class: center, middle, inverse, title-slide .title[ # Project Progress Report ] .author[ ### Lin Yu ] .date[ ### 2022-08-08 ] --- background-image: url(data:image/png;base64,#https://www.clipartkey.com/mpngs/m/193-1933360_meeting-clipart-meeting-room-animated-meeting.png) background-position: 50% 50% class: center, bottom, inverse #
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--- class: center,inverse, middle # Background --- class: middle - **研究背景/假设**:重医三院2017-2021年数据显示,因哮喘急性加重住院逐年减少;假设AECOPD住院率的下降与哮喘使用控制性药物增多,以及规范治疗增加有关; -- - **研究目的**: 1. 描述门诊哮喘患者数量自2017年至2021年的变化趋势; 2. 探究使用哮喘药物与哮喘急性加重住院率之间的相关性。 --- class: center,inverse, middle # Timeline ---
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![](data:image/png;base64,#asthma_timeline.png) --- class: inverse # Background -- ### **Title**: Dynamic model updating (DMU) approach for statistical learning model building with missing data
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-- ### **Publication info**: - ### Year: 2021 - ### Author(s): Rahi Jain and Wei Xu, Dalla Lana School of Public Health, University of Toronto - ### Journal name: BMC Bioinformatics ISSN 1471-2105 (IF: 3.169) - ### Article type: methodology article --- # Some Techniques - ### Complete Cases Analysis (CCA) - Easy to conduct, but provides biased estimates in cases when data is not missing completely at random (MCAR) - ### Imputation approach - Single imputation: - mean, median, etc.:underestimates the variance of the predictor and ignores the relationship between the predictors - regression: y = combination of linear other predictors,relies on the linear relationship - Multiple imputation: PMM handles MCAR and MAR types of missing data. It considers uncertainty (variance) (note: MICE package in R, for performing MI), in cases when missing values are in the tail of a distribution, predictive mean matching (PMM) have biased imputation. --- class: inverse # Rationale -- ### Data collection is not simultaneous -- ### Instead of replacing the missing values with predicted value to compete the dataset -- ### Dynamic model updating(DMU) approach focuses on building the model on incomplete info rather than on imputed info. --- class: center,inverse, middle # How --- class: middle ![](data:image/png;base64,#DMU_figure1.png) --- -- .pull-left[ ![](data:image/png;base64,#DMU_figure21.png) ] -- .pull-right[ ![](data:image/png;base64,#DMU_figure22.png) - #### Hierarchical clustering: it partitions the samples based on the distance of a sample with other samples. - #### The similar samples have lower distance among them as compared to dissimilar samples ] --- # Hierarchical clustering ![](data:image/png;base64,#hierarchical_clustering.png)