ISSN: 0443-511
e-ISSN: 2448-5667
Herramientas del artículo
Envíe este artículo por correo electrónico (Inicie sesión)
Enviar un correo electrónico al autor/a (Inicie sesión)
Tamaño de fuente

Open Journal Systems

Expresión numérica del curso clínico de la enfermedad. Manejo de datos / Numerical expression of the clinical course of the disease. Data management

Juan Osvaldo Talavera, Ivonne Roy-García, Sofía Teresa Díaz-Torres, Lino Teresa Palacios-Cruz, Alejandro Noguez-Ramos, Marcela Pérez-Rodríguez, Miguel Ángel Martínez, Jessica E. Silva-Guzmán, Rodolfo Rivas-Ruiz



El manejo de datos “tras bambalinas” se refiere a los procesos de recopilación, limpieza, imputación y demarcación; los cuales, aun siendo indispensables, usualmente suelen ser descuidados, por lo que generan información errónea. Durante la recopilación son errores: omisión de covariables, desvío del objetivo, y calidad insuficiente. La omisión de covariables distorsiona el resultado atribuido a la maniobra principal. El desvío del objetivo primario es común cuando el desenlace es raro, tardado o subjetivo y promueve la sustitución por variables subrogadas no equivalentes. Además, la calidad insuficiente, sucede por instrumentos inadecuados, omisión del procedimiento de medición, o medición fuera de contexto -como atribución a destiempo o equivalente-. Por otro lado, la limpieza implica identificar valores erróneos, extremos y faltantes, que podrán ser o no imputados, dependiendo del porcentaje se imputará comúnmente por la medida de resumen. Nunca se imputan los valores de la maniobra ni del desenlace, ni se eliminan pacientes por falta de valores. Finalmente, la demarcación de cada variable busca un significado clínico en referencia al desenlace, para ello se sigue una secuencia jerárquica de criterios: 1) estudio clínico previo, 2) acuerdo de expertos, 3) juicio clínico del investigador/investigadores y 4) estadística. Actuar sin controles de calidad en el manejo de datos provoca frecuentemente mentiras involuntarias y confunde en lugar de esclarecer



Data management “behind the scenes” refers to collection, cleaning, imputation, and demarcation; and despite of being indispensable processes, they are usually neglected and thus, generate erroneous information. During the collection are errors: omission of covariates, deviation from the objective, and insufficient quality. The omission of covariates distorts the result attributed to the main manoeuvre. Deviation from the primary objective commonly occurs when the outcome is rare, delayed, or subjective and promotes substitution by non-equivalent surrogate variables. Moreover, insufficient quality occurs due to inadequate instruments, omission of the measurement procedure, or measurements out of context, such as attribution at the wrong time or equivalent. Furthermore, cleaning implies identifying erroneous, extreme, and missing values, which may or may not be imputed, depending on the percentage. The values of the manoeuvre or the outcome are never imputed, nor are patients eliminated due to a lack of values. Finally, the demarcation of each variable seeks to give it a clinical meaning about the outcome, for which a hierarchical sequence of criteria is followed: 1) previous clinical study, 2) expert agreement, 3) clinical judgment of the investigator/investigators, and 4) statistics. Acting without quality controls in data management frequently causes involuntary lies and confuses instead of clarifying.

Palabras clave

Recolección de Datos; Manejo de Datos; Epidemiología Clínica; Estadística / Data Collection; Data Management; Clinical Epidemiology; Statistics

Texto completo:



Talavera JO, Roy-García IA, Pérez-Rodríguez M, Palacios-Cruz L, Rivas-Ruíz R. De vuelta a la clínica. Métodos II. Arquitectura de la investigación clínica. Interacción sujeto, maniobras y enfermedad a través del tiempo. Gac Med Mex. 2020;156(5):438-446. doi:10.24875/GMM.20000159.

Talavera J. Juicio Clínico: el método científico aplicado a la clínica. Rev Med Inst Mex Seguro Soc. 2019;57(5):267-268.

Rothman KJ, Greenland S. Causation and causal inference in epidemiology. Am J Public Health. 2005;95(SUPPL. 1):S144-S150.

Evans AS, Henle W, Huebner R, Johnson R, Lilienfeld A. Causation and Disease: The Henle-Koch Postulates Revisited. Yale J Biol Med. 1976;49:175-195.

Yekushalmu J, Palmer CE. On the methodology of investigations of etiologic factors un chronic diseases. Journal of Chronic Disease. Published online 1959:27-40.

Elster J. Ulises y Las Sirenas. 1st ed. Fondo de Cultura Económica. México; 1979.

Bradford-Hill A. The Environment and Disease: Association or Causation? Proc R Soc Med. Published online. 1965:295-300.

Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. 3rd ed. (Seigafuse S, Bierig L, eds.). Wolters Kluwer, Lippincott Williams & Wilkins; 2008. 9. Spinoza B. Ethics Proved in Geometrical Order. 1st ed. (Kisner MJ, ed.). Cambdridge University Press; 2018.

Feinstein AR. Clinical biostatistics XLVII. Scientific standards vs. statistical associations and biologic logic in the analysis of causation. Clin Pharmacol Ther. 1979;25(4):481-492. doi:10.1002/cpt1979254481.

Feinstein AR. Statistical reductionism and clinicians’ delinquencies in humanistic research. Clin Pharmacol Ther. 1999; 60(3):211-217.

Talavera JO, Wacher-Rodarte NH, Rivas-Ruiz R. Investigación clínica III. Estudios de Causalidad. Rev Med Inst Mex Seguro Soc. 2011;49(3):289-294.

López-Facundo N, Talavera JO, Tejocote-Romero I. Mortalidad temprana en niños con leucemia linfoblástica aguda en un país en vías de desarrollo; factores asociados con el pronóstico. Gaceta Médica de Oncología. 2008;7(3):93-101.

López-Facundo NA, Tejocote-Romero I, Rodríguez-Castillejos C, Jaimes-García Y. Impacto de la obesidad en el pronóstico de supervivencia y recaída en niños con leucemia aguda linfoblástica del estado de México. Gaceta Mexicana de Oncologia. 2015;14(5):242-249. doi: 10.1016/j.gamo.2015.11.004.

Rivas-Ruiz R, Talavera JO. Investigación clínica VII. Búsqueda sistemática: cómo localizar artículos para resolver una pregunta clínica. Rev Med Inst Mex Seguro Soc. 2012;50(1): 53-58.

Horwitz R, Singer BH, Makuch RW, Viscoli CM. Can Treatment That Is Helpful on Average Be Harmful to Some Patients? A Study of thk Conflicting information Needs of Clinical Inquiry and Drug Regulation. J Clin Epidemiol. 1996;49 (4):395-400.

UK Prospective Diabetes Study (UKPDS) Group. Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). The Lancet. 352AD;352:855-865.

Donner T, Muñoz M. Update on Insulin Therapy for Type 2 Diabetes. J Clin Endocrinol Metabol. 2012;97(5):1405-1413.

Delahanty LM, Nathan DM. Research Navigating the Course of Clinical Practice in Diabetes. J Am Diet Assoc. 2004;104 (12):1846-1853. doi: 10.1016/j.jada.2004.09.028.

Guyatt G, Sackett D, Taylor W, Chong J, Roberts R, Pugsley S. Determining optimal therapy- Randomized Trials in individual patients. New England Journal of Medicine. 1986;314 (14):889-892.

Guyatt GH, Keller JL, Jaeschke R, Rosenbloom D, Adachi JD, Newhouse MT. The n-of-1 Randomized Controlled Trial: Clinical Usefulness. Our Three-Year Experience. Ann Intern Med. 1990;112:293-299.

Levin WC, Fink DJ, Porter FS, Hall TC, Loeb V. Cooperative Clinical Investigation. A Modality of Medical Science. JAMA. 1974;227(11):1295-1296.

Feinstein AR. Clinical biostatistics XLVIII. Efficacy of different research structures in preventing bias in the analysis of causation. Clin Pharmacol Ther. 1979;26(1):129-141. doi: 10.1002/ cpt1979261129.

Feinstein AR. Clinical biostatistics XLI. Hard science, soft data, and the challenges of choosing clinical variables in research. Clin Pharmacol Ther. 1977;22(4):485-498. doi: 10.1002/ cpt1977224485.

Streiner DL, Norman GR. “Precision” and “accuracy”: Two terms that are neither. J Clin Epidemiol. 2006;59:327-330. doi: 10.1016/j.jclinepi.2005.09.005.

Feinstein AR. Clinimetrics. 1st ed. Yale University Press; 1987.

Lawlor DA, Hart CL, Hole DJ, Smith GD. Reverse Causality and Confounding and the Associations of Overweight and Obesity with Mortality. Obesity. 2006;14(12):2294-2304.

28. Banack HR, Bea JW, Kaufman JS, et al. The effects of reverse causality and selective attrition on the relationship between Body Mass Index and mortality in postmenopausal women. Am J Epidemiol. 2019;188(10):1838-1848. doi: 10.1093/aje/ kwz160.

Vasudeva D, Asi R. An examination of the role of conceptualization and operationalization in empirical social research. ZENITH International Journal of Multidisciplinary Research. 2013;3:108-114.

Feinstein AR. Clinical biostatistics. XLV. The purposes and functions of criteria. Clin Pharmacol Ther. 1978;24(4):479-492. doi: 10.1002/cpt1978244479.

Dixon WJ. Analysis of Extreme Values. The Annals of Mathematical Statistics. 1950;21(4):488-506. 32. Portney LG. Foundations of Clinical Research: Aplications to Evidence-Based Medicine. 4th ed. F.A. Davis; 2020.

Streiner DL. Statistics Commentary Series: Commentary # 10 - Dealing with Drop-Outs. J Clin Psychopharmacol. 2015;35 (5):496-498. doi: 10.1097/JCP.0000000000000299.

Little RJA, Rubin DB. Statistical Analysis with Missing Data. 3rd ed. Wiley; 2020.

Nguyen CD, Carlin JB, Lee KJ. Practical strategies for handling breakdown of multiple imputation procedures. Emerg Themes Epidemiol. 2021;18(5). doi: 10.1186/s12982-021-00095-3.

Padgett CR, Skilbeck CE, Summers MJ. Missing data: The importance and impact of missing data from clinical research. Brain Impairment. 2014;15(1):1-9. doi: 10.1017/BrImp.2014.2.

Steele AJ, Denaxas SC, Shah AD, Hemingway H, Luscombe NM. Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease. PLoS One. 2018; 13(8). doi: 10.1371/journal.pone.0202344.

Feinstein AR. Clinical biostatistics III. The architecture of clinical research. Clin Pharmacol Ther. 1970;11(3):432-441. doi: 10.1002/cpt1970113432.

Feinstein AR. Clinical biostatistics IV. The architecture of clinical research (continued). Clin Pharmacol Ther. 1970;11(4):595- 610. doi: 10.1002/cpt1970114595.

Feinstein AR. Clinical biostatistics V. The architecture of clinical research (concluded). Clin Pharmacol Ther. 1970;11(5):755- 771. doi: 10.1002/cpt1970115755.

Talavera JO, Roy-García I, Palacios-Cruz L, Rivas-Ruiz R, Hoyo I, Pérez-Rodríguez M. De vuelta a la clínica. Métodos I. Diseños de investigación. Mayor calidad de información, mayor certeza a la respuesta. Gac Med Mex. 2019;155(4):399- 405. doi: 10.24875/GMM.19005226.

Belk RA, Pilling M, Rogers KD, Lovell K, Young A. The theoretical and practical determination of clinical cut-offs for the British Sign Language versions of PHQ-9 and GAD-7. BMC Psychiatry. 2016;16(372):1-12. doi: 10.1186/s12888-016-1078-0.

He L, Khanal P, Morse CI, Williams A, Thomis M. Differentially methylated gene patterns between age-matched sarcopenic and non-sarcopenic women. J Cachexia Sarcopenia Muscle. 2019;10(6):1295-1306. doi: 10.1002/jcsm.12478.

Gordijn SJ, Beune IM, Thilaganathan B, et al. Consensus definition of fetal growth restriction: a Delphi procedure. Ultrasound Obstet Gynecol. 2016;48:333-339. doi: 10.1002/ uog.15884.

Tinazzi M, Geroin C, Bhidayasiri R, et al. Task Force Consensus on Nosology and Cut-Off Values for Axial Postural Abnormalities in Parkinsonism. Mov Disord Clin Pract. 2022; 9(5):594-603. doi: 10.1002/mdc3.13460.

Duckett Jones T. The Diagnosis of Reumatic Fever. JAMA. 1944;126(8):481-484.

Xu H, Simonet F, Luo ZC. Optimal birth weight percentile cut-offs in defining small- or large-for-gestational-age. Acta Paediatr. 2010;99(4):550-555. doi: 10.1111/j.1651-2227.2009.01674.x.

Brown JD, Alipour-Haris G, Pahor M, Manini TM. Association between a Deficit Accumulation Frailty Index and Mobility Outcomes in Older Adults: Secondary Analysis of the Lifestyle Interventions and Independence for Elders (LIFE) Study. J Clin Med. 2020;9(11):1-13. doi: 10.3390/jcm9113757.

González R, Hidalgo G, Salazar J, Preciado M. Instrumento Para Medir La Calidad de Vida En El Trabajo CVT-GOHISALO. Manual Para Su Aplicación e Interpretación. 1st ed. Editorial de la Luna; 2010.

Gholami T, Pahlavian AH, Akbarzadeh M, Motamedzade M, Moghaddam RH. The role of burnout syndrome as a mediator for the effect of psychosocial risk factors on the intensity of musculoskeletal disorders: a structural equation modeling approach. International Journal of Occupational Safety and Ergonomics. 2016;22(2):283-290. doi: 10.1080/10803548.2016.1147876.

Enlaces refback

  • No hay ningún enlace refback.