{'fraction_non_alphanumeric': 0.01267605633802817, 'fraction_numerical': 0.0, 'mean_word_length': 5.548387096774194, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A comparison of different methods for handling measurements affected by medication use

In epidemiological research it is common to encounter measurements affected by medication use, such as blood pressure lowered by antihypertensive drugs. When one is interested in the relation between the variables not affected by medication, ignoring medication use can cause bias. Several methods have been proposed, but the problem is often ignored or handled with generic methods, such as excluding individuals on medication or adjusting for medication use in the analysis. This study aimed to investigate methods for handling measurements affected by medication use when one is interested in the relation between the unaffected variables and to provide guidance for how to optimally handle the problem. We focused on linear regression and distinguish between the situation where the affected measurement is an exposure, confounder or outcome. In the Netherlands Epidemiology of Obesity study and in several simulated settings, we compared generic and more advanced methods, such as substituting or adding a fixed value to the treated values, regression calibration, censored normal regression, Heckmans treatment model and multiple imputation methods. We found that often-used methods such as adjusting for medication use could result in substantial bias and that methods for handling medication use should be chosen cautiously.