Allison Dunning MS Research Biostatistician Weill Cornell Medical College Outline Background Motivation Methods Data Management Results Conclusion Background Results from the primary openlabel clinical trial have previously been published in the New England Journal of Medicine ID: 137682
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Slide1
Correctly modeling CD4 cell count in Cox regression analysis of HIV-positive patients
Allison Dunning, M.S.
Research Biostatistician
Weill Cornell Medical CollegeSlide2
OutlineBackground
Motivation
Methods
Data Management
Results
ConclusionSlide3
BackgroundResults from the primary open-label clinical trial have previously been published in the New England Journal of Medicine.Slide4
BackgroundResults of the clinical trial have
shown that starting
antiretroviral therapy
earlier (‘Early’) rather than waiting for onset of symptoms (‘Standard’) in HIV patients significantly decreases mortality.
Between 2005 and 2008 a total of 816 participants – 408 per group – were enrolled and followed.
After stopping the clinical trail all participants were immediately put on antiretroviral therapy.
Researchers have continued to follow and collect data on the 816 participants.Slide5
MotivationAs a follow-up, researchers are interested in determining if ‘Early’ therapy significantly decreases time to first Tuberculosis (TFTB) diagnosis.
CD4 cell count has long been considered a measure of overall health in HIV patients.
Therefore investigators felt it was important to adjust for CD4 cell count in the analysis of TFTB diagnosis.Slide6
MotivationThe problem arose of how best to adjust for CD4 cell count.
Typically CD4 recorded at the beginning of the study is used for analysis; known as baseline CD4 cell count.
Per protocol CD4 cell counts were collected every 6 months for all participants.
Investigators felt it was important to account for changing CD4 cell counts, especially after therapy initiation, in the analysis.Slide7
MotivationOur analysis was not interested in predicting survival just whether or not drug start time was a predictor of TB diagnosis
.
In order to allow survival analysis to account for changing CD4 cell counts
we
decided to conduct a Cox Proportional Hazards Regression analysis using a mixture of fixed and time-dependent
covariates.Slide8
What is a Time Dependent CovariateTime-dependent covariates are those that may change in value over the study period
Most variables in survival analysis are collected at one time point, typically at the start of the study, these include demographic and risk factor variables
Sometimes we may collect a lab variable or risk factor that can vary over the study period.Slide9
Example of Time Dependent Variables
Lab Values:
Blood Pressure
Most studies will only use blood pressure collected at start of study, sometimes called baseline blood pressure.
However, in theory, blood pressure could be collected at multiple time during the study period.
Risk Factors:
Smoking Status
Again this can be collected only at start of study, or baseline or could be tracked over time
Some patients may quit smoking, start smoking, or quit and relapse smoking during the study period.Slide10
Fixed CovariatesFixed Covariates is a term used to represent variables that stay constant, or do not change, during the study period.
These are typically things like patient gender, race/ethnicity, risk factors such as diabetes or hypertension, etc.
We as researchers must develop a method to analyze time to event data while including both these fixed covariate and time-dependent covariatesSlide11
MethodsSTATA 12.0 was used to perform two
Cox
regression models to analyze the effect of ART start time on TFTB.
The
first model included baseline CD4 cell count only as a predictor
While
the second model treated CD4 cell count as a time-varying predictor.
Both models were adjusted for history of TB diagnosis prior to clinical trial and baseline BMISlide12
MethodsRegular Cox Proportional
Hazards Model:
Log[h
i
(t)] =
α
(t) +
β
1
x
i1
+ … +
β
k
x
ik
Where
α
(t) = log [
λ
0
(t)]
Proportional Hazards Model with time-varying covariate:
Log[h
i
(t)] =
α
(t) +
β
1
x
i1
+
β
2
x
i2
(t)
Where
α
(t
) = log
[
λ
0
(t)]Slide13
Data ManagementProblems we encountered:
Missing CD4 cell count
Some
patients missed a scheduled lab visit during the study, therefore CD4 cell count was missing for
one
of the six month intervals
.
Multiple CD4 cell counts within a six month interval
For various reasons, several patients visited the lab multiple times within a six month interval, therefore multiple CD4 cell counts were collected in the six month time frame.Slide14
Data ManagementWhat we did – Missing Data:
If only one interval was missing, the previous CD4 cell count was used in a carry the last forward approach
If at least two
consecutive
intervals were missing, the patient was excluded from the study; 13 patients in total were
excluded for this reason.
What we did – Multiple Observations:
The minimum CD4 cell count collected in the six month interval was the value used in analysis for that time frame.Slide15Slide16
Results – Regular Cox RegressionSlide17
ResultsRegular cox regression analysis showed that ‘Early’ therapy results in a significant decrease in TFTB, after adjustment for previous TB diagnosis, baseline BMI, and baseline CD4 cell count. Slide18
Data ManagementData was collected with one row per participant:Slide19
Data ManagementIn STATA, using reshape command, we reformatted dataset for analysis:Slide20Slide21
Results – Cox Regression with time-dependent covariatesSlide22
ResultsWhen treating
CD4 cell count as time-varying predictor in Cox regression,
we find that
ART start time
is not
a significant predictor of TFTB
.Slide23
ConclusionFailing to adjust for the change in CD4 cell counts over time led to reporting that ‘Early’ therapy significantly reduces risk of TB diagnosis. Modeled correctly, the effect becomes non-significant. This result has substantial consequence on treatment decision making.Slide24
ConclusionOur results help us to consider that TFTB diagnosis in HIV positive patients is not associated with start time of ART when overall patient health is considered
.
Further analysis is needed before we are comfortable making this conclusion.Slide25
Looking ForwardWe are currently in the process of further examining the relationship between CD4 cell count and ART start.
Currently collecting data to examine time from ART start to first TB diagnosis
. For the Early group this data does not change, however, for the Standard group this may have a significant effect on the analysis.Slide26
AcknowledgementsDaniel W. Fitzgerald, M.DSean Collins, M.D
Sandra H. Rua,
Ph.DSlide27
Thank Youald2018@med.cornell.edu