2021
DOI: 10.1016/j.jclinepi.2021.02.019
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Missing at random assumption made more plausible: evidence from the 1958 British birth cohort

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Cited by 80 publications
(89 citation statements)
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“…However, it must be acknowledged that confounding-adjusted analyses using complete cases were highly imprecise due to missing data in the exposures, outcome and confounding factors. However, these estimates are likely to be biased, as they are based on the sample that is highly selective due to attrition [ 68 ].…”
Section: Discussionmentioning
confidence: 99%
“…However, it must be acknowledged that confounding-adjusted analyses using complete cases were highly imprecise due to missing data in the exposures, outcome and confounding factors. However, these estimates are likely to be biased, as they are based on the sample that is highly selective due to attrition [ 68 ].…”
Section: Discussionmentioning
confidence: 99%
“…Third, it is possible that our examination of moderation by residential mobility could be under-powered through dichotomizing moving status, but this will need to be tested in other data sets with more consistent collection of mobility data over time. Fourth, as in any longitudinal study, attrition occurred across both samples [39,40]. However, attrition bias is most likely to create underestimates of neighborhood effects on health over time, given that cohort members residing in the most deprived neighborhoods, with the highest BMIs, would be the most likely to leave this study; both through non-response and mortality.…”
Section: Discussionmentioning
confidence: 99%
“…Despite being embedded in long standing studies, surveys during the pandemic were selective. While we corrected for this using weights derived for each study, bias due to selective non-response cannot be excluded (25). Similarly, bias due to unmeasured confounding cannot be ruled out and could be influential considering the small magnitude of the risk ratios observed.…”
Section: Discussionmentioning
confidence: 99%