Validation to correct for outcome misclassification bias

Lanes, Stephan; Beachler, Daniel C.

doi:10.1002/pds.5601

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…Real-world data sources are complex, and the investigator must carefully consider whether the data on hand are sufficient to answer the research question. For example, a study that relies solely on claims data for outcome ascertainment may suffer from out-come misclassification bias (Lanes and Beachler 2023). This bias can be addressed through medical record validation for a random subset of patients, followed by quantitative bias analysis (Lanes and Beachler 2023).…”

Section: Discussionmentioning

confidence: 99%

“…As feasible, existing validation studies for the exposure and outcome should be referenced, or new validation efforts undertaken. The results of such validation studies can inform study estimates via quantitative bias analyses (Lanes and Beachler 2023). The study team may also consider biases arising from unmeasured confounding and plan quantitative bias analyses to explore how unmeasured confounding may impact estimates.…”

Section: Quality Control and Sensitivity Analyses (Step 8)mentioning

confidence: 99%

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A step-by-step guide to causal study design using real-world data

Hoffman,

Gangan,

Chen

et al. 2024

Health Serv Outcomes Res Method

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Due to the need for generalizable and rapidly delivered evidence to inform healthcare decision-making, real-world data have grown increasingly important to answer causal questions. However, causal inference using observational data poses numerous challenges, and relevant methodological literature is vast. We endeavored to identify underlying unifying themes of causal inference using real-world healthcare data and connect them into a single schema to aid in observational study design, and to demonstrate this schema using a previously published research example. A multidisciplinary team (epidemiology, biostatistics, health economics) reviewed the literature related to causal inference and observational data to identify key concepts. A visual guide to causal study design was developed to concisely and clearly illustrate how the concepts are conceptually related to one another. A case study was selected to demonstrate an application of the guide. An eight-step guide to causal study design was created, integrating essential concepts from the literature, anchored into conceptual groupings according to natural steps in the study design process. The steps include defining the causal research question and the estimand; creating a directed acyclic graph; identifying biases and design and analytic techniques to mitigate their effect, and techniques to examine the robustness of findings. The cardiovascular case study demonstrates the applicability of the steps to developing a research plan. This paper used an existing study to demonstrate the relevance of the guide. We encourage researchers to incorporate this guide at the study design stage in order to elevate the quality of future real-world evidence.

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Section: Discussionmentioning

confidence: 99%

Section: Quality Control and Sensitivity Analyses (Step 8)mentioning

confidence: 99%

A step-by-step guide to causal study design using real-world data

Hoffman,

Gangan,

Chen

et al. 2024

Health Serv Outcomes Res Method

View full text Add to dashboard Cite

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“…In comparison, the algorithm used in the present study was based on the algorithm developed in the Food and Drug Administration’s Mini-Sentinel study but included two steps: a screening algorithm to enable estimates of sensitivity and a predictive model algorithm. The screening algorithm had a PPV of 65% (95% CI, 60–71%) and a presumed sensitivity close to 100% ( 20 ). The performance characteristics for the predictive model algorithm (at the selected probability threshold) were a PPV of 94% (95% CI, 91–98%), sensitivity of 92%, and specificity of 89% ( 8 ).…”

Section: Discussionmentioning

confidence: 99%

Risk of Anaphylaxis Among New Users of GLP-1 Receptor Agonists: A Cohort Study

Anthony,

Aroda,

Parlett

et al. 2024

Diabetes Care

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OBJECTIVE To assess risk of anaphylaxis among patients with type 2 diabetes mellitus who are initiating therapy with a glucagon-like peptide-1 receptor agonist (GLP-1 RA), with a focus on those starting lixisenatide therapy. RESEARCH DESIGN AND METHODS A cohort study was conducted in three large, U.S. claims databases (2017–2021). Adult (aged ≥18 years) new users of a GLP-1 RA who had type 2 diabetes mellitus and ≥6 months enrollment in the database before GLP-1 RA initiation (start of follow-up) were included. GLP-1 RAs evaluated were lixisenatide, an insulin glargine/lixisenatide fixed-ratio combination (FRC), exenatide, liraglutide or insulin degludec/liraglutide FRC, dulaglutide, and semaglutide (injectable and oral). The first anaphylaxis event during follow-up was identified using a validated algorithm. Incidence rates (IRs) and 95% CIs were calculated within each medication cohort. The unadjusted IR ratio (IRR) comparing anaphylaxis rates in the lixisenatide cohort with all other GLP-1 RAs combined was analyzed post hoc. RESULTS There were 696,089 new users with 456,612 person-years of exposure to GLP-1 RAs. Baseline demographics, comorbidities, and use of other prescription medications in the 6 months before the index date were similar across medication cohorts. IRs (95% CIs) per 10,000 person-years were 1.0 (0.0–5.6) for lixisenatide, 6.0 (3.6–9.4) for exenatide, 5.1 (3.7–7.0) for liraglutide, 3.9 (3.1–4.8) for dulaglutide, and 3.6 (2.6–4.9) for semaglutide. The IRR (95% CI) for the anaphylaxis rate for the lixisenatide cohort compared with the pooled other GLP-1 RA cohort was 0.24 (0.01–1.35). CONCLUSIONS Anaphylaxis is rare with GLP-1 RAs. Lixisenatide is unlikely to confer higher risk of anaphylaxis than other GLP-1 RAs.

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“…Studies that use these algorithms should also consider quantitative bias analysis or other approaches to account for the influence of misclassification. 28,29 Our study was conducted using prescription data from EHR, and the algorithms tested may perform differently based on administrative dispensing data, though quantitative bias analysis suggested such misclassification would have little impact on algorithm performance. Similarly, because data were gathered from only three clinical institutions, the findings may not generalize to patients treated at other centers.…”

Section: Discussionmentioning

confidence: 99%

“…While the performance of refined algorithms was improved from that of the original algorithm, these algorithms were not completely accurate and were less accurate as proxies for stricter definitions of JIA flare. Studies that use these algorithms should also consider quantitative bias analysis or other approaches to account for the influence of misclassification 28,29 . Our study was conducted using prescription data from EHR, and the algorithms tested may perform differently based on administrative dispensing data, though quantitative bias analysis suggested such misclassification would have little impact on algorithm performance.…”

Section: Discussionmentioning

confidence: 99%

Validation of new medication use algorithms as proxies for worsening disease activity in patients with juvenile idiopathic arthritis

Saito,

Gabbeta,

Mulvihill

et al. 2024

Pharmacoepidemiology and Drug

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PurposeTo facilitate claims‐based research on populations with juvenile idiopathic arthritis (JIA), we sought to validate an algorithm of new medication use as a proxy for worsening JIA disease activity.MethodsUsing electronic health record data from three pediatric centers, we defined new JIA medication use as (re)initiation of disease‐modifying antirheumatic drugs or glucocorticoids (oral or intra‐articular). Data were collected from 201 randomly selected subjects with (101) or without (100) new medication use. We assessed the positive predictive value (PPV) and negative predictive value (NPV) based on a reference standard of documented worsening of JIA disease activity. The algorithm was refined to optimize test characteristics.ResultsOverall, the medication‐based algorithm had suboptimal performance in representing worsening JIA disease activity (PPV 69.3%, NPV 77.1%). However, algorithm performance improved for definitions specifying longer times after JIA diagnosis (≥1‐year post‐diagnosis: PPV 82.9%, NPV 80.0%) or after initiation of prior JIA treatment (≥1‐year post‐treatment: PPV 89.7%, NPV 80.0%).ConclusionAn algorithm for new JIA medication use appears to be a reasonable proxy for worsening JIA disease activity, particularly when specifying new use ≥1 year since initiating a prior JIA medication. This algorithm will be valuable for conducting research on JIA populations within administrative claims databases.

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Validation to correct for outcome misclassification bias

Cited by 8 publications

References 25 publications

A step-by-step guide to causal study design using real-world data

A step-by-step guide to causal study design using real-world data

Risk of Anaphylaxis Among New Users of GLP-1 Receptor Agonists: A Cohort Study

Validation of new medication use algorithms as proxies for worsening disease activity in patients with juvenile idiopathic arthritis

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