Purpose: Epidemiologists often think about the balance between study error and cost-efficiency in terms of study design and strategies to reduce random error. We less often consider cost-efficiencies in terms of dealing with systematic errors that arise within a study, such as in deciding how to measure study variables and misclassification implications.
Methods: Given the information used to inform a study size calculation, the expected study data can be simulated during study planning, and the impact of anticipated biases can be estimated using quantitative bias analysis. This would allow investigators and stakeholders to identify areas where better data collection through more valid instruments is critical and where additional investment will not yield strong validity benefits. This could promote better use of study resources and help increase investigators' chances of funding by demonstrating they have thought through biases and have a plan for mitigating the impact.
Results: We demonstrate how this would work with a practical example using the relationship between smoking during pregnancy as measured on birth certificates and incident breast cancer.
Conclusions: We show that although exposure sensitivity would likely be poor, spending more money to get a better smoking measure is unlikely to yield more valid estimates.
Keywords: Confounding; Grant planning; Information bias; Quantitative bias analysis; Selection bias.
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