Our article, entitled
"Double robust and efficient estimation of a prognostic model for events in the presence of dependent censoring"
has been published in the journal Biostatistics and is now available on Pub Med at http://www.ncbi.nlm.nih.gov/pubmed/26224070 .
This article presents two simple double robust estimators of a mean ("marginal") model when subjects are longitudinally censored. In our example, we were interested in modeling the probability of negative health outcomes in subjects with HIV under the scenario where all subjects remained on treatment that successfully controlled viral load. Subjects were artificially censored when they did not continue on treatment, which therefore required modeling adjustment.
In an effort to make double robust computation more accessible, we provide code for fitting similar models (with example usage). Thanks to the referees and the journal editors for encouraging open access code!
"Double robust and efficient estimation of a prognostic model for events in the presence of dependent censoring"
has been published in the journal Biostatistics and is now available on Pub Med at http://www.ncbi.nlm.nih.gov/pubmed/26224070 .
This article presents two simple double robust estimators of a mean ("marginal") model when subjects are longitudinally censored. In our example, we were interested in modeling the probability of negative health outcomes in subjects with HIV under the scenario where all subjects remained on treatment that successfully controlled viral load. Subjects were artificially censored when they did not continue on treatment, which therefore required modeling adjustment.
In an effort to make double robust computation more accessible, we provide code for fitting similar models (with example usage). Thanks to the referees and the journal editors for encouraging open access code!
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