Preprint / Versión 1

El ensayo clínico diana para inferencia causal en estudios observacionales

Autores/as

  • Isaac Jacobo Núñez Saavedra Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán - Universidad Nacional Autónoma de México, México. https://orcid.org/0000-0001-8859-9115
  • Martín Lajous Instituto Nacional de Salud Pública, México.

DOI:

https://doi.org/10.62059/LatArXiv.preprints.70

Palabras clave:

Ensayo clínico diana, Emulación, Ensayos clínicos, Inferencia causal

Resumen

El uso de estudios observacionales para inferencia causal es una necesidad, ya que no hay ensayos clínicos aleatorizados disponibles siempre. Para reducir fuentes de sesgo presentes en estudios observacionales “clásicos”, se ha propuesto utilizar el marco del ensayo clínico diana. Bajo este marco, se definen al menos los siguientes componentes  del protocolo del ensayo clínico pragmático que hubiéramos querido realizar: criterios de elegibilidad, estrategias de tratamiento, métodos de asignación, periodo de seguimiento, desenlace, contrastes causales y plan de análisis. Posteriormente, estos se modifican en base a los datos observacionales disponibles y se utilizan para emular el ensayo clínico diana lo más de cerca posible. Esto resulta en estudios observacionales con menos sesgo y resultados más confiables. Sin embargo, es importante considerar las limitaciones de este enfoque, ya que sigue tratándose de un estudio observacional y no de un experimento aleatorizado.

Referencias

Hernán MA. The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data. Am J Public Health. 2018;108(5):616-619. doi:10.2105/AJPH.2018.304337

Hernan MA. A definition of causal effect for epidemiological research. J Epidemiol Community Health. 2004;58(4):265-271. doi:10.1136/jech.2002.006361

Hernán MA. Methods of Public Health Research — Strengthening Causal Inference from Observational Data. N Engl J Med. 2021;385(15):1345-1348. doi:10.1056/NEJMp2113319

Collins R, Bowman L, Landray M, Peto R. The Magic of Randomization versus the Myth of Real-World Evidence. N Engl J Med. 2020;382(7):674-678. doi:10.1056/NEJMsb1901642

Murad MH, Asi N, Alsawas M, Alahdab F. New evidence pyramid. Evid Based Med. 2016;21(4):125-127. doi:10.1136/ebmed-2016-110401

Hernán MA, Robbins J. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC

Hernán MA, Alonso A, Logan R, et al. Observational Studies Analyzed Like Randomized Experiments: An Application to Postmenopausal Hormone Therapy and Coronary Heart Disease. Epidemiology. 2008;19(6):766-779. doi:10.1097/EDE.0b013e3181875e61

Dickerman BA, García-Albéniz X, Logan RW, Denaxas S, Hernán MA. Avoidable flaws in observational analyses: an application to statins and cancer. Nat Med. 2019;25(10):1601-1606. doi:10.1038/s41591-019-0597-x

Hernán MA, Taubman SL. Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes. 2008;32(S3):S8-S14. doi:10.1038/ijo.2008.82

Lajous M. Inferencia causal en análisis basados en datos de vigilancia epidemiológica para Covid-19. Salud Pública México. 2021;63(4):459-460. doi:10.21149/12777

Lodi S, Phillips A, Logan R, et al. Comparative effectiveness of immediate antiretroviral therapy versus CD4-based initiation in HIV-positive individuals in high-income countries: observational cohort study. Lancet HIV. 2015;2(8):e335-e343. doi:10.1016/S2352-3018(15)00108-3

When to Initiate Combined Antiretroviral Therapy to Reduce Mortality and AIDS-Defining Illness in HIV-Infected Persons in Developed Countries: An Observational Study. Ann Intern Med. 2011;154(8):509. doi:10.7326/0003-4819-154-8-201104190-00001

The INSIGHT START Study Group. Initiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection. N Engl J Med. 2015;373(9):795-807. doi:10.1056/NEJMoa1506816

Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016;79:70-75. doi:10.1016/j.jclinepi.2016.04.014

Matthews AA, Danaei G, Islam N, Kurth T. Target trial emulation: applying principles of randomised trials to observational studies. BMJ. Published online August 30, 2022:e071108. doi:10.1136/bmj-2022-071108

Hernán MA, Robins JM. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1. Am J Epidemiol. 2016;183(8):758-764. doi:10.1093/aje/kwv254

Danaei G, Rodríguez LAG, Cantero OF, Logan R, Hernán MA. Observational data for comparative effectiveness research: An emulation of randomised trials of statins and primary prevention of coronary heart disease. Stat Methods Med Res. 2013;22(1):70-96. doi:10.1177/0962280211403603

Matthews AA, Young JC, Kurth T. The target trial framework in clinical epidemiology: principles and applications. J Clin Epidemiol. 2023;164:112-115. doi:10.1016/j.jclinepi.2023.10.008

Dahabreh IJ, Metthews A, Steingrimsson J, Scharfstein D, Stuart EA. Using Trial and Observational Data to Assess Effectiveness: Trial Emulation, Transportability, Benchmarking, and Joint Analysis. Epidemiol Rev.

Schulz KF, Altman DG, Moher D. CONSORT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomized Trials. Ann Intern Med. 2010;152(11):726-732.

Hansford HJ, Cashin AG, Jones MD, et al. Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials: A Systematic Review. JAMA Netw Open. 2023;6(9):e2336023. doi:10.1001/jamanetworkopen.2023.36023

Núñez I, Soto-Mota A. Uneven Resources Threaten Causal Consistency in Randomized Trials. Epidemiology. 2023;34(4):531-534. doi:10.1097/EDE.0000000000001616

Dang LE, Balzer LB. Start with the Target Trial Protocol, Then Follow the Roadmap for Causal Inference. Epidemiology. 2023;34(5):619-623. doi:10.1097/EDE.0000000000001637

Núñez I. Canine Confounders. Significance. 2022;19(4):24-27. doi:10.1111/1740-9713.01670

VanderWeele TJ, Shpitser I. A New Criterion for Confounder Selection. Biometrics. 2011;67(4):1406-1413. doi:10.1111/j.1541-0420.2011.01619.x

Núñez I. The importance of using disease causal models in studies of preventive interventions: Learning from preeclampsia research. Prev Med. 2023;177:107790. doi:10.1016/j.ypmed.2023.107790

Rudolph KE, Keyes KM. Voluntary Firearm Divestment and Suicide Risk: Real- World Importance in the Absence of Causal Identification. 2023;34(1).

Suissa S. Immortal Time Bias in Pharmacoepidemiology. Am J Epidemiol. 2008;167(4):492-499. doi:10.1093/aje/kwm324

Howe CJ, Cole SR, Lau B, Napravnik S, Eron JJ. Selection Bias Due to Loss to Follow Up in Cohort Studies: Epidemiology. 2016;27(1):91-97. doi:10.1097/EDE.0000000000000409

Millard LAC, Fernández-Sanlés A, Carter AR, et al. Exploring the impact of selection bias in observational studies of COVID-19: a simulation study. Int J Epidemiol. 2023;52(1):44-57. doi:10.1093/ije/dyac221

Haneuse S, Arterburn D, Daniels MJ. Assessing Missing Data Assumptions in EHR-Based Studies: A Complex and Underappreciated Task. JAMA Netw Open. 2021;4(2):e210184. doi:10.1001/jamanetworkopen.2021.0184

Hughes RA, Heron J, Sterne JAC, Tilling K. Accounting for missing data in statistical analyses: multiple imputation is not always the answer. Int J Epidemiol. 2019;48(4):1294-1304. doi:10.1093/ije/dyz032

Hernán MA, Hernández-Díaz S. Beyond the intention-to-treat in comparative effectiveness research. Clin Trials. 2012;9(1):48-55. doi:10.1177/1740774511420743

Swanson SA, Studdert DM, Zhang Y, Prince L, Miller M. Handgun Divestment and Risk of Suicide. Epidemiology. 2023;34(1):99-106. doi:10.1097/EDE.0000000000001549

Matthews AA, Dahabreh IJ, Fröbert O, et al. Benchmarking Observational Analyses Before Using Them to Address Questions Trials Do Not Answer: An Application to Coronary Thrombus Aspiration. Am J Epidemiol. 2022;191(9):1652-1665. doi:10.1093/aje/kwac098

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PrePrint online

2024-05-24

Declaración de disponibilidad de datos

No fueron utilizados datos originales para este manuscrito.