Propensity score matching for estimating a marginal hazard ratio.

Propensity score matching is commonly used to draw causal inference from observational survival data. However, its asymptotic properties have yet to be established, and variance estimation is still open to debate. We derive the statistical properties of the propensity score matching estimator of the marginal causal hazard ratio based on matching with replacement and a fixed number of matches. We also propose a double-resampling technique for variance estimation that takes into account the uncertainty due to propensity score estimation prior to matching.

Reweighting estimators to extend the external validity of clinical trials: methodological considerations.

Methods to extend the strong internal validity of randomized controlled trials to reliably estimate treatment effects in target populations are gaining attention. This paper enumerates steps recommended for undertaking such extended inference, discusses currently viable choices for each one, and provides recommendations. We demonstrate a complete extended inference from a clinical trial studying a pharmaceutical treatment for Alzheimer's disease (AD) to a realistic target population of European residents diagnosed with AD. This case study highlights approaches to overcoming practical difficulties and demonstrates limitations of reliably extending inference from a trial to a real-world population.

Predicting Response to Bolus Insulin Therapy in Patients With Type 2 Diabetes.

BACKGROUND: The aim of this study was to develop a predictive model to classify people with type 2 diabetes (T2D) into expected levels of success upon bolus insulin initiation. METHODS: Machine learning methods were applied to a large nationally representative insurance claims database from the United States (dNHI database; data from 2007 to 2017). We trained boosted decision tree ensembles (XGBoost) to assign people into Class 0 (never meeting HbA1c goal), Class 1 (meeting but not maintaining HbA1c goal), or Class 2 (meeting and maintaining HbA1c goal) based on the demographic and clinical data available prior to initiating bolus insulin. The primary objective of the study was to develop a model capable of determining at an individual level, whether people with T2D are likely to achieve and maintain HbA1c goals. HbA1c goal was defined at <8.0% or reduction of baseline HbA1c by >1.0%. RESULTS: Of 15 331 people with T2D (mean age, 53.0 years; SD, 8.7), 7800 (50.9%) people met HbA1c goal but failed to maintain that goal (Class 1), 4510 (29.4%) never attained this goal (Class 0), and 3021 (19.7%) people met and maintained this goal (Class 2). Overall, the model's receiver operating characteristic (ROC) was 0.79 with greater performance on predicting those in Class 2 (ROC = 0.92) than those in Classes 0 and 1 (ROC = 0.71 and 0.62, respectively). The model achieved high area under the precision-recall curves for the individual classes (Class 0, 0.46; Class 1, 0.58; Class 2, 0.71). CONCLUSIONS: Predictive modeling using routine health care data reasonably accurately classified patients initiating bolus insulin who would achieve and maintain HbA1c goals, but less so for differentiation between patients who never met and who did not maintain goals. Prior HbA1c was a major contributing parameter for the predictions.

Evaluation of VTE, MACE, and Serious Infections Among Patients with RA Treated with Baricitinib Compared to TNFi: A Multi-Database Study of Patients in Routine Care Using Disease Registries and Claims Databases.

INTRODUCTION: The aim of this work is to evaluate baricitinib safety with respect to venous thromboembolism (VTE), major adverse cardiovascular events (MACE), and serious infection relative to tumor necrosis factor inhibitors (TNFi) in patients with rheumatoid arthritis (RA). METHODS: Patients with RA from 14 real-world data sources (three disease registries, eight commercial and three government health insurance claims databases) in the United States (n = 9), Europe (n = 3), and Japan (n = 2) were analyzed using a new user active comparator design. Propensity score matching (1:1) controlled for potential confounding. Meta-analysis of incidence rate ratios (IRR) and incidence rate differences (IRD) for each outcome, from each data source was executed using modified Poisson regression and Cochran-Mantel-Haenszel analysis. RESULTS: Of 9013 eligible baricitinib-treated patients, 7606 were propensity score-matched with TNFi-treated patients, contributing 5879 and 6512 person-years of baricitinib and TNFi exposure, respectively. Across data sources, 97 patients (56 baricitinib) experienced VTE during follow-up, 93 experienced MACE (54 baricitinib), and 321 experienced serious infection (176 baricitinib). Overall IRRs comparing baricitinib with TNFi treatment were 1.51 (95% CI 1.10, 2.08) for VTE, 1.54 (95% CI 0.93, 2.54) for MACE, and 1.36 (95% CI 0.86, 2.13) for serious infection. IRDs for VTE, MACE, and serious infection, respectively, were 0.26 (95% CI -0.04, 0.57), 0.22 (95% CI -0.07, 0.52), and 0.57 (95% CI -0.07, 1.21) per 100 person-years greater for baricitinib than TNFi. CONCLUSIONS: Overall results suggest increased risk of VTE with baricitinib versus TNFi, with consistent point estimates from the two largest data sources. A numerically greater risk was observed for MACE and serious infection when comparing baricitinib versus TNFi, with different point estimates from the two largest data sources. Findings from this study and their impact on clinical practice should be considered in context of limitations and other evidence regarding the safety and efficacy of baricitinib and other Janus kinase inhibitors. TRIAL REGISTRATION: EU PAS Register ( http://encepp.eu ), identifier #32271.

Causal analyses with target trial emulation for real-world evidence removed large self-inflicted biases: systematic bias assessment of ovarian cancer treatment effectiveness.

BACKGROUND AND OBJECTIVES: Drawing causal conclusions from real-world data (RWD) poses methodological challenges and risk of bias. We aimed to systematically assess the type and impact of potential biases that may occur when analyzing RWD using the case of progressive ovarian cancer. METHODS: We retrospectively compared overall survival with and without second-line chemotherapy (LOT2) using electronic medical records. Potential biases were determined using directed acyclic graphs. We followed a stepwise analytic approach ranging from crude analysis and multivariable-adjusted Cox model up to a full causal analysis using a marginal structural Cox model with replicates emulating a reference randomized controlled trial (RCT). To assess biases, we compared effect estimates (hazard ratios [HRs]) of each approach to the HR of the reference trial. RESULTS: The reference trial showed an HR for second line vs. delayed therapy of 1.01 (95% confidence interval [95% CI]: 0.82-1.25). The corresponding HRs from the RWD analysis ranged from 0.51 for simple baseline adjustments to 1.41 (95% CI: 1.22-1.64) accounting for immortal time bias with time-varying covariates. Causal trial emulation yielded an HR of 1.12 (95% CI: 0.96-1.28). CONCLUSION: Our study, using ovarian cancer as an example, shows the importance of a thorough causal design and analysis if one is expecting RWD to emulate clinical trial results.

Comparative Effectiveness of Dexamethasone in Hospitalized COVID-19 Patients in the United States.

INTRODUCTION: To compare the mortality of hospitalized patients with COVID-19 between those that required supplemental oxygen and received dexamethasone with a comparable set of patients who did not receive dexamethasone. METHODS: We utilized the Premier Health Database to identify hospitalized adult patients with COVID-19 from July 1, 2020-January 31, 2021. Index date was when patients first initiated oxygen therapy. The primary endpoint was in-hospital mortality for patients receiving dexamethasone versus those not receiving dexamethasone 1-day pre- to 1-day post-index period. Secondary endpoints included 28-day mortality, time to in-hospital mortality, progression to invasive mechanical ventilation or death, time to discharge, and proportion discharged alive by day 28. Twenty-three models using weighting, matching, stratification, and regression were deployed through the concept of frequentist model average (FMA) to estimate the effect of dexamethasone on all-cause mortality up to the 28-day hospitalization period. RESULTS: A total of 1,208,881 patients with COVID-19 were screened; as an inpatient 255,216 used oxygen, and 251,536 were included in the analysis. In the dexamethasone group, odds of in-hospital mortality were higher than those of the comparator (FMA: odds ratio [OR] 1.15, 95% CI 1.08, 1.22). Using a best fit model, OR for in-hospital mortality was non-significant for the dexamethasone group compared with the comparator (OR 1.02, 95% CI 0.92, 1.14). Dexamethasone treatment was associated with poorer outcomes versus the comparator group across the majority of secondary endpoints, except for number of days in hospital, which was lower in the dexamethasone group versus the comparator group (mean difference - 2.14, 95% CI - 2.43, - 1.47). CONCLUSIONS: Hospitalized adult patients with COVID-19 who required supplemental oxygen and received dexamethasone did not have a survival benefit versus similar patients not receiving dexamethasone. The dexamethasone group was not associated with favorable responses for outcomes such as progression to death or mechanical ventilation and time to in-hospital death.

A first step towards best practice recommendations for the design and statistical analyses of pragmatic clinical trials: A modified Delphi approach.

AIM: Pragmatic clinical trials (PCTs) are randomized trials implemented through routine clinical practice, where design parameters of traditional randomized controlled trials are modified to increase generalizability. However, this may introduce statistical challenges. We aimed to identify these challenges and discuss possible solutions leading to best practice recommendations for the design and analysis of PCTs. METHODS: A modified Delphi method was used to reach consensus among a panel of 11 experts in clinical trials and statistics. Statistical issues were identified in a focused literature review and aggregated with insights and possible solutions from experts collected through a series of survey iterations. Issues were ranked according to their importance. RESULTS: Twenty-seven articles were included and combined with experts' insight to generate a list of issues categorized into participants, recruiting sites, randomization, blinding and intervention, outcome (selection and measurement) and data analysis. Consensus was reached about the most important issues: risk of participants' attrition, heterogeneity of "usual care" across sites, absence of blinding, use of a subjective endpoint and data analysis aligned with the trial estimand. Potential issues should be anticipated and preferably be addressed in the trial protocol. The experts provided solutions regarding data collection and data analysis, which were considered of equal importance. DISCUSSION: A set of important statistical issues in PCTs was identified and approaches were suggested to anticipate and/or minimize these through data analysis. Any impact of choosing a pragmatic design feature should be gauged in the light of the trial estimand.

Diagnosis, consultation, treatment, and impact of migraine in the US: Results of the OVERCOME (US) study.

OBJECTIVE: The ObserVational survey of the Epidemiology, tReatment and Care of MigrainE (OVERCOME; United States) study is a multicohort, longitudinal web survey that assesses symptomatology, consulting, diagnosis, treatment, and impact of migraine in the United States. BACKGROUND: Regularly updating population-based views of migraine in the United States provides a method for assessing the quality of ongoing migraine care and identifying unmet needs. METHODS: The OVERCOME (US) 2018 migraine cohort involved: (I) creating a demographically representative sample of US adults using quota sampling (n = 97,478), (II) identifying people with active migraine in the past year via a validated migraine diagnostic questionnaire and/or self-reported medical diagnosis of migraine (n = 24,272), and (III) assessing consultation, diagnosis, and treatment of migraine (n = 21,143). The current manuscript evaluated whether those with low frequency episodic migraine (LFEM; 0-3 monthly headache days) differed from other categories on outcomes of interest. RESULTS: Among the migraine cohort (n = 21,143), 19,888 (94.1%) met our International Classification of Headache Disorders, 3rd edition-based case definition of migraine and 12,905 (61.0%) self-reported a medical diagnosis of migraine. Respondents' mean (SD) age was 42.2 (15.0) years; 15,697 (74.2%) were women. Having at least moderate disability was common (n = 8965; 42.4%) and around half (n = 10,783; 51.0%) had consulted a medical professional for migraine care in the past year. Only 4792 (22.7%) of respondents were currently using a triptan. Overall, 8539 (40.4%) were eligible for migraine preventive medication and 3555 (16.8%) were currently using migraine preventive medication. Those with LFEM differed from moderate and high frequency episodic migraine and chronic migraine on nearly all measures of consulting, diagnosis, and treatment. CONCLUSION: The OVERCOME (US) 2018 cohort revealed slow but steady progress in diagnosis and preventive treatment of migraine. However, despite significant impact among the population, many with migraine have unmet needs related to consulting for migraine, migraine diagnosis, and getting potentially beneficial migraine treatment. Moreover, it demonstrated the heterogeneity and varying unmet needs within episodic migraine.

Practical recommendations on double score matching for estimating causal effects.

Unlike in randomized clinical trials (RCTs), confounding control is critical for estimating the causal effects from observational studies due to the lack of treatment randomization. Under the unconfoundedness assumption, matching methods are popular because they can be used to emulate an RCT that is hidden in the observational study. To ensure the key assumption hold, the effort is often made to collect a large number of possible confounders, rendering dimension reduction imperative in matching. Three matching schemes based on the propensity score (PSM), prognostic score (PGM), and double score (DSM, ie, the collection of the first two scores) have been proposed in the literature. However, a comprehensive comparison is lacking among the three matching schemes and has not made inroads into the best practices including variable selection, choice of caliper, and replacement. In this article, we explore the statistical and numerical properties of PSM, PGM, and DSM via extensive simulations. Our study supports that DSM performs favorably with, if not better than, the two single score matching in terms of bias and variance. In particular, DSM is doubly robust in the sense that the matching estimator is consistent requiring either the propensity score model or the prognostic score model is correctly specified. Variable selection on the propensity score model and matching with replacement is suggested for DSM, and we illustrate the recommendations with comprehensive simulation studies. An R package is available at https://github.com/Yunshu7/dsmatch.

Predicting optimal treatment regimens for patients with HR+/HER2- breast cancer using machine learning based on electronic health records.

Aim: To predict optimal treatments maximizing overall survival (OS) and time to treatment discontinuation (TTD) for patients with metastatic breast cancer (MBC) using machine learning methods on electronic health records. Patients/methods: Adult females with HR+/HER2- MBC on first- or second-line systemic therapy were eligible. Random survival forest (RSF) models were used to predict optimal regimen classes for individual patients and each line of therapy based on baseline characteristics. Results: RSF models suggested greater use of CDK4 & 6 inhibitor-based therapies may maximize OS and TTD. RSF-predicted optimal treatments demonstrated longer OS and TTD compared with nonoptimal treatments across line of therapy (hazard ratios = 0.44∼0.79). Conclusion: RSF may help inform optimal treatment choices and improve outcomes for patients with HR+/HER2- MBC.

A comparison of reweighting estimators of average treatment effects in real world populations.

Regulatory agencies typically evaluate the efficacy and safety of new interventions and grant commercial approval based on randomized controlled trials (RCTs). Other major healthcare stakeholders, such as insurance companies and health technology assessment agencies, while basing initial access and reimbursement decisions on RCT results, are also keenly interested in whether results observed in idealized trial settings will translate into comparable outcomes in real world settings-that is, into so-called "real world" effectiveness. Unfortunately, evidence of real world effectiveness for new interventions is not available at the time of initial approval. To bridge this gap, statistical methods are available to extend the estimated treatment effect observed in a RCT to a target population. The generalization is done by weighting the subjects who participated in a RCT so that the weighted trial population resembles a target population. We evaluate a variety of alternative estimation and weight construction procedures using both simulations and a real world data example using two clinical trials of an investigational intervention for Alzheimer's disease. Our results suggest an optimal approach to estimation depends on the characteristics of source and target populations, including degree of selection bias and treatment effect heterogeneity.

Replication of randomized clinical trial results using real-world data: paving the way for effectiveness decisions.

The FDA is preparing guidance about using real-world evidence (RWE) to support decisions about product effectiveness. Several ongoing efforts aim to replicate randomized clinical trial (RCT) results using RWE with the intent of identifying circumstances and methods that provide valid evidence of drug effects. Lack of agreement may not be due to faulty methods but rather to the challenges with emulating RCTs, differences in healthcare settings and patient populations, differences in effect measures and data analysis, bias, and/or the efficacy-effectiveness gap. In fact, for some decisions, RWE may lead to better understanding of how treatments work in usual care settings than a more constrained view from RCTs. Efforts to reconcile the role and opportunities for generating complementary evidence from RWE and RCTs will advance regulatory science.

The Association Between Adherence to Insulin Therapy and Health Care Costs for Adults with Type 2 Diabetes: Evidence from a U.S. Retrospective Claims Database.

BACKGROUND: Research has shown that many patients with type 2 diabetes (T2D) are not adherent to their medication regimen. OBJECTIVE: To examine the association between adherence to insulin therapy and all-cause health care costs for patients with T2D. METHODS: This study used the IQVIA PharMetrics Plus Linkable to Ambulatory Electronic Medical Record data from January 1, 2012, through September 30, 2017. Patients were included if they were identified with T2D and initiated therapy on basal insulin (BAS) or basal-bolus (BAS-BOL) combination at any time from January 1, 2013, through October 1, 2016. Patients aged < 18 years, who used an insulin pump, identified as pregnant, or did not have continuous insurance coverage from 1 year before initiation on insulin therapy through 1 year after initiation were excluded. Descriptive statistics compared patient characteristics and costs (in U.S. 2017 dollars) between patients who were adherent or nonadherent to their insulin therapy in the 1-year postperiod, where adherence was defined as having proportion of days covered (PDC) of at least 80%. In addition, generalized linear models were used to compare costs between adherent and nonadherent patients, while controlling for patient characteristics, previous general health and comorbidities, resource utilization, medication use and type of insulin. RESULTS: 13,296 patients were included in the BAS cohort (5,502 adherent; 7,794 nonadherent) and 10,069 in the BAS-BOL cohort (2,006 adherent; 8,063 nonadherent). Adherent patients had significantly lower all-cause total unadjusted costs following initiation on BAS ($29,322 vs. $31,888, P = 0.0134) and BAS-BOL combination ($36,229 vs. $40,147, P = 0.0078). Drug costs comprised 39.5%-45.4% of costs among adherent patients and 23.0%-25.9% of costs among nonadherent patients. Multivariable analyses revealed that adherent patients had significantly lower adjusted all-cause total costs than nonadherent patients in the BAS cohort ($30,127 vs. $37,049, 95% CI for difference -$8,460 to -$5,384) and the BAS-BOL cohort ($36,603 vs. $44,702, 95% CI for difference -$9,129 to -$6,980). CONCLUSIONS: In patients with T2D who initiated BAS or BAS-BOL combination therapy, adherence was associated with significantly lower all-cause total health care costs, despite significantly higher drug costs. These results illustrate the potential economic benefits associated with adherence to insulin therapy. DISCLOSURES": Eli Lilly and Company funded this study and was responsible for study design and execution. Bajpai, Eby, Faries, and Haynes are employees and own stock in Eli Lilly and Company. Lage received compensation from Eli Lilly and Company for her work on this research project.

Reweighting Randomized Controlled Trial Evidence to Better Reflect Real Life - A Case Study of the Innovative Medicines Initiative.

Evidence from randomized controlled trials available for timely health technology assessments of new pharmacological treatments and regulatory decision making may not be generalizable to local patient populations, often resulting in decisions being made under uncertainty. In recent years, several reweighting approaches have been explored to address this important question of generalizability to a target population. We present a case study of the Innovative Medicines Initiative to illustrate the inverse propensity score reweighting methodology, which may allow us to estimate the expected treatment benefit if a clinical trial had been run in a broader real-world target population. We learned that identifying treatment effect modifiers, understanding and managing differences between patient characteristic data sets, and balancing the closeness of trial and target patient populations with effective sample size are key to successfully using this methodology and potentially mitigating some of this uncertainty around local decision making.

Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study.

BACKGROUND: Rheumatoid arthritis (RA) is a condition with symptoms that vary over time. The typical 3- to 6-month interval between physician visits may lead to patients failing to recall or underreporting symptoms experienced during the interim. Wearable digital technology enables the regular passive collection of patients' biometric and activity data. If it is shown to be strongly related to data captured by patient-reported outcome (PRO) measures, information collected passively from wearable digital technology could serve as an objective proxy or be complementary to patients' subjective experience of RA symptoms. OBJECTIVE: The goal of this study is to characterize the extent to which digital measures collected from a consumer-grade smartwatch agree with measures of RA disease activity and other PROs collected via a smartphone app. METHODS: This observational study will last 6 months for each participant. We aim to recruit 250 members of the ArthritisPower registry with an RA diagnosis who will receive a smartwatch to wear for the period of the study. From the ArthritisPower mobile app on their own smartphone device, participants will be prompted to answer daily and weekly electronic PRO (ePRO) measures for the first 3 months. RESULTS: The study was launched in December 2018 and will require up to 18 months to complete. Study results are expected to be published by the end of 2021. CONCLUSIONS: The completion of this study will provide important data regarding the following: (1) the relationship between passively collected digital measures related to activity, heart rate, and sleep collected from a smartwatch with ePROs related to pain, fatigue, physical function, and RA flare entered via smartphone app; (2) determine predictors of adherence with smartwatch and smartphone app technology; and (3) assess the effect of study-specific reminders on adherence with the smartwatch. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14665.

The Effectiveness of a Private Orthopaedic Practice-Based Osteoporosis Management Service to Reduce the Risk of Subsequent Fractures.

BACKGROUND: Osteoporosis is prevalent in the United States, with an increasing need for management. In this study, we evaluated the effectiveness of a private orthopaedic practice-based osteoporosis management service (OP MS) in reducing subsequent fracture risk and improving other aspects of osteoporosis management of patients who had sustained fractures. METHODS: This was a retrospective cohort study using the 100% Medicare data set for Michigan residents with any vertebral; hip, pelvic or femoral; or other nonvertebral fracture during the period of April 1, 2010 to September 30, 2014. Patients who received OP MS care with a follow-up visit within 90 days of the first fracture, and those who did not seek OP MS care but had a physician visit within 90 days of the first fracture, were considered as exposed and unexposed, respectively (first follow-up visit = index date). Eligible patients with continuous enrollment in Medicare Parts A and B for the 90-day pre-index period were followed until the earliest of death, health-plan disenrollment, or study end (December 31, 2014) to evaluate rates of subsequent fracture, osteoporosis medication prescriptions filled, and bone mineral density (BMD) assessments. Health-care costs were evaluated among patients with 12 months of post-index continuous enrollment. Propensity-score matching was used to balance differences in baseline characteristics. Each exposed patient was matched to an unexposed patient within ± 0.01 units of the propensity score. After propensity-score matching, Cox regression examined the hazard ratio (HR) of clinical and economic outcomes in the exposed and unexposed cohorts. RESULTS: Two well-matched cohorts of 1,304 patients each were produced. The exposed cohort had a longer median time to subsequent fracture (998 compared with 743 days; log-rank p = 0.001), a lower risk of subsequent fracture (HR = 0.8; 95% confidence interval [CI] = 0.7 to 0.9), and a higher likelihood of having osteoporosis medication prescriptions filled (HR = 1.7; 95% CI = 1.4 to 2.0) and BMD assessments (HR = 4.3; 95% CI = 3.7 to 5.0). The total 12-month costs ($25,306 compared with $22,896 [USD]; p = 0.082) did not differ significantly between the cohorts. CONCLUSIONS: A private orthopaedic practice-based OP MS effectively reduced subsequent fracture risk, likely through coordinated and ongoing comprehensive patient care, without a significant overall higher cost. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Addressing unmeasured confounding in comparative observational research.

PURPOSE: Observational pharmacoepidemiological studies can provide valuable information on the effectiveness or safety of interventions in the real world, but one major challenge is the existence of unmeasured confounder(s). While many analytical methods have been developed for dealing with this challenge, they appear under-utilized, perhaps due to the complexity and varied requirements for implementation. Thus, there is an unmet need to improve understanding the appropriate course of action to address unmeasured confounding under a variety of research scenarios. METHODS: We implemented a stepwise search strategy to find articles discussing the assessment of unmeasured confounding in electronic literature databases. Identified publications were reviewed and characterized by the applicable research settings and information requirements required for implementing each method. We further used this information to develop a best practice recommendation to help guide the selection of appropriate analytical methods for assessing the potential impact of unmeasured confounding. RESULTS: Over 100 papers were reviewed, and 15 methods were identified. We used a flowchart to illustrate the best practice recommendation which was driven by 2 critical components: (1) availability of information on the unmeasured confounders; and (2) goals of the unmeasured confounding assessment. Key factors for implementation of each method were summarized in a checklist to provide further assistance to researchers for implementing these methods. CONCLUSION: When assessing comparative effectiveness or safety in observational research, the impact of unmeasured confounding should not be ignored. Instead, we suggest quantitatively evaluating the impact of unmeasured confounding and provided a best practice recommendation for selecting appropriate analytical methods.

Healthcare utilization and costs of children with attention deficit/hyperactivity disorder initiating atomoxetine versus extended-release guanfacine.

OBJECTIVES: To compare 1-year direct healthcare costs and utilization among children and adolescents initiating non-stimulant medications atomoxetine (ATX) or extended-release guanfacine (GXR). METHODS: In this retrospective, observational cohort study, children and adolescents aged 6-17 years with attention deficit/hyperactivity disorder (ADHD) who had ≥1 prescription claim for ATX or GXR between December 31, 2009 and January 1, 2011 were identified in the MarketScan Commercial or Multi-State Medicaid claims databases. The first claim was set as the index. Patients with no claims for other ADHD medications that overlapped with the days' supply for the index therapy during the post-period were classified as initiating monotherapy. All-cause and ADHD-related utilization and costs (2011 US$) and treatment patterns (adherence and persistence) were evaluated during the 12 months following index. Propensity score adjustment accounted for differences in patient characteristics, and bootstrapping was used for comparisons. RESULTS: A total of 13,239 children and adolescents with ADHD met the study criteria (4,411 ATX initiators and 8,828 GXR initiators). There were 2,699 ATX monotherapy patients. In propensity-score-adjusted analyses, mean all-cause total costs were significantly less for monotherapy ATX initiators than for GXR initiators ($7,553 vs $10,639; difference = -$3,086, p < .0001), as were mean ADHD-related total costs ($3,213 vs $4,544; difference = -$1,330, p < .0001). Monotherapy ATX initiators had significantly fewer all-cause and ADHD-related total medical visits and ∼22 days shorter persistence to index therapy (p < .0001). Results were similar for secondary analyses comparing all ATX with all GXR initiators, regardless of monotherapy or combination regimen, and comparing only monotherapy initiators. CONCLUSIONS: Children and adolescents with ADHD who initiated ATX monotherapy incurred lower all-cause and ADHD-related total healthcare costs than patients who initiated GXR. This was due in part to less healthcare resource utilization and slightly shorter persistence for ATX patients. These findings may aid decision-making and inform future studies, but must be tempered due to inherent observational research limitations.

Evaluating different strategies for estimating treatment effects in observational studies.

Since the introduction of the propensity score (PS), methods for estimating treatment effects with observational data have received growing attention in the literature. Recent research has added substantially to the number of available statistical approaches for controlling confounding in such analyses. However, researchers need guidance to decide on the optimal analytic strategy for any given scenario. To address this gap, we conducted simulations evaluating both well-established methods (regression, PS weighting, stratification, and matching) and more recently proposed approaches (tree-based methods, local control, entropy balancing, genetic matching, prognostic scoring). The simulation scenarios included tree-based and smooth regression models as true data-generation mechanisms. We evaluated an extensive number of analysis strategies combining different treatment choices and outcome models. Key findings include 1) the lack of a single best strategy across all potential scenarios; 2) the importance of appropriately addressing interactions in the treatment choice model and/or outcome model; and 3) a tree-structured treatment choice model and a polynomial outcome model with second-order interactions performed well. One limitation to this initial assessment is the lack of heterogeneous simulation scenarios allowing treatment effects to vary by patient.

Impact of Proton Pump Inhibitor Use on the Comparative Effectiveness and Safety of Prasugrel Versus Clopidogrel: Insights From the Treatment With Adenosine Diphosphate Receptor Inhibitors: Longitudinal Assessment of Treatment Patterns and Events After Acute Coronary Syndrome (TRANSLATE-ACS) Study.

BACKGROUND: Proton pump inhibitors (PPIs) reduce gastrointestinal bleeding events but may alter clopidogrel metabolism. We sought to understand the comparative effectiveness and safety of prasugrel versus clopidogrel in the context of proton pump inhibitor (PPI) use. METHODS AND RESULTS: Using data on 11 955 acute myocardial infarction (MI) patients treated with percutaneous coronary intervention at 233 hospitals and enrolled in the TRANSLATE-ACS study, we compared whether discharge PPI use altered the association of 1-year adjusted risks of major adverse cardiovascular events (MACE; death, MI, stroke, or unplanned revascularization) and Global Use of Strategies To Open Occluded Arteries (GUSTO) moderate/severe bleeding between prasugrel- and clopidogrel-treated patients. Overall, 17% of prasugrel-treated and 19% of clopidogrel-treated patients received a PPI at hospital discharge. At 1 year, patients discharged on a PPI versus no PPI had higher risks of MACE (adjusted hazard ratio [HR] 1.38, 95% confidence interval [CI] 1.21-1.58) and GUSTO moderate/severe bleeding (adjusted HR 1.55, 95% CI 1.15-2.09). Risk of MACE was similar between prasugrel and clopidogrel regardless of PPI use (adjusted HR 0.88, 95% CI 0.62-1.26 with PPI, adjusted HR 1.07, 95% CI 0.90-1.28 without PPI, interaction P=0.31). Comparative bleeding risk associated with prasugrel versus clopidogrel use differed based on PPI use but did not reach statistical significance (adjusted HR 0.73, 95% CI 0.36-1.48 with PPI, adjusted HR 1.34, 95% CI 0.79-2.27 without PPI, interaction P=0.17). CONCLUSIONS: PPIs did not significantly affect the MACE and bleeding risk associated with prasugrel use, relative to clopidogrel. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01088503.