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Even if these assumptions hold, one would nonetheless fail to estimate direct effects using standard methods, for example, stratification or regression modeling, when the treatment influences confounding factors. For such situations, the sequential g\u2010estimation method for structural nested mean models has been developed for estimating controlled direct effects in point\u2010treatment situations. In this study, we demonstrate that this method can be applied to longitudinal data with time\u2010varying treatments and repeatedly measured intermediate variables. We sequentially estimate the parameters in two structural nested mean models: one for a repeatedly measured intermediate and the other one for direct effects of a time\u2010varying treatment. The method was applied to data from a large primary prevention trial for coronary events, in which pravastatin was used to lower the cholesterol levels in patients with moderate hypercholesterolemia. Copyright \u00a9 2014 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/sim.6162","type":"journal-article","created":{"date-parts":[[2014,4,3]],"date-time":"2014-04-03T06:18:41Z","timestamp":1396505921000},"page":"3214-3228","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Estimation of controlled direct effects in time\u2010varying treatments using structural nested mean models: application to a primary prevention trial for coronary events with pravastatin"],"prefix":"10.1002","volume":"33","author":[{"given":"Tomohiro","family":"Shinozaki","sequence":"first","affiliation":[{"name":"Department of Biostatistics School of Public Health, The University of Tokyo Tokyo 113\u20100033 Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutaka","family":"Matsuyama","sequence":"additional","affiliation":[{"name":"Department of Biostatistics School of Public Health, The University of Tokyo Tokyo 113\u20100033 Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasuo","family":"Ohashi","sequence":"additional","affiliation":[{"name":"Department of Biostatistics School of Public Health, The University of Tokyo Tokyo 113\u20100033 Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2014,4,3]]},"reference":[{"key":"e_1_2_12_2_1","first-page":"411","volume-title":"Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence","author":"Pearl J","year":"2001"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.0000133317.49796.0E"},{"key":"e_1_2_12_4_1","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1016\/S0195-668X(03)00347-6","article-title":"For the third joint task force of european and other societies on cardiovascular disease prevention in clinical practice. 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