Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (6 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2021)

In many non-randomized, observational settings, treatment assignment mechanisms are opaque and researchers may not trust an assumption of conditional ignorability or the covariate-adjustment-based identification it allows. The Stability-Controlled Quasi-Experiment (SCQE, 2019) avoids this adjustment approach and makes no direct comparison between treated and untreated units. In settings with a change in the usage rate of some treatment of interest between successive cohorts, SCQE transforms an assumption about cohort-wide counterfactual outcome trends into estimates of the treatment's effect on those units that were impacted by the treatment change.

For example, imagine two consecutive cohorts of patients, the latter featuring non-randomized use of a new treatment. SCQE can identify and estimate an Average Treatment Effect among the Treated (ATT). The assumption required is a proposed value of δ, the unobservable difference between the cohorts’ average outcomes that we would have seen had the treatment not been introduced.

SCQE is a partial identification strategy, presenting these effects across values of δ. In doing so, it ties claims about beneficial, harmful, or null effects to the corresponding δ values we would need to defend in order to support them, helping to avoid overconfidence in "suggestive" point estimates. In many applications, δ may also be more intuitive and easy to reason with than sensitivity analyses for conditional-ignorability-based results.

In this thesis, I (1) present new inferential tools for SCQE, including summary-statistic-based inference for use when unit-level data is unavailable or limited by data sharing restrictions; (2) extend the method to accommodate expert-informed prior distributions on δ, applications with only one cohort, and existing treatments experiencing changes in usage rates; (3) apply the method to evaluations of a tuberculosis prophylactic in Tanzania, early COVID-19 treatments, and in-hospital rapid response alerts; and (4) discuss important considerations, conceptual guidance, and best practices for practitioners.

    Cover page: Causal Inference Outside of Randomized Trials with the Stability-Controlled Quasi-Experiment: Extensions and Considerations
    • Thesis
    • Peer Reviewed
    UCLA Electronic Theses and Dissertations (2019)

    A new causal inference method called the Stability-Controlled Quasi-Experiment (SCQE) was recently proposed for identifying the effect of a treatment on units in a population that are treated. It allows for non-randomized or otherwise unknown means of selection into treatment, relying only on an assumption about the stability of the non-treatment outcomes of the population. We apply SCQE to the case of a national public health intervention in Tanzania to prevent active tuberculosis in HIV-positive patients by prescribing Isoniazid (IPT). Our preliminary results suggest much of the treatment is given to patients at low risk for developing active tuberculosis. In the process, we offer best practices and discuss challenges faced during its implementation. We share steps currently underway to generate confidence in these results, extend the method further, and derive valid standard errors.

      Cover page: The Stability-Controlled Quasi-Experiment: Preliminary Report on Applications to Tuberculosis Prevention in Tanzania
      • Article
      CEGA Working Papers (2019)
      When determining the effectiveness of a new treatment, randomized trials are not always possible or desirable. The stability-controlled quasi-experiment (SCQE) (Hazlett, 2019) is an observational approach that replaces the usual “no-unobserved confounding” assumption with one on the change in non-treatment outcome between successive cohorts, or the “baseline trend.” We extend this method to allow variance estimation and inference, and apply it for the first time by examining whether isoniazid preventive therapy (IPT) reduced tuberculosis (TB) incidence among 26,715 HIV patients in Tanzania. After IPT became available in the clinics we studied, a non-random 25% of patients received it. Within a year, fewer than 1% of patients on IPT developed TB, compared to 16% of the untreated. Regression adjustment using available covari-ates produces an equally large and highly significant estimate of -15 percentage point (pp) [95%CI: -16.6, -13.7]. While those estimates may generate confidence in IPT’s effectiveness, they cannot eliminate confounding. By contrast, SCQE reveals that the average treatment effect on the treated must be small and indistinguishable from zero, if we assume the baseline trend was flat over the study period. Rather, to argue that IPT was beneficial requires claiming that the (non-treatment) incidence rate rose by at least 0.5 pp per year. This is plausible, but far from certain. The SCQE approach has broad applicability and will sometimes lead to definitive claims of effectiveness. In this case, it usefully aids in protecting against over-confidence in claims that IPT was effective.
        Cover page: Inference without randomization or ignorability: A stability­ controlled quasi-experiment on the prevention of tuberculosis
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2020)

        Objectives

        To investigate the effectiveness of hydroxychloroquine and dexamethasone on coronavirus disease (COVID-19) mortality using patient data outside of randomized trials.

        Design

        Phenotypes derived from electronic health records were analyzed using the stability-controlled quasi-experiment (SCQE) to provide a range of possible causal effects of hydroxy-chloroquine and dexamethasone on COVID-19 mortality.

        Setting and participants

        Data from 2,007 COVID-19 positive patients hospitalized at a large university hospital system over the course of 200 days and not enrolled in randomized trials were analyzed using SCQE. For hyrdoxychloroquine, we examine a high-use cohort (n=766, days 1 to 43) and a later, low-use cohort (n=548, days 44 to 82). For dexamethasone, we examine a low-use cohort (n=614, days 44 to 101) and high-use cohort (n=622, days 102 to 200).

        Outcome measure

        14-day mortality, with a secondary outcome of 28-day mortality.

        Results

        Hydroxycholoroquine could only have been significantly (p<0.05) beneficial if baseline mortality was at least 6.4 percentage points (55%) lower among patients in the later (low-use) than the earlier (high-use) cohort. Hydroxychloroquine instead proves significantly harmful if baseline mortality rose from one cohort to the next by just 0.3 percentage points. Dexamethasone significantly reduced mortality risk if baseline mortality in the later (high-use) cohort (days 102-200) was higher than, the same as, or up to 1.5 percentage points lower than that in the earlier (low-use) cohort (days 44-101). It could only prove significantly harmful if mortality improved from one cohort to the next by 6.8 percentage points due to other causes—an assumption implying an unlikely 84% reduction in mortality due to other causes, leaving an in-hospital mortality rate of just 1.3%.

        Conclusions

        The assumptions required for a beneficial effect of hydroxychloroquine on 14 day mortality are difficult to sustain, while the assumptions required for hydroxychloroquine to be harmful are difficult to reject with confidence. Dexamethasone, by contrast, was beneficial under a wide range of plausible assumptions, and was only harmful if a nearly impossible assumption is met. More broadly, the SCQE reveals what inferences can be credibly supported by evidence from non-randomized uses of experimental therapies, making it a useful tool when randomized trials have not yet produced clear evidence or to provide corroborative evidence from different populations.
          Cover page: Credible learning of hydroxychloroquine and dexamethasone effects on COVID-19 mortality outside of randomized trials
          • Article
          • Peer Reviewed
          UC Davis Previously Published Works (2018)
          OBJECTIVES:Interventions that focus on educating patients appear to be the most effective in directing healthcare utilization to more appropriate venues. We sought to evaluate the effects of mailed information and a brief scripted educational phone call from an emergency physician (EP) on subsequent emergency department (ED) utilization by low-risk adults with a recent treat-and-release ED visit. STUDY DESIGN:Patients were randomized into 3 groups for post-ED follow-up: EP phone call with mailed information, mailed information only, and no educational intervention. Each intervention group was compared with a set of matched controls. METHODS:We undertook this study in 6 EDs within an integrated healthcare delivery system. Overall, 9093 patients were identified; the final groups were the phone group (n = 609), mail group (n = 771), and matched control groups for each (n = 1827 and n = 1542, respectively). Analysis was stratified by age (<65 and ≥65 years). Patients were educated about available venues of care delivery for their future medical needs. The primary outcome was the rate of 6-month ED utilization after the intervention compared with the 6-month utilization rate preceding the intervention. RESULTS:Compared with matched controls, subsequent ED utilization decreased by 22% for patients 65 years or older in the phone group (P = .04) and by 27% for patients younger than 65 years in the mail group (P = .03). CONCLUSIONS:ED utilization subsequent to a low-acuity ED visit decreased after a brief post-ED education intervention by an EP explaining alternative venues of care for future medical needs. Response to the method of communication (phone vs mail) varied significantly by patient age.
            Cover page: Impact of emergency physician-provided patient education about alternative care venues.
            • Article
            • Peer Reviewed
            UCLA Previously Published Works (2021)
            Coronavirus disease 2019 (COVID-19) has exposed health care disparities in minority groups including Hispanics/Latinxs (HL). Studies of COVID-19 risk factors for HL have relied on county-level data. We investigated COVID-19 risk factors in HL using individual-level, electronic health records in a Los Angeles health system between March 9, 2020, and August 31, 2020. Of 9,287 HL tested for SARS-CoV-2, 562 were positive. HL constituted an increasing percentage of all COVID-19 positive individuals as disease severity escalated. Multiple risk factors identified in Non-Hispanic/Latinx whites (NHL-W), like renal disease, also conveyed risk in HL. Pre-existing nonrheumatic mitral valve disorder was a risk factor for HL hospitalization but not for NHL-W COVID-19 or HL influenza hospitalization, suggesting it may be a specific HL COVID-19 risk. Admission laboratory values also suggested that HL presented with a greater inflammatory response. COVID-19 risk factors for HL can help guide equitable government policies and identify at-risk populations.
              Top