322
Views
0
CrossRef citations to date
0
Altmetric
Original Articles

A simulation study on implementing marginal structural models in an observational study with switching medication based on a biomarker

&
Pages 350-361 | Received 18 Jan 2017, Accepted 30 Oct 2016, Published online: 04 Dec 2017

References

  • Aalen, O. O., Cook, R. J., Roysland, K. (2015). Does Cox analysis of a randomized survival study yield a causal treatment effect? Lifetime Data Analysis 21:579–593.
  • Aalen, O. O., et al. (2014). Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms. Statistical Methods in Medical Research 25:2294–2314.
  • Abbott, R. D. (1985). Logistic regression in survival analysis. American Journal of Epidemiology 121:465–471.
  • Bang, H., Robins, J. M. (2005). Doubly-robust estimation in missing data and causal inference models. Biometrics 61:962–973.
  • Bodnar, L., Davidian, M., Siega-Riz A. M., Tsiatis A. (2004). Marginal structural models for analyzing causal effects of time-dependent treatments: An application in perinatal epidemiology. American Journal of Epidemiology 159:926–934.
  • Buonaccorsi, J., Eugene Demidenko, E., Tosteson, T. (1995). Estimation in longitudinal random effects models with measurement error. Statistica Sinica 10:885–903.
  • Buonaccorsi, J. (2000). Prediction in the presence of measurement error: General discussion and an example predicting defolation. Biometrics 51:1562–1569.
  • Cain, L., Robins, J., Lanoy, E., Logan, R., Costagliola, D., Hernan, M. (2010). When to start treatment? A systematic approach to the comparison of dynamic regimes using observational data. The International Journal of Biostatistics 6:1–24.
  • Carroll, R., Ruppert, D., Stefanski, L., Crainiceanu, C. (2006). Measurement Error in Nonlinear Models: A Modern Perspective. Boca Raton, Florida: Chapman & Hall.
  • Cole, S. R., Hernan, M. A. (2008). Constructing inverse probability weight for marginal structural models. American Journal of Epidemiology 168:656–664.
  • Cole, S. R., Jacobson, L. P., Tien, P. C. Kingsley, L., Chmiel, J. S., Anastos, K. (2010). Using Marginal structural measurement error models to estimate the long-term effect of Antiretroviral Therapy on incident AIDS or Death. American Journal of Epidemiology 171:113–122.
  • D’Agostino, R. B., Lee, M. L., Belanger, A. J., Anderson, K., Kannel, W. B. (1990). Relation of pooled logistic regression to time dependent Cox regression analysis: The Framingham heart study. Statistics in Medicine 9:1501–1515.
  • Daniel, R. M., Cousens, S. N., De Stavola, D. L., Kenward, M. G., Sterne, J. A. C. (2013). Statistics in Medicine, 1584–1618.
  • De Meyer, G., Shapiro, F. (2003). Biomarker Development: The Road to Clinical Utility, 23–27. Boston, USA: Current Drug Discovery.
  • Douglas, E. F., Kadziola, Z. A. (2010). Analysis of Observational Health Care Data Using SAS, 211–230. North Carolina, USA: SAS Institute Inc.
  • Ewings, F. M., Ford, D., Walder, S., Carpenter, J., Copas, A. (2014). Optimal CD4 count for initiating HIV treatment: Impact of CD4 observation frequency and grace periods, and performance of dynamic marginal structural models. Epidemiology 25:194–202.
  • Funk, M. J., Westreich, D., Wiesen, C., Sturmer, T., Brookhart, M., Davidian, M. (2011). Doubly Robust estimation of causal effects. American Journal of Epidemiology 173:761–767.
  • Goetghebeur, E., Vansteelandt, S. (2005). Structural mean models for compliance analysis in randomized clinical trials and the impact of errors on measures of exposure. Stat Methods Med Res 14(4):397–415.
  • Gruber, S., Logan, R. W., Jarrin, I., Monge, S., Hernan, M. A. (2015). Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets, Statistics in Medicine 34:106–117.
  • Havercroft, W. G., Didelez, V. (2012). Simulating from marginal structural models with time-dependent confounding. Statistics in Medicine 31:4190–4206.
  • Hernan, M., Brumback, B., Robins, J. (2000). Marginal structural models to estimate the causal effect of ziodvudine on survival of HIV-positive. Epidemiology 11:561–570.
  • Hernan, M., Brumback, B., Robins, J. (2001). Marginal structural models to estimate the joint causal effect of non-randomized treatments. Journal of the American Statistical Association 96:440–448.
  • Hernan, M., Brumback, B., Robins, J. (2002). Estimating the causal effect of zidovudine on CD4 count with a marginal structural model for repeated measures. Statistics in Medicine 21:1689–1709.
  • Hernan, M., Lanoy, E., Costagliola, D., Robins, J. (2006). Comparison of dynamic treatment regimens via inverse probability weighting. Clinical Pharmacology & Toxicology 98:237–242.
  • HIV-CAUSAL Collaboration. (2010). The effect of combined antiretroviral therapy on the overall mortality of HIV-infected individuals. AIDS (London, England) 24:123–137.
  • Liang, H., Wu, H., Carroll, R. J. (2003). The relationship between virologic and immunologic response in AIDS clinical research using mixed-effects varying-coefficient models with measurement error. Biostatistics 4:297–312.
  • Liang, X., Zucker, D., Li, Y., Spiegelman, D. (2011). Survival analysis with error-prone time-varying covariates: A risk set calibration approach. Biometrics 67:50–58.
  • Lunceford, J. K., Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study. Statistics in Medicine 23:2937–2960.
  • Mortimer, K. M., Neugebauer, R., Van Der Laan, M., Tager, I. B. (2005). An application of model-fitting procedure for marginal structural models. American Journal of Epidemiology 162:382–388.
  • Neugebaur, R., Van Der Laan, M. J. (2006). Causal effects in longitudinal studies: Definition and maximum likelihood estimation. Comput Stat Dada Anal 2006(51):1664–1675.
  • Peal, J. (2000). Causality: Models, Reasoning, and Inference. New York: Cambridge University Press.
  • Prentice, R. (1982). Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika 69:331–342.
  • Qoronfleh, M. W., Lindpaintner, K. (2010). Protein Biomarker Immunoassays: Opportunity and Challenges, 19–28. London: RJ Communications Ltd, Drug Discovery World.
  • Robins, J. (1998). Correction for non-compliance in equivalence trials. Statistics in Medicine 17:269–302.
  • Robins, J. M. (2003). General methodological considerations. Journal Econometrics 112:89–106.
  • Robins, J., Hernan, M. (2008). Estimation of the causal effects of time-varying exposures. In G. Fitzmaurice, M. Davidian, G. Verbeke, G. Molenberghs (eds), Longitudinal Data Analysis, 553–599. New York: Chapman and Hall/CRC Press.
  • Robins, J., Hernan, M., Brumback, B. (2000). Marginal structural models and causal inference in Epidemiology. Epidemiology 11:550–560.
  • Robins, J., Rotnitzky, A., Zhao, L. P. (1994). Estimation of regression coefficients when some of the regressors are not always observed. Journal of the American Statistical Association 89:846–866.
  • Rosenbaum, P. R., Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55.
  • Rosner, B., Willett, W., Spiegelman, D. (1989). Correction of logistic regression relative risk estimates and confidence interval for systematic within-person measurement errors. Statistics in Medicine 8(9):1051–1069.
  • Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66(5):688–701.
  • Stefanski, L., Carroll, R. (1985). Covariate measurement error in logistic regression. Annals of Statistics 13:1335–1351.
  • Tosteson, T., Buonaccorsi, J., Demidenko, E. (1997). Covariate measurement error and the estimation of random effect parameters in a mixed model for longitudinal data. Statistics in Medicine 17:1959–1971.
  • Van Der Laan, M. J., Robins, J. M. (2003). Unified Methods for Censored Longitudinal Data and Causality. New York: Springer-Verlag.
  • Wallace, M. P., Moodie, E. (2015). Doubly-robust dynamic treatment regimen estimation via weighted least squares. Biometric 71:639–644.
  • Wooldridge, J. M. (2007). Inverse probability weighed estimation for general missing data problems. Journal of Econometrics 141:1281–1301.
  • Wu, L. (2010). Mixed Effects Models for Complex Data. Boca Raton, FL: A Chapman & Hall CRC Press.
  • Xiao, Y., Abrahamowicz, M., Moodie, E. (2010). Accuracy of conventional and marginal structural Cox model estimators: A simulation study. The International Journal of Biostatistics 6(2):13.
  • Ziger, S., Diggle, P. (1994). Semiparametric models for longitudinal data with application to CD4+ cell numbers in HIV seroconverters. Biometrics 50:689–699.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.