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Relevant bibliographies by topics / Power and sample size

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Academic literature on the topic 'Power and sample size'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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Journal articles on the topic "Power and sample size"

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KILIC, Selim. "Sample size, power concepts and sample size calculation." Journal of Mood Disorders 2, no. 3 (2012): 140. http://dx.doi.org/10.5455/jmood.20120921043306.

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HOGAN, JOSEPH W., and JEFFREY F. PEIPERT. "Power and Sample Size." Clinical Obstetrics and Gynecology 41, no. 2 (1998): 257–66. http://dx.doi.org/10.1097/00003081-199806000-00006.

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Whitney, JoAnne D. "Sample Size and Power." Journal of Wound, Ostomy and Continence Nursing 26, no. 6 (1999): 314–19. http://dx.doi.org/10.1097/00152192-199911000-00009.

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Sheps, Sam. "Sample Size and Power." Journal of Investigative Surgery 6, no. 6 (1993): 469–75. http://dx.doi.org/10.3109/08941939309141636.

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Krzywinski, Martin, and Naomi Altman. "Power and sample size." Nature Methods 10, no. 12 (2013): 1139–40. http://dx.doi.org/10.1038/nmeth.2738.

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Sedgwick, P. "Sample size and power." BMJ 343, sep07 4 (2011): d5579. http://dx.doi.org/10.1136/bmj.d5579.

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Haas, Janet P. "Sample size and power." American Journal of Infection Control 40, no. 8 (2012): 766–67. http://dx.doi.org/10.1016/j.ajic.2012.05.020.

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Pingsmann, A. "Sample Size and Statistical Power." Journal of Bone and Joint Surgery-American Volume 82, no. 9 (2000): 1361. http://dx.doi.org/10.2106/00004623-200009000-00028.

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Freedman, Kevin B., and Joseph Bernstein. "Sample Size and Statistical Power." Journal of Bone and Joint Surgery-American Volume 82, no. 9 (2000): 1361. http://dx.doi.org/10.2106/00004623-200009000-00029.

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Krzywinski, Martin, and Naomi Altman. "Erratum: Power and sample size." Nature Methods 11, no. 2 (2014): 210. http://dx.doi.org/10.1038/nmeth0214-210d.

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Dissertations / Theses on the topic "Power and sample size"

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You, Zhiying. "Power and sample size of cluster randomized trials." Thesis, Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2009r/you.pdf.

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Gibbons, Christopher. "Determination of power and sample size for Levene's test." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447667.

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Cunningham, Tina. "Power and Sample Size for Three-Level Cluster Designs." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/148.

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Over the past few decades, Cluster Randomized Trials (CRT) have become a design of choice in many research areas. One of the most critical issues in planning a CRT is to ensure that the study design is sensitive enough to capture the intervention effect. The assessment of power and sample size in such studies is often faced with many challenges due to several methodological difficulties. While studies on power and sample size for cluster designs with one and two levels are abundant, the evaluation of required sample size for three-level designs has been generally overlooked. First, the nesting effect introduces more than one intracluster correlation into the model. Second, the variance structure of the estimated treatment difference is more complicated. Third, sample size results required for several levels are needed. In this work, we developed sample size and power formulas for the three-level data structures based on the generalized linear mixed model approach. We derived explicit and general power and sample size equations for detecting a hypothesized effect on continuous Gaussian outcomes and binary outcomes. To confirm the accuracy of the formulas, we conducted several simulation studies and compared the results. To establish a connection between the theoretical formulas and their applications, we developed a SAS user-interface macro that allowed the researchers to estimate sample size for a three-level design for different scenarios. These scenarios depend on which randomization level is assigned and whether or not there is an interaction effect.

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Chang, Yu-Wei. "Sample Size Determination for a Three-arm Biosimilar Trial." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/298932.

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Statistics<br>Ph.D.<br>The equivalence assessment usually consists of three tests and is often conducted through a three-arm clinical trial. The first two tests are to demonstrate the superiority of the test treatment and the reference treatment to placebo, and they are followed by the equivalence test between the test treatment and the reference treatment. The equivalence is commonly defined in terms of mean difference, mean ratio or ratio of mean differences, i.e. the ratio of the mean difference of the test and placebo to the mean difference of the reference and placebo. In this dissertation, the equivalence assessment for both continuous data and discrete data are discussed. For the continuous case, the test of the ratio of mean differences is applied. The advantage of this test is that it combines a superiority test of the test treatment over the placebo and an equivalence test through one hypothesis. For the discrete case, the two-step equivalence assessment approach is studied for both Poisson and negative binomial data. While a Poisson distribution implies that population mean and variance are the same, the advantage of applying a negative binomial model is that it accounts for overdispersion, which is a common phenomenon of count medical endpoints. The test statistics, power function, and required sample size examples for a three-arm equivalence trial are given for both continuous and discrete cases. In addition, discussions on power comparisons are complemented with numerical results.<br>Temple University--Theses

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Guan, Tianyuan. "Sample Size Calculations in Simple Linear Regression: A New Approach." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627667392849137.

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Tongur, Can. "Small sample performances of two tests for overidentifying restrictions." Thesis, Uppsala University, Department of Economics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6367.

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<p>Two new specification tests for overidentifying restrictions proposed by Hahn and Hausman (2002:b) are here tested and compared to the classical Sargan test. Power properties are found to be very similar in overall performance, while Sargan generally has better size than the new tests. Also, size is distorted for one of the new tests, thus a tendency to reject prevails. In addition, sometimes severe bias is found which affects the tests’ performances, something that differs from earlier studies.</p>

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Tong, Bo. "More accurate two sample comparisons for skewed populations." Diss., Kansas State University, 2016. http://hdl.handle.net/2097/35783.

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Doctor of Philosophy<br>Department of Statistics<br>Haiyan Wang<br>Various tests have been created to compare the means of two populations in many scenarios and applications. The two-sample t-test, Wilcoxon Rank-Sum Test and bootstrap-t test are commonly used methods. However, methods for skewed two-sample data set are not well studied. In this dissertation, several existing two sample tests were evaluated and four new tests were proposed to improve the test accuracy under moderate sample size and high population skewness. The proposed work starts with derivation of a first order Edgeworth expansion for the test statistic of the two sample t-test. Using this result, new two-sample tests based on Cornish Fisher expansion (TCF tests) were created for both cases of common variance and unequal variances. These tests can account for population skewness and give more accurate test results. We also developed three new tests based on three transformations (T[subscript i] test, i = 1; 2; 3) for the pooled case, which can be used to eliminate the skewness of the studentized statistic. In this dissertation, some theoretical properties of the newly proposed tests are presented. In particular, we derived the order of type I error rate accuracy of the pooled two-sample t-test based on normal approximation (TN test), the TCF and T[subscript i] tests. We proved that these tests give the same theoretical type I error rate under skewness. In addition, we derived the power function of the TCF and TN tests as a function of the population parameters. We also provided the detailed conditions under which the theoretical power of the two-sample TCF test is higher than the two-sample TN test. Results from extensive simulation studies and real data analysis were also presented in this dissertation. The empirical results further confirm our theoretical results. Comparing with commonly used two-sample parametric and nonparametric tests, our new tests (TCF and Ti) provide the same empirical type I error rate but higher power.

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Sawrie, David Franklin. "Preemptive power analysis for the consulting statistician novel applications of internal pilot design and information based monitoring systems /." Thesis, Birmingham, Ala. : University of Alabama at Birmingham, 2007. https://www.mhsl.uab.edu/dt/2009r/sawrie.pdf.

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Bell, Melanie L., Amy L. Whitehead, and Steven A. Julious. "Guidance for using pilot studies to inform the design of intervention trials with continuous outcomes." DOVE MEDICAL PRESS LTD, 2018. http://hdl.handle.net/10150/627081.

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Background: A pilot study can be an important step in the assessment of an intervention by providing information to design the future definitive trial. Pilot studies can be used to estimate the recruitment and retention rates and population variance and to provide preliminary evidence of efficacy potential. However, estimation is poor because pilot studies are small, so sensitivity analyses for the main trial's sample size calculations should be undertaken. Methods: We demonstrate how to carry out easy-to-perform sensitivity analysis for designing trials based on pilot data using an example. Furthermore, we introduce rules of thumb for the size of the pilot study so that the overall sample size, for both pilot and main trials, is minimized. Results: The example illustrates how sample size estimates for the main trial can alter dramatically by plausibly varying assumptions. Required sample size for 90% power varied from 392 to 692 depending on assumptions. Some scenarios were not feasible based on the pilot study recruitment and retention rates. Conclusion: Pilot studies can be used to help design the main trial, but caution should be exercised. We recommend the use of sensitivity analyses to assess the robustness of the design assumptions for a main trial.

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Senteney, Michael H. "A Monte Carlo Study to Determine Sample Size for Multiple Comparison Procedures in ANOVA." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou160433478343909.

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Books on the topic "Power and sample size"

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Verma, J. P., and Priyam Verma. Determining Sample Size and Power in Research Studies. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5204-5.

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Desu, M. M. Sample size methodology. Academic Press, 1990.

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Salar, Kemal. Sample size for correlation estimates. Naval Postgraduate School, 1989.

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Chow, Shein-Chung. Sample size calculations in clinical research. 2nd ed. Chapman & Hall/CRC, 2007.

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Daplyn, M. G. Sample size determination for formal surveys. Pakistan Agricultural Research Council, 1994.

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Brush, Gary G. How to choose the proper sample size. American Society for Quality Control, 1988.

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Zarnoch, Stanley J. Determining sample size for tree utilization surveys. U.S. Dept. of Agriculture, Forest Service, Southern Research Station, 2004.

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Reiser, B. Sample size choice for strength stress models. University of Toronto, Dept. of Statistics, 1988.

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Handbook of sample size guidelines for clinical trials. CRC Press, 1989.

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1938-, Herrendörfer Günter, ed. Experimental design: Sample size determination and block designs. D. Reidel Pub. Co., 1986.

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Book chapters on the topic "Power and sample size"

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Cleophas, Ton J., Aeilko H. Zwinderman, and Toine F. Cleophas. "Power, Sample Size." In Statistics Applied to Clinical Trials: Self-Assessment Book. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0285-1_3.

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Lyman, Stephen. "Power and Sample Size." In Basic Methods Handbook for Clinical Orthopaedic Research. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58254-1_20.

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Lee, Hang. "Sample Size and Power." In Foundations of Applied Statistical Methods. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02402-8_8.

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Murphy, Jessica, Eric K. Duku, Achilles Thoma, and Charles H. Goldsmith. "Power and Sample Size." In Evidence-Based Surgery. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05120-4_29.

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Lin, Lawrence, A. S. Hedayat, and Wenting Wu. "Sample Size and Power." In Statistical Tools for Measuring Agreement. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0562-7_4.

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Stanberry, Larissa. "Power and Sample Size." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1192.

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Neale, Michael C., and Lon R. Cardon. "Power and Sample Size." In Methodology for Genetic Studies of Twins and Families. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-8018-2_9.

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Garrett-Mayer, Elizabeth. "Power and Sample Size." In Principles and Practice of Clinical Trials. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-52677-5_213-1.

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Case, L. Douglas, and Walter T. Ambrosius. "Power and Sample Size." In Topics in Biostatistics. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-530-5_19.

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Armstrong, Richard A., and Anthony C. Hilton. "Statistical Power and Sample Size." In Statistical Analysis in Microbiology: Statnotes. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch9.

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Conference papers on the topic "Power and sample size"

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Ocheredko, Oleksandr. "MCMC Bootstrap Based Approach to Power and Sample Size Evaluation." In Annual Meeting of the International Society for Data Science and Analytics. ISDSA Press, 2020. http://dx.doi.org/10.35566/isdsa2019c5.

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Goodhue, D., W. Lewis, and R. Thompson. "PLS, Small Sample Size, and Statistical Power in MIS Research." In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06). IEEE, 2006. http://dx.doi.org/10.1109/hicss.2006.381.

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Mestre, Xavier, Ben A. Johnson, and Yuri I. Abramovich. "Source Power Estimation for Array Processing Applications under Low Sample Size Constraints." In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366381.

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Dong, Guangling, Fenghua He, Yu Yao, Hui Zhao, and Chi He. "Sample size determination method for credibility evaluation based on statistical power analysis." In 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2014. http://dx.doi.org/10.1109/wcica.2014.7053546.

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Sakai, Tetsuya. "Statistical Significance, Power, and Sample Sizes." In SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2016. http://dx.doi.org/10.1145/2911451.2911492.

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Ostwald, Dirk, Sebastian Schneider, Rasmus Bruckner, and Lilla Horvath. "Power, positive predictive value, and sample size calculations for random field theory-based fMRI inference." In 2019 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1050-0.

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Rafajlovski, G., K. Najdenkoski, L. Nikoloski, and H. Haidvogl. "Power quality monitoring and sample size analysis beyond EN 50160 and IEC 61000-4-30." In 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.0574.

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Adamec, Václav. "Power of heteroskedasticity tests in presence of various types of skedastic function and sample size." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4992278.

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Takai, Shun, Thomas J. Smith, and Marcos Esterman. "A Power Analysis to Determine Appropriate Sample Size for the Study of Student Design-Team Effectiveness." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22345.

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Abstract Forming collaborative teams is a critical first step in team-project-based design courses as team composition directly affects not only teamwork processes and outcomes but also teamwork skills and experience. While various approaches have been used to form teams, the best methodology has not been found due to a lack of understanding of how team compositions impact team performance and teamwork learning. We need to establish a team effectiveness model for student design teams that describes relationships between team characteristics and team performance or teamwork learning. One of many challenges in such an effort is to estimate an appropriate sample size to achieve statistically significant results before starting data collection. In this paper, we demonstrate a power analysis for determining an appropriate sample size, i.e., the number of student teams, before we study the effectiveness of student design-teams. We first present a hypothesized team effectiveness model for student design teams that shows possible relationships among team factors. We then illustrate a statistical analysis procedure for studying the team effectiveness model using structural equation modeling (SEM) or path analysis. We finally demonstrate a power analysis of SEM for determining the appropriate sample size for studying the team effectiveness model.

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Pissoort, D., T. Claeys, D. Vanoost, F. Vanhee, J. Catrysse, and Christian Brull. "Influence of sample shape and size on the shielding effectiveness of EMI when characterized with the stripline test method." In 2017 IEEE International Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMCSI). IEEE, 2017. http://dx.doi.org/10.1109/isemc.2017.8077949.

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Reports on the topic "Power and sample size"

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W Djimeu, Eric, and Deo-Gracias Houndolo. Power calculation for causal inference in social science: sample size and minimum detectable effect determination. International Initiative for Impact Evaluation (3ie), 2015. http://dx.doi.org/10.23846/wp0026.

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Lo, Andrew, and A. Craig MacKinlay. The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation. National Bureau of Economic Research, 1988. http://dx.doi.org/10.3386/t0066.

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Hansen, Clifford. Sample size for PV lifetime project. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1367408.

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Valenciano, Marilyn. Developmental sentence scoring sample size comparison. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.3108.

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Callan, Peggy. Developmental sentence scoring sample size comparison. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6053.

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Zarnoch, Stanley J., James W. Bentley, and Tony G. Johnson. Determining sample size for tree utilization surveys. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2004. http://dx.doi.org/10.2737/srs-rp-34.

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Zarnoch, Stanley J., James W. Bentley, and Tony G. Johnson. Determining sample size for tree utilization surveys. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2004. http://dx.doi.org/10.2737/srs-rp-34.

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Royset, Johannes O. On Sample Size Control in Sample Average Approximations for Solving Smooth Stochastic Programs. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada513136.

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McConnell, Brendon, and Marcos Vera-Hernandez. Going beyond simple sample size calculations: a practitioner's guide. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.ifs.2015.1517.

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Davidson, J. R. Verification of the Accuracy of Sample-Size Equation Calculations for Visual Sample Plan Version 0.9C. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/786780.

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