Estimating Marginal Hazard Ratios by Simultaneously Using A Set of Propensity Score Models: A Multiply Robust Approach

The inverse probability weighted Cox model is frequently used to estimate marginal hazard ratios. Its validity requires a crucial condition that the propensity score model is correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in empirical likelihood theory. The proposed estimator is multiply robust in that it is guaranteed to be consistent when a set of postulated propensity score models contains a correctly specified model. Our simulation studies demonstrate satisfactory finite sample performance of the proposed method in terms of consistency and efficiency. We apply the proposed method to compare the risk of postoperative hospitalization between sleeve gastrectomy and Roux-en-Y gastric bypass using data from a large medical claims and billing database.We further extend the development to multi-site studies to enable each site to postulate multiple site-specific propensity score models

Conditional Empirical Likelihood Approach to Statistical Analysis with Missing Data.

This dissertation focuses on the development of conditional empirical likelihood (CEL) based methods for mean regression analysis when the outcome is subject to missingness. It considers the cases of cross-sectional data, longitudinal data with dropout, and unbalanced longitudinal data. Unlike the existing estimating functions based estimators, the proposed estimators do not require to model any higher order moments of the data beyond the missingness mechanism and the conditional mean of the outcome. In both cases of cross-sectional data and longitudinal data with dropout, under the missing at random (MAR) mechanism and certain regularity conditions, the proposed CEL based augmented inverse probability weighted (CEL-AIPW) estimator is doubly robust and locally efficient. Specifically, the CEL-AIPW estimator is consistent if either the missingness mechanism or the conditional mean of the outcome given the observed data at each level of missingness is correctly modeled, and attains the semiparametric efficiency bound when both quantities are correctly modeled. In the case of unbalanced longitudinal data, the unbalanced follow-up visits are dealt with via stratification according to distinctive follow-up patterns. Such a strategy implicitly assumes the missing completely at random (MCAR) mechanism, the same mechanism assumed by the popular generalized estimating equations (GEE) method. The proposed CEL estimator achieves the same efficiency as that of the GEE estimator obtained employing the true variance-covariance matrix of the longitudinal outcomes. In all three cases, the proposed estimators are implemented through a nested optimization, and the detailed Newton-Raphson algorithm is described for each case. Certain issues related to the numerical implementation are also discussed. The asymptotic distributions of all proposed estimators are derived, and the finite sample performances with comparisons to some existing estimators are examined using simulation experiments. As for the application in the three cases, we analyze the data collected from an intervention study for adolescents of parents with HIV, the data from the national cooperative gallstone study, and the data from the Kenya primary school nutritional intervention study, respectively.PhDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99966/1/peisong_1.pd

Portal Vein Thrombosis in Liver Cirrhosis

In liver cirrhosis, portal vein thrombosis (PVT), which is defined as thrombosis that occurs within the main portal vein and intrahepatic portal branches, is one of the most common complications. High incidence of PVT in the setting of liver cirrhosis is mainly due to hypercoagulable state and altered dynamic of blood flow in the portal vein. The clinical manifestations of PVT are variable among different patients, so the diagnosis of PVT is mainly dependent on the imaging examinations, like ultrasound, computed tomography and magnetic resonance imaging. The overall goal of treatment for PVT can be summarized as reducing risk factors of PVT, thus to prevent further expansion of thrombus and maintain portal patency and prevent and treat the symptoms of PVT by anticoagulants, local thrombolysis, transjugular intrahepatic portosystemic shunt and/or surgery. In future, due to the progress in vascular imaging and innovation in clinical anti-thrombotic drug, PVT could be prevented and cured effectively

A general framework for quantile estimation with incomplete data

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148388/1/rssb12309.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148388/2/rssb12309-sup-0001-TableS1-S4.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148388/3/rssb12309_am.pd

Robust Causal Inference of Drug-drug Interactions

There is growing interest in developing causal inference methods for multi-valued treatments with a focus on pairwise average treatment effects. Here we focus on a clinically important, yet less-studied estimand: causal drug-drug interactions (DDIs), which quantifies the degree to which the causal effect of drug A is altered by the presence versus the absence of drug B. Confounding adjustment when studying the effects of DDIs can be accomplished via inverse probability of treatment weighting (IPTW), a standard approach originally developed for binary treatments and later generalized to multi-valued treatments. However, this approach generally results in biased results when the propensity score model is misspecified. Motivated by the need for more robust techniques, we propose two empirical likelihood-based weighting approaches that allow for specifying a set of propensity score models, with the second method balancing user-specified covariates directly, by incorporating additional, nonparametric constraints. The resulting estimators from both methods are consistent when the postulated set of propensity score models contains a correct one; this property has been termed multiple robustness. We then evaluate their finite sample performance through simulation. The results demonstrate that the proposed estimators outperform the standard IPTW method in terms of both robustness and efficiency. Finally, we apply the proposed methods to evaluate the impact of renin-angiotensin system inhibitors (RAS-I) on the comparative nephrotoxicity of nonsteroidal anti-inflammatory drugs (NSAID) and opioids, using data derived from electronic medical records from a large multi-hospital health system.Comment: 33 pages, 9 figures and 2 table

Exploring the big data paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS)

Selection bias poses a challenge to statistical inference validity in non-probability surveys. This study compared estimates of the first-dose COVID-19 vaccination rates among Indian adults in 2021 from a large non-probability survey, COVID-19 Trends and Impact Survey (CTIS), and a small probability survey, the Center for Voting Options and Trends in Election Research (CVoter), against benchmark data from the COVID Vaccine Intelligence Network (CoWIN). Notably, CTIS exhibits a larger estimation error (0.39) compared to CVoter (0.16). Additionally, we investigated the estimation accuracy of the CTIS when using a relative scale and found a significant increase in the effective sample size by altering the estimand from the overall vaccination rate. These results suggest that the big data paradox can manifest in countries beyond the US and it may not apply to every estimand of interest

Concentrations and distribution of biogenic barium in surface sediments of Prydz Bay, Antarctica

Concentrations of biogenic barium were investigated in surface sediments of Prydz Bay, Antarctica, during the 21st and 27th CHINARE cruises. Factors controlling the observed distribution are explored. Biogenic barium concentrations obtained from a sequential extraction procedure are compared with total concentrations obtained from the normative calculation based on a total digestion, and differences in the results are examined. Concentrations of biogenic barium, calculated by the normative calculation, were much higher than the concentrations obtained through sequential extraction; this discrepancy is the result of the occurrence of barium associated with Mn/Fe oxides, which represents an important component of total barium in these sediments. Concentrations of biogenic barium obtained from the sequential extraction range from 104 to 445 μg∙g-1, and the average concentration was 227 μg∙g-1. The highest concentrations of biogenic barium occur in the central area of the bay, where the seawater is more stable, while lower values occur in the bank and the ice shelf. Biogenic barium is significantly linearly correlated with biogenic barium and organic carbon, and similar in distribution of Chl a, which may indicate that primary productivity of phytoplankton in the surface water column is the main environmental factor regulating barium concentration and distribution

Distribution of transparent exopolymer particles and their response to phytoplankton community structure changes in the Amundsen Sea, Antarctica

To understand the response of transparent exopolymer particles (TEP) to the changes in phytoplankton communities caused by melting sea ice, we collected samples from the polynya and open ocean affected by the Antarctic circumpolar current in the Amundsen Sea. TEP, pigments, and other environmental factors were analyzed. The results showed that high TEP content was mainly found in the polynya, and was higher in the surface layer than in the deep layer. The main factor that affected TEP distribution was the phytoplankton community. In the polynya area, the phytoplankton were dominated by low-iron Haptophyta. In the Antarctic circumpolar current region affected by ice-melting water, the dominant species was diatom type II. Our results revealed that low-iron Haptophyta may be the main contributors to TEP content