I am sending this on behalf of a colleague who is not a member of the mailbase.
I hope that's all right.
-Brett Larive
Cleveland Clinic Foundation
Hi,
I am a statistician for a clinical trial with up to 6.5 years of follow-up
with a death rate of approximately 15% per year, and a loss-to-follow-up
rate for other reasons of approximately 7% per year. In addition to
evaluation of mortality, a key objective of the study is to assess the
impact of the randomized treatment interventions on changes in continuous
parameters which are measured at regular intervals while the patient
remains in follow-up. Existing methods for incorporating informative censoring
in mixed effects models appear to be appropriate for dealing with censoring
for causes other than death, but seem problematic for high rates of censoring
due to death itelf. The problem is that most methods involve estimation of
parameters attributed to the full population of patients who started the
trial. This seems highly artificial when a high percentage of the patients
are dead by several years into the study. Does anyone have experience with
methods for longitudinal data which treat censoring by death differently than
other types of censoring?
Thank you very much.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|