Uso de medidas alternativas para determinar la presencia de heterogeneidad en un metaanálisis
El presente trabajo presenta dos tipos de medidas alternativas que fueron desarrolladas en 2017 para cuantifi car la presencia de heterogeneidad y las aplica en 2 conjuntos de datos distintos. Los datos utilizados hacen parte de 2 revisiones sistemáticas. Una relacionada con niveles de LDL-c en paci...
- Autores:
-
Gil Velásquez, José de Jesús
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/84169
- Palabra clave:
- Cholesterol
Colesterol
Análisis biológico
Biological analysis
Metaanálisis
Modelo de efectos fijos
Modelo de efectos aleatorios
Heterogeneidad
Epidemiología
Meta-analysis
Fixed-effects model
Random-effects model
Heterogeneity
Epidemiology
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Uso de medidas alternativas para determinar la presencia de heterogeneidad en un metaanálisis |
| dc.title.translated.eng.fl_str_mv |
Use of alternative measures to establish the presence of heterogeneity in a meta-analysis |
| title |
Uso de medidas alternativas para determinar la presencia de heterogeneidad en un metaanálisis |
| spellingShingle |
Uso de medidas alternativas para determinar la presencia de heterogeneidad en un metaanálisis Cholesterol Colesterol Análisis biológico Biological analysis Metaanálisis Modelo de efectos fijos Modelo de efectos aleatorios Heterogeneidad Epidemiología Meta-analysis Fixed-effects model Random-effects model Heterogeneity Epidemiology |
| title_short |
Uso de medidas alternativas para determinar la presencia de heterogeneidad en un metaanálisis |
| title_full |
Uso de medidas alternativas para determinar la presencia de heterogeneidad en un metaanálisis |
| title_fullStr |
Uso de medidas alternativas para determinar la presencia de heterogeneidad en un metaanálisis |
| title_full_unstemmed |
Uso de medidas alternativas para determinar la presencia de heterogeneidad en un metaanálisis |
| title_sort |
Uso de medidas alternativas para determinar la presencia de heterogeneidad en un metaanálisis |
| dc.creator.fl_str_mv |
Gil Velásquez, José de Jesús |
| dc.contributor.advisor.none.fl_str_mv |
Rodríguez Malagón, María Nelcy |
| dc.contributor.author.none.fl_str_mv |
Gil Velásquez, José de Jesús |
| dc.subject.decs.eng.fl_str_mv |
Cholesterol |
| topic |
Cholesterol Colesterol Análisis biológico Biological analysis Metaanálisis Modelo de efectos fijos Modelo de efectos aleatorios Heterogeneidad Epidemiología Meta-analysis Fixed-effects model Random-effects model Heterogeneity Epidemiology |
| dc.subject.decs.spa.fl_str_mv |
Colesterol |
| dc.subject.lemb.spa.fl_str_mv |
Análisis biológico |
| dc.subject.lemb.eng.fl_str_mv |
Biological analysis |
| dc.subject.proposal.spa.fl_str_mv |
Metaanálisis Modelo de efectos fijos Modelo de efectos aleatorios Heterogeneidad Epidemiología |
| dc.subject.proposal.eng.fl_str_mv |
Meta-analysis Fixed-effects model Random-effects model Heterogeneity Epidemiology |
| description |
El presente trabajo presenta dos tipos de medidas alternativas que fueron desarrolladas en 2017 para cuantifi car la presencia de heterogeneidad y las aplica en 2 conjuntos de datos distintos. Los datos utilizados hacen parte de 2 revisiones sistemáticas. Una relacionada con niveles de LDL-c en pacientes con Alzheimer y sin demencia. La otra referida al desarrollo de complicaciones por COVID en personas fumadoras y que nunca han fumado. (Texto tomado de la fuente) |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-07-07T20:24:32Z |
| dc.date.available.none.fl_str_mv |
2023-07-07T20:24:32Z |
| dc.date.issued.none.fl_str_mv |
2023 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/84169 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/84169 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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Agresti, A. (2002). Categorical data analysis. Wiley. Aitkin, M. (1999). Meta-analysis by random effect modelling in generalized linear models. Statistics in medicine, 18:2343-2351. Balduzzi, S., Rucker, G., and Schwarzer, G. (2019). How to perform a meta-analysis with r: a practical tutorial. Evidence-Based Mental Health, pages 153-160. Berkey, C. S., Hoaglin, D. C., Mosteller, F., and Colditz, G. A. (1995). A random-effects regression model for meta-analysis. Statistics in medicine, 14:395-411. Borenstein, M., Hedges, L. V., Higgins, J. P., and Rothsein, H. (2009). Introduction to Meta-Analysis. Wiley. Brockwell, S. E. and Gordon, I. R. (2001). A comparison of statistical methods for meta-analysis. Statistics in medicine, 20:825-840. Canal, S. Y., Alvarez, N. G., and Vargas, J. A. (2011). Cartas de control T2 multivariadas usando R y SAS. Universidad Nacional de Colombia. Centro Cochrane Iberoamericano, t. (2012). Ma- nual Cochrane de Revisiones Sistematicas de Intervenciones, version 5.1.0 [actualizada en marzo de 2011] [Internet]. Barcelona: Centro Cochrane Iberoamericano. Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., and Borges, B. (2021). shiny: Web Application Framework for R. R Foundation for Statistical Computing. Collins, R., Peto, R., MacMahon, S., Hebert, P., Fiebach, N. H., Eberlein, K. A., Godwin, J., Qizilbash, N., Taylor, J. O., and Hennekens, C. H. (1990). Blood pressure, stroke, and coronary heart disease. part 2, short-term reductions in blood pressure: overview of randomised drug trials in their epidemiological context. The Lancet, 335:827-838. Demidenko, E. (2013). Mixed models: theory and applications with R. John Wiley & Sons. DerSimonian, R. and Laird, N. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials, 7:177-188. Dewey, M. (2020). CRAN Task View: Meta-Analysis. R Foundation for Statistical Computing, dewey.myzen.co.uk. Dowle, M. and Srinivasan, A. (2019). data.table: Extension of `data.frame`. R Foundation for Statistical Computing. Emerson, J. D. (1994). Combining estimates of the odds ratio: the state of the art. Statistical methods in medical research, 3:157-178. Goodwin, P. J. and Boyd, N. F. (1988). Mammographic parenchymal pattern and breast cancer risk: a critical appraisal of the evidence. American Journal of Epidemiology, 127:1097-1108. Hardy, R. J. and Thompson, S. G. (1996). A likelihood approach to meta-analysis with random effects. Statistics in medicine, 15:619-629. Hedges, L. V. and Olkin, I. (1985). Statistical Method for Meta- Analysis. Academic Press, Orlando. Hennekens, C. H. and Buring, J. E. (1987). Epidemiology in medicine. Little, Brown and Company, Boston, Toronto. Higgins, J. P. T. and Thompson, S. G. (2002). Quantifying heterogeneity in a metaanalysis. Statistics in Medicine, 21:1539-1558. Jimenez, C. A., Lopez, D., Alonso, A., Aleixandre, R., Solano, S., and de Granda, J. I. (2021). Covid-19 y tabaquismo: revision sistematica y metaanalisis de la evidencia. Achivos de bronconeumologia, 57:21-34. Johnson, R. A. and Wichern, D. W. (2002). Applied multivariate statistical analysis. Prentice Hall. Lin, L. (2017). Statistical methods for meta-analysis. Tesis de doctorado, University of minnesota. Lin, L. and Chu, H. (2022). altmeta: Alternative Meta-Analysis Methods. R Foundation for Statistical Computing. Macmahon, S., Peto, R., Cutler, J., Collins, R., Sorlie, P., Neaton, J., Abbott, R., Godwin, J., Dyer, A., and Stamler, J. (1990). Blood pressure, stroke, and coronary heart disease. part 1, prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. The Lancet, 335:765-774. Mayorga, A. J. H. (2004). Inferencia Estadística. Universidad Nacional de Colombia. Mcneish, D. (2016). On using bayesian methods to address small sample problems. Structural equation modeling: a multidisciplinary journal, 00:1-24. Nelder, J. A. and Wedderburn, R. W. (1972). Generalized linear models. Journal of the royal statistical society, 135:370-384. Piggot, T. D. (2012). Advances in Meta-Analysis. Springer. R Core Team (2020). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Ruiz, A. and Morillo, L. E. (2004). Epidemiologia clinica: investigacion clinica aplicada. Editorial medica panamericana. Schloerke, B. Allen, J. (2021). plumber: An API Generator for R. R Foundation for Statistical Computing. Schwarzer, G., Carpenter, J. R., and Rucker, G. (2015). Meta-Analysis with R. Springer. Smyth, G. K. (1989). Generalized linear models with varying dispersion. Journal of the royal statistical society, 51:47-60. Tang, J. and Liu, J. L. (2000). Misleading funnel plot for detection of bias in meta-analysis. Journal of Clinical Epidemiology, 53:477-484. Urruatia, G., Tort, S., and Bon ll, X. (2005). Metaanáalisis (quorum). Medicina Clínica (Barcelona), 125:32-37. Whitehead, A. and Whitehead, J. (1991). A general parametric approach to the metaanalysis of randomized clinical trials. Statistics in Medicine, 10:1665-1677. Whitehead, J. (1997). The Design and Analysis of Sequential Clinical Trials. Statistics in Practice. Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. Wickham, H. (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R Foundation for Statistical Computing. Wickham, H. (2020). forcats: Tools for Working with Categorical Variables (Factors). R Foundation for Statistical Computing. Wickham, H., Francois, R., Henry, L., and Muller, K. (2020). dplyr: A Grammar of Data Manipulation. R Foundation for Statistical Computing. Wickham, H. and Henry, L. (2020a). purrr: Functional Programming Tools. R Foundation for Statistical Computing. Wickham, H. and Henry, L. (2020b). tidyr: Tidy Messy Data. R Foundation for Statistical Computing. Wickham, H., Hester, J., and Francois, R. (2018). readr: Read Rectangular Text Data. R Foundation for Statistical Computing. Wickham, H. and Muller, K. (2021). tibble: Simple Data Frames. R Foundation for Statistical Computing. Yusuf, S. (1987). Obtaining medically meaningful answers from a overview of randomized clinical trials. Statistics in medicine, 6:281-286. Zhou, Z., Liang, Y., Zhang, X., Xu, J., Lin, J., Zhang, R., Kang, K., Liu, C., Zhao, C., and Zhao, M. (2020). Low-density lipoprotein cholesterol and alzheimer's disease: a systematic review and meta-analysis. Frontiers in aging neuroscience, 12. |
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Facultad de Ciencias |
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Bogotá,Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rodríguez Malagón, María Nelcy8f33b8ce435339c585ab853c80915b24Gil Velásquez, José de Jesúsda17408fe68969ef30f806595d9b01902023-07-07T20:24:32Z2023-07-07T20:24:32Z2023https://repositorio.unal.edu.co/handle/unal/84169Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/El presente trabajo presenta dos tipos de medidas alternativas que fueron desarrolladas en 2017 para cuantifi car la presencia de heterogeneidad y las aplica en 2 conjuntos de datos distintos. Los datos utilizados hacen parte de 2 revisiones sistemáticas. Una relacionada con niveles de LDL-c en pacientes con Alzheimer y sin demencia. La otra referida al desarrollo de complicaciones por COVID en personas fumadoras y que nunca han fumado. (Texto tomado de la fuente)This work presents two types of alternative measures developed in 2017 in order to quantify the presence of heterogeneity and applied them into 2 different datasets. Data came from two systematic reviews. The first one refer to the LDL-c levels in patients with Alzheimer and non-demantia. The other related to the development of COVID complications in smokers and people who have never smoked.MaestríaMagíster en Ciencias - Estadísticaxv, 46páginasapplication/pdfspaUso de medidas alternativas para determinar la presencia de heterogeneidad en un metaanálisisUse of alternative measures to establish the presence of heterogeneity in a meta-analysisTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá,ColombiaUniversidad Nacional de Colombia - Sede BogotáAgresti, A. (2002). Categorical data analysis. Wiley.Aitkin, M. (1999). Meta-analysis by random effect modelling in generalized linear models. Statistics in medicine, 18:2343-2351.Balduzzi, S., Rucker, G., and Schwarzer, G. (2019). How to perform a meta-analysis with r: a practical tutorial. Evidence-Based Mental Health, pages 153-160.Berkey, C. S., Hoaglin, D. C., Mosteller, F., and Colditz, G. A. (1995). A random-effects regression model for meta-analysis. Statistics in medicine, 14:395-411.Borenstein, M., Hedges, L. V., Higgins, J. P., and Rothsein, H. (2009). Introduction to Meta-Analysis. Wiley.Brockwell, S. E. and Gordon, I. R. (2001). A comparison of statistical methods for meta-analysis. Statistics in medicine, 20:825-840.Canal, S. Y., Alvarez, N. G., and Vargas, J. A. (2011). Cartas de control T2 multivariadas usando R y SAS. Universidad Nacional de Colombia.Centro Cochrane Iberoamericano, t. (2012). Ma- nual Cochrane de Revisiones Sistematicas de Intervenciones, version 5.1.0 [actualizada en marzo de 2011] [Internet]. Barcelona: Centro Cochrane Iberoamericano.Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., and Borges, B. (2021). shiny: Web Application Framework for R. R Foundation for Statistical Computing.Collins, R., Peto, R., MacMahon, S., Hebert, P., Fiebach, N. H., Eberlein, K. A., Godwin, J., Qizilbash, N., Taylor, J. O., and Hennekens, C. H. (1990). Blood pressure, stroke, and coronary heart disease. part 2, short-term reductions in blood pressure: overview of randomised drug trials in their epidemiological context. The Lancet, 335:827-838.Demidenko, E. (2013). Mixed models: theory and applications with R. John Wiley & Sons.DerSimonian, R. and Laird, N. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials, 7:177-188.Dewey, M. (2020). CRAN Task View: Meta-Analysis. R Foundation for Statistical Computing, dewey.myzen.co.uk.Dowle, M. and Srinivasan, A. (2019). data.table: Extension of `data.frame`. R Foundation for Statistical Computing.Emerson, J. D. (1994). Combining estimates of the odds ratio: the state of the art. Statistical methods in medical research, 3:157-178.Goodwin, P. J. and Boyd, N. F. (1988). Mammographic parenchymal pattern and breast cancer risk: a critical appraisal of the evidence. American Journal of Epidemiology, 127:1097-1108.Hardy, R. J. and Thompson, S. G. (1996). A likelihood approach to meta-analysis with random effects. Statistics in medicine, 15:619-629.Hedges, L. V. and Olkin, I. (1985). Statistical Method for Meta- Analysis. Academic Press, Orlando.Hennekens, C. H. and Buring, J. E. (1987). Epidemiology in medicine. Little, Brown and Company, Boston, Toronto.Higgins, J. P. T. and Thompson, S. G. (2002). Quantifying heterogeneity in a metaanalysis. Statistics in Medicine, 21:1539-1558.Jimenez, C. A., Lopez, D., Alonso, A., Aleixandre, R., Solano, S., and de Granda, J. I. (2021). Covid-19 y tabaquismo: revision sistematica y metaanalisis de la evidencia. Achivos de bronconeumologia, 57:21-34.Johnson, R. A. and Wichern, D. W. (2002). Applied multivariate statistical analysis. Prentice Hall.Lin, L. (2017). Statistical methods for meta-analysis. Tesis de doctorado, University of minnesota.Lin, L. and Chu, H. (2022). altmeta: Alternative Meta-Analysis Methods. R Foundation for Statistical Computing.Macmahon, S., Peto, R., Cutler, J., Collins, R., Sorlie, P., Neaton, J., Abbott, R., Godwin, J., Dyer, A., and Stamler, J. (1990). Blood pressure, stroke, and coronary heart disease. part 1, prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. The Lancet, 335:765-774.Mayorga, A. J. H. (2004). Inferencia Estadística. Universidad Nacional de Colombia.Mcneish, D. (2016). On using bayesian methods to address small sample problems. Structural equation modeling: a multidisciplinary journal, 00:1-24.Nelder, J. A. and Wedderburn, R. W. (1972). Generalized linear models. Journal of the royal statistical society, 135:370-384.Piggot, T. D. (2012). Advances in Meta-Analysis. Springer.R Core Team (2020). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.Ruiz, A. and Morillo, L. E. (2004). Epidemiologia clinica: investigacion clinica aplicada. Editorial medica panamericana.Schloerke, B. Allen, J. (2021). plumber: An API Generator for R. R Foundation for Statistical Computing.Schwarzer, G., Carpenter, J. R., and Rucker, G. (2015). Meta-Analysis with R. Springer.Smyth, G. K. (1989). Generalized linear models with varying dispersion. Journal of the royal statistical society, 51:47-60.Tang, J. and Liu, J. L. (2000). Misleading funnel plot for detection of bias in meta-analysis. Journal of Clinical Epidemiology, 53:477-484.Urruatia, G., Tort, S., and Bon ll, X. (2005). Metaanáalisis (quorum). Medicina Clínica (Barcelona), 125:32-37.Whitehead, A. and Whitehead, J. (1991). A general parametric approach to the metaanalysis of randomized clinical trials. Statistics in Medicine, 10:1665-1677.Whitehead, J. (1997). The Design and Analysis of Sequential Clinical Trials. Statistics in Practice.Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.Wickham, H. (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R Foundation for Statistical Computing.Wickham, H. (2020). forcats: Tools for Working with Categorical Variables (Factors). R Foundation for Statistical Computing.Wickham, H., Francois, R., Henry, L., and Muller, K. (2020). dplyr: A Grammar of Data Manipulation. R Foundation for Statistical Computing.Wickham, H. and Henry, L. (2020a). purrr: Functional Programming Tools. R Foundation for Statistical Computing.Wickham, H. and Henry, L. (2020b). tidyr: Tidy Messy Data. R Foundation for Statistical Computing.Wickham, H., Hester, J., and Francois, R. (2018). readr: Read Rectangular Text Data. R Foundation for Statistical Computing.Wickham, H. and Muller, K. (2021). tibble: Simple Data Frames. R Foundation for Statistical Computing.Yusuf, S. (1987). Obtaining medically meaningful answers from a overview of randomized clinical trials. Statistics in medicine, 6:281-286.Zhou, Z., Liang, Y., Zhang, X., Xu, J., Lin, J., Zhang, R., Kang, K., Liu, C., Zhao, C., and Zhao, M. (2020). Low-density lipoprotein cholesterol and alzheimer's disease: a systematic review and meta-analysis. Frontiers in aging neuroscience, 12.CholesterolColesterolAnálisis biológicoBiological analysisMetaanálisisModelo de efectos fijosModelo de efectos aleatoriosHeterogeneidadEpidemiologíaMeta-analysisFixed-effects modelRandom-effects modelHeterogeneityEpidemiologyEstudiantesInvestigadoresORIGINAL1026259070.2023.pdf1026259070.2023.pdfTesis de maestría en Estadísticaapplication/pdf973075https://repositorio.unal.edu.co/bitstream/unal/84169/4/1026259070.2023.pdf89f2d10369648a914d028aed0106d720MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84169/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53THUMBNAIL1026259070.2023.pdf.jpg1026259070.2023.pdf.jpgGenerated Thumbnailimage/jpeg3532https://repositorio.unal.edu.co/bitstream/unal/84169/5/1026259070.2023.pdf.jpgfb04af4cb7a4ceaf9847283506cc83a7MD55unal/84169oai:repositorio.unal.edu.co:unal/841692024-08-12 01:59:17.96Repositorio Institucional Universidad Nacional de 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