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

Risk of pre-eclampsia in first and subsequent pregnancies: prospective cohort study

Objective To investigate whether pre-eclampsia is more common in first pregnancies solely because fewer affected women, who presumably have a higher risk of recurrence, go on to have subsequent pregnancies

Variance Estimation in Inverse Probability Weighted Cox Models

Inverse probability weighted Cox models can be used to estimate marginal hazard ratios under different treatments interventions in observational studies. To obtain variance estimates, the robust sandwich variance estimator is often recommended to account for the induced correlation among weighted observations. However, this estimator does not incorporate the uncertainty in estimating the weights and tends to overestimate the variance, leading to inefficient inference. Here we propose a new variance estimator that combines the estimation procedures for the hazard ratio and weights using stacked estimating equations, with additional adjustments for the sum of non-independent and identically distributed terms in a Cox partial likelihood score equation. We prove analytically that the robust sandwich variance estimator is conservative and establish the asymptotic equivalence between the proposed variance estimator and one obtained through linearization by Hajage et al., 2018. In addition, we extend our proposed variance estimator to accommodate clustered data. We compare the finite sample performance of the proposed method with alternative methods through simulation studies. We illustrate these different variance methods in an inverse probability weighted application to estimate the marginal hazard ratio for postoperative hospitalization under sleeve gastrectomy versus Roux-en-Y gastric bypass in a large medical claims and billing database. To facilitate implementation of the proposed method, we have developed an R package ipwCoxCSV

Use of Antidiabetic drugs during pregnancy among U.S. women with Livebirth deliveries in the Mini-Sentinel system

BACKGROUND: As the prevalence of diabetes mellitus increases in the population, the exposure to antidiabetic drugs (ADDs) during pregnancies is expected to grow, as has been seen over the last decade. The objective of this study was to estimate the prevalence of ADD use during pregnancy among women in the Mini-Sentinel Distributed Database (MSDD) who delivered a liveborn infant. METHODS: We identified qualifying livebirth pregnancies among women aged 10 to 54 years in the MSDD from 2001 to 2013. ADD use was estimated using outpatient pharmacy dispensing claims and days-supplied among three cohorts: all livebirth pregnancies, pregnancies among women with pre-existing diabetes, and pregnancies among women without prior ADD use. RESULTS: Among the 1.9 million pregnancies in the MSDD that resulted in a livebirth from 2001 to 2013, 4.4% were exposed to an ADD. Of the 15,606 pregnancies (0.8%) with pre-existing diabetes, 92.8% were also exposed during the pregnancy period. The most commonly used product in these pregnancies was insulin (75.6% of pregnancies). In contrast, in pregnancies of women without prior ADD use, the most commonly used products were glyburide and insulin, and most of these users were diagnosed with gestational diabetes. CONCLUSIONS: Patterns of ADD use during pregnancy described here, along with changes in disease incidence and management, highlight the importance of continuing surveillance of ADD utilization patterns and examining the safety and effectiveness of these products in pregnancy

Antibiotics and oral contraceptive failure - a case-crossover study

Background: Evidence on the association between antibiotic use and combined oral contraceptive (COC) failure is controversial. We examined the effect of concomitant antibiotic treatment on the risk of breakthrough pregnancy among COC users. Study Designs: We performed a case-crossover study of 1330 COC failure cases among 17,721 women from the Slone Epidemiology Center Birth Defects Study (1997-2008) and among 25,941 women from the National Birth Defects Prevention Study (NBDPS, 1997-2005). Self-matched odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by comparing antibiotic use between the 4 weeks before conception ("case period") and the 4-8 weeks before conception ("control period") using conditional logistic regression. A case time-control analysis was conducted using nonusers of COCs with unplanned pregnancies as controls. Results: For the combined data, the self-matched OR was 1.08 (95% CI: 0.63-1.84) and the case time-control OR was 1.12 (0.63-1.98) for antibiotics overall. The results did not appreciably differ when adjusted for characteristics that might vary between the case and control period. However, among COC failure cases from the NBDPS, allowing a 1-month gap between the case and control period resulted in a self-matched OR of 1.45 (0.85-2.50) and a case time-control OR of 1.55 (0.86-2.79) for antibiotics overall. Conclusions: We did not find an association between concomitant antibiotic use and the risk of breakthrough pregnancy among COC users. However, due to limited power and potential carryover effects, findings from this study cannot rule out an elevated risk of COC failure among antibiotic users. (C) 2011 Elsevier Inc. All rights reserved

Antiemetic use among pregnant women in the United States: the escalating use of ondansetron

PURPOSE: To examine ondansetron use in pregnancy in the context of other antiemetic use among a large insured United States population of women delivering live births. METHODS: We assessed ondansetron and other antiemetic use among pregnant women delivering live births between 2001 and 2015 in 15 data partners contributing data to the Mini-Sentinel Distributed Database. We identified live birth pregnancies using a validated algorithm, and all forms of ondansetron and other available antiemetics were identified using National Drug Codes or procedure codes. We assessed the prevalence of antiemetic use by trimester, calendar year, and formulation. RESULTS: In over 2.3 million pregnancies, the prevalence of ondansetron, promethazine, metoclopramide, or doxylamine/pyridoxine use anytime in pregnancy was 15.2, 10.3, 4.0, and 0.4%, respectively. Ondansetron use increased from \u3c1% of pregnancies in 2001 to 22.2% in 2014, with much of the increase attributable to oral ondansetron beginning in 2006. Promethazine and metoclopramide use increased modestly between 2001 (13.8%, 3.2%) and 2006 (16.0%, 6.0%) but decreased annually through 2014 (8.0%, 3.2%). Doxylamine/pyridoxine, approved for management of nausea and vomiting in pregnancy in 2013, was used in 1.8% of pregnancies in 2014. For all antiemetics, use was highest in the first trimester. CONCLUSIONS: We observed a marked increase in ondansetron use by study year, prescribed to nearly one-quarter of insured pregnant women in 2014, occurring in conjunction with decreased use of promethazine and metoclopramide. Given the widespread use of ondansetron in pregnancy, data establishing product efficacy and methodologically rigorous evaluation of post-marketing safety are needed

Stakeholders\u27 views on data sharing in multicenter studies

AIM: To understand stakeholders\u27 views on data sharing in multicenter comparative effectiveness research studies and the value of privacy-protecting methods. MATERIALS and METHODS: Semistructured interviews with five US stakeholder groups. RESULTS: We completed 11 interviews, involving patients (n = 15), researchers (n = 10), Institutional Review Board and regulatory staff (n = 3), multicenter research governance experts (n = 2) and healthcare system leaders (n = 4). Perceptions of the benefits and value of research were the strongest influences toward data sharing; cost and security risks were primary influences against sharing. Privacy-protecting methods that share summary-level data were acknowledged as being appealing, but there were concerns about increased cost and potential loss of research validity. CONCLUSION: Stakeholders were open to data sharing in multicenter studies that offer value and minimize security risks

Causal Inference from Longitudinal Studies with Baseline Randomization

We describe analytic approaches for study designs that, like large simple trials, can be better characterized as longitudinal studies with baseline randomization than as either a pure randomized experiment or a purely observational study. We (i) discuss the intention-to-treat effect as an effect measure for randomized studies, (ii) provide a formal definition of causal effect for longitudinal studies, (iii) describe several methods -- based on inverse probability weighting and g-estimation -- to estimate such effect, (iv) present an application of these methods to a naturalistic trial of antipsychotics on symptom severity of schizophrenia, and (v) discuss the relative advantages and disadvantages of each method