88 lines
3.0 KiB
Plaintext
88 lines
3.0 KiB
Plaintext
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% Usage: knitr an
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\documentclass{article}
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\usepackage{relsize,setspace} % used by latex(describe( ))
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\usepackage{needspace}
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\usepackage{longtable,epic} % used by print(..., latex=TRUE)
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\usepackage{url} % used in bibliography
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\usepackage[superscript,nomove]{cite} % use if \cite is used and superscripts wanted
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% Remove nomove if you want superscripts after punctuation in citations
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\usepackage{lscape} % for landscape mode tables
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\textwidth 6.75in % set dimensions before fancyhdr
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\textheight 9.25in
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\topmargin -.875in
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\oddsidemargin -.125in
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\evensidemargin -.125in
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\usepackage{fancyhdr} % this and next line are for fancy headers/footers
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\pagestyle{fancy}
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\newcommand{\bc}{\begin{center}} % abbreviate
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\newcommand{\ec}{\end{center}}
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\newcommand{\code}[1]{{\smaller\texttt{#1}}}
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\newcommand{\R}{{\normalfont\textsf{R}}{}}
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\newcommand{\vr}[1]{\texttt{#1}}
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\newcommand{\mc}[2]{\multicolumn{#1}{c}{#2}}
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\title{Analysis of Race and Severe Aortic Stenosis}
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\author{Frank Harrell\\\smaller Department of Biostatistics\\\smaller Vanderbilt University School of Medicine}
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\begin{document}
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\maketitle
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\tableofcontents
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<<echo=FALSE>>=
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require(rms)
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@
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\section{Univariable Descriptive Statistics}
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<<describe,results='asis'>>=
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Load(ras)
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ras <-
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within(ras, {
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label(age) <- 'Age'
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diabetes <- ifelse(diabetes.after.review=='No', 0,
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ifelse(diabetes.after.review=='Yes', 1,
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ifelse(diabetes.search=='No', 0,
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ifelse(diabetes.search=='Yes', 1,
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NA))))
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htn <- ifelse(htn.after.review=='No', 0,
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ifelse(htn.after.review=='Yes', 1,
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ifelse(htn.search=='No', 0,
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ifelse(htn.search=='Yes', 1,
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NA))))
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dialysis <- ifelse(dialysis.after.review=='No', 0,
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ifelse(dialysis.after.review=='Yes', 1,
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ifelse(dialysis.search=='No', 0,
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NA)))
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cad <- ifelse(cad.after.review=='No', 0,
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ifelse(cad.after.review=='Yes', 1,
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ifelse(cad.search=='No', 0,
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ifelse(cad.search=='Yes', 1,
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NA))))
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gender <- factor(ifelse(gender=='Female', 'Female',
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ifelse(gender=='Male', 'Male',
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NA)))
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})
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latex(describe(ras), file='')
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@
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\section{Missing Data}
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Let's look at patterns of missing values, especially which variables
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are missing on the same patients.
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<<naclus,top=2>>=
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r <- subset(ras, select=c(gender,race,age,bmi,ldl,diabetes,htn,dialysis,
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cad,statin,sasr,sas.etiology,creatinine))
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dd <- datadist(r)
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n <- naclus(subset(r, select=-sas.etiology))
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naplot(n, which='na per obs')
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@
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<<naplot,top=2,rt=1,ps=8>>=
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options(datadist='dd')
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naplot(n, which='mean na')
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@
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<<naplot2>>=
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plot(n)
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@
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\end{document}
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