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Stat Methods Med Res. 2017 Aug;26(4):1969-1981. doi: 10.1177/0962280215593974. Epub 2015 Jul 9.

Estimating the ratio of multivariate recurrent event rates with application to a blood transfusion study.

Author information

1
1 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA.
2
2 Division of Clinical and Translational Sciences, Department of Internal Medicine, The University of Texas Medical School at Houston, Houston, USA.
3
3 Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Sciences Center at Houston, Houston, USA.
4
4 Division of Biostatistics, School of Public Health, The University of Texas Health Sciences Center at Houston, Houston, USA.
5
5 Center for Translational Injury Research, Division of Acute Care Surgery, Department of Surgery, The University of Texas Health Science Center at Houston, Houston, USA.
6
6 Department of Biomedical Engineering, Wake Forest University, Winston-Salem, USA.
7
7 Department of Biostatistics, School of Public Health, Johns Hopkins University, Baltimore, USA.

Abstract

In comparative effectiveness studies of multicomponent, sequential interventions like blood product transfusion (plasma, platelets, red blood cells) for trauma and critical care patients, the timing and dynamics of treatment relative to the fragility of a patient's condition is often overlooked and underappreciated. While many hospitals have established massive transfusion protocols to ensure that physiologically optimal combinations of blood products are rapidly available, the period of time required to achieve a specified massive transfusion standard (e.g. a 1:1 or 1:2 ratio of plasma or platelets:red blood cells) has been ignored. To account for the time-varying characteristics of transfusions, we use semiparametric rate models for multivariate recurrent events to estimate blood product ratios. We use latent variables to account for multiple sources of informative censoring (early surgical or endovascular hemorrhage control procedures or death). The major advantage is that the distributions of latent variables and the dependence structure between the multivariate recurrent events and informative censoring need not be specified. Thus, our approach is robust to complex model assumptions. We establish asymptotic properties and evaluate finite sample performance through simulations, and apply the method to data from the PRospective Observational Multicenter Major Trauma Transfusion study.

KEYWORDS:

Informative censoring; multivariate recurrent event; rate ratio; transfusion medicine

PMID:
26160825
PMCID:
PMC5088066
DOI:
10.1177/0962280215593974
[Indexed for MEDLINE]
Free PMC Article

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