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CREATE Biostatistics Core THRio Statistical Considerations
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  1. CREATE Biostatistics CoreTHRio Statistical Considerations Analysis Plan

  2. Design Review Intervention: Train clinic staff to implement PPD testing procedures among those without prior TB or INH; give INH prophylaxis to those testing positive.

  3. Phased Clinic Entry Into Intervention Status 1/2 3/4 5/6 7/8 9/10 29 Clinic entry to intervention period Control period Follow-up period Intervention period 1 3 5 7 9 29 36 42 Month

  4. First—Need to Consider Analytic Approach • Study will take place over 2.5 years, and there may be a strong temporal trend in TB incidence • Perfectly control for calendar time by comparing, ON EACH DAY, TB incidence in clinics that are still in control status to incidence in clinics that are in intervention status • Assume Poisson process with time-varying intensity: where is the person-days of exposure in the ith clinic on the tth day, represents the effect of the tth day is the log rate ratio comparing those in the intervention status ( =1) to those in control status ( =0)

  5. Analytic Approach (continued) • Condition on each day’s risk set; form partial likelihood, comparing covariates of incident cases to those of the other patients: eliminates • Use clinic-level bootstrap, or robust variance estimator, to account for within-clinic correlation over time

  6. But… • Big delay between intervention in a clinic and intervention in an individual in the clinic (as per Pacheco’s K-M graphs) • So—new primary analysis: One covariate is fit, which tracks intervention status: on a given day for a given patient, it is the proportion of patients in that patient’s clinic who have had a clinic visit since initiation of the intervention in that clinic.

  7. Interpretation of Main Analysis • The interpretation of the coefficient of this covariate is that it represents a log hazard ratio comparing a clinic whose entire patient population has had a visit during intervention phase to clinics in control phase. No distinction is made between an intervention phase clinic with no patients who have made a visit in that phase, and clinics in control phase.

  8. Other Analyses A. Same as Primary, except with the endpoint of the complement of TB-free survival (i.e. time to earliest of TB or death). This will rely on merging in the mortality data base. The idea is to make sure to capture those who leave a clinic, get TB without it being noted, and then die. B. Original primary calculation: use of a covariate that is 0 if the patient’s clinic on a given day is in control phase, 1 if it is in intervention phase. This will have reduced power compared to the primary calculation, due to the large lag in a clinic entering intervention status and the potential receipt of the intervention by individual patients.

  9. Other Analyses (continued) These (C,D,E,F) will be conducted among the subgroup of patients who are “eligible” for the intervention, i.e. who have not had prior TB or INH prophylaxis. C. Use of a covariate that is 0 for a patient who has not yet made a visit to his or her clinic during the clinic’s intervention phase, 1 on the day of the patient’s first visit to the clinic during its intervention phase. This may have more power than the primary calculation, but may have some bias due to a potential correlation between an individual’s frequency of attendance and risk of TB (which could be the case if a seldom-attender is taking fewer of the prescribed ARVs). • Among clinics in intervention phase only: Use of a covariate that is 0 for a person who has not had a TST, and 1 as of the day of TST reading (after 1 September 2005). This measures the value of a TST; it is not a randomized comparison, but is a better measure than in control status clinics where TST tends to be given to those thought to be more susceptible to TB. This measures the impact of initiating the intervention at the individual level.

  10. Other Analyses (continued) E. INH effectiveness: use of a covariate that is 0 unless a patient has started INH prophylaxis, at which point it becomes 1. F. Intervention effects on processes—time-to-event analyses, accounting for within-clinic correlation, that: • Compare time from first visit when “eligible” to first TST between clinics on intervention and control status. • Compare time from positive TST to initiation of INH prophylaxis between clinics on intervention and control status.

  11. Tertiary Analyses • Further elaboration of the foregoing analyses will incorporate relevant patient-level covariates: • Time-varying: Age, CD4, HIV viral load, HAART, time on HAART, PPD result Fixed: Gender • We will have a great deal of data on opportunistic infections. They may be used as potential confounders in the foregoing analyses. It may also be of interest to look at the association between HAART, CD4 and OIs in this population. A major use of these data will be for descriptive purposes. • Other analyses will be specific to those who are adherent to INH prophylaxis (>80% meds, or 180 days). For example, we can estimate rates among the intervention status person-years, comparing adherent to not, with a timeline starting 12 months after initiation of IPT for each patient.

  12. 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.