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Correctly Modelling CD4 Cell Count in Cox Regression Analysis of HIV Positive Patients

  • Allison Dunning


    (Weill Cornell Medical College)

  • Sean Collins
  • Dan Fitzgerald
  • Sandra H. Rua
Registered author(s):

    Previous trial has shown that starting ART therapy earlier (‘Early’) rather than waiting for onset of symptoms (‘Standard’) in HIV patients significantly decreases mortality. As a follow-up, researchers are interested in determining if ‘Early’ therapy significantly decreases time to first Tuberculosis (TFTB) diagnosis, when adjusting for CD4 cell count, a known strong predictor. STATA 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. Regular 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. Treating CD4 cell count as time-varying predictor in Cox regression, however, we determine that ART start time was not a significant predictor of TFTB. Failing 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.

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    Paper provided by Stata Users Group in its series 2013 Stata Conference with number 16.

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    Date of creation: 01 Aug 2013
    Date of revision:
    Handle: RePEc:boc:norl13:16
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