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Statistical methods for evaluating the correlation between timeline follow-back data and daily process data with applications to research on alcohol and marijuana use

机译：用于评估时间线跟踪数据与日常过程数据之间的相关性的统计方法，并使用应用程序研究酒精和大麻使用

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Wanjun Liu; Runze Li; Marc A. Zimmerman; Maureen A. Walton; Rebecca M. Cunningham; Anne Buu;
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1. Statistical methods for evaluating the correlation between timeline follow-back data and daily process data with applications to research on alcohol and marijuana use [J] . Liu Wanjun, Li Runze, Zimmerman Marc A., Addictive behaviors . 2019,第期

机译：用于评估时间线跟踪数据与日常流程数据之间的相关性的统计方法，并使用应用程序研究酒精和大麻使用
2. How good is fine-grained Timeline Follow-back data? Comparing 30-day TLFB and repeated 7-day TLFB alcohol consumption reports on the person and daily level. [J] . Hoeppner BB, Stout RL, Jackson KM, Addictive behaviors . 2010,第12期

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6. How good is fine-grained Timeline Follow-back data? Comparing 30-day TLFB and repeated 7-day TLFB alcohol consumption reports on the person and daily level [O] . Bettina B. Hoeppner, Robert L. Stout, Kristina M. Jackson, -1

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Statistical methods for evaluating the correlation between timeline follow-back data and daily process data with applications to research on alcohol and marijuana use

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