Bridged Treatment Comparisons: an Illustrative Application in HIV Treatment.

Comparisons of treatments, interventions, or exposures are of central interest in epidemiology, but direct comparisons are not always possible due to practical or ethical reasons. Here, we detail a fusion approach to compare treatments across studies. The motivating example entails comparing the risk of the composite outcome of death, AIDS, or greater than a 50% CD4 cell count decline in people with HIV when assigned triple versus mono antiretroviral therapy, using data from the AIDS Clinical Trial Group (ACTG) 175 (mono versus dual therapy) and ACTG 320 (dual versus triple therapy). We review a set of identification assumptions and estimate the risk difference using an inverse probability weighting estimator that leverages the shared trial arms (dual therapy). A fusion diagnostic based on comparing the shared arms is proposed that may indicate violation of the identification assumptions. Application of the data fusion estimator and diagnostic to the ACTG trials indicates triple therapy results in a reduction in risk compared to monotherapy in individuals with baseline CD4 counts between 50 and 300 cells/mm3. Bridged treatment comparisons address questions that none of the constituent data sources could address alone, but valid fusion-based inference requires careful consideration of the underlying assumptions.

Comparing PCSK9 Monoclonal Antibody Treatment Strategies Following Myocardial Infarction Using Negative Control Outcomes: A Target Trial Emulation Study.

BACKGROUND: Initiation of proprotein convertase subtilisin/kexin type 9 monoclonal antibody (PCSK9 mAb) for lipid-lowering following myocardial infarction (MI) is likely affected by patients' prognostic factors, potentially leading to bias when comparing real-world treatment effects. METHODS: Using target-trial emulation, we assessed potential confounding when comparing two treatment strategies post-MI: initiation of PCSK9 mAb within 1 year and no initiation of PCSK9 mAb. We identified MI hospitalizations during July 2015-June 2020 for patients aged ≥18 years in Optum's de-identified Clinformatics Data Mart (CDM) and MarketScan, and those aged ≥66 in the US Medicare claims database. We estimated a 3-year counterfactual cumulative risk and risk difference (RD) for 10 negative control outcomes using the clone-censor-weight approach to address time-varying confounding and immortal person-time. RESULTS: PCSK9 mAb initiation within 1-year post-MI was low (0.7% in MarketScan and 0.4% in both CDM and Medicare databases). In CDM, there was a lower risk for cancer (RD = -3.6% [95% CI: -4.3%, -2.9%]), decubitus ulcer (RD = -7.7% [95% CI: -11.8%, -3.7%]), fracture (RD = -8.1% [95% CI: -9.6%, -6.6%]), influenza vaccine (RD = -9.3% [95% CI: -17.5%, -1.1%]), and visual test (RD = -0.6% [95% CI: -0.7%, -0.6%]) under the PCSK9 mAb initiation versus no initiation strategy. Similar differences persisted in the MarketScan and Medicare databases. In each database, ezetimibe and low-density lipoprotein testing were unbalanced between treatment strategies. CONCLUSION: A comparative effectiveness study of these treatments using the current approach would likely bias results due to the low number of PCSK9 mAb initiators.

Comparative effectiveness of alternative intervals between first and second doses of the mRNA COVID-19 vaccines.

The optimal interval between the first and second doses of COVID-19 mRNA vaccines has not been thoroughly evaluated. Employing a target trial emulation approach, we compared the effectiveness of different interdose intervals among >6 million mRNA vaccine recipients in Georgia, USA, from December 2020 to March 2022. We compared three protocols defined by interdose interval: recommended by the Food and Drug Administration (FDA) (17-25 days for Pfizer-BioNTech; 24-32 days for Moderna), late-but-allowable (26-42 days for Pfizer-BioNTech; 33-49 days for Moderna), and late ( ≥ 43 days for Pfizer-BioNTech; ≥50 days for Moderna). In the short-term, the risk of SARS-CoV-2 infection was lowest under the FDA-recommended protocol. Longer-term, the late-but-allowable protocol resulted in the lowest risk (risk ratio on Day 120 after the first dose administration compared to the FDA-recommended protocol: 0.83 [95% confidence interval: 0.82-0.84]). Here, we showed that delaying the second dose by 1-2 weeks may provide stronger long-term protection.

Characterizing Imbalance in the Tails of the Propensity Score Distribution.

Understanding characteristics of patients with propensity scores in the tails of the propensity score (PS) distribution has relevance for inverse-probability-of-treatment-weighted and PS-based estimation in observational studies. Here we outline a method for identifying variables most responsible for extreme propensity scores. The approach is illustrated in 3 scenarios: 1) a plasmode simulation of adult patients in the National Ambulatory Medical Care Survey (2011-2015) and 2) timing of dexamethasone initiation and 3) timing of remdesivir initiation in patients hospitalized for coronavirus disease 2019 from February 2020 through January 2021. PS models were fitted using relevant baseline covariates, and tails of the PS distribution were defined using asymmetric first and 99th percentiles. After fitting of the PS model in each original data set, values of each key covariate were permuted and model-agnostic variable importance measures were examined. Visualization and variable importance techniques were helpful in identifying variables most responsible for extreme propensity scores and may help identify individual characteristics that might make patients inappropriate for inclusion in a study (e.g., off-label use). Subsetting or restricting the study sample based on variables identified using this approach may help investigators avoid the need for trimming or overlap weights in studies.

Controlling for Differential Regression-To-The-Mean via Propensity Scores: A Simulation Study.

Purpose: Regression-to-the-mean (RTM) is a statistical phenomenon that may occur in epidemiologic studies where inclusion in the study cohort is contingent upon experiencing a laboratory/clinical measurement beyond a defined threshold. When differential across treatment groups, RTM could bias the final study estimate. This poses substantial challenges in observational studies that index patients upon experiencing extreme laboratory or clinical values. Our objective was to investigate propensity score-based methods as a tool for mitigating this source of bias via simulation. Methods: We simulated a noninterventional comparative effectiveness study, comparing treatment with romiplostim to standard-of-care therapies for immune thrombocytopenia (ITP), a disease characterized by low platelet counts. Platelet counts were generated from normal distributions according to the underlying ITP severity, a strong confounder of treatment and outcome. Patients were assigned treatment probabilities based upon ITP severity, which created varied levels of differential and non-differential RTM. Treatments were compared via the difference in median platelet counts during 23 weeks of follow-up. We calculated four summary metrics of the platelet counts measured prior to cohort entry and built six propensity score models to adjust for those variables. We adjusted for these summary metrics using inverse probability of treatment weights. Results: Across all simulated scenarios, propensity score adjustment reduced bias and increased precision of the treatment effect estimator. Adjusting for combinations of the summary metrics was most effective at reducing bias. Adjusting for the mean of prior platelet counts or the difference between the cohort-qualifying platelet count and the largest prior count eliminated the most bias when assessed individually. Conclusion: These results suggest that differential RTM could be reasonably addressed by propensity score models with summaries of historical laboratory values. This approach can be easily applied to any comparative effectiveness or safety study, though investigators should carefully consider the best summary metric for their data.

Pragmatic considerations for negative control outcome studies to guide non-randomized comparative analyses: A narrative review.

PURPOSE: This narrative review describes the application of negative control outcome (NCO) methods to assess potential bias due to unmeasured or mismeasured confounders in non-randomized comparisons of drug effectiveness and safety. An NCO is assumed to have no causal relationship with a treatment under study while subject to the same confounding structure as the treatment and outcome of interest; an association between treatment and NCO then reflects the potential for uncontrolled confounding between treatment and outcome. METHODS: We focus on two recently completed NCO studies that assessed the comparability of outcome risk for patients initiating different osteoporosis medications and lipid-lowering therapies, illustrating several ways in which confounding may result. In these studies, NCO methods were implemented in claims-based data sources, with the results used to guide the decision to proceed with comparative effectiveness or safety analyses. RESULTS: Based on this research, we provide recommendations for future NCO studies, including considerations for the identification of confounding mechanisms in the target patient population, the selection of NCOs expected to satisfy required assumptions, the interpretation of NCO effect estimates, and the mitigation of uncontrolled confounding detected in NCO analyses. We propose the use of NCO studies prior to initiating comparative effectiveness or safety research, providing information on the potential presence of uncontrolled confounding in those comparative analyses. CONCLUSIONS: Given the increasing use of non-randomized designs for regulatory decision-making, the application of NCO methods will strengthen study design, analysis, and interpretation of real-world data and the credibility of the resulting real-world evidence.

Effectiveness of Remdesivir Treatment Protocols Among Patients Hospitalized with COVID-19: A Target Trial Emulation.

BACKGROUND: Remdesivir is recommended for certain hospitalized patients with COVID-19. However, these recommendations are based on evidence from small randomized trials, early observational studies, or expert opinion. Further investigation is needed to better inform treatment guidelines with regard to the effectiveness of remdesivir among these patients. METHODS: We emulated a randomized target trial using chargemaster data from 333 US hospitals from 1 May 2020 to 31 December 2021. We compared three treatment protocols: remdesivir within 2 days of hospital admission, no remdesivir within the first 2 days of admission, and no remdesivir ever. We used baseline comorbidities recorded from encounters up to 12 months before admission and identified the use of in-hospital medications, procedures, and oxygen supplementation from charges. We estimated the cumulative incidence of mortality or mechanical ventilation/extracorporeal membrane oxygenation with an inverse probability of censoring weighted estimator. We conducted analyses in the total population as well as in subgroups stratified by level of oxygen supplementation. RESULTS: A total of 274,319 adult patients met the eligibility criteria for the study. Thirty-day in-hospital mortality risk differences for patients adhering to the early remdesivir protocol were -3.1% (95% confidence interval = -3.5%, -2.7%) compared to no early remdesivir and -3.7% (95% confidence interval -4.2%, -3.2%) compared to never remdesivir, with the strongest effect in patients needing high-flow oxygen. For mechanical ventilation/extracorporeal membrane oxygenation, risk differences were minimal. CONCLUSIONS: We estimate that, among hospitalized patients with COVID-19, remdesivir treatment within 2 days of admission reduced 30-day in-hospital mortality, particularly for patients receiving supplemental oxygen on the day of admission.

Illustration of 2 Fusion Designs and Estimators.

"Fusion" study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information.

DAG With Omitted Objects Displayed (DAGWOOD): a framework for revealing causal assumptions in DAGs.

Directed acyclic graphs (DAGs) are frequently used in epidemiology as a method to encode causal inference assumptions. We propose the DAGWOOD framework to bring many of those encoded assumptions to the forefront. DAGWOOD combines a root DAG (the DAG in the proposed analysis) and a set of branch DAGs (alternative hidden assumptions to the root DAG). All branch DAGs share a common ruleset, and must 1) change the root DAG, 2) be a valid DAG, and either 3a) change the minimally sufficient adjustment set or 3b) change the number of frontdoor paths. Branch DAGs comprise a list of assumptions which must be justified as negligible. We define two types of branch DAGs: exclusion branch DAGs add a single- or bidirectional pathway between two nodes in the root DAG (e.g., direct pathways and colliders), while misdirection branch DAGs represent alternative pathways that could be drawn between objects (e.g., creating a collider by reversing the direction of causation for a controlled confounder). The DAGWOOD framework 1) organizes causal model assumptions, 2) reinforces best DAG practices, 3) provides a framework for evaluation of causal models, and 4) can be used for generating causal models.

Reflection on modern methods: combining weights for confounding and missing data.

Inverse probability weights are increasingly used in epidemiological analysis, and estimation and application of weights to address a single bias are well discussed in the literature. Weights to address multiple biases simultaneously (i.e. a combination of weights) have almost exclusively been discussed related to marginal structural models in longitudinal settings where treatment weights (estimated first) are combined with censoring weights (estimated second). In this work, we examine two examples of combined weights for confounding and missingness in a time-fixed setting in which outcome or confounder data are missing, and the estimand is the marginal expectation of the outcome under a time-fixed treatment. We discuss the identification conditions, construction of combined weights and how assumptions of the missing data mechanisms affect this construction. We use a simulation to illustrate the estimation and application of the weights in the two examples. Notably, when only outcome data are missing, construction of combined weights is straightforward; however, when confounder data are missing, we show that in general we must follow a specific estimation procedure which entails first estimating missingness weights and then estimating treatment probabilities from data with missingness weights applied. However, if treatment and missingness are conditionally independent, then treatment probabilities can be estimated among the complete cases.

Use of negative control outcomes to assess the comparability of patients initiating lipid-lowering therapies.

PURPOSE: Clinical trials have demonstrated efficacy of proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i) in reducing risk of cardiovascular disease events, but effectiveness in routine clinical care has not been well-studied. We used negative control outcomes to assess potential confounding in an observational study of PCSK9i versus ezetimibe or high-intensity statin. METHODS: Using commercial claims, we identified U.S. adults initiating PCSK9i, ezetimibe, or high-intensity statin in 2015-2018, with other lipid-lowering therapy (LLT) use in the year prior (LLT cohort) or atherosclerotic cardiovascular disease (ASCVD) in the past 90 days (ASCVD cohort). We compared initiators of PCSK9i to ezetimibe and high-intensity statin by estimating one-year risks of negative control outcomes influenced by frailty or health-seeking behaviors. Inverse probability of treatment and censoring weighted estimators of risk differences (RDs) were used to evaluate residual confounding after controlling for covariates. RESULTS: PCSK9i initiators had lower one-year risks of negative control outcomes associated with frailty, such as decubitus ulcer in the ASCVD cohort (PCSK9i vs. high-intensity statin RD = -3.5%, 95% confidence interval (CI): -4.6%, -2.5%; PCSK9i vs. ezetimibe RD = -1.3%, 95% CI: -2.1%, -0.6%), with similar but attenuated associations in the LLT cohort. Lower risks of accidents and fractures were also observed for PCSK9i, varying by cohort. Risks were similar for outcomes associated with health-seeking behaviors, although trended higher for PCSK9i in the ASCVD cohort. CONCLUSIONS: Observed associations suggest lower frailty and potentially greater health-seeking behaviors among PCSK9i initiators, particularly those with a recent ASCVD diagnosis, with the potential to bias real-world analyses of treatment effectiveness.

Lessons Learned Using Real-World Data to Emulate Randomized Trials: A Case Study of Treatment Effectiveness for Newly Diagnosed Immune Thrombocytopenia.

Regulatory agencies are increasingly considering real-world evidence (RWE) to support label expansions of approved medicines. We conducted a comparative effectiveness study to emulate a proposed randomized trial of romiplostim vs. standard-of-care (SOC) therapy among patients with recently diagnosed (≤12 months) immune thrombocytopenia (ITP), that could support expansion of the romiplostim label. We discuss challenges that we encountered and solutions that were developed to address those challenges. Study size was a primary concern, particularly for romiplostim initiators, given the rarity of ITP and the stringent trial eligibility criteria. For this reason, we leveraged multiple data sources (Nordic Country Patient Registry for Romiplostim; chart review study of romiplostim initiators in Europe; Flatiron Health EMR linked with MarketScan claims). Additionally, unlike the strictly controlled clinical trial setting, platelet counts were not measured at regular intervals in the observational data sources, and therefore the end point of durable platelet response often used in trials could not be reliably measured. Instead, the median platelet count was chosen as the primary end point. Ultimately, while we observed a slightly higher median platelet count in the romiplostim group vs. SOC, precision was limited because of small study size (median difference was 11 × 109 /L (95% CI: -59, 81)). We underscore the importance of conducting comprehensive feasibility assessments to identify fit-for-purpose data sources with sufficient sample size, data elements, and follow-up. Beyond technical challenges, we also discuss approaches to increase the credibility of RWE, including systematic incorporation of clinical expertise into study design decisions, and separation between decision makers and the data.

Poverty, Deprivation, and Mortality Risk Among Women With HIV in the United States.

BACKGROUND: Prior studies suggest neighborhood poverty and deprivation are associated with adverse health outcomes including death, but evidence is limited among persons with HIV, particularly women. We estimated changes in mortality risk from improvement in three measures of area-level socioeconomic context among participants of the Women's Interagency HIV Study. METHODS: Starting in October 2013, we linked geocoded residential census block groups to the 2015 Area Deprivation Index (ADI) and two 2012-2016 American Community Survey poverty variables, categorized into national tertiles. We used parametric g-computation to estimate, through March 2018, impacts on mortality of improving each income or poverty measure by one and two tertiles maximum versus no improvement. RESULTS: Of 1596 women with HIV (median age 49), 91 (5.7%) were lost to follow-up and 83 (5.2%) died. Most women (62%) lived in a block group in the tertile with the highest proportions of individuals with income:poverty <1; 13% lived in areas in the tertile with the lowest proportions. Mortality risk differences comparing a one-tertile improvement (for those in the two highest poverty tertiles) in income:poverty <1 versus no improvement increased over time; the risk difference was -2.2% (95% confidence interval [CI] = -3.7, -0.64) at 4 years. Estimates from family income below poverty level (-1.0%; 95% CI = -2.7, 0.62) and ADI (-1.5%; 95% CI = -2.8, -0.21) exposures were similar. CONCLUSIONS: Consistent results from three distinct measures of area-level socioeconomic environment support the hypothesis that interventions to ameliorate neighborhood poverty or deprivation reduce mortality risk for US women with HIV. See video abstract at, http://links.lww.com/EDE/B863.

Effectiveness and Safety of Romiplostim Among Patients with Newly Diagnosed, Persistent and Chronic Immune Thrombocytopenia in European Clinical Practice.

INTRODUCTION: Romiplostim has been approved in Europe since 2009 to treat patients with chronic primary immune thrombocytopenia (ITP). Using real-world data from seven European countries, we measured the effectiveness and safety outcomes within 24 weeks following romiplostim initiation by duration of ITP: less than 3 months ("newly diagnosed"), 3-12 months ("persistent"), and more than 12 months ("chronic"). METHODS: Adults with ITP and ≥ 1 romiplostim administration between 2009 and 2012 were included. Endpoints included durable platelet response, median platelet count, rescue therapy, bleeding and adverse events. We used inverse probability of censoring weighted estimators to estimate cumulative risk of each outcome. There were 64 newly diagnosed, 50 persistent, and 226 chronic ITP patients at romiplostim initiation. RESULTS: Durable platelet response at 24 weeks ranged from 32% [confidence interval (CI): 18-46%] in newly diagnosed patients to 53% (CI 37-68%) in persistent patients. Median platelet count during follow-up ranged from 88 (CI 80-96) × 109/L in chronic patients to 131 (CI 102-160) × 109/L in newly diagnosed patients. CONCLUSION: Regardless of ITP duration, over half of patients discontinued concomitant ITP medications. Few adverse events were observed. Although only approved for chronic patients, estimates of the romiplostim treatment effect were similar across patients being managed in European clinical practice, regardless of ITP duration at romiplostim initiation.

Estimating the Effectiveness of Rotavirus Vaccine Schedules.

BACKGROUND: Important questions exist regarding the comparative effectiveness of alternative childhood vaccine schedules; however, optimal approaches to studying this complex issue are unclear. METHODS: We applied methods for studying dynamic treatment regimens to estimate the comparative effectiveness of different rotavirus vaccine (RV) schedules for preventing acute gastroenteritis-related emergency department (ED) visits or hospitalization. We studied the effectiveness of six separate protocols: one- and two-dose monovalent rotavirus vaccine (RV1); one-, two-, and three-dose pentavalent rotavirus vaccine (RV5); and no RV vaccine. We used data on all infants to estimate the counterfactual cumulative risk for each protocol. Infants were censored when vaccine receipt deviated from the protocol. Inverse probability of censoring-weighted estimation addressed potentially informative censoring by protocol deviations. A nonparametric group-based bootstrap procedure provided statistical inference. RESULTS: The method yielded similar 2-year effectiveness estimates for the full-series protocols; weighted risk difference estimates comparing unvaccinated children to those adherent to either full-series (two-dose RV1, three-dose RV5) corresponded to four fewer hospitalizations and 12 fewer ED visits over the 2-year period per 1,000 children. We observed dose-response relationships, such that additional doses further reduced risk of acute gastroenteritis. Under a theoretical intervention to fully vaccinate all children, the 2-year risk differences comparing full to observed adherence were 0.04% (95% CI = 0.03%, 0.05%) for hospitalizations and 0.17% (95% CI = 0.14%, 0.19%) for ED visits. CONCLUSIONS: The proposed approach can generate important evidence about the consequences of delaying or skipping vaccine doses, and the impact of interventions to improve vaccine schedule adherence.