Time-sensitive testing pressures and COVID-19 outcomes: are socioeconomic inequalities over the first year of the pandemic explained by selection bias?

Carter, Alice R; Clayton, Gemma L; Borges, M Carolina; Howe, Laura D; Hughes, Rachael A; Smith, George Davey; Lawlor, Deborah A; Tilling, Kate; Griffith, Gareth J

doi:10.1186/s12889-023-16767-5

Cited by 1 publication

(1 citation statement)

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“…Testing for SARS-CoV-2 was highly restricted during this period and so data on SARS-CoV-2 infection were missing for the majority of participants (over 98% of the study sample). Data on SARS-CoV-2 infection were suspected to be MNAR since testing among the majority of the UK population (i.e., non-healthcare workers) was mainly restricted to those who experienced symptoms of COVID-19 (63). Observed factors associated with the chance of being tested in UKB included having higher BMI, being a current smoker, having a pre-existing condition (such as asthma, diabetes, or hypertension), being female, and having a university degree or higher (60).…”

Section: Methodsmentioning

confidence: 99%

Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study

Kawabata,

Major-Smith,

Clayton

et al. 2024

Preprint

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Background: Bias from data missing not at random (MNAR) is a persistent concern in health-related research. A bias analysis quantitatively assesses how conclusions change under different assumptions about missingness using bias parameters which govern the magnitude and direction of the bias. Probabilistic bias analysis specifies a prior distribution for these parameters, explicitly incorporating available information and uncertainty about their true values. A Bayesian approach combines the prior distribution with the data's likelihood function whilst a Monte Carlo approach samples the bias parameters directly from the prior distribution. No study has compared a Monte Carlo approach to a fully Bayesian approach in the context of a bias analysis to MNAR missingness. Methods: We propose an accessible Monte Carlo probabilistic bias analysis which uses a well-known imputation method. We designed a simulation study based on a motivating example from the UK Biobank study, where a large proportion of the outcome was missing and missingness was suspected to be MNAR. We compared the performance of our Monte Carlo probabilistic bias analysis to a principled Bayesian probabilistic bias analysis, complete case analysis (CCA) and missing at random implementations of inverse probability weighting (IPW) and multiple imputation (MI). Results: Estimates of CCA, IPW and MI were substantially biased, with 95% confidence interval coverages of 7-64%. Including auxiliary variables (i.e., variables not included in the substantive analysis which are predictive of missingness and the missing data) in MI's imputation model amplified the bias due to assuming missing at random. With reasonably accurate and precise information about the bias parameter, the Monte Carlo probabilistic bias analysis performed as well as the fully Bayesian approach. However, when very limited information was provided about the bias parameter, only the Bayesian approach was able to eliminate most of the bias due to MNAR whilst the Monte Carlo approach performed no better than the CCA, IPW and MI. Conclusion: Our proposed Monte Carlo probabilistic bias analysis approach is easy to implement in standard software and is a viable alternative to a Bayesian approach. We caution careful consideration of choice of auxiliary variables when applying imputation where data may be MNAR.

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Section: Methodsmentioning

confidence: 99%