臨床大數(shù)據(jù)研究系列文獻(xiàn)分享第5篇，由浙江大學(xué)章仲恒老師撰寫的臨床大數(shù)據(jù)系列專欄文章發(fā)表在 Annals of Translational雜志，這篇文章主要介紹的是介紹Logistic回歸模型的構(gòu)建策略软驰。這里只做學(xué)習(xí)交流锭亏，版權(quán)歸原作者所有。
摘要
Logistic回歸是解決醫(yī)學(xué)文獻(xiàn)中混雜因素的最常用模型之一碑隆。本文介紹了如何使用R執(zhí)行有目的的選擇模型構(gòu)建策略。作者著重于介紹使用似然比檢驗(yàn)來查看刪除變量是否會(huì)對(duì)模型擬合產(chǎn)生重大影響劫狠。還應(yīng)檢查已刪除的變量，以確定它是否對(duì)剩余協(xié)變量的重要調(diào)整懦砂。應(yīng)檢查交互作用罚随，以弄清協(xié)變量之間的復(fù)雜關(guān)系及其對(duì)響應(yīng)變量的協(xié)同作用潮改。應(yīng)該檢查模型的擬合優(yōu)度 goodness-of-fit（GOF）。換句話說脏答，擬合模型如何反映真實(shí)數(shù)據(jù)趾疚。 Hosmer-Lemeshow GOF檢驗(yàn)是用于Logistic回歸模型的最廣泛的檢驗(yàn)。
介紹
Logistic回歸模型是研究變量對(duì)醫(yī)學(xué)文獻(xiàn)中二項(xiàng)式結(jié)果的獨(dú)立影響的最廣泛使用的模型之一以蕴。但是糙麦，許多研究并未明確提出模型建立策略，從而損害了結(jié)果的可靠性和可重復(fù)性丛肮。文獻(xiàn)中報(bào)道了多種模型構(gòu)建策略赡磅，例如有目的地選擇變量，逐步選擇和最佳子集宝与。但是焚廊，究竟哪一種方法好還沒有被證明，也不得而知习劫，模型構(gòu)建策略是“部分科學(xué)咆瘟，部分統(tǒng)計(jì)方法以及部分經(jīng)驗(yàn)和常識(shí)”。模型構(gòu)建的原理是選擇盡可能少的變量诽里，但是模型（簡約模型）仍然反映了數(shù)據(jù)的真實(shí)結(jié)果袒餐。在本文中，作者介紹了如何在R中執(zhí)行有目的的選擇谤狡。變量選擇是模型構(gòu)建的第一步灸眼。其他步驟將在后續(xù)文章中介紹。
附上原文
參考文獻(xiàn)
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