Research Projects
Forecasting risk in acute
myocardial infarction
Impact of the censoring distribution
on time-to-event problems in the presence of competing
risks
A model to predict
risk of recurrent events in the LIPID cardiovascular
trial
A
method to adjust for differential
background treatments in long-term
trials
Models to predict
breast cancer metastasis to internal mammary
nodes
Forecasting
risk in acute myocardial infarction
Existing short-term risk assessment strategies in acute
myocardial infarction are limited to Western populations. We have
proposed risk models for prediction of mortality after acute
myocardial infarction based on the geographically diverse
HERO-2 trial. HERO-2 randomised 17 073 patients to either
unfractionated heparin or bivalirudin in conjunction with
streptokinase to treat ST-segment-elevation myocardial
infarction. Patients were recruited from 46 countries from Europe,
Russia, North America, Latin America and Asia, including
Australia and New Zealand. We also examined variations in
outcomes across geographical regions and propose new methods for
comparing the calibration and ranking performance of risk
strategies.
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Impact of the censoring
distribution on time-to-event problems in the presence of competing
risks
Methods accounting for competing risks in time-to-event problems
are becoming common in mainstream statistical analyses. Standard
approaches include those based on log-rank type tests (Gray) and
cumulative incidence regression (Fine and Gray). These approaches
are based on weighting competing events by the censoring
distribution. The usual cumulative incidence regression uses
weights based on the pooled censoring distribution. However,
the effects of the pattern of events and censoring in these
approaches is still unclear. We are examining two aspects of this
problem: the amount of competing risk present (by using
a proportional-hazards model), and the pattern of censoring
between groups in the presence of competing risks.
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A
model to predict risk of recurrent events in
the LIPID cardiovascular trial
Traditional methods for analysing clinical and epidemiological
data have focused on the first occurrence of the outcome or event
being measured. These methods can be unsuitable for analysing
recurring events because a first event may signal another one; that
is, recurrent events are not independent of each other.
A new study focused on recurring events in the CTC's
multicentre trial, LIPID, which had shown that lipid-lowering with
a statin prevented a coronary event. We investigated recurrent
events and whether risk factors were different for first and
recurrent events. A semiparametric proportional-hazards model and a
parametric conditional model were both found to be useful tools for
exploring the biological cardiovascular process. The analysis also
showed that the study drug, pravastatin, prevented first and second
cardiovasacular events to a similar degree.
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A
method to adjust for differential
background treatments in long-term trials
An advance in trial methods arose from difficulties in the
statistical analysis of the FIELD diabetes trial. In this large
international trial, 9795 patients were randomly assigned to
fenofibrate or placebo and followed up for an average of 5 years.
Cardiovascular outcomes were measured.
Over the 5 years of the trial, many patients started taking
newly approved cholesterol-lowering drugs, confounding the effect
of the study drug. FIELD investigators and CTC statisticians
devised a novel method using the results of other clinical trials
to adjust the estimates of efficacy of the study drug - a
method with potential for wide application in long-term trials.
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Models to predict
breast cancer metastasis to internal mammary nodes
An important prognostic factor in breast cancer is the status of
the internal mammary lymph nodes, that is, whether there is tumour
in the nodes near the middle of the chest. These nodes are less
accessible than axillary lymph nodes and less likely to be
visualised with radioisotope mapping or to be biopsied. Models to
predict metastasis in these lymph nodes have been developed on the
basis of anatomy and tumour biology. These will assist cancer
clinicians to make decisions about treatment when the status of
these lymph nodes is not known.
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