Quantitative bias analysis for study and grant planning

Fox, Matthew P.; Lash, Timothy L.

doi:10.1016/j.annepidem.2020.01.013

Cited by 14 publications

(5 citation statements)

References 17 publications

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“…These alternative methods can be used prior to data collection, to optimize resource allocation by identifying where additional data would be most impactful in minimizing bias. 28,29 One can also perform the exercise of evaluating bias strengths that would lead to a null effect estimate or an effect estimate whose confidence interval includes the null, as in the E-value. 30 By understanding the bias strengths that would 'explain away' the observed effect, one gets a better idea of how likely the effect is to be nonnull.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous adjustment of uncontrolled confounding, selection bias and misclassification in multiple-bias modelling

Brendel¹,

Torres²,

Arah

2023

International Journal of Epidemiology

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Background Adjusting for multiple biases usually involves adjusting for one bias at a time, with careful attention to the order in which these biases are adjusted. A novel, alternative approach to multiple-bias adjustment involves the simultaneous adjustment of all biases via imputation and/or regression weighting. The imputed value or weight corresponds to the probability of the missing data and serves to ‘reconstruct’ the unbiased data that would be observed based on the provided assumptions of the degree of bias. Methods We motivate and describe the steps necessary to implement this method. We also demonstrate the validity of this method through a simulation study with an exposure-outcome relationship that is biased by uncontrolled confounding, exposure misclassification, and selection bias. Results The study revealed that a non-biased effect estimate can be obtained when correct bias parameters are applied. It also found that incorrect specification of every bias parameter by +/-25% still produced an effect estimate with less bias than the observed, biased effect. Conclusions Simultaneous multi-bias analysis is a useful way of investigating and understanding how multiple sources of bias may affect naive effect estimates. This new method can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies.

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Section: Discussionmentioning

confidence: 99%

Simultaneous adjustment of uncontrolled confounding, selection bias and misclassification in multiple-bias modelling

Brendel¹,

Torres²,

Arah

2023

International Journal of Epidemiology

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“…It may also show that certain improvements to study design are futile; if the amount of an unavoidable bias greatly attenuates the anticipated risk ratio estimate, investing resources into reducing another type of bias may not be worth it. 37,38 There are a number of caveats and limitations to this approach. Although the calculations involved in our approach are simple, the entire process of assessing bias should not be.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Multiple-bias Sensitivity Analysis Using Bounds

2021

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Confounding, selection bias, and measurement error are well-known sources of bias in epidemiologic research. Methods for assessing these biases have their own limitations. Many quantitative sensitivity analysis approaches consider each type of bias individually, although more complex approaches are harder to implement or require numerous assumptions. By failing to consider multiple biases at once, researchers can underestimate-or overestimate-their joint impact. We show that it is possible to bound the total composite bias owing to these three sources and to use that bound to assess the sensitivity of a risk ratio to any combination of these biases. We derive bounds for the total composite bias under a variety of scenarios, providing researchers with tools to assess their total potential impact. We apply this technique to a study where unmeasured confounding and selection bias are both concerns and to another study in which possible differential exposure misclassification and confounding are concerns. The approach we describe, though conservative, is easier to implement and makes simpler assumptions than quantitative bias analysis. We provide R functions to aid implementation.

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“…It may also show that certain improvements to study design are futile; if the amount of an unavoidable bias greatly attenuates the anticipated risk ratio estimate, investing resources into reducing another type of bias may not be worth it. 39,40 The multi-bias E-value presents an even more straightforward alternative: calculate the joint minimum of the bias parameters that would be required to "explain away" an observed risk ratio.…”

Section: Discussionmentioning

confidence: 99%

Multiple-bias sensitivity analysis using bounds

Smith,

Mathur,

VanderWeele

2020

Preprint

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Unmeasured confounding, selection bias, and measurement error are well-known sources of bias in epidemiologic research. Methods for assessing these biases have their own limitations. Many quantitative sensitivity analysis approaches consider each type of bias individually, while more complex approaches are harder to implement or require numerous assumptions. By failing to consider multiple biases at once, researchers can underestimate -or overestimate -their joint impact. We show that it is possible to bound the total composite bias due to these three sources, and to use that bound to assess the sensitivity of a risk ratio to any combination of these biases. We derive bounds for the total composite bias under a variety of scenarios, providing researchers with tools to assess their total potential impact. We apply this technique to a study where unmeasured confounding and selection bias are both concerns, and to another study in which possible differential exposure misclassification and unmeasured confounding are concerns. We also show that a "multi-bias E-value"can describe the minimal strength of joint bias-parameter association necessary for an observed risk ratio to be compatible with a null causal effect (or with other pre-specified effect sizes). This may provide intuition about the relative impacts of each type of bias. The approach we describe is easy to implement with minimal assumptions, and we provide R functions to do so.

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Quantitative bias analysis for study and grant planning

Cited by 14 publications

References 17 publications

Simultaneous adjustment of uncontrolled confounding, selection bias and misclassification in multiple-bias modelling

Simultaneous adjustment of uncontrolled confounding, selection bias and misclassification in multiple-bias modelling

Multiple-bias Sensitivity Analysis Using Bounds

Multiple-bias sensitivity analysis using bounds

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