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Journal articles on the topic 'Power and sample size'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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

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7

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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8

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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9

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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10

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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11

Kim, Hyungjin Myra. "Sample Size Determination and Power." International Statistical Review 83, no. 1 (2015): 168–69. http://dx.doi.org/10.1111/insr.12095_8.

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12

Van Mullekom, Jennifer H. "Sample Size and Power Determination." Journal of Quality Technology 47, no. 2 (2015): 205–6. http://dx.doi.org/10.1080/00224065.2015.11918126.

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13

Krzywinski, Martin, and Naomi Altman. "Erratum: Power and sample size." Nature Methods 12, no. 9 (2015): 893. http://dx.doi.org/10.1038/nmeth0915-893e.

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14

Dupont, William D., and Walton D. Plummer. "Power and sample size calculations." Controlled Clinical Trials 11, no. 2 (1990): 116–28. http://dx.doi.org/10.1016/0197-2456(90)90005-m.

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15

Bagiella, Emilia, and Helena Chang. "Power analysis and sample size calculation." Journal of Molecular and Cellular Cardiology 133 (August 2019): 214–16. http://dx.doi.org/10.1016/j.yjmcc.2019.01.006.

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16

Mascha, Edward J., and Thomas R. Vetter. "Significance, Errors, Power, and Sample Size." Anesthesia & Analgesia 126, no. 2 (2018): 691–98. http://dx.doi.org/10.1213/ane.0000000000002741.

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17

Ingram, Richard. "Power analysis and sample size estimation." NT Research 3, no. 2 (1998): 132–39. http://dx.doi.org/10.1177/174498719800300210.

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18

Francis, Kennon. "Sample size determination: power in research." Trends in Pharmacological Sciences 6 (January 1985): 350–52. http://dx.doi.org/10.1016/0165-6147(85)90163-4.

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19

Columb, M. O., and A. Stevens. "Power analysis and sample size calculations." Current Anaesthesia & Critical Care 19, no. 1 (2008): 12–14. http://dx.doi.org/10.1016/j.cacc.2007.03.011.

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20

Denne, Jonathan S. "Sample size recalculation using conditional power." Statistics in Medicine 20, no. 17-18 (2001): 2645–60. http://dx.doi.org/10.1002/sim.734.

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21

Columb, MO, and MS Atkinson. "Statistical analysis: sample size and power estimations." BJA Education 16, no. 5 (2016): 159–61. http://dx.doi.org/10.1093/bjaed/mkv034.

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22

Harvey, Bart J., and Thomas A. Lang. "Hypothesis Testing, Study Power, and Sample Size." Chest 138, no. 3 (2010): 734–37. http://dx.doi.org/10.1378/chest.10-0067.

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23

McClelland, Gary H. "Increasing statistical power without increasing sample size." American Psychologist 55, no. 8 (2000): 963–64. http://dx.doi.org/10.1037/0003-066x.55.8.963.

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24

Schober, Patrick, and Thomas R. Vetter. "Sample Size and Power in Clinical Research." Anesthesia & Analgesia 129, no. 2 (2019): 323. http://dx.doi.org/10.1213/ane.0000000000004316.

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25

Phillips, Ceib. "Sample size and power: What Is enough?" Seminars in Orthodontics 8, no. 2 (2002): 67–76. http://dx.doi.org/10.1053/sodo.2002.32074.

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26

Bernstein, Bruce A. "An Introduction to Sample Size and Power." Journal of Developmental & Behavioral Pediatrics 29, no. 6 (2008): 516–22. http://dx.doi.org/10.1097/dbp.0b013e3181900224.

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27

McCrum-Gardner, Evie. "Sample size and power calculations made simple." International Journal of Therapy and Rehabilitation 17, no. 1 (2010): 10–14. http://dx.doi.org/10.12968/ijtr.2010.17.1.45988.

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28

Streiner, David L. "Sample Size and Power in Psychiatric Research*." Canadian Journal of Psychiatry 35, no. 7 (1990): 616–20. http://dx.doi.org/10.1177/070674379003500712.

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29

Brown, Todd M. "Power and sample size in clinical studies." Journal of Nuclear Cardiology 22, no. 6 (2015): 1314–15. http://dx.doi.org/10.1007/s12350-015-0288-z.

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30

Blackwelder, William C., and John J. Gart. "Sample size and power for comparing vaccines." Controlled Clinical Trials 12, no. 5 (1991): 643. http://dx.doi.org/10.1016/0197-2456(91)90137-b.

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31

Gaskill, Brianna N., and Joseph P. Garner. "Power to the People: Power, Negative Results and Sample Size." Journal of the American Association for Laboratory Animal Science 59, no. 1 (2020): 9–16. http://dx.doi.org/10.30802/aalas-jaalas-19-000042.

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The practical application of statistical power is becoming an increasingly important part of experimental design, data analysis, and reporting. Power is essential to estimating sample size as part of planning studies and obtaining ethical approval for them. Furthermore, power is essential for publishing and interpreting negative results. In this manuscript, we review what power is, how it can be calculated, and reporting recommendations if a null result is found. Power can be thought of as reflecting the signal to noise ratio of an experiment. The conventional wisdom that statistical power is driven by sample size (which increases the signal in the data), while true, is a misleading oversimplification. Relatively little discussion covers the use of experimental designs which control and reduce noise. Even small improvements in experimental design can achieve high power at much lower sample sizes than (for instance) a simple t test. Failure to report experimental design or the proposed statistical test on animal care and use protocols creates a dilemma for IACUCs, because it is unknown whether sample size has been correctly calculated. Traditional power calculations, which are primarily provided for animal number justifications, are only available for simple, yet low powered, experimental designs, such as paired t tests. Thus, in most controlled experimental studies, the only analyses for which power can be calculated are those that inheriently have low statistical power; these analyses should not be used because they require more animals than necessary. We provide suggestions for more powerful experimental designs (such as randomized block and factorial designs) that increase power, and we describe methods to easily calculate sample size for these designs that are suitable for IACUC number justifications. Finally we also provide recommendations for reporting negative results, so that readers and reviewers can determine whether an experiment had sufficient power. The use of more sophisticated designs in animal experiments will inevitably improve power, reproducibility, and reduce animal use.
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32

Ruvuna, Francis. "Unequal Center Sizes, Sample Size, and Power in Multicenter Clinical Trials." Drug Information Journal 38, no. 4 (2004): 387–94. http://dx.doi.org/10.1177/009286150403800409.

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33

Self, Steven G., and Robert H. Mauritsen. "Power/Sample Size Calculations for Generalized Linear Models." Biometrics 44, no. 1 (1988): 79. http://dx.doi.org/10.2307/2531897.

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34

Rompas, Alexander, Charalampos Tsirmpas, Athanasios Anastasiou, Dimitra Iliopoulou, and Dimitris Koutsouris. "Statistical Power and Sample Size in Personalized Medicine." International Journal of Systems Biology and Biomedical Technologies 2, no. 2 (2013): 72–88. http://dx.doi.org/10.4018/ijsbbt.2013040105.

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Personalized medicine (PM) is a rapidly growing field of healthcare and medicine. The advantage of a personalized medicine is the availability of each person’s unique genetic and genomic print. The healthcare that incorporates personalized medicine provides coordinated, continuous patient-specific data. The goal of personalized medicine is to promote health wellness, satisfaction, and to increase the likelihood of a successful disease prevention, detection and treatment. This form of medicine, apart from patient’s personal data and medicine-biological measurements, uses genomic information data to understand the molecular structure of the disease and to optimize health care strategies and drug therapies. Clinical trials that investigate personalized approaches are subject to special rules, for example, pertain the selection of participating patients. In personalized medicine, a certain genetic profile must be identified so that the treatment can work. This is why potential participants are first screened and selected accordingly for clinical trials. The special design of such clinical trials has an impact on the evaluation of data collected during the given study.
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35

Cai, Jianwen, and Donglin Zeng. "Sample Size/Power Calculation for Case-Cohort Studies." Biometrics 60, no. 4 (2004): 1015–24. http://dx.doi.org/10.1111/j.0006-341x.2004.00257.x.

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36

Jones, S. R. "An introduction to power and sample size estimation." Emergency Medicine Journal 20, no. 5 (2003): 453–58. http://dx.doi.org/10.1136/emj.20.5.453.

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37

Gatsonis, Constantine, and Allan R. Sampson. "Multiple correlation: Exact power and sample size calculations." Psychological Bulletin 106, no. 3 (1989): 516–24. http://dx.doi.org/10.1037/0033-2909.106.3.516.

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38

Brakenhoff, TB, KCB Roes, and S. Nikolakopoulos. "Bayesian sample size re-estimation using power priors." Statistical Methods in Medical Research 28, no. 6 (2018): 1664–75. http://dx.doi.org/10.1177/0962280218772315.

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The sample size of a randomized controlled trial is typically chosen in order for frequentist operational characteristics to be retained. For normally distributed outcomes, an assumption for the variance needs to be made which is usually based on limited prior information. Especially in the case of small populations, the prior information might consist of only one small pilot study. A Bayesian approach formalizes the aggregation of prior information on the variance with newly collected data. The uncertainty surrounding prior estimates can be appropriately modelled by means of prior distributions. Furthermore, within the Bayesian paradigm, quantities such as the probability of a conclusive trial are directly calculated. However, if the postulated prior is not in accordance with the true variance, such calculations are not trustworthy. In this work we adapt previously suggested methodology to facilitate sample size re-estimation. In addition, we suggest the employment of power priors in order for operational characteristics to be controlled.
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39

Laska, Eugene M., Morris Meisner, and Carole Siegel. "Power and Sample Size in Cost- Effectiveness Analysis." Medical Decision Making 19, no. 3 (1999): 339–43. http://dx.doi.org/10.1177/0272989x9901900312.

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40

Yang, M. C. K., J. J. Yang, R. A. McIndoe, and J. X. She. "Microarray experimental design: power and sample size considerations." Physiological Genomics 16, no. 1 (2003): 24–28. http://dx.doi.org/10.1152/physiolgenomics.00037.2003.

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Gene expression analysis using high-throughput microarray technology has become a powerful approach to study systems biology. The exponential growth in microarray experiments has spawned a number of investigations into the reliability and reproducibility of this type of data. However, the sample size requirements necessary to obtain statistically significant results has not had as much attention. We report here statistical methods for the determination of the sufficient number of subjects necessary to minimize the false discovery rate while maintaining high power to detect differentially expressed genes. Two experimental designs were considered: 1) a comparison between two groups at a single time point, and 2) a comparison of two experimental groups with sequential time points. Computer programs are available for the methods discussed in this paper and are adaptable to more complicated situations.
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41

Kemal, Ozgur. "Power Analysis and Sample Size, When and Why?" Turkish Archives of Otorhinolaryngology 58, no. 1 (2020): 3–4. http://dx.doi.org/10.5152/tao.2020.0330.

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42

Lee, Mei-Ling Ting, and G. A. Whitmore. "Power and sample size for DNA microarray studies." Statistics in Medicine 21, no. 23 (2002): 3543–70. http://dx.doi.org/10.1002/sim.1335.

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43

Gillett, Raphael. "An Average Power Criterion for Sample Size Estimation." Statistician 43, no. 3 (1994): 389. http://dx.doi.org/10.2307/2348574.

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44

Rabbee, N., B. A. Coull, C. Mehta, N. Patel, and P. Senchaudhuri. "Power and sample size for ordered categorical data." Statistical Methods in Medical Research 12, no. 1 (2003): 73–84. http://dx.doi.org/10.1191/0962280203sm317ra.

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45

Lin, Wei-Jiun, Huey-Miin Hsueh, and James J. Chen. "Power and sample size estimation in microarray studies." BMC Bioinformatics 11, no. 1 (2010): 48. http://dx.doi.org/10.1186/1471-2105-11-48.

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46

Jung, Sin-Ho, and S. Stanley Young. "Power and Sample Size Calculation for Microarray Studies." Journal of Biopharmaceutical Statistics 22, no. 1 (2011): 30–42. http://dx.doi.org/10.1080/10543406.2010.500066.

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47

Smith, A. B. "Feel the power: sample size and meaningful effects." British Journal of Oral and Maxillofacial Surgery 56, no. 8 (2018): 650–52. http://dx.doi.org/10.1016/j.bjoms.2018.07.008.

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48

Zhang, Lanju, Lu Cui, and Bo Yang. "Optimal flexible sample size design with robust power." Statistics in Medicine 35, no. 19 (2016): 3385–96. http://dx.doi.org/10.1002/sim.6931.

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49

Kang, Hyun. "Sample size determination and power analysis using the G*Power software." Journal of Educational Evaluation for Health Professions 18 (July 30, 2021): 17. http://dx.doi.org/10.3352/jeehp.2021.18.17.

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Appropriate sample size calculation and power analysis have become major issues in research and publication processes. However, the complexity and difficulty of calculating sample size and power require broad statistical knowledge, there is a shortage of personnel with programming skills, and commercial programs are often too expensive to use in practice. The review article aimed to explain the basic concepts of sample size calculation and power analysis; the process of sample estimation; and how to calculate sample size using G*Power software (latest ver. 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) with 5 statistical examples. The null and alternative hypothesis, effect size, power, alpha, type I error, and type II error should be described when calculating the sample size or power. G*Power is recommended for sample size and power calculations for various statistical methods (F, t, χ2, Z, and exact tests), because it is easy to use and free. The process of sample estimation consists of establishing research goals and hypotheses, choosing appropriate statistical tests, choosing one of 5 possible power analysis methods, inputting the required variables for analysis, and selecting the “Calculate” button. The G*Power software supports sample size and power calculation for various statistical methods (F, t, χ2, z, and exact tests). This software is helpful for researchers to estimate the sample size and to conduct power analysis.
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50

Grüner, Sven. "Sample Size Calculation in Economic Experiments." Jahrbücher für Nationalökonomie und Statistik 240, no. 6 (2020): 791–823. http://dx.doi.org/10.1515/jbnst-2019-0020.

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AbstractClinical studies and economic experiments are often conducted with randomized controlled trials. In clinical studies, power calculations are carried out as a standard. But what’s about economic experiments? After describing the basic idea of the calculation procedure in a brief tutorial, I tackle the practice of sample size calculations in the field of experimental economics by considering the publications of 5 economic journals in the period 2000–2018. These are two top-ranked economic journals (Quarterly Journal of Economics and American Economic Review), the leading field journals in the area of experimental economics (Experimental Economics) and behavioral sciences (Journal of Economic Behavior and Organization), and a leading field journal in environmental economics (Environmental and Resource Economics). In contrast to clinical drug trials, sample size calculations have rarely been carried out by experimental economists. But the number of power calculations has slightly increased in recent years, especially in the top-ranked journals of economics. However, this can be partly explained by the fact that field experiments (in which scholars pay more attention to power analyses than in lab experiments these days) play an important role in these journals.
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