Which approach helps minimize loss to follow-up?

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Multiple Choice

Which approach helps minimize loss to follow-up?

Explanation:
Minimizing loss to follow-up is about keeping data complete for the period you’re studying. Excluding patients who are unlikely to remain in the database directly reduces the chance of missing follow-up data, so the dataset you analyze stays more complete. This approach makes attrition less of a problem in the analysis. However, it does come with trade-offs: it can introduce selection bias and reduce generalizability because the study population is no longer fully representative of all patients initially eligible. The other options don’t directly prevent missing follow-up data. Relying on self-report only can introduce reporting errors and may still miss information; a cross-sectional design isn’t about follow-up at all, so it doesn’t address longitudinal attrition; including everyone regardless of follow-up can leave you with substantial missing data and biased results when you try to analyze outcomes over time.

Minimizing loss to follow-up is about keeping data complete for the period you’re studying. Excluding patients who are unlikely to remain in the database directly reduces the chance of missing follow-up data, so the dataset you analyze stays more complete. This approach makes attrition less of a problem in the analysis.

However, it does come with trade-offs: it can introduce selection bias and reduce generalizability because the study population is no longer fully representative of all patients initially eligible.

The other options don’t directly prevent missing follow-up data. Relying on self-report only can introduce reporting errors and may still miss information; a cross-sectional design isn’t about follow-up at all, so it doesn’t address longitudinal attrition; including everyone regardless of follow-up can leave you with substantial missing data and biased results when you try to analyze outcomes over time.

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