@article{aa6e33b061ef47deb2696a357aea8349,
title = "Analyzing evidence-based falls prevention data with significant missing information using variable selection after multiple imputation",
abstract = "Falls are the leading cause of fatal and non-fatal injuries among older adults. Evidence-based fall prevention programs are delivered nationwide, largely supported by funding from the Administration for Community Living (ACL), to mitigate fall-related risk. This study utilizes data from 39 ACL grantees in 22 states from 2014 to 2017. The large amount of missing values for falls efficacy in this national database may lead to potentially biased statistical results and make it challenging to implement reliable variable selection. Multiple imputation is used to deal with missing values. To obtain a consistent result of variable selection in multiply-imputed datasets, multiple imputation-stepwise regression (MI-stepwise) and multiple imputation-least absolute shrinkage and selection operator (MI-LASSO) methods are used. To compare the performances of MI-stepwise and MI-LASSO, simulation studies were conducted. In particular, we extended prior work by considering several circumstances not covered in previous studies, including an extensive investigation of data with different signal-to-noise ratios and various missing data patterns across predictors, as well as a data structure that allowed the missingness mechanism to be missing not at random (MNAR). In addition, we evaluated the performance of MI-LASSO method with varying tuning parameters to address the overselection issue in cross-validation (CV)-based LASSO.",
keywords = "Multiple imputation, Rubin's rules, data simulation, fall prevention, falls efficacy, group LASSO penalty, stepwise regression, variable selection",
author = "Yujia Cheng and Yang Li and {Lee Smith}, Matthew and Changwei Li and Ye Shen",
note = "Funding Information: Evidence-based fall prevention programs [,] were delivered nationwide as part of the Patient Protection & Affordable Care Act (ACA) to help older adults reduce falls and fall-related risks. The ongoing implementation and dissemination of these programs are supported by grants from ACL and technical assistance from the National Council on Aging (NCOA)'s National Falls Prevention Resource Center []. Eight evidence-based fall prevention programs are included in this initiative, which includes data from 39 grantees spanning 22 states from 2014 to 2017 []. Using a national data repository [], participants' information was collected using surveys before (pre-intervention) and immediately after (post-intervention) the intervention. Participant attendance details were recorded by attendance log. Grantees reported data about the program and workshops delivered, which were uploaded in the repository. Generally, in terms of outcome measures, four aspects of participant data are collected: (a) physical improvement; (b) mental improvement; (c) program influence on daily activities and environment; and (d) comments of the program (collected post-intervention only). Funding Information: This work was supported by National Natural Science Foundation of China[71771211]NCOA, Lewin Subcontract Agreement[TLG14007-5176.20]ACL, Prevention and Public Health Fund-NCOA, National Falls Prevention Resource Center Cooperative Agreement Grant[90FP0023], National Bureau of Statistics of China Research Fund (2019LD07). Dr. Y Li is supported by Platform of Public Health & Disease Control and Prevention, Major Innovation & Planning Interdisciplinary Platform for the “Double-First Class” Initiative, Renmin University of China. The Administration for Community Living/Administration on Aging (ACL/AoA) is the primary funding source for this national study. The findings, conclusions and opinions expressed do not necessarily represent official Administration for Community Living/Administration on Aging policy. The National Council on Aging (NCOA) served as the Technical Assistance Resource Center for this initiative and collected data on program participation from grantees. Publisher Copyright: {\textcopyright} 2021 Informa UK Limited, trading as Taylor & Francis Group.",
year = "2023",
doi = "10.1080/02664763.2021.1985090",
language = "English (US)",
volume = "50",
pages = "724--743",
journal = "Journal of Applied Statistics",
issn = "0266-4763",
publisher = "Routledge",
number = "3",
}