2015 |
Mojaverian, Nassim; Moodie, Erica E M; Bliu, Alex; Klein, Marina B The Impact of Sparse Follow-up on Marginal Structural Models for Time-to-Event Data Journal Article American Journal of Epidemiology, 2015. Abstract | Links | BibTeX | Tags: Available-case analysis, Last observation carried forward, Marginal structural models, Missing data, Multiple imputation, Survival analysis @article{Mojaverian2015, title = {The Impact of Sparse Follow-up on Marginal Structural Models for Time-to-Event Data}, author = {Nassim Mojaverian and Erica E. M. Moodie and Alex Bliu and Marina B. Klein}, url = {https://www.ncbi.nlm.nih.gov/pubmed/26589708}, doi = {10.1093/aje/kwv152}, year = {2015}, date = {2015-12-15}, journal = {American Journal of Epidemiology}, abstract = {The impact of risk factors on the amount of time taken to reach an endpoint is a common parameter of interest. Hazard ratios are often estimated using a discrete-time approximation, which works well when the by-interval event rate is low. However, if the intervals are made more frequent than the observation times, missing values will arise. We investigated common analytical approaches, including available-case (AC) analysis, last observation carried forward (LOCF), and multiple imputation (MI), in a setting where time-dependent covariates also act as mediators. We generated complete data to obtain monthly information for all individuals, and from the complete data, we selected "observed" data by assuming that follow-up visits occurred every 6 months. MI proved superior to LOCF and AC analyses when only data on confounding variables were missing; AC analysis also performed well when data for additional variables were missing completely at random. We applied the 3 approaches to data from the Canadian HIV-Hepatitis C Co-infection Cohort Study (2003-2014) to estimate the association of alcohol abuse with liver fibrosis. The AC and LOCF estimates were larger but less precise than those obtained from the analysis that employed MI. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.}, keywords = {Available-case analysis, Last observation carried forward, Marginal structural models, Missing data, Multiple imputation, Survival analysis}, pubstate = {published}, tppubtype = {article} } The impact of risk factors on the amount of time taken to reach an endpoint is a common parameter of interest. Hazard ratios are often estimated using a discrete-time approximation, which works well when the by-interval event rate is low. However, if the intervals are made more frequent than the observation times, missing values will arise. We investigated common analytical approaches, including available-case (AC) analysis, last observation carried forward (LOCF), and multiple imputation (MI), in a setting where time-dependent covariates also act as mediators. We generated complete data to obtain monthly information for all individuals, and from the complete data, we selected "observed" data by assuming that follow-up visits occurred every 6 months. MI proved superior to LOCF and AC analyses when only data on confounding variables were missing; AC analysis also performed well when data for additional variables were missing completely at random. We applied the 3 approaches to data from the Canadian HIV-Hepatitis C Co-infection Cohort Study (2003-2014) to estimate the association of alcohol abuse with liver fibrosis. The AC and LOCF estimates were larger but less precise than those obtained from the analysis that employed MI. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. |
Research Papers
2015 |
The Impact of Sparse Follow-up on Marginal Structural Models for Time-to-Event Data Journal Article American Journal of Epidemiology, 2015. |