Browsing by Subject "Causal Inference"

Now showing 1 - 4 of 4
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    Causal Estimators for Non-Standard Scenarios: Individual Versus Population-Level Causal Effects in Transplantation Treatment Regimes, and Clinical Trials where Compliance is Measured with Error
    (2017-07) Verdoliva Boatman, Jeffrey
    Inferring and explaining causal relationships is frequently one of the primary goals in public health research. Randomized controlled trials (RCTs) are the gold standard for establishing causal effects, but often RCTs are infeasible or unethical, and we must rely on observational data for inference. Even in the case where RCTs can be conducted causal inference is often difficult due to patient noncompliance. Statistical methods for causal inference are needed in such cases. Although there exist well-established statistical causal inference methods, in this dissertation we develop methods for non-standard scenarios. In Chapter 2, we consider treatment regimes for solid organ transplantation. In Chapters 3 and 4, we consider estimating causal effects in RCTs in the presence of noncompliance. In all cases, we develop novel weighted estimators that are similar to inverse probability of compliance weighted estimators, but the weights are a ratio of probabilities rather than an inverse probability. For solid organ transplantation, these weights are needed so that our estimators have the desired interpretation, and in the case of RCTs in the presence of noncompliance, these weights are needed so that our estimators actually correspond to a causal effect of interest when traditional assumptions about noncompliance are not valid.
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    Constrained Likelihood Inference in Instrumental Variable Regression with Invalid Instruments and Its Application to GWAS Summary Data
    (2021-05) Xue, Haoran
    There has been increasing interest in instrumental variables regression for causal inference. In genetics, transcriptome-wide association studies (TWAS), also known as PrediXcan, have recently emerged as a widely applied tool to discover causal/target genes by integrating an outcome GWAS dataset with another gene expression/ transcriptome GWAS (called eQTL) dataset; they can not only boost statistical power but also offer biological insights by identifying (putative) causal genes for a GWAS trait, e.g. low-density lipoprotein cholesterol (LDL). Statistically TWAS apply (two-sample) two-stage least squares (2SLS) with multiple correlated SNPs as instrumental variables (IVs) to predict/impute gene expression, in contrast to typical (two-sample) Mendelian randomization (MR) approaches using independent SNPs as IVs, which are expected to be lower-powered. However, some of the SNPs used may not be valid IVs as a result of their (horizontal) pleiotropic/direct effects on the trait not mediated through the gene of interest, leading to false conclusions by TWAS (or MR). We propose a general inferential method for possibly high-dimensional data to account for confounding and invalid IVs while selecting valid IVs simultaneously via two-stage constrained maximum likelihood; we develop a theory for the likelihood method subject to a truncated L1-constraint approximating the L0-constraint for asymptotically valid and efficient statistical inference on causal effects. We demonstrate both theoretically and numerically the superior performance of the proposed method over the standard 2SLS/TWAS and other methods. We apply the methods to identify causal genes for LDL by integrating GWAS summary data with eQTL data.
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    Flattening the Eviction Curve: A Quasi-Experimental Evaluation of the Brooklyn Center Tenant Protection Ordinance
    (2024-02-29) Gramlich, Jack
    This paper uses two quasi-experimental methods—synthetic control (SC) and difference-in-differences (DiD)—to evaluate the effects of the 2022 Brooklyn Center Tenant Protection Ordinance. The ordinance was adopted at a time when eviction filings were on the rise across the state. Descriptive statistics provide an indication that after the ordinance was adopted, Brooklyn Center’s eviction rate did not increase by as much as the eviction rate in other parts of suburban Hennepin County. For SC models, I compared Brooklyn Center to most other Hennepin County cities. I found statistically significant evidence that the ordinance reduced eviction rates in the period 37-48 weeks after policy adoption. This result survived several placebo tests (though it was sensitive to whether Brooklyn Park was included in the donor pool). Results for filing rates did not survive all placebo tests. For DiD, I drew from a sample of most block groups in suburban Hennepin County. Conditioning on pre-treatment covariates via doubly robust DiD, I found the policy brought reduced eviction rates and filing rates in some of the first eight months after policy adoption. DiD models survived a wide variety of robustness checks. SC and DiD provided consistent evidence of reduced eviction rates in some periods of time. The two methods produced mixed evidence on filing rates, and did not produce strong evidence of policy effects for other outcomes. This paper concludes that when evictions spiked across Minnesota following the expiration of COVID-19 eviction moratorium policies, the City of Brooklyn Center flattened the eviction curve.
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    Incident cataracts following protracted low-dose occupational ionizing radiation exposures in United States medical radiologic technologists: Statistical methods for exploring heterogeneity of effects and improving causal inference
    (2016-02) Meyer, Craig
    Background: Medical radiologic technologists are routinely exposed to protracted low-dose occupational ionizing radiation. The U.S. Rad Tech (USRT Study) was begun in 1982 by the National Cancer Institute in collaboration with the University of Minnesota School of Public and the American Registry of Radiologic Technologists to investigate potential health risks from occupational ionizing radiation. Ionizing radiation exposures have been associated with cataracts, which if left untreated can lead to visual impairment or blindness. Phenotypes of cataracts are characterized by their location in the eye lens and include posterior subcapsular and cortical cataracts (most commonly associated with ionizing radiation), and nuclear cataracts (associated with age). Methods that allow investigators to flexibly examine the extent of heterogeneity across many covariate strata are needed to help characterize the extent of any heterogeneity. One such potential method is boosted regression trees, a machine learning ensemble model that is particularly well suited to prediction while incorporating interactions. As prediction is becoming increasingly important for epidemiologic investigations (causal inference methods commonly require the use of prediction), exploration of the utility of machine learning methods in epidemiology is warranted. Occupational epidemiologic cohort studies are often susceptible to selection bias from the healthy worker survivor effect (HWSE), whereby less healthy individuals leave work and accrue less exposure compared to healthier individuals who stay at work and continue to accrue exposure. As a result, the association between exposure and an outcome may be attenuated, or even reversed in some cases. G-methods are a family of analytical tools that were developed to address situations that may be affected from time-varying confounding and structural bias as seen in the HWSE. One such method, the parametric g-formula, is a rigorous computational model that has been used to correct effects estimates for potential bias from the HWSE. Objective: The overall objective of this research is to explore the relationship between protracted low-dose exposures to occupational ionizing radiation and the risk of cataracts in medical radiologic technologists in the United States and its territories, and to propose methodologic techniques to help estimate causal effects in such settings. The overall objective of this research will be accomplished in three separate manuscripts. Manuscript 1: Aim: To estimate the overall association between protracted exposure to low-dose occupational ionizing radiation and incident cataracts in medical radiologic technologists. Methods: Cox regression models were used to model time to cataract predicted by ionizing radiation. Technologists were followed from year first worked as a radiologic technologist starting at age 18 or older, until report of cataracts or administrative censoring at the third survey. Results: After adjustment for birth year, sex, and race / ethnicity (N=69,798), ionizing radiation was significantly associated with increased hazard of cataracts with a time-varying effect (p<0.001) that while initially elevated, decreased over time. Hazard ratios of cataract per 10-mSv increment of radiation were statistically significant at age 20 [HR=1.09; 95% CI = (1.04, 1.14)] and age 30 [HR = 1.04; 95% CI = (1.00, 1.09)], but were not significant after age 30. Sensitivity analyses indicated strong evidence that selection bias from the HWSE were present and may have explained the time-varying effect. Additionally, a literature review found five population-based studies of cataract subtype prevalence over time, and indicated that there was potential for misclassification of cataracts in the USRT study that may have biased effect estimates. Manuscript 2: Aim: Use boosted regression trees to fully characterize the distribution of the effect of occupational ionizing radiation on cataracts in medical radiologic technologists. Methods: A boosted regression tree model was used to build a prediction model of cataracts. The cohort was restricted to those ages 24–44 at baseline (N=43,513). Predictions from the model were used to calculate risk differences of cataracts between high dose (75th percentile of observed badge dose: 61.31 mSv) and low-dose (25th percentile of observed badge dose: 23.90 mSv) occupational ionizing radiation in strata of potential effect modifiers. Results: Overall, there was a significant population average effect [RD=0.002; 95% CI = (0.002, 0.015)]. Additionally, subgroups were found with larger risks than the population average including those born earliest, those with diabetes, macular degeneration, glaucoma, or were overweight (BMI > 25) at baseline. Those who were youngest and those without macular degeneration conversely had lower risk differences compared to the average. Manuscript 3: Aim: Use the parametric G-formula to adjust effect estimates for the healthy worker survivor effect in the estimated risk of incident radiogenic cataracts in medical radiologic technologists. Methods: The parametric g-formula was used to estimate cataract risks under different hypothetical scenarios limiting badge dose in five-year periods to the 80th percentile (badge dose ≤ 18.38 mSv), 60th percentile (badge dose ≤ 9.06 mSv), 40th percentile (badge dose ≤ 4.47 mSv), and 20th percentile (badge dose ≤ 2.08 mSv) of observed dose, and a 5-mSv reduction in dose estimates in each period over follow-up (N=69,798). Cumulative incidence risks and risks conditional on survival of cataracts from these treatment regimes were compared to the status quo (no intervention of dose) with risk differences and 95% confidence intervals. Substantively important differences in both cumulative incidence of cataracts and conditional risks of cataracts between the natural course and treatment regimes were found. There was evidence that decreasing the dose of radiation exposure could reduce the risk of cataracts, even at relatively early ages. Conclusion: Overall, our results indicate that low-dose occupational ionizing radiation exposures elevate the risks of cataracts in medical radiologic technologists in the USRT Study, as our three manuscripts found significant associations between occupational ionizing radiation and cataract risks. Additionally, methods were proposed to explore heterogeneity of effects and improve the causal interpretation of effect estimates in the association between ionizing radiation and cataracts. Validation of cataracts is warranted and future studies would benefit from information regarding phenotypes of cataracts.