Statistics & Probability Letters 77, no. 12 (July 2007): 1345–53. http://dx.doi.org/10.1016/j.spl.2007.03.020.

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Souza, Elaine Aparecida de, Isaias Olívio Geraldi, Magno Antonio Patto Ramalho, and Fernando de Lelis Garcia Bertolucci. "Experimental alternatives for evaluation of progenies and clones in eucalyptus breeding programs." Revista Árvore 27, no. 4 (August 2003): 427–34. http://dx.doi.org/10.1590/s0100-67622003000400002.

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The feasibility of using augmented block designs and spatial analysis methods for early stage selection in eucalyptus breeding programs was tested. A total of 113 half-sib progenies of Eucalyptus urophylla and eight clones were evaluated in an 11 x 11 triple lattice experiment at two locations: Posto da Mata (Bahia, Brazil) and São Mateus (Minas Gerais, Brazil). Four checks were randomly allocated within each block. Plots consisted of 15 m long rows containing 6 plants spaced 3 m apart. The girth at breast height (cm/plant) was evaluated at 19 and 26 months of age. Variance analyses were performed according to the following methods: lattice design, randomized complete block design, augmented block design, Papadakis method, moving means method, and check plots. Comparisons among different methods were based on the magnitude of experimental errors and precision of the estimates of genetic and phenotypic parameters. General results indicated that augmented block design is useful to evaluate progenies and clones in early selection in eucalyptus breeding programs using moderate and low selection intensities. However, this design is not suitable for estimating genetic and phenotypic parameters due to its low precision. Check plots, nearest neighbour, Papadakis (1937), and moving means methods were efficient in removing the heterogeneity within blocks. These efficiencies were compared to that in lattice analysis for estimation of genetic and phenotypic parameters.

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GÜL, Hasan Hüseyin, and Hülya BAYRAK. "Proposed nonparametric tests for the ordered alternative in a completely randomized and randomized block mixed design." Cumhuriyet Science Journal 42, no. 1 (March 29, 2021): 191–200. http://dx.doi.org/10.17776/csj.762235.

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Tharangani, Himali, Changwen Lu, Liansheng Zhao, Lu Ma, Xusheng Guo, William P. Weiss, and Dengpan Bu. "Estimation of between-Cow Variability in Nutrient Digestion of Lactating Dairy Cows Fed Corn-Based Diets." Animals 10, no. 8 (August 6, 2020): 1363. http://dx.doi.org/10.3390/ani10081363.

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The objective of this study was to estimate cow variability that can be used to determine the optimal sample size for digestibility trials using lactating dairy cows. Experimental design was randomized complete block design having three blocks and three dietary treatments. Three similarly managed nearby intensive farms were considered as blocks, and three diets were formulated to have 0.7, 1.0, and 1.3 neutral detergent fiber (NDF): starch ratio. In each farm, 18 cows were assigned for each dietary treatment and five sample sizes per each treatment group were simulated by simple random sampling of data from 18, 15, 12, 9 and 6 cows respectively. Intake was not affected by diet or sample size (p > 0.05). Estimated cow variability (as standard deviation) for digestibility of dry matter, NDF and starch were 3.8 g/kg, 5.1 g/kg and 3.3 g/kg, respectively. A major implication of this study is that cow variability is greatest for NDF digestibility and the use of a minimum of 12 cows per dietary treatment is adequate to reliably detect treatment effects on the digestibility of NDF, starch and dry matter using cows fed in groups with randomized block design under these experimental conditions.

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BOYLE, CAROLYN R., and ROY D. MONTGOMERY. "An Application of the Augmented Randomized Complete Block Design to Poultry Research ,." Poultry Science 75, no. 5 (May 1996): 601–7. http://dx.doi.org/10.3382/ps.0750601.

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Kula, Kamile, and Ayşen Apaydin. "Estimation of the Randomized Complete Block Design Parameters with Fuzzy Goal Programming." Mathematical and Computational Applications 16, no. 3 (December 1, 2011): 721–27. http://dx.doi.org/10.3390/mca16030721.

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HU, LiangPing, and YuanJie JIA. "Statistical analysis and SAS solution for quantitative data in randomized block design." Pharmaceutical Care and Research 11, no. 5 (October 31, 2011): 330–32. http://dx.doi.org/10.5428/pcar20110504.

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Jo, Sungdong, and Dongjae Kim. "Nonparametric procedures using aligned method and joint placement in randomized block design." Journal of the Korean Data and Information Science Society 24, no. 1 (January 31, 2013): 95–103. http://dx.doi.org/10.7465/jkdi.2013.24.1.95.

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A. H. Elamir, Elsayed. "Analysis of Mean Absolute Deviation for Randomized Block Design under Laplace Distribution." American Journal of Theoretical and Applied Statistics 4, no. 3 (2015): 138. http://dx.doi.org/10.11648/j.ajtas.20150403.19.

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Giancristofaro, Rosa Arboretti, Livio Corain, and Susanna Ragazzi. "A Comparison among Combination-Based Permutation Statistics for Randomized Complete Block Design." Communications in Statistics - Simulation and Computation 41, no. 7 (August 2012): 964–79. http://dx.doi.org/10.1080/03610918.2012.625752.

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Shiraishi, Taka-Aki, and Shin-Ichi Matsuda. "Closed testing procedures for all pairwise comparisons in a randomized block design." Communications in Statistics - Theory and Methods 47, no. 15 (October 23, 2017): 3571–87. http://dx.doi.org/10.1080/03610926.2017.1359302.

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Kaiser, Lee D. "Nonexistence of blue's under a randomization model for the randomized block design." Journal of Statistical Planning and Inference 22, no. 1 (May 1989): 63–69. http://dx.doi.org/10.1016/0378-3758(89)90065-7.

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Hilgers, R. D., and M. Tamm. "Chronological Bias in Randomized Clinical Trials Arising from Different Types of Unobserved Time Trends." Methods of Information in Medicine 53, no. 06 (2014): 501–10. http://dx.doi.org/10.3414/me14-01-0048.

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SummaryBackground: In clinical trials patients are commonly recruited sequentially over time incurring the risk of chronological bias due to (unobserved) time trends. To minimize the risk of chronological bias, a suitable randomization procedure should be chosen.Objectives: Considering different time trend scenarios, we aim at a detailed evaluation of the extent of chronological bias under permuted block randomization in order to provide recommendations regarding the choice of randomization at the design stage of a clinical trial and to assess the maximum extent of bias for a realized sequence in the analysis stage.Methods: For the assessment of chronological bias we consider linear, logarithmic and stepwise trends illustrating typical changes during recruitment in clinical practice. Bias and variance of the treatment effect estimator as well as the empirical type I error rate when applying the t-test are investigated. Different sample sizes, block sizes and strengths of time trends are considered.Results: Using large block sizes, a notable bias exists in the estimate of the treatment effect for specific sequences. This results in a heavily inflated type I error for realized worst-case sequences and an enlarged mean squared error of the treatment effect estimator. Decreasing the block size restricts these effects of time trends. Already applying permuted block randomization with two blocks instead of the random allocation rule achieves a good reduction of the mean squared error and of the inflated type I error. Averaged over all sequences, the type I error of the t-test is far below the nominal significance level due to an overestimated variance.Conclusions: Unobserved time trends can induce a strong bias in the treatment effect estimate and in the test decision. Therefore, already in the design stage of a clinical trial a suitable randomization procedure should be chosen. According to our results, small block sizes should be preferred, but also medium block sizes are sufficient to restrict chronological bias to an acceptable extent if other contrary aspects have to be considered (e.g. serious risk of selection bias). Regardless of the block size, a blocked ANOVA should be used because the t-test is far too conservative, even for weak time trends.

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Deshpande, Dr Vidya V. "Repeated Randomised Block Design." International Journal of Scientific Research 2, no. 1 (June 1, 2012): 171–72. http://dx.doi.org/10.15373/22778179/jan2013/58.

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Katsileros, Anastasios, and Christos Koukouvinos. "Evaluation of experimental designs in durum wheat trials." Biometrical Letters 52, no. 2 (December 1, 2015): 105–14. http://dx.doi.org/10.1515/bile-2015-0010.

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Abstract Variability among experimental plots may be a relevant problem in field genotype experiments, especially when a large number of entries are involved. Four field trials on 24 durum wheat genotypes were conducted in 2013/14 in order to evaluate the efficiency of Incomplete Block, Alpha and Augmented designs in comparison with the traditional Randomized Complete Block Design (RCBD). The results showed that the RCBD can be replaced by an Alpha design, which provides better control of variability among the experimental units when the number of treatments to be tested in an experiment exceeds twenty. The ranking of the genotypes across the four designs was not constant.

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Novita, Elida, Idah Andriyani, Zakina Romadona, and Hendra Andiananta Pradana. "Effect of Variations on the Type and Size of Organic Waste on Compost Water Content and Growth of Chili Plant." Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan 17, no. 1 (January 20, 2020): 19–28. http://dx.doi.org/10.14710/presipitasi.v17i1.19-28.

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Organic waste is potential as a compost material. It was supported by the amount of nutrient content. The compost block is a technology for utilizing organic material as a plant nursery. The research objective was to examine the variants of type and size of organic matter effect on the water content in compost blocks and vegetative growth of chili plants. The materials type of compost block were coffee pulp, tobacco petiole, and rice husks. Size variations in compost block were 10, 40 and 80 mesh. Experimental design on the types and sizes of organic materials using a Completely Randomized Design. The experimental results show that the highest value of water content in compost block with the watering of the same volume amount 51.22 mL of water is coffee pulp material with a range value of 40 - 50%. Coffee pulp and rice husk as compost block material influenced the vegetative growth of chili plants. The statistical tests that show a significant effect on the type and size of organic waste material on the chili plant growth i.e height and leaf area plants. The results become a consideration for variation type and size of organic waste choice for compost block materials.

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Shin, Jason J., Chris L. McCrum, Craig S. Mauro, and Dharmesh Vyas. "Pain Management After Hip Arthroscopy: Systematic Review of Randomized Controlled Trials and Cohort Studies." American Journal of Sports Medicine 46, no. 13 (October 13, 2017): 3288–98. http://dx.doi.org/10.1177/0363546517734518.

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Background: Hip arthroscopy is often associated with significant postoperative pain and opioid-associated side effects. Effective pain management after hip arthroscopy improves patient recovery and satisfaction and decreases opioid-related complications. Purpose: To collect, examine, and provide a comprehensive review of the available evidence from randomized controlled trials and comparative studies on pain control after hip arthroscopy. Study Design: Systematic review. Methods: Using the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, a systematic review of the literature for postoperative pain control after hip arthroscopy was performed using electronic databases. Only comparative clinical studies with level 1 to 3 evidence comparing a method of postoperative pain control with other modalities or placebo were included in this review. Case series and studies without a comparative cohort were excluded. Results: Several methods of pain management have been described for hip arthroscopy. A total of 14 studies met our inclusion criteria: 3 on femoral nerve block, 3 on lumbar plexus block, 3 on fascia iliaca block, 4 on intra-articular injections, 2 on soft tissue surrounding surgical site injection, and 2 on celecoxib (4 studies compared 2 or more methods of analgesia). The heterogeneity of the studies did not allow for pooling of data. Single-injection femoral nerve blocks and lumbar plexus blocks provided improved analgesia, but increased fall rates were observed. Fascia iliaca blocks do not provide adequate pain relief when compared with surgical site infiltration with local anesthetic and are associated with increased risk of cutaneous nerve deficits. Patients receiving lumbar plexus block experienced significantly decreased pain compared with fascia iliaca block. Portal site and periacetabular injections provide superior analgesia compared with intra-articular injections alone. Preoperative oral celecoxib, compared with placebo, resulted in earlier time to discharge and provided significant pain relief up to 24 hours. Conclusion: Perioperative nerve blocks provide effective pain management after hip arthroscopy but must be used with caution to decrease risk of falls. Intra-articular and portal site injections with local anesthetics and preoperative celecoxib can decrease opioid consumption. There is a lack of high-quality evidence on this topic, and further research is needed to determine the best approach to manage postoperative pain and optimize patient satisfaction.

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Shiraishi, Taka-aki, and Shin-ichi Matsuda. "Nonparametric Closed Testing Procedures for All Pairwise Comparisons in a Randomized Block Design." Japanese Journal of Biometrics 40, no. 1 (August 1, 2019): 1–14. http://dx.doi.org/10.5691/jjb.40.1.

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Azadeh, A., S. M. Asadzadeh, R. Jafari-Marandi, S. Nazari-Shirkouhi, G. Baharian Khoshkhou, S. Talebi, and A. Naghavi. "Optimum estimation of missing values in randomized complete block design by genetic algorithm." Knowledge-Based Systems 37 (January 2013): 37–47. http://dx.doi.org/10.1016/j.knosys.2012.06.014.

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Journal articles on the topic 'Randomized block design'

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