Approaches to inverse-probability-of-treatment–weighted estimation with concurrent treatments

Objectives: In a setting with two concurrent treatments, inverse-probability-of-treatment weights can be used to estimate the joint treatment effects or the marginal effect of one treatment while taking the other to be a confounder. We explore these two approaches in a study of intravenous iron use in hemodialysis patients treated concurrently with epoetin alfa (EPO). Study Design and Setting: We linked US Renal Data System data with electronic health records (2004–2008) from a large dialysis provider. Using a retrospective cohort design with 776,203 records from 117,050 regular hemodialysis patients, we examined a composite outcome: mortality, myocardial infarction, or stroke. Results: With EPO as a joint treatment, inverse-probability-of-treatment weights were unstable, confidence intervals for treatment effects were wide, covariate balance was unsatisfactory, and the treatment and outcome models were sensitive to omission of the baseline EPO covariate. By handling EPO exposure as a confounder instead of a joint treatment, we derived stable weights and balanced treatment groups on measured covariates. Conclusions: In settings with concurrent treatments, if only one treatment is of interest, then including the other in the treatment model as a confounder may result in more stable treatment effect estimates. Otherwise, extreme weights may necessitate additional analysis steps

Counterpoint: The Treatment Decision Design

The comparative new-user design is a principled approach to learning about the relative risks and benefits of starting different treatments in patients who have no history of use of the treatments being studied. Vandenbroucke and Pearce (Am J Epidemiol. 2015;182(10):826–833) discuss some problems inherent in incident exposure designs and argue that epidemiology may be harmed by a rigid requirement that follow-up can only begin at first exposure. In the present counterpoint article, a range of problems in pharmacoepidemiology that do not necessarily require that observation begin at first exposure are discussed. For example, among patients who are past or current users of a medication, we might want to know whether treatment should be augmented, switched, restarted, or discontinued. To answer these questions, a generalization of the new-user design, the treatment decision design, which identifies cohorts anchored at times when treatment decisions are being made, such as the evaluation of laboratory parameters, is discussed. The design aims to provide estimates that are directly relevant to physicians and patients, helping them to better understand the risks and benefits of the different treatment choices that they are considering

A Semiparametric Model Selection Criterion with Applications to the Marginal Structural Model

Estimators for the parameter of interest in semiparametric models often depend on a guessed model for the nuisance parameter. The choice of the model for the nuisance parameter can affect both the finite sample bias and efficiency of the resulting estimator of the parameter of interest. In this paper we propose a finite sample criterion based on cross validation that can be used to select a nuisance parameter model from a list of candidate models. We show that expected value of this criterion is minimized by the nuisance parameter model that yields the estimator of the parameter of interest with the smallest mean-squared error relative to the expected value of an initial consistent reference estimator. In a simulation study, we examine the performance of this criterion for selecting a model for a treatment mechanism in a marginal structural model (MSM) of point treatment data. For situations where all possible models cannot be evaluated, we outline a forward/backward model selection algorithm based on the cross validation criterion proposed in this paper and show how it can be used to select models for multiple nuisance parameters. We evaluate the performance of this algorithm in a simulation study of the one-step estimator of the parameter of interest in a MSM where models for both a treatment mechanism and a conditional expectation of the response need to be selected. Finally, we apply the forward model selection algorithm to a MSM analysis of the relationship between boiled water use and gastrointestinal illness in HIV positive men

On-label and off-label use of high-dose influenza vaccine in the United States, 2010–2012

High-dose inactivated, influenza vaccine was licensed by the FDA in December 2009 for adults aged 65 y and older. The ACIP did not issue or state a preference for a specific vaccine in the elderly population. The extent of its on-label and off-label use is unknown. Using the MarketScan Commercial Claims and Encounters and the Medicare Supplemental database, we identified individuals who received the high-dose influenza vaccine or the standard, seasonal trivalent influenza vaccine between January 1, 2010 and December 31, 2012. For people aged ≥65 y, we used multivariable regression to assess the association between patient and provider level variables and high-dose influenza vaccine versus standard influenza vaccine. We characterized all off-label high-dose vaccine administered to people younger than 65 y of age, and investigated whether sicker patients were targeted for off-label use by examining the association between various comorbid conditions and receipt of the high-dose vaccine among adults aged 18–64. Among patients aged ≥65 y who received an influenza vaccine, 18.4% received the high-dose vaccine. Uptake was minimal in 2010, but 25% and 32% of influenza shots were the high-dose formulation in 2011 and 2012, respectively. Almost 27,000 seniors received a second high-dose vaccine with a median of 368 d (IQR: 350–387 days) between doses. Older age, family practice physicians, and having PPO insurance were positively associated with receiving high-dose vaccine. There were 36,624 off-label high-dose vaccines administered. Half of the patients receiving off-label doses were aged 50–64. Adults aged 18–64 y receiving high-dose vaccine were more likely to have chronic comorbidities than people receiving standard influenza vaccine; however, there was not one specific illness that seemed to be targeted by physicians. In the first 3 y since licensure, use of the high-dose vaccine among seniors has been limited. The safety of this vaccine should be monitored closely among 2 groups of people - seniors receiving repeat doses and people <65

Controlling Time-Dependent Confounding by Health Status and Frailty: Restriction Versus Statistical Adjustment

Nonexperimental studies of preventive interventions are often biased because of the healthy-user effect and, in frail populations, because of confounding by functional status. Bias is evident when estimating influenza vaccine effectiveness, even after adjustment for claims-based indicators of illness. We explored bias reduction methods while estimating vaccine effectiveness in a cohort of adult hemodialysis patients. Using the United States Renal Data System and linked data from a commercial dialysis provider, we estimated vaccine effectiveness using a Cox proportional hazards marginal structural model of all-cause mortality before and during 3 influenza seasons in 2005/2006 through 2007/2008. To improve confounding control, we added frailty indicators to the model, measured time-varying confounders at different time intervals, and restricted the sample in multiple ways. Crude and baseline-adjusted marginal structural models remained strongly biased. Restricting to a healthier population removed some unmeasured confounding; however, this reduced the sample size, resulting in wide confidence intervals. We estimated an influenza vaccine effectiveness of 9% (hazard ratio = 0.91, 95% confidence interval: 0.72, 1.15) when bias was minimized through cohort restriction. In this study, the healthy-user bias could not be controlled through statistical adjustment; however, sample restriction reduced much of the bias

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

Evidence of Sample Use Among New Users of Statins: Implications for Pharmacoepidemiology

Epidemiologic studies of prescription medications increasingly rely on large administrative healthcare databases. These data do not capture patients’ use of medication samples. This could potentially bias studies of short-term effects where date of initiation may be inaccurate

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

Instrumental variable methods in comparative safety and effectiveness research

Instrumental variable (IV) methods have been proposed as a potential approach to the common problem of uncontrolled confounding in comparative studies of medical interventions, but IV methods are unfamiliar to many researchers. The goal of this article is to provide a non-technical, practical introduction to IV methods for comparative safety and effectiveness research. We outline the principles and basic assumptions necessary for valid IV estimation, discuss how to interpret the results of an IV study, provide a review of instruments that have been used in comparative effectiveness research, and suggest some minimal reporting standards for an IV analysis. Finally, we offer our perspective of the role of IV estimation vis-à-vis more traditional approaches based on statistical modeling of the exposure or outcome. We anticipate that IV methods will be often underpowered for drug safety studies of very rare outcomes, but may be potentially useful in studies of intended effects where uncontrolled confounding may be substantial