Abstract
Background: Failure to appropriately account for unmeasured confounding may lead to erroneous conclusions. Quantitative bias analysis (QBA) can be used to quantify the potential impact of unmeasured confounding or how much unmeasured confounding would be needed to change a study’s conclusions. Currently, QBA methods are not routinely implemented, partly due to a lack of knowledge about accessible software. Also, comparisons of QBA methods have focussed on analyses with a binary outcome.
Methods: We conducted a systematic review of the latest developments in QBA software published between 2011 to 2021. Our inclusion criteria were software that did not require adaption (i.e., code changes) before application, was still available in 2022, and accompanied by documentation. Key properties of each software tool were identified. We provide a detailed description of programs applicable for a linear regression analysis, illustrate their application using two data examples and provide code to assist researchers in future use of these programs.
Results: Our review identified 21 programs with 62% created post 2016. All are implementations of a deterministic QBA with 81% available in the free software R. There are programs applicable when the analysis of interest is a regression of binary, continuous or survival outcomes, and for matched and mediation analyses. We identified five programs implementing differing QBAs for a continuous outcome: treatSens, causalsens, sensemakr, EValue, and konfound. When applied to our illustrative examples, causalsens incorrectly indicated sensitivity to unmeasured confounding whereas the other four programs indicated robustness. sensemakr performs the most detailed QBA and includes a benchmarking feature for multiple unmeasured confounders.
Conclusions: Software is now available to implement a QBA for a range of different analyses. However, the diversity of methods, even for the same analysis of interest, presents challenges to their widespread uptake. Provision of detailed QBA guidelines would be highly beneficial.
Methods: We conducted a systematic review of the latest developments in QBA software published between 2011 to 2021. Our inclusion criteria were software that did not require adaption (i.e., code changes) before application, was still available in 2022, and accompanied by documentation. Key properties of each software tool were identified. We provide a detailed description of programs applicable for a linear regression analysis, illustrate their application using two data examples and provide code to assist researchers in future use of these programs.
Results: Our review identified 21 programs with 62% created post 2016. All are implementations of a deterministic QBA with 81% available in the free software R. There are programs applicable when the analysis of interest is a regression of binary, continuous or survival outcomes, and for matched and mediation analyses. We identified five programs implementing differing QBAs for a continuous outcome: treatSens, causalsens, sensemakr, EValue, and konfound. When applied to our illustrative examples, causalsens incorrectly indicated sensitivity to unmeasured confounding whereas the other four programs indicated robustness. sensemakr performs the most detailed QBA and includes a benchmarking feature for multiple unmeasured confounders.
Conclusions: Software is now available to implement a QBA for a range of different analyses. However, the diversity of methods, even for the same analysis of interest, presents challenges to their widespread uptake. Provision of detailed QBA guidelines would be highly beneficial.
| Original language | English |
|---|---|
| Article number | 111 |
| Journal | BMC Medical Research Methodology |
| Volume | 23 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 4 May 2023 |
Bibliographical note
Funding Information:RAH and EK are supported by a Sir Henry Dale Fellowship that is jointly funded by the Wellcome Trust and the Royal Society (grant 215408/Z/19/Z), and KT works in the MRC Integrative Epidemiology Unit, which is supported by the University of Bristol and the Medical Research Council (grants MC_UU_00011/3).
Funding Information:
We thank the study executives of NHANES, and Dr. P. C. Elwood (MRC Epidemiology Unit, South Wales) and Prof. Y. Ben-Shlomo (University of Bristol) for permitting access to the BCG study data.
Publisher Copyright:
© 2023, The Author(s).