Quantitative bias analysis in practice: review of software for regression with unmeasured confounding

Kawabata, Emily; Tilling, Kate; Hughes, Rachael A.

doi:10.1186/s12874-023-01906-8

Cited by 5 publications

(1 citation statement)

References 63 publications

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“…Further, for a recent panel session at the ISPOR-US annual conference (Boston, USA; May 2023) one of the co-authors (SD) had identified over 20 NICE HTA submissions where tipping point analyses were applied to individual RCTs. Tipping point analysis is also included as a main function in over 21 software packages for QBA [ 19 ].…”

Section: Tipping Point Analysis Of Missing Covariate Datamentioning

confidence: 99%

Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers

Thorlund,

Duffield,

Popat

et al. 2024

J. Comp. Eff. Res.

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Development of medicines in rare oncologic patient populations are growing, but well-powered randomized controlled trials are typically extremely challenging or unethical to conduct in such settings. External control arms using real-world data are increasingly used to supplement clinical trial evidence where no or little control arm data exists. The construction of an external control arm should always aim to match the population, treatment settings and outcome measurements of the corresponding treatment arm. Yet, external real-world data is typically fraught with limitations including missing data, measurement error and the potential for unmeasured confounding given a nonrandomized comparison. Quantitative bias analysis (QBA) comprises a collection of approaches for modelling the magnitude of systematic errors in data which cannot be addressed with conventional statistical adjustment. Their applications can range from simple deterministic equations to complex hierarchical models. QBA applied to external control arm represent an opportunity for evaluating the validity of the corresponding comparative efficacy estimates. We provide a brief overview of available QBA approaches and explore their application in practice. Using a motivating example of a comparison between pralsetinib single-arm trial data versus pembrolizumab alone or combined with chemotherapy real-world data for RET fusion-positive advanced non-small cell lung cancer (aNSCLC) patients (1–2% among all NSCLC), we illustrate how QBA can be applied to external control arms. We illustrate how QBA is used to ascertain robustness of results despite a large proportion of missing data on baseline ECOG performance status and suspicion of unknown confounding. The robustness of findings is illustrated by showing that no meaningful change to the comparative effect was observed across several ‘tipping-point’ scenario analyses, and by showing that suspicion of unknown confounding was ruled out by use of E-values. Full R code is also provided.

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Section: Tipping Point Analysis Of Missing Covariate Datamentioning

confidence: 99%

Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers

Thorlund,

Duffield,

Popat

et al. 2024

J. Comp. Eff. Res.

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An introduction to causal mediation analysis

Qin

2024

Asia Pacific Educ. Rev.

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Causal mediation analysis has gained increasing attention in recent years. This article guides empirical researchers through the concepts and challenges of causal mediation analysis. I first clarify the difference between traditional and causal mediation analysis and highlight the importance of adjusting for the treatment-by-mediator interaction and confounders of the treatment–mediator, treatment–outcome, and mediator–outcome relationships. I then introduce the definition of causal mediation effects under the potential outcomes framework and different methods for the identification and estimation of the effects. After that, I highlight the importance of conducting a sensitivity analysis to assess the sensitivity of analysis results to potential unmeasured confounding. I also list various statistical software that can conduct causal mediation analysis and sensitivity analysis and provide suggestions for writing a causal mediation analysis paper. Finally, I briefly introduce some extensions that I made with my colleagues, including power analysis, multisite causal mediation analysis, causal moderated mediation analysis, and relaxing the assumption of no post-treatment confounding.

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The mockery that confounds better treatment of confounding in epidemiology: The change in estimate fallacy

Burstyn

2024

Global Epidemiology

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Quantitative bias analysis in practice: review of software for regression with unmeasured confounding

Cited by 5 publications

References 63 publications

Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers

Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers

An introduction to causal mediation analysis

The mockery that confounds better treatment of confounding in epidemiology: The change in estimate fallacy

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