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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

"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. [ABSTRACT FROM AUTHOR]

The authors offer a response to the article "Invited Commentary: Combining Information to Answer Epidemiologic Questions about a Target Population" by I. J. Dahabreh. They talk about the data combined in fusion study designs, case on estimating a misclassified and transported proportion, and introduction of the oncept of learning functions.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]