Inverse Probability Weighted Estimating Equations for Randomized Trials in Transfusion Medicine
Abstract
Thrombocytopenia is a condition characterized by extremely low platelet counts, which puts
patients at elevated risk of morbidity and mortality because of bleeding. Trials in transfusion
medicine are routinely designed to assess the effect of experimental platelet products on patients
platelet counts. In such trials, patients may receive multiple platelet transfusions over a predefined
period of treatment, and a response is available from each such administration. The resulting data
comprised multiple responses per patient, and although it is natural to want to use this data in testing
for treatment effects, naive analyses of the multiple responses can yield biased estimates of the
probability of response and associated treatment effects. These biases arise because only subsets
of the patients randomized contribute response data on the second and subsequent administrations
of therapy and the balance between treatment groups with respect to potential confounding factors
is lost. We discuss the design and analysis issues involved in this setting and make recommendations
for the design of future platelet transfusion trials.
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Cite this version of the work
Richard J. Cook, Ker-Ai Lee, Meaghan Cuerden, Cecilia Cotton
(2013).
Inverse Probability Weighted Estimating Equations for Randomized Trials in Transfusion Medicine. UWSpace.
http://hdl.handle.net/10012/10256
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