Identification of causal effects on binary outcomes using structural mean models

Structural mean models (SMMs) were originally formulated to estimate causal effects among those selecting treatment in randomized controlled trials affected by nonignorable noncompliance. It has already been established that SMMs can identify these causal effects in randomized placebo-controlled trials under fairly weak assumptions. SMMs are now being used to analyze other types of study where identification depends on a no effect modification assumption. We highlight how this assumption depends crucially on the unknown causal model that generated the data, and so is difficult to justify. However, we also highlight that, if treatment selection is monotonic, additive and multiplicative SMMs do identify local (or complier) causal effects, but that the double-logistic SMM estimator does not without further assumptions. We clarify the proper interpretation of inferences from SMMs by means of an application and a simulation study. © 2010 The Author

Disease transmission models for public health decision making: analysis of epidemic and endemic conditions caused by waterborne pathogens.

Developing effective policy for environmental health issues requires integrating large collections of information that are diverse, highly variable, and uncertain. Despite these uncertainties in the science, decisions must be made. These decisions often have been based on risk assessment. We argue that two important features of risk assessment are to identify research needs and to provide information for decision making. One type of information that a model can provide is the sensitivity of making one decision over another on factors that drive public health risk. To achieve this goal, a risk assessment framework must be based on a description of the exposure and disease processes. Regarding exposure to waterborne pathogens, the appropriate framework is one that explicitly models the disease transmission pathways of pathogens. This approach provides a crucial link between science and policy. Two studies--a Giardia risk assessment case study and an analysis of the 1993 Milwaukee, Wisconsin, Cryptosporidium outbreak--illustrate the role that models can play in policy making

Patterns of Use of Human Papillomavirus and Other Adolescent Vaccines in the United States

AbstractPurposeThe purpose of the study was to describe the patterns of use of universally recommended adolescent vaccines in the United States.MethodsWe identified 11-year-olds using the MarketScan insurance claims database (2009–2014). Human papillomavirus (HPV), tetanus-diphtheria-acellular pertussis (Tdap), and meningococcal (MenACWY) vaccination claims were identified using diagnosis and procedure codes. Generalized linear models estimated vaccination incidence rates and correlates of adolescent vaccination and timely vaccination.ResultsAmong 1,691,223 adolescents, receipt of Tdap (52.1%) and MenACWY (45.8%) vaccinations exceeded receipt of HPV vaccination (18.4%). While both sexes had similar Tdap and MenACWY vaccination proportions, girls received HPV vaccination more frequently than boys (21.9% vs. 15.1%). Adolescents received HPV vaccination later (mean age: 11.8 years) than Tdap or MenACWY vaccination (mean age: 11.2 years for both). Half of vaccinated adolescents received Tdap and MenACWY vaccination only; however, coadministration with HPV vaccine increased with birth cohort. Western adolescents had the highest incidence rates of HPV vaccination, and Southern adolescents had the lowest. Rural adolescents were less likely than urban adolescents to receive each vaccination except in the Northeast, where they were more likely to receive HPV vaccination (incidence rate ratio: 1.09, 95% confidence interval: 1.2005–1.13). Timely HPV vaccination was associated with female sex, urbanicity, Western residence, and later birth cohort.ConclusionsHPV vaccination occurred later than Tdap or MenACWY vaccination and was less frequent in boys and rural adolescents. Girls, Western and urban residents, and younger birth cohorts were more likely to receive timely HPV vaccination. Vaccine coadministration increased over time and may encourage timely and complete vaccination coverage

Timing and predictors of severe rotavirus gastroenteritis among unvaccinated infants in low- and middle-income countries

Delays in rotavirus vaccine schedule could improve performance in low- and middle-income countries (LMICs). However, delaying the first dose could be detrimental if infants experience severe rotavirus gastroenteritis (RVGE) early in life. Our objective was to describe the timing and predictors of severe RVGE in unvaccinated children in LMICs. We analysed the placebo arms from two clinical trials (cohort 1: NCT00241644; cohort 2: NCT00362648). We estimated the rate, cumulative incidence (per 1000 infants) and age distribution of severe RVGE episodes. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals (CI) for the association between baseline factors and severe RVGE. Cumulative incidence at 6 months of age was 23/1000 (95% CI 15-30) in cohort 1 and 6/1000 (95% CI 3-8) in cohort 2. Early antibiotic use (compared with no use) was associated with 2.03 (95% CI 1.18-3.48) and 1.41 (95% CI 0.80-2.51) times the rate of severe RVGE in cohorts 1 and 2, respectively. The cumulative incidence of severe RVGE was low at 6 months of age, suggesting that a 4-week delay in the vaccination schedule may not result in a large number of severe RVGE episodes prior to vaccine receipt. Copyright © Cambridge University Press 2018

Selection of confounding variables should not be based on observed associations with exposure

In observational studies, selection of confounding variables for adjustment is often based on observed baseline incomparability. The aim of this study was to evaluate this selection strategy. We used clinical data on the effects of inhaled long-acting beta-agonist (LABA) use on the risk of mortality among patients with obstructive pulmonary disease to illustrate the impact of selection of confounding variables for adjustment based on baseline comparisons. Among 2,394 asthma and COPD patients included in the analyses, the LABA ever-users were considerably older than never-users, but cardiovascular co-morbidity was equally prevalent (19.9% vs. 19.9%). Adjustment for cardiovascular co-morbidity status did not affect the crude risk ratio (RR) for mortality: crude RR 1.19 (95% CI 0.93–1.51) versus RR 1.19 (95% CI 0.94–1.50) after adjustment for cardiovascular co-morbidity. However, after adjustment for age (RR 0.95, 95% CI 0.76–1.19), additional adjustment for cardiovascular co-morbidity status did affect the association between LABA use and mortality (RR 1.01, 95% CI 0.80–1.26). Confounding variables should not be discarded based on balanced distributions among exposure groups, because residual confounding due to the omission of confounding variables from the adjustment model can be relevant

Driving Catalyst Reoxidation in Wacker Cyclizations with Acetal-Based Metal-Hydride Abstractors

In traditional Wacker processes, Pd(II) becomes reduced to Pd(0) after C-O bond formation and β-H elimination and must be reoxidized to the electrophilic Pd(II) state via a stoichiometric oxidant like benzoquinone, CuCl2, or O2. We report herein a Pt-catalyzed Wacker-type process that regenerates the electrophilic Pt2+ state by H− abstraction from a [Pt]-H using an oxocarbenium ion generated from an acetal or ketal under acidic conditions

Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: Nonsteroidal antiinflammatory drugs and short-term mortality in the elderly

Little is known about optimal application and behavior of exposure propensity scores (EPS) in small studies. In a cohort of 103,133 elderly Medicaid beneficiaries in New Jersey, the effect of nonsteroidal antiinflammatory drug use on 1-year all-cause mortality was assessed (1995-1997) based on the assumption that there is no protective effect and that the preponderance of any observed effect would be confounded. To study the comparative behavior of EPS, disease risk scores, and "conventional" disease models, the authors randomly resampled 1,000 subcohorts of 10,000, 1,000, and 500 persons. The number of variables was limited in disease models, but not EPS and disease risk scores. Estimated EPS were used to adjust for confounding by matching, inverse probability of treatment weighting, stratification, and modeling. The crude rate ratio of death was 0.68 for users of nonsteroidal antiinflammatory drugs. "Conventional" adjustment resulted in a rate ratio of 0.80 (95% confidence interval: 0.77, 0.84). The rate ratio closest to 1 (0.85) was achieved by inverse probability of treatment weighting (95% confidence interval: 0.82, 0.88). With decreasing study size, estimates remained further from the null value, which was most pronounced for inverse probability of treatment weighting (n = 500: rate ratio = 0.72, 95% confidence interval: 0.26, 1.68). In this setting, analytic strategies using EPS or disease risk scores were not generally superior to "conventional" models. Various ways to use EPS and disease risk scores behaved differently with smaller study size

Experimental and computational study of alkane dehydrogenation catalyzed by a carbazolide-based rhodium PNP pincer complex

A rhodium complex based on the bis-phosphine carbazolide pincer ligand was investigated in the context of alkane dehydrogenation and in comparison with its iridium analogue