3:30pm - 4:30pm | Biostat Seminar: Without Randomization or Ignorability: A Stability-Controlled Quasi-Experiment on the Prevention of Tuberculosis

Wednesday, December 4, 2019

Chad Hazlett, Assistant Professor
Department of Political Science and Statistics
University of California, Los Angeles

Wednesday, December 04, 2019
3:30pm - 4:30pm, CHS 33-105A
Refreshments served at 3:00 PM in room 51-254 CHS

When determining the effectiveness of a new treatment, randomized trials are not always
possible or ethical, or we may wish to estimate the effect a treatment has actually had, among
a population that has already received it, through a selection process that is unknown. The
stability-controlled quasi-experiment (SCQE) (Hazlett, 2019) replaces randomization or the
assumption of no-unobserved confounding with an assumption on the outcome's ``baseline
trend,'' or more precisely, the change in average non-treatment potential outcome across
successive cohorts. We describe and extend this method, and provide its first direct
application: examining the real world effectiveness of isoniazid preventive therapy (IPT) to
reduce tuberculosis (TB) incidence among people living with HIV in Tanzania. Since IPT
became available in the clinics we studied, 27% of new patients received it, selected through
an unknown process. Within a year, 16% of those not on IPT developed TB, compared to
fewer than 1% of those taking IPT. We find that (i) despite this compelling naive comparison, if
the baseline trend is assumed to be flat, the effect of IPT on TB incidence would be -2
percentage points (pp) with a confidence interval of [-10, 5]; (ii) to argue that IPT was
beneficial requires believing that the (non-treatment) incidence rate would have risen by at
least 0.5pp per year in the absence of the treatment; and (iii) to argue IPT was not harmful
requires arguing that the baseline trend did not fall by more than 1pp per year. We also find
that those who were given treatment may have been less likely to develop TB anyway. This
illustrates how the SCQE approach extracts valid causal information from observational data
while protecting against over-confidence.