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Relevant bibliographies by topics / Chi-square test

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Academic literature on the topic 'Chi-square test'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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Journal articles on the topic "Chi-square test"

1

Kilic, Selim. "Chi-square Test." Journal of Mood Disorders 6, no. 3 (2016): 180. http://dx.doi.org/10.5455/jmood.20160803110534.

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Daya, Salim. "Chi-square test." Evidence-based Obstetrics & Gynecology 3, no. 1 (March 2001): 3–4. http://dx.doi.org/10.1054/ebog.2001.0223.

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Franke, Todd Michael, Timothy Ho, and Christina A. Christie. "The Chi-Square Test." American Journal of Evaluation 33, no. 3 (November 8, 2011): 448–58. http://dx.doi.org/10.1177/1098214011426594.

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Pandis, Nikolaos. "The chi-square test." American Journal of Orthodontics and Dentofacial Orthopedics 150, no. 5 (November 2016): 898–99. http://dx.doi.org/10.1016/j.ajodo.2016.08.009.

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Germogenov, A. P., and A. F. Ronzhin. "A Sequential Chi-Square Test." Theory of Probability & Its Applications 29, no. 2 (January 1985): 397–403. http://dx.doi.org/10.1137/1129052.

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Wu, Jyh-Shyang, and Wen-Shuenn Deng. "Averaged shifted chi-square test." Journal of Nonparametric Statistics 24, no. 1 (March 2012): 39–57. http://dx.doi.org/10.1080/10485252.2011.608849.

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Koletsi, Despina, and Nikolaos Pandis. "The chi-square test for trend." American Journal of Orthodontics and Dentofacial Orthopedics 150, no. 6 (December 2016): 1066–67. http://dx.doi.org/10.1016/j.ajodo.2016.10.001.

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Leorato, S. "A chi-square-type test for covariances." Journal of Nonparametric Statistics 18, no. 2 (February 2006): 159–80. http://dx.doi.org/10.1080/10485250600687051.

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บวรกิติวงศ์, สุชาดา. "ความแกร่งของสถิติทดสอบไคสแควร์ Robustness of the Chi-square Test." Journal of Education Studies 44, no. 3 (July 2016): 212–20. http://dx.doi.org/10.58837/chula.educu.44.3.14.

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Rofiqoh, Siti Nur Indah, Ririn Triratnasari, Nelly Rahmatillah, Fatin Fadhilah Hasib, and Alimin Alimin. "Uji chi-square pada Penelitian Ekonomi Syariah." International Conference on Islamic Economic (ICIE) 2, no. 1 (July 13, 2023): 152–61. http://dx.doi.org/10.58223/icie.v2i1.200.

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In Islamic economic research, we may collect categorical variable data. Using statistical analysis, chi-square test can be an alternative to evaluate those data. Furthermore, the analysis of chi-square test can be aimed at evaluating the differences between study groups according to the assign degree of error. This article also explains the purpose of chi-square test, the Karl Person chi-square hypothesis, what type of data can use in chi-square test, the assumptions of chi-square analysis and also the rule to analyze categorical variables data in sharia economics research.

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Dissertations / Theses on the topic "Chi-square test"

1

Roberts, Georgia Ruth Carleton University Dissertation Psychology. "Contributions to chi-squared tests with survey data." Ottawa, 1985.

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De, Champlain André F. "Assessing test dimensionality using two approximate chi-square statistics." Thesis, University of Ottawa (Canada), 1992. http://hdl.handle.net/10393/7848.

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Cheng, Kai-ho. "The Chi-square test when the expected frequencies are less than 5." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39558708.

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鄭啟豪 and Kai-ho Cheng. "The Chi-square test when the expected frequencies are less than 5." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39558708.

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Mullen, Jerry D. (Jerry Davis). "A Comparison of Some Continuity Corrections for the Chi-Squared Test in 3 x 3, 3 x 4, and 3 x 5 Tables." Thesis, North Texas State University, 1987. https://digital.library.unt.edu/ark:/67531/metadc331001/.

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This study was designed to determine whether chis-quared based tests for independence give reliable estimates (as compared to the exact values provided by Fisher's exact probabilities test) of the probability of a relationship between the variables in 3 X 3, 3 X 4 , and 3 X 5 contingency tables when the sample size is 10, 20, or 30. In addition to the classical (uncorrected) chi-squared test, four methods for continuity correction were compared to Fisher's exact probabilities test. The four methods were Yates' correction, two corrections attributed to Cochran, and Mantel's correction. The study was modeled after a similar comparison conducted on 2 X 2 contingency tables and published by Michael Haber.

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Steele, Michael C., and n/a. "The Power of Categorical Goodness-Of-Fit Statistics." Griffith University. Australian School of Environmental Studies, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20031006.143823.

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The relative power of goodness-of-fit test statistics has long been debated in the literature. Chi-Square type test statistics to determine 'fit' for categorical data are still dominant in the goodness-of-fit arena. Empirical Distribution Function type goodness-of-fit test statistics are known to be relatively more powerful than Chi-Square type test statistics for restricted types of null and alternative distributions. In many practical applications researchers who use a standard Chi-Square type goodness-of-fit test statistic ignore the rank of ordinal classes. This thesis reviews literature in the goodness-of-fit field, with major emphasis on categorical goodness-of-fit tests. The continued use of an asymptotic distribution to approximate the exact distribution of categorical goodness-of-fit test statistics is discouraged. It is unlikely that an asymptotic distribution will produce a more accurate estimation of the exact distribution of a goodness-of-fit test statistic than a Monte Carlo approximation with a large number of simulations. Due to their relatively higher powers for restricted types of null and alternative distributions, several authors recommend the use of Empirical Distribution Function test statistics over nominal goodness-of-fit test statistics such as Pearson's Chi-Square. In-depth power studies confirm the views of other authors that categorical Empirical Distribution Function type test statistics do not have higher power for some common null and alternative distributions. Because of this, it is not sensible to make a conclusive recommendation to always use an Empirical Distribution Function type test statistic instead of a nominal goodness-of-fit test statistic. Traditionally the recommendation to determine 'fit' for multivariate categorical data is to treat categories as nominal, an approach which precludes any gain in power which may accrue from a ranking, should one or more variables be ordinal. The presence of multiple criteria through multivariate data may result in partially ordered categories, some of which have equal ranking. This thesis proposes a modification to the currently available Kolmogorov-Smirnov test statistics for ordinal and nominal categorical data to account for situations of partially ordered categories. The new test statistic, called the Combined Kolmogorov-Smirnov, is relatively more powerful than Pearson's Chi-Square and the nominal Kolmogorov-Smirnov test statistic for some null and alternative distributions. A recommendation is made to use the new test statistic with higher power in situations where some benefit can be achieved by incorporating an Empirical Distribution Function approach, but the data lack a complete natural ordering of categories. The new and established categorical goodness-of-fit test statistics are demonstrated in the analysis of categorical data with brief applications as diverse as familiarity of defence programs, the number of recruits produced by the Merlin bird, a demographic problem, and DNA profiling of genotypes. The results from these applications confirm the recommendations associated with specific goodness-of-fit test statistics throughout this thesis.

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Steele, Michael C. "The Power of Categorical Goodness-Of-Fit Statistics." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366717.

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The relative power of goodness-of-fit test statistics has long been debated in the literature. Chi-Square type test statistics to determine 'fit' for categorical data are still dominant in the goodness-of-fit arena. Empirical Distribution Function type goodness-of-fit test statistics are known to be relatively more powerful than Chi-Square type test statistics for restricted types of null and alternative distributions. In many practical applications researchers who use a standard Chi-Square type goodness-of-fit test statistic ignore the rank of ordinal classes. This thesis reviews literature in the goodness-of-fit field, with major emphasis on categorical goodness-of-fit tests. The continued use of an asymptotic distribution to approximate the exact distribution of categorical goodness-of-fit test statistics is discouraged. It is unlikely that an asymptotic distribution will produce a more accurate estimation of the exact distribution of a goodness-of-fit test statistic than a Monte Carlo approximation with a large number of simulations. Due to their relatively higher powers for restricted types of null and alternative distributions, several authors recommend the use of Empirical Distribution Function test statistics over nominal goodness-of-fit test statistics such as Pearson's Chi-Square. In-depth power studies confirm the views of other authors that categorical Empirical Distribution Function type test statistics do not have higher power for some common null and alternative distributions. Because of this, it is not sensible to make a conclusive recommendation to always use an Empirical Distribution Function type test statistic instead of a nominal goodness-of-fit test statistic. Traditionally the recommendation to determine 'fit' for multivariate categorical data is to treat categories as nominal, an approach which precludes any gain in power which may accrue from a ranking, should one or more variables be ordinal. The presence of multiple criteria through multivariate data may result in partially ordered categories, some of which have equal ranking. This thesis proposes a modification to the currently available Kolmogorov-Smirnov test statistics for ordinal and nominal categorical data to account for situations of partially ordered categories. The new test statistic, called the Combined Kolmogorov-Smirnov, is relatively more powerful than Pearson's Chi-Square and the nominal Kolmogorov-Smirnov test statistic for some null and alternative distributions. A recommendation is made to use the new test statistic with higher power in situations where some benefit can be achieved by incorporating an Empirical Distribution Function approach, but the data lack a complete natural ordering of categories. The new and established categorical goodness-of-fit test statistics are demonstrated in the analysis of categorical data with brief applications as diverse as familiarity of defence programs, the number of recruits produced by the Merlin bird, a demographic problem, and DNA profiling of genotypes. The results from these applications confirm the recommendations associated with specific goodness-of-fit test statistics throughout this thesis.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Australian School of Environmental Studies
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8

Stasiukaitytė, Irma. "Infarkto gydymo įvairiais vaistais statistiniai tyrimo metodai." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2004. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2004~D_20040610_174400-77084.

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The goal of the present thesis is to ascertain the impact of different drugs, intended for the infarction treatment; investigation of the other factors, which may stipulate bleeding in the course of the operation and within the post-operation period. The investigation was carried out in two stages. During the first stage the data was accumulated for processing (investigation of the sample homogeneity and normality); the second stage implied solution of the statistical tasks (solution of the tasks, which correspond to the goals of the thesis). The methods of data analysis and the models of binary logistic and linear logistic regression were applied. 89 patients, who survived the myocarditis infarction, were investigated and it was ascertained that there is no huge difference in between the tranexamic acid and aprotinin. The bleeding complications may be caused by aspirin, which has been used before the operation. One of the complications, i.e. the drainage, may be predicted, judging from the amount of haemoglobin, haematocrit in the blood as well as creatinin. The model of the binary logistic regression assisted us in drawing the conclusion that smoking, hypothermia, euroscore and other factors produce an impact upon the bleeding complications.

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Pang, Xiao L. "Assessing the performance of the approximate chi-square and Stout's T statistics with different test structures." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0010/NQ52277.pdf.

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Wu, Baohua. "Data Driven Approaches to Testing Homogeneity of Intraclass Correlation Coefficients." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/math_theses/92.

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The test of homogeneity for intraclass correlation coefficients has been one of the active topics in statistical research. Several chi-square tests have been proposed to test the homogeneity of intraclass correlations in the past few decades. The big concern for them is that these methods are seriously biased when sample sizes are not large. In this thesis, data driven approaches are proposed to testing the homogeneity of intraclass correlation coefficients of several populations. Through simulation study, data driven methods have been proved to be less biased and accurate than some commonly used chi-square tests.

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Books on the topic "Chi-square test"

1

Graham, G. The chi-square method to test the normality of the distribution of time and magnitude residuals of the South African National Seismological Network. Pretoria: Geological Survey, Dept. of Mineral and Energy Affairs, Republic of South Africa, 1987.

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Greenwood, P. E. A guide to chi-squared testing. New York: Wiley, 1996.

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3

Srivastava, M. S. Comparison of approximate saddlepoint and saddlepoint method with Edgeworth expansion. Toronto: University of Toronto, Dept. of Statistics, 1987.

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Apodaca, Mark. Evaluation of phase 1 screening for the 1996 Agricultural Resource Management Study. Fairfax, Va: U.S. Dept. of Agriculture, National Agricultural Statistics Service, Research Division, 1998.

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Yau, Wai Kwok. Approximation of tail probability of a linear combination of noncentral chi-squares by saddlepoint method. Toronto: University of Toronto, Dept. of Statistics, 1988.

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6

Böhning, Dankmar. On minimizing chi-square distances under the hypothesis of homogeneity of independence for a two-way contingency table. Osnabrück: Fachbereich Psychologie, Universität Osnabrück, 1985.

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Stewart, Connie, and Rose McCloskey. Learn to Use the Chi-Square Homogeneity Test in Minitab With Data From a 2015 Health Care Observational Study. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526488237.

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Scott Jones, Julie. Learn to Clean and Prepare Data for a Chi-Square Test of Independence in SPSS Using Data From the CSEW (2013/14) (Teaching Dataset). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529605266.

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Consortium for Mathematics and Its Applications (U.S.), Chedd-Angier Production Company, American Statistical Association, and Annenberg Media, eds. Against all odds--inside statistics: Disc 3, programs 9-12. S. Burlington, VT: Annenberg Media, 2011.

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Balakrishnan, N., M. S. Nikulin, and Vassilly Voinov. Chi-Squared Goodness of Fit Tests with Applications. Elsevier Science & Technology Books, 2013.

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Book chapters on the topic "Chi-square test"

1

Tallarida, Ronald J., and Rodney B. Murray. "Chi-Square Test." In Manual of Pharmacologic Calculations, 140–42. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4974-0_43.

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Schumacker, Randall, and Sara Tomek. "Chi-Square Test." In Understanding Statistics Using R, 169–75. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6227-9_8.

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Panneerselvam, R. "Chi-Square Test." In Business Statistics Using Excel, 373–91. London: Routledge India, 2023. http://dx.doi.org/10.4324/9781032671703-11.

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Mikyo Oh, Deborah, and Fred Pyrczak. "Chi-Square Test." In Making Sense of Statistics, 205–10. 8th ed. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003299356-38.

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Luo, Yanxia, and Hongbo Liu. "Chi-Square Test." In Textbook of Medical Statistics, 125–40. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7390-3_9.

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Rayat, Charan Singh. "Chi-Square Test (χ2 – Test)." In Statistical Methods in Medical Research, 69–79. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0827-7_9.

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Miah, Abdul Quader. "The Chi-Square Test." In Applied Statistics for Social and Management Sciences, 171–81. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0401-8_8.

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Das, Basant Kumar, Dharm Nath Jha, Sanjeev Kumar Sahu, Anil Kumar Yadav, Rohan Kumar Raman, and M. Kartikeyan. "Chi-Square Test of Significance." In Concept Building in Fisheries Data Analysis, 81–94. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4411-6_5.

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Gooch, Jan W. "Chi-square Test of Association." In Encyclopedic Dictionary of Polymers, 973. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15176.

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Gooch, Jan W. "Chi-square Test of Homogeneity." In Encyclopedic Dictionary of Polymers, 973. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15177.

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Conference papers on the topic "Chi-square test"

1

Farrow, Emily, Junbo Li, Farhan Zaki, and Ashwin Lall. "Accessible Streaming Algorithms for the Chi-Square Test." In SSDBM 2020: 32nd International Conference on Scientific and Statistical Database Management. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3400903.3400905.

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Zhiyong, Cai, Shen Ying, and Shen Changxiang. "Detection of Insertional Covert Channels Using Chi-square Test." In 2009 International Conference on Multimedia Information Networking and Security. IEEE, 2009. http://dx.doi.org/10.1109/mines.2009.296.

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Ouyang, Jie, Nilesh Patel, and Ishwar K. Sethi. "Chi-Square Test Based Decision Trees Induction in Distributed Environment." In 2008 IEEE International Conference on Data Mining Workshops. IEEE, 2008. http://dx.doi.org/10.1109/icdmw.2008.37.

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Kalla, Dinesh, and Arvind Chandrasekaran. "Heart Disease Prediction using Chi-Square Test and Linear Regression." In 9th International Conference on Artificial Intelligence. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130712.

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Heart disease is most common disease reported currently in the United States among both the genders and according to official statistics about fifty percent of the American population is suffering from some form of cardiovascular disease. This paper performs chi square tests and linear regression analysis to predict heart disease based on the symptoms like chest pain and dizziness. This paper will help healthcare sectors to provide better assistance for patients suffering from heart disease by predicting it in beginning stage of disease. Chi square test is conducted to identify whether there is a relation between chest pain and heart disease cases in the United States by analyzing heart disease dataset from IEEE Data Port. The test results and analysis show that males in the United States are most likely to develop heart disease with the symptoms like chest pain, dizziness, shortness of breath, fatigue, and nausea. This test also shows that there is a week corelation of 0.5 is identified which shows people with all ages including teens can face heart diseases and its prevalence increase with age. Also, the tests indicate that 90 percent of the participant who are facing severe chest pain is suffering from heart disease where majority of the successful heart disease identified is in males and only 10 percent participants are identified as healthy. The evaluated p-values are much greater than the statistical threshold of 0.05 which concludes factors like sex, Exercise angina, Cholesterol, old peak, ST_Slope, obesity, and blood sugar play significant role in onset of cardiovascular disease. We have tested the dataset with prediction model built on logistic regression and observed an accuracy of 85.12 percent.

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Rajput, Saeed A., A. S. Pandya, S. Saxena, and Steve Ostroff. "Evaluating mobile phone handoff behavior using chi-square statistical test." In Southeastcon 2008. IEEE, 2008. http://dx.doi.org/10.1109/secon.2008.4494322.

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Liu, Xintong, Qing Yu, Yutian Hu, Wenting Lu, Aipan Zhanga, and Xujiang Bo. "Cause Analysis of Ship Accidents Based on Chi-Square Test." In Proceedings of the 2nd International Conference on Big Data Economy and Digital Management, BDEDM 2023, January 6-8, 2023, Changsha, China. EAI, 2023. http://dx.doi.org/10.4108/eai.6-1-2023.2330244.

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Oakes, Michael, Robert Gaaizauskas, Helene Fowkes, Anna Jonsson, Vincent Wan, and Micheline Beaulieu. "A method based on the chi-square test for document classification." In the 24th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/383952.384080.

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Belaoued, Mohamed, Smaine Mazouzi, Seddari Noureddine, and Bougueroua Salah. "Using Chi-Square test and heuristic search for detecting metamorphic malware." In 2015 First International Conference on New Technologies of Information and Communication (NTIC). IEEE, 2015. http://dx.doi.org/10.1109/ntic.2015.7368758.

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Anezakis, Vardis-Dimitris, Konstantinos Demertzis, and Lazaros Iliadis. "Classifying with fuzzy chi-square test: The case of invasive species." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5043910.

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Marisa, Hanifa, Kamel Ariffin Mohd Atan, and Isthrinayagy S. Krishnarajah. "Reconnaissance On Chi-Square Test Procedure For Determining Two Species Association." In INTERNATIONAL CONFERENCE ON MATHEMATICAL BIOLOGY 2007: ICMB07. AIP, 2008. http://dx.doi.org/10.1063/1.2883836.

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Reports on the topic "Chi-square test"

1

Conover, W. J., D. D. Cox, and H. F. Martz. A chi-square goodness-of-fit test for non-identically distributed random variables: with application to empirical Bayes. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/645488.

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Kwesiga, Victoria, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Compliance to GMP guidelines for Herbal Manufacturers in East Africa: A Position Paper. Purdue University, November 2021. http://dx.doi.org/10.5703/1288284317428.

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With the global increase in the use of traditional and complementary remedies for the prevention and treatment of illness, the quality and safety of these medicines have become a significant concern for all regulatory authorities. Herbal medicines are the most commonly used form of traditional and complementary medicines in the world and the efficacy and safety of herbal medicines, like conventional medicines, largely depends on their quality from planting to harvesting, preprocessing and final processing. Due to the inherent complexity of herbal medicines, often containing an array of active compounds, the primary processing of herbal medicines has a direct influence on their quality. Quality concerns are the reason why the medicines regulatory agencies insist that manufacturers of medicines strictly follow Good Manufacturing Practices since it is an essential tool to prevent instances of contamination, mix-ups, deviations, failures and errors. However, a strict application of GMP requirements is expensive and would drive the prices of the manufactured products up. As a result, a maturity level grading of facilities is proposed as a way of justifying the costs incurred for manufacturers desiring to reach a broader market and investing in continuous improvement. 36 Good Manufacturing Practice (GMP) inspection reports of local herbal manufacturers conducted by National Drug Authority were analyzed to establish the type and extent of deficiencies to GMP requirements for local herbal manufacturers in Uganda. The different GMP chapters and related sub-parameters constituted the variables used for the analysis of conformity to requirements. The primary outcome variable was the conclusion regarding compliance or noncompliance of the inspected local herbal manufacturing facility. GMP parameters that were frequently defaulted by local herbal manufacturers and the corresponding frequencies were identified. The Pearson Chi-square test was applied independently on each category to find the association that existed between conformity and the questions in each category. Only 22% (8) of the 30 inspected facilities were found to comply with GMP requirements, as per National Drug Authority (NDA) guidelines; while the majority of the facilities, 28 (78%), were found not to comply. Of the facilities inspected, 25 were undergoing GMP inspection for the first time. A total of 1,236 deficiency observations were made in the 36 inspection reports reviewed for the study. The mean for all deficiencies was 34.3, and the standard deviation was 15.829. 91.5% of the facilities did not have mechanisms for a record of market complaints; 80.9% did not meet documentation requirements; 78.9% did not have quality control measures in place, and 65.7% did not meet stores requirements. By encouraging a culture of self/voluntary improvement through the introduction of listing of manufacturers based on a maturity level grading, the National Drug Authority will improve the Herbal Medicines sector as per the mandate of improving the herbal medicine industry. Also, increased sensitization of all relevant stakeholders regarding the requirements for GMP should be intensified.

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McDonagh, Marian S., Jesse Wagner, Azrah Y. Ahmed, Rongwei Fu, Benjamin Morasco, Devan Kansagara, and Roger Chou. Living Systematic Review on Cannabis and Other Plant-Based Treatments for Chronic Pain. Agency for Healthcare Research and Quality (AHRQ), October 2021. http://dx.doi.org/10.23970/ahrqepccer250.

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Objectives. To evaluate the evidence on benefits and harms of cannabinoids and similar plant-based compounds to treat chronic pain. Data sources. Ovid® MEDLINE®, PsycINFO®, Embase®, the Cochrane Library, and SCOPUS® databases, reference lists of included studies, submissions received after Federal Register request were searched to July 2021. Review methods. Using dual review, we screened search results for randomized controlled trials (RCTs) and observational studies of patients with chronic pain evaluating cannabis, kratom, and similar compounds with any comparison group and at least 1 month of treatment or followup. Dual review was used to abstract study data, assess study-level risk of bias, and rate the strength of evidence. Prioritized outcomes included pain, overall function, and adverse events. We grouped studies that assessed tetrahydrocannabinol (THC) and/or cannabidiol (CBD) based on their THC to CBD ratio and categorized them as high-THC to CBD ratio, comparable THC to CBD ratio, and low-THC to CBD ratio. We also grouped studies by whether the product was a whole-plant product (cannabis), cannabinoids extracted or purified from a whole plant, or synthetic. We conducted meta-analyses using the profile likelihood random effects model and assessed between-study heterogeneity using Cochran’s Q statistic chi square and the I2 test for inconsistency. Magnitude of benefit was categorized into no effect or small, moderate, and large effects. Results. From 2,850 abstracts, 20 RCTs (N=1,776) and 7 observational studies (N=13,095) assessing different cannabinoids were included; none of kratom. Studies were primarily short term, and 75 percent enrolled patients with a variety of neuropathic pain. Comparators were primarily placebo or usual care. The strength of evidence (SOE) was low, unless otherwise noted. Compared with placebo, comparable THC to CBD ratio oral spray was associated with a small benefit in change in pain severity (7 RCTs, N=632, 0 to10 scale, mean difference [MD] −0.54, 95% confidence interval [CI] −0.95 to −0.19, I2=28%; SOE: moderate) and overall function (6 RCTs, N=616, 0 to 10 scale, MD −0.42, 95% CI −0.73 to −0.16, I2=24%). There was no effect on study withdrawals due to adverse events. There was a large increased risk of dizziness and sedation and a moderate increased risk of nausea (dizziness: 6 RCTs, N=866, 30% vs. 8%, relative risk [RR] 3.57, 95% CI 2.42 to 5.60, I2=0%; sedation: 6 RCTs, N=866, 22% vs. 16%, RR 5.04, 95% CI 2.10 to 11.89, I2=0%; and nausea: 6 RCTs, N=866, 13% vs. 7.5%, RR 1.79, 95% CI 1.20 to 2.78, I2=0%). Synthetic products with high-THC to CBD ratios were associated with a moderate improvement in pain severity, a moderate increase in sedation, and a large increase in nausea (pain: 6 RCTs, N=390 to 10 scale, MD −1.15, 95% CI −1.99 to −0.54, I2=39%; sedation: 3 RCTs, N=335, 19% vs. 10%, RR 1.73, 95% CI 1.03 to 4.63, I2=0%; nausea: 2 RCTs, N=302, 12% vs. 6%, RR 2.19, 95% CI 0.77 to 5.39; I²=0%). We found moderate SOE for a large increased risk of dizziness (2 RCTs, 32% vs. 11%, RR 2.74, 95% CI 1.47 to 6.86, I2=0%). Extracted whole-plant products with high-THC to CBD ratios (oral) were associated with a large increased risk of study withdrawal due to adverse events (1 RCT, 13.9% vs. 5.7%, RR 3.12, 95% CI 1.54 to 6.33) and dizziness (1 RCT, 62.2% vs. 7.5%, RR 8.34, 95% CI 4.53 to 15.34). We observed a moderate improvement in pain severity when combining all studies of high-THC to CBD ratio (8 RCTs, N=684, MD −1.25, 95% CI −2.09 to −0.71, I2=50%; SOE: moderate). Evidence on whole-plant cannabis, topical CBD, low-THC to CBD, other cannabinoids, comparisons with active products, and impact on use of opioids was insufficient to draw conclusions. Other important harms (psychosis, cannabis use disorder, and cognitive effects) were not reported. Conclusions. Low to moderate strength evidence suggests small to moderate improvements in pain (mostly neuropathic), and moderate to large increases in common adverse events (dizziness, sedation, nausea) and study withdrawal due to adverse events with high- and comparable THC to CBD ratio extracted cannabinoids and synthetic products in short-term treatment (1 to 6 months). Evidence for whole-plant cannabis, and other comparisons, outcomes, and PBCs were unavailable or insufficient to draw conclusions. Small sample sizes, lack of evidence for moderate and long-term use and other key outcomes, such as other adverse events and impact on use of opioids during treatment, indicate that more research is needed.

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Chou, Roger, Jesse Wagner, Azrah Y. Ahmed, Benjamin J. Morasco, Devan Kansagara, Shelley Selph, Rebecca Holmes, and Rongwei Fu. Living Systematic Review on Cannabis and Other Plant-Based Treatments for iii Chronic Pain: 2022 Update. Agency for Healthcare Research and Quality (AHRQ), September 2022. http://dx.doi.org/10.23970/ahrqepccer250update2022.

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Abstract:

Objectives. To update the evidence on benefits and harms of cannabinoids and similar plant-based compounds to treat chronic pain using a living systematic review approach. Data sources. Ovid® MEDLINE®, PsycINFO®, Embase®, the Cochrane Library, and SCOPUS® databases; reference lists of included studies; and submissions received after Federal Register request were searched to April 4, 2022. Review methods. Using dual review, we screened search results for randomized controlled trials (RCTs) and observational studies of patients with chronic pain evaluating cannabis, kratom, and similar compounds with any comparison group and at least 1 month of treatment or followup. Dual review was used to abstract study data, assess study-level risk of bias, and rate the strength of evidence (SOE). Prioritized outcomes included pain, overall function, and adverse events. We grouped studies that assessed tetrahydrocannabinol (THC) and/or cannabidiol (CBD) based on their THC to CBD ratio and categorized them as comparable THC to CBD ratio, high-THC to CBD ratio, and low-THC to CBD ratio. We also grouped studies by whether the product was a whole-plant product (cannabis), cannabinoids extracted or purified from a whole plant, or a synthetic product. We conducted meta-analyses using the profile likelihood random effects model and assessed between-study heterogeneity using Cochran’s Q statistic chi square test and the I2 statistic. Magnitude of benefit was categorized as no effect or small, moderate, and large effects. Results. From 3,283 abstracts, 21 RCTs (N=1,905) and 8 observational studies (N=13,769) assessing different cannabinoids were included; none evaluated kratom. Studies were primarily short term, and 59 percent enrolled patients with neuropathic pain. Comparators were primarily placebo or usual care. The SOE was low unless otherwise noted. Compared with placebo, comparable THC to CBD ratio oral spray was associated with a small benefit in change in pain severity (7 RCTs, N=632, 0 to10 scale, mean difference [MD] −0.54, 95% confidence interval [CI] −0.95 to −0.19, I2=39%; SOE: moderate) and overall function (6 RCTs, N=616, 0 to 10 scale, MD −0.42, 95% CI −0.73 to −0.16, I2=32%). There was no effect on study withdrawals due to adverse events. There was a large increased risk of dizziness and sedation, and a moderate increased risk of nausea (dizziness: 6 RCTs, N=866, 31.0% vs. 8.0%, relative risk [RR] 3.57, 95% CI 2.42 to 5.60, I2=0%; sedation: 6 RCTs, N=866, 8.0% vs. 1.2%, RR 5.04, 95% CI 2.10 to 11.89, I2=0%; and nausea: 6 RCTs, N=866, 13% vs. 7.5%, RR 1.79, 95% CI 1.19 to 2.77, I2=0%). Synthetic products with high-THC to CBD ratios were associated with a moderate improvement in pain severity, a moderate increase in sedation, and a large increase in nausea (pain: 6 RCTs, N=390, 0 to 10 scale, MD −1.15, 95% CI −1.99 to −0.54, I2=48%; sedation: 3 RCTs, N=335, 19% vs. 10%, RR 1.73, 95% CI 1.03 to 4.63, I2=28%; nausea: 2 RCTs, N=302, 12.3% vs. 6.1%, RR 2.19, 95% CI 0.77 to 5.39; I²=0%). We also found moderate SOE for a large increased risk of dizziness (2 RCTs, 32% vs. 11%, RR 2.74, 95% CI 1.47 to 6.86, I2=40%). Extracted whole-plant products with high-THC to CBD ratios (oral) were associated with a large increased risk of study withdrawal due to adverse events (1 RCT, 13.9% vs. 5.7%, RR 3.12, 95% CI 1.54 to 6.33) and dizziness (1 RCT, 62.2% vs. 7.5%, RR 8.34, 95% CI 4.53 to 15.34); outcomes assessing benefit were not reported or insufficient. We observed a moderate improvement in pain severity when combining all studies of high-THC to CBD ratio (8 RCTs, N=684, MD −1.25, 95% CI −2.09 to −0.71, I2=58%; SOE: moderate). Evidence (including observational studies) on whole-plant cannabis, topical or oral CBD, low-THC to CBD, other cannabinoids, comparisons with active products or between cannabis-related products, and impact on use of opioids was insufficient to draw conclusions. Other important harms (psychosis, cannabis use disorder, and cognitive effects) were not reported. Conclusions. Low to moderate strength evidence suggests small to moderate improvements in pain (mostly neuropathic), and moderate to large increases in common adverse events (dizziness, sedation, nausea) with high- and comparable THC to CBD ratio extracted cannabinoids and synthetic products during short-term treatment (1 to 6 months); high-THC to CBD ratio products were also associated with increased risk of withdrawal due to adverse events. Evidence for whole-plant cannabis and other comparisons, outcomes, and plant-based compounds was unavailable or insufficient to draw conclusions. Small sample sizes, lack of evidence for moderate and long-term use and other key outcomes, such as other adverse events and impact on use of opioids during treatment, indicate that more research is needed.

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Chou, Roger, Azrah Y. Ahmed, Christina Bougatsos, Benjamin J. Morasco, Rebecca Holmes, Terran Gilbreath, and Rongwei Fu. Living Systematic Review on Cannabis and Other Plant-Based Treatments for Chronic Pain: 2022 Update—Surveillance Report 2. Agency for Healthcare Research and Quality (AHRQ), January 2023. http://dx.doi.org/10.23970/ahrqepccer250.2022updatesr2.

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Abstract:

Objectives. To update the evidence on benefits and harms of cannabinoids and similar plant-based compounds to treat chronic pain using a living systematic review approach. Data sources. Ovid® MEDLINE®, PsycINFO®, Embase®, the Cochrane Library, and SCOPUS® databases; reference lists of included studies; and submissions received after Federal Register request were searched to October 24, 2022. Review methods. Using dual review, we screened search results for randomized controlled trials (RCTs) and observational studies of patients with chronic pain evaluating cannabis, kratom, and similar compounds with any comparison group and at least 1 month of treatment or followup. Dual review was used to abstract study data, assess study-level risk of bias, and rate the strength of evidence (SOE). Prioritized outcomes included pain, overall function, and adverse events. We grouped studies that assessed tetrahydrocannabinol (THC) and/or cannabidiol (CBD) based on their THC to CBD ratio and categorized them as comparable THC to CBD ratio, high-THC to CBD ratio, and low-THC to CBD ratio. We also grouped studies by whether the product was a whole-plant product (cannabis), cannabinoids extracted or purified from a whole plant, or a synthetic product. We conducted meta-analyses using the profile likelihood random effects model and assessed between-study heterogeneity using Cochran’s Q statistic chi square test and the I2 statistic. Magnitude of benefit was categorized as no effect or small, moderate, and large effects. Results. From a total of 3,568 abstracts, 21 RCTs (N=1,905) and 9 observational studies (N=15,079) assessing different cannabinoids were included; none evaluated kratom. Studies were primarily short term, and 60 percent enrolled patients with neuropathic pain. Comparators were primarily placebo or usual care. The SOE was low unless otherwise noted. Compared with placebo, comparable THC to CBD ratio oral spray was associated with a small benefit in pain severity (7 RCTs, N=632, 0 to 10 scale, mean difference [MD] −0.54, 95% confidence interval [CI] −0.95 to −0.19, I2=39%; SOE: moderate) and overall function (6 RCTs, N=616, 0 to 10 scale, MD −0.42, 95% CI −0.73 to −0.16, I2=32%). There was no effect on study withdrawals due to adverse events. There was a large increased risk of dizziness and sedation, and a moderate increased risk of nausea (dizziness: 6 RCTs, N=866, 31.0% vs. 8.0%, relative risk [RR] 3.57, 95% CI 2.42 to 5.60, I2=0%; sedation: 6 RCTs, N=866, 8.0% vs. 1.2%, RR 5.04, 95% CI 2.10 to 11.89, I2=0%; and nausea: 6 RCTs, N=866, 13% vs. 7.5%, RR 1.79, 95% CI 1.19 to 2.77, I2=0%). Synthetic products with high-THC to CBD ratios were associated with a moderate improvement in pain severity, a moderate increase in sedation, and a large increase in nausea (pain: 6 RCTs, N=390, 0 to 10 scale, MD −1.15, 95% CI −1.99 to −0.54, I2=48%; sedation: 3 RCTs, N=335, 19% vs. 10%, RR 1.73, 95% CI 1.03 to 4.63, I2=28%; nausea: 2 RCTs, N=302, 12.3% vs. 6.1%, RR 2.19, 95% CI 0.77 to 5.39; I²=0%). We also found moderate SOE for a large increased risk of dizziness (2 RCTs, 32% vs. 11%, RR 2.74, 95% CI 1.47 to 6.86, I2=40%). Extracted whole-plant products with high-THC to CBD ratios (oral) were associated with a large increased risk of study withdrawal due to adverse events (1 RCT, 13.9% vs. 5.7%, RR 3.12, 95% CI 1.54 to 6.33) and dizziness (1 RCT, 62.2% vs. 7.5%, RR 8.34, 95% CI 4.53 to 15.34); outcomes assessing benefit were not reported or insufficient. We observed a moderate improvement in pain severity when combining all studies of high-THC to CBD ratio (8 RCTs, N=684, MD −1.25, 95% CI −2.09 to −0.71, I2=58%; SOE: moderate). Evidence (including observational studies) on whole-plant cannabis, topical or oral CBD, low-THC to CBD, other cannabinoids, comparisons with active products or between cannabis-related products, and impact on use of opioids was insufficient to draw conclusions. Other important harms (psychosis, cannabis use disorder, and cognitive effects) were not reported. Conclusions. Low to moderate strength evidence suggests small to moderate improvements in pain (mostly neuropathic), and moderate to large increases in common adverse events (dizziness, sedation, nausea) with high and comparable THC to CBD ratio extracted cannabinoids and synthetic products during short-term treatment (1 to 6 months); high-THC to CBD ratio products were also associated with increased risk of withdrawal due to adverse events. Evidence for whole-plant cannabis and other comparisons, outcomes, and plant-based compounds was unavailable or insufficient to draw conclusions. Small sample sizes, lack of evidence for moderate and long-term use and other key outcomes, such as other adverse events and impact on use of opioids during treatment, indicate that more research is needed.

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