REFERENCES
https://doi.org/10.1002/pds.1614
1. Grothey A, Sobrero AF, Shields AF, et al. Duration of adjuvant
chemotherapy for stage III colon cancer. New England Journal of
Medicine 2018; 378: 1177-1188.2. Rothman KJ and Suissa S. Exclusion of
immortal person-time. Pharmacoepidemiology and drug safety 2008;
17: 1036-1036. DOI: .3. Suissa S. Immortal time bias in
pharmacoepidemiology. American journal of epidemiology 2008; 167:
492-499.4. Duchesneau ED, Jackson BE, Webster-Clark M, et al. The
Timing, the Treatment, the Question: Comparison of Epidemiologic
Approaches to Minimize Immortal Time Bias in Real-World Data Using a
Surgical Oncology Example. Cancer epidemiology, biomarkers &
prevention : a publication of the American Association for Cancer
Research, cosponsored by the American Society of Preventive Oncology 2022; 31: 2079-2086. 2022/08/20. DOI: 10.1158/1055-9965.epi-22-0495.5.
Hernán MA, Sauer BC, Hernández-Díaz S, et al. Specifying a target trial
prevents immortal time bias and other self-inflicted injuries in
observational analyses. Journal of clinical epidemiology 2016;
79: 70-75. 2016/05/31. DOI: 10.1016/j.jclinepi.2016.04.014.6. Cain LE,
Robins JM, Lanoy E, et al. When to start treatment? A systematic
approach to the comparison of dynamic regimes using observational data.The international journal of biostatistics 2010; 6.7. Zhao SS,
Lyu H and Yoshida K. Versatility of the clone-censor-weight approach:
response to “trial emulation in the presence of immortal-time bias”.International Journal of Epidemiology 2020; 50: 694-695. DOI:
10.1093/ije/dyaa223.8. Hernán MA and Robins JM. Using Big Data to
Emulate a Target Trial When a Randomized Trial Is Not Available.Am J Epidemiol 2016; 183: 758-764. 2016/03/20. DOI:
10.1093/aje/kwv254.9. Hernán MA. Methods of Public Health Research -
Strengthening Causal Inference from Observational Data. The New
England journal of medicine 2021; 385: 1345-1348. 2021/10/02. DOI:
10.1056/NEJMp2113319.10. Huitfeldt A, Kalager M, Robins JM, et al.
Methods to estimate the comparative effectiveness of clinical strategies
that administer the same intervention at different times. Current
epidemiology reports 2015; 2: 149-161.11. Emilsson L, García-Albéniz X,
Logan RW, et al. Examining bias in studies of statin treatment and
survival in patients with cancer. JAMA oncology 2018; 4:
63-70.12. Garcia-Albeniz X, Chan J, Paciorek A, et al. Immediate versus
deferred initiation of androgen deprivation therapy in prostate cancer
patients with PSA-only relapse. An observational follow-up study.European Journal of Cancer 2015; 51: 817-824.13. Young JG, Herńan
MA and Robins JM. Identification, estimation and approximation of risk
under interventions that depend on the natural value of treatment using
observational data. Epidemiol Methods 2014; 3: 1-19. DOI:
10.1515/em-2012-0001.14. Gonzales A, Guruswamy G and Smith SR. Synthetic
data in health care: A narrative review. PLOS Digit Health 2023;
2: e0000082. 20230106. DOI: 10.1371/journal.pdig.0000082.15. Young JG,
Logan RW, Robins JM, et al. Inverse probability weighted estimation of
risk under representative interventions in observational studies.J Am Stat Assoc 2019; 114: 938-947. 20180810. DOI:
10.1080/01621459.2018.1469993.16. Wanis KN, Sarvet AL, Wen L, et al.
Grace periods in comparative effectiveness studies of sustained
treatments. Journal of the Royal Statistical Society Series A:
Statistics in Society 2024; 187: 796-810. DOI:
10.1093/jrsssa/qnae002.17. Hernán MA. How to estimate the effect of
treatment duration on survival outcomes using observational data.Bmj 2018; 360: k182. 20180201. DOI: 10.1136/bmj.k182.18. Gaber
CE, Hanson KA, Kim S, et al. The Clone-Censor-Weight Method in
Pharmacoepidemiologic Research: Foundations and Methodological
Implementation. Current Epidemiology Reports 2024; 11: 164-174.
DOI: 10.1007/s40471-024-00346-2.19. Hernan MA and Robins JM.Causal Inference: What If . CRC Press, 2024.20. Rothman KJ,
Greenland S and Lash TL. Modern epidemiology . Wolters Kluwer
Health/Lippincott Williams & Wilkins Philadelphia, 2008.21. Westreich
D. Epidemiology by design: a causal approach to the health
sciences . Oxford University Press, 2019.22. Robins JM, Hernan MA and
Brumback B. Marginal structural models and causal inference in
epidemiology. Lww, 2000, p. 550-560.23. Kulesa A, Krzywinski M, Blainey
P, et al. Sampling distributions and the bootstrap. Nat Methods 2015; 12: 477-478. DOI: 10.1038/nmeth.3414.24. Cole SR and Hernán MA.
Constructing inverse probability weights for marginal structural models.Am J Epidemiol 2008; 168: 656-664. 20080805. DOI:
10.1093/aje/kwn164.25. Young JC, Conover MM and Funk MJ. Measurement
error and misclassification in electronic medical records: methods to
mitigate bias. Curr Epidemiol Rep 2018; 5: 343-356. 20180910.
DOI: 10.1007/s40471-018-0164-x.26. Peskoe SB, Arterburn D, Coleman KJ,
et al. Adjusting for selection bias due to missing data in electronic
health records-based research. Statistical Methods in Medical
Research 2021; 30: 2221-2238. DOI: 10.1177/09622802211027601.27.
Waernbaum I and Pazzagli L. Model misspecification and bias for inverse
probability weighting estimators of average causal effects. Biom
J 2023; 65: e2100118. 20220831. DOI: 10.1002/bimj.202100118.28. Acton
EK, Willis AW and Hennessy S. Core concepts in pharmacoepidemiology: Key
biases arising in pharmacoepidemiologic studies. Pharmacoepidemiol
Drug Saf 2023; 32: 9-18. 20221020. DOI: 10.1002/pds.5547.29. Boyne DJ,
Brenner DR, Gupta A, et al. Head-to-head comparison of FOLFIRINOX versus
gemcitabine plus nab-paclitaxel in advanced pancreatic cancer: a target
trial emulation using real-world data. Ann Epidemiol 2023; 78:
28-34. 20221220. DOI: 10.1016/j.annepidem.2022.12.005.