CREATE Biostatistics Core

THRio Statistical Considerations

• Analysis of baseline data—esp. truncation• Analysis of main study data—esp. correlation

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Baseline analysis

Outcome = TB diagnosis in baseline follow-up period

Primary Exposure =1. No HAART & No IPT 2. HAART3. IPT4. Both HAART & IPT

Sept 12003

Sept 12005

Outcome = TB diagnosis in baseline follow-up period

Primary Exposure =1. No HAART & No IPT 2. HAART3. IPT4. Both HAART & IPT

Sept 12003

Sept 12005

Outcome = TB diagnosis in baseline follow-up period

Primary Exposure =1. No HAART & No IPT 2. HAART3. IPT4. Both HAART & IPT

Sept 12003

Sept 12005

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Study definitions

Start = Sept 1, 2003 or HIV diagnosis date if between

Sept 1, 2003 and Sept 1, 2005

End = TB diagnosis date or Sept 1, 2005

IPT date = Date that IPT began

HAART date = Date that HAART began

HIV dx date = Earliest of HIV diagnosis date, initial CD4

date, HAART start date

TB dx date = Date that tuberculosis diagnosis reported

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Table 3: Incidence Rate by exposure category

Exposure category

Person-Years

TB cases

IR (per 100

PYs)

Naïve 3,865 1574.06

(3.45-4.75)

HAART only 11,629 2291.97

(1.72-2.24)

IPT only 395 51.27

(0.41-2.95)

Both 1,253 131.04

(0.55-1.78)

TOTAL 17,142 4042.36

(2.13-2.60)

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THRio Baseline Analysis

• Question: How much should we worry about bias due to truncation / prevalent cohort?

--Sickest, by defn, will die earlier. Had to have made at least one visit to a clinic between 1 Sept 2003 and 1 Sept 2005. Not included if died before 1 Sept 2003. Also, someone who died in Nov 2003 would have had little chance to be included.

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truncation…

I’m thinking this is somewhat mitigated by controlling for CD4/VL.

--Like lining up an analysis of time from HIV seroconversion to TB by estimated conversion time, but staggering entry into risk set according to when came into the study.

• We have 95% of data on month of first HIV dx.

But data would get ‘thin’ if staggered!

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Thinness

= entry, at risk

Calendar timeline

Time since HIV Dx timeline

1 Sept 2003

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Handling Correlation

• Currently, plan to form daily risk sets, do conditional logistic regression, with a dummy variable for whether each of the 29 clinics is in intervention status on that day (same as Cox model to TB)

• Correlation can be handled with a sandwich covariance estimator; or, by bootstrapping entire clinic histories

• Q: sandwich not a great idea when have lots of obs per cluster and few clusters; but what if those lots of obs only have a few events? Perhaps 10-20 TB events per clinic.