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CREATE Biostatistics Core THRio Statistical Considerations

CREATE Biostatistics Core THRio Statistical Considerations. Analysis of baseline data—esp. truncation Analysis of main study data—esp. correlation. Outcome =. Outcome =. Outcome =. TB diagnosis in baseline follow. TB diagnosis in baseline follow. TB diagnosis in baseline follow. -.

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CREATE Biostatistics Core THRio Statistical Considerations

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  1. CREATE Biostatistics CoreTHRio Statistical Considerations Analysis of baseline data—esp. truncation Analysis of main study data—esp. correlation

  2. Outcome = Outcome = Outcome = TB diagnosis in baseline follow TB diagnosis in baseline follow TB diagnosis in baseline follow - - - up period up period up period Primary Exposure = Primary Exposure = Primary Exposure = 1. No HAART & No IPT 1. No HAART & No IPT 1. No HAART & No IPT 2. HAART 2. HAART 2. HAART 3. IPT 3. IPT 3. IPT 4. Both HAART & IPT 4. Both HAART & IPT 4. Both HAART & IPT Sept 1 Sept 1 Sept 1 Sept 1 Sept 1 Sept 1 2003 2003 2003 2005 2005 2005 Baseline analysis

  3. 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

  4. Table 3: Incidence Rate by exposure category

  5. 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.

  6. 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!

  7. Thinness = entry, at risk Calendar timeline 1 Sept 2003 Time since HIV Dx timeline

  8. 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.

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