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Relevant bibliographies by topics / Marginal Structural Cox models

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Journal articles

Academic literature on the topic 'Marginal Structural Cox models'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Marginal Structural Cox models"

1

Ali, R. Ayesha, M. Adnan Ali, and Zhe Wei. "On computing standard errors for marginal structural Cox models." Lifetime Data Analysis 20, no. 1 (2013): 106–31. http://dx.doi.org/10.1007/s10985-013-9255-7.

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2

Enders, Dirk, Susanne Engel, Roland Linder, and Iris Pigeot. "Robust versus consistent variance estimators in marginal structural Cox models." Statistics in Medicine 37, no. 24 (2018): 3455–70. http://dx.doi.org/10.1002/sim.7823.

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3

Xiao, Yongling, Erica E. M. Moodie, and Michal Abrahamowicz. "Comparison of Approaches to Weight Truncation for Marginal Structural Cox Models." Epidemiologic Methods 2, no. 1 (2013): 1–20. http://dx.doi.org/10.1515/em-2012-0006.

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4

Westreich, D., S. R. Cole, P. C. Tien, et al. "Time Scale and Adjusted Survival Curves for Marginal Structural Cox Models." American Journal of Epidemiology 171, no. 6 (2010): 691–700. http://dx.doi.org/10.1093/aje/kwp418.

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5

Karim, Mohammad Ehsanul, John Petkau, Paul Gustafson, Robert W. Platt, and Helen Tremlett. "Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies." Statistical Methods in Medical Research 27, no. 6 (2016): 1709–22. http://dx.doi.org/10.1177/0962280216668554.

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In longitudinal studies, if the time-dependent covariates are affected by the past treatment, time-dependent confounding may be present. For a time-to-event response, marginal structural Cox models are frequently used to deal with such confounding. To avoid some of the problems of fitting marginal structural Cox model, the sequential Cox approach has been suggested as an alternative. Although the estimation mechanisms are different, both approaches claim to estimate the causal effect of treatment by appropriately adjusting for time-dependent confounding. We carry out simulation studies to asse

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6

Westreich, Daniel, Stephen R. Cole, Enrique F. Schisterman, and Robert W. Platt. "A simulation study of finite-sample properties of marginal structural Cox proportional hazards models." Statistics in Medicine 31, no. 19 (2012): 2098–109. http://dx.doi.org/10.1002/sim.5317.

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7

Burne, Rebecca M., and Michal Abrahamowicz. "Adjustment for time-dependent unmeasured confounders in marginal structural Cox models using validation sample data." Statistical Methods in Medical Research 28, no. 2 (2017): 357–71. http://dx.doi.org/10.1177/0962280217726800.

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Large databases used in observational studies of drug safety often lack information on important confounders. The resulting unmeasured confounding bias may be avoided by using additional confounder information, frequently available in smaller clinical “validation samples”. Yet, no existing method that uses such validation samples is able to deal with unmeasured time-varying variables acting as both confounders and possible mediators of the treatment effect. We propose and compare alternative methods which control for confounders measured only in a validation sample within marginal structural C

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8

Bruneau, Pierre, Fahim Ashkar, and Bernard Bobée. "SMPLNORM : Un modèle simple pour obtenir les probabilités conjointes de deux débits et le niveau qui en dépend." Canadian Journal of Civil Engineering 21, no. 5 (1994): 883–95. http://dx.doi.org/10.1139/l94-094.

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Most bivariate models assume the same type of marginal distribution, with two parameters, for two variables (gamma, type I extreme values, and so forth). The disadvantage of these models is that it is often difficult to make adjustments for observed flows. This study shows the application flexibility of a program that calculates the joint probability of two variables, Q1, and Q2, with marginal distributions that have three parameters. The program can also provide the probability of nonexceedence of a third variable, H, mathematically related to the first two variables. Two applications are dis

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9

Santacatterina, Michele, Celia García‐Pareja, Rino Bellocco, Anders Sönnerborg, Anna Mia Ekström, and Matteo Bottai. "Optimal probability weights for estimating causal effects of time‐varying treatments with marginal structural Cox models." Statistics in Medicine 38, no. 10 (2018): 1891–902. http://dx.doi.org/10.1002/sim.8080.

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10

Madden, Jamie M., Finbarr P. Leacy, Lina Zgaga, and Kathleen Bennett. "Fitting Marginal Structural and G-Estimation Models Under Complex Treatment Patterns: Investigating the Association Between De Novo Vitamin D Supplement Use After Breast Cancer Diagnosis and All-Cause Mortality Using Linked Pharmacy Claim and Registry Data." American Journal of Epidemiology 189, no. 3 (2019): 224–34. http://dx.doi.org/10.1093/aje/kwz243.

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Abstract Studies have shown that accounting for time-varying confounding through time-dependent Cox proportional hazards models may provide biased estimates of the causal effect of treatment when the confounder is also a mediator. We explore 2 alternative approaches to addressing this problem while examining the association between vitamin D supplementation initiated after breast cancer diagnosis and all-cause mortality. Women aged 50–80 years were identified in the National Cancer Registry Ireland (n = 5,417) between 2001 and 2011. Vitamin D use was identified from linked prescription data (n

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