# Ring et al. 2017 Serum creatinine spline fits and residuals

#### Caroline Ring

#### 2018-01-23

    library(httk)
    library(survey)
    library(data.table)
    library(ggplot2)
    all.reth <- levels(nhanes_mec_svy$variables[, ridreth1])
    all.gendr <- levels(nhanes_mec_svy$variables[, riagendr])

Plot serum creatinine vs. age with spline fits.

    par(mfcol=c(5,2),
        mar=c(2,2,2.5,2)+0.1,
        oma=c(4,4,4,0),
        mgp=c(1,1,0))

    for (gendr in all.gendr){
      for (r in all.reth){
        grsub <- subset(nhanes_mec_svy, riagendr==gendr & ridreth1==r & !is.na(ridexagm) & !is.na(loglbxscr))
        svyplot(loglbxscr~ridexagm, grsub, style="transparent",
                basecol="gray50", xlab=NA, ylab=NA, cex.axis=1.5)
        lines(sort(unique(grsub$variables[,ridexagm])), predict(spline_serumcreat[gender==gendr & reth==r,
                                                                   sc_spline[[1]]],
                                      sort(unique(grsub$variables[,ridexagm])))$y)
        title(paste(gendr, r), line=0.5, cex.main=1.5)
      }
    }
    title(xlab="Age, months",
          ylab="Log serum creatinine, log g/dL",
          outer=TRUE,
          line=2, cex.lab=2)
    title(main="Serum creatinine vs. age, with spline fits",
          outer=TRUE,
          line=1, cex.main=2)

Plot log serum creatinine residuals vs. age.

    par(mfcol=c(5,2),
        mar=c(2,2,2.5,2)+0.1,
        oma=c(4,4,4,0),
        mgp=c(1,1,0))

    for (gendr in all.gendr){
      for (r in all.reth){
        grsub <- subset(nhanes_mec_svy, riagendr==gendr & ridreth1==r & !is.na(ridexagm) & !is.na(loglbxscr))
        grsub <- update(grsub, loglbxscr_resid=loglbxscr-
                          predict(spline_serumcreat[gender==gendr &
                                                        reth==r,
                                                      sc_spline[[1]]],
                                  grsub$variables[,ridexagm])$y)
        svyplot(loglbxscr_resid~ridexagm, grsub, style="transparent",
                xlab=NA, ylab=NA, cex.axis=1.5)
        title(paste(gendr, r), line=0.5, cex.main=1.5)
        }
      }
    title(xlab="Age, months",
          ylab="Log serum creatinine residual, log g/dL",
          outer=TRUE,
          line=2, cex.lab=2)
    title(main = "Serum creatinine residuals vs. age",
          outer=TRUE, line=1, cex.main=2)

Make QQ plots of log serum creatinine residual quantiles vs. normal quantiles.

    par(mfcol=c(5,2),
        mar=c(2,2,2.5,2)+0.1,
        oma=c(4,4,4,0),
        mgp=c(1,1,0))

    for (gendr in all.gendr){
      for (r in all.reth){
        grsub <- subset(nhanes_mec_svy, riagendr==gendr & ridreth1==r & !is.na(ridexagm) & !is.na(loglbxscr))
        grsub <- update(grsub, logscresid=loglbxscr-
                          predict(spline_serumcreat[gender==gendr &
                                                        reth==r,
                                                      sc_spline[[1]]],
                                  grsub$variables[,ridexagm])$y)
    qprobs <- ppoints(n=length(grsub$prob))
    norm.q <- qnorm(p=qprobs)
    logscresid.q <- svyquantile(~logscresid,
                                design=grsub,
                                quantiles=qprobs)
    plot(y=logscresid.q,
         x=norm.q, #Plot weighted points
         type='p',
         col="gray70",
         xlab=NA, ylab=NA)
    #Add qqline: passing through first and third quartiles
    x <- qnorm(p=c(0.25, 0.75))
    y <- as.vector(svyquantile(~logscresid,
                               design=grsub,
                               quantiles=c(0.25, 0.75)))
    m <- diff(y)/diff(x)
    intercept <- -m*x[1]+y[1]
    abline(a=intercept, b=m)
    title(paste(gendr, r), line=0.5, cex.main=1.5)
    }
    }
    title(xlab='Normal quantiles',
         ylab='log SC resid quantiles',
         outer=TRUE,
         line=2,
         cex.lab=2)

Clearly, the log residuals aren’t normally distributed for most combinations of race and gender. For this reason, I decided to use a kernel density estimate of the residual distribution as a way to non-parametrically estimate the residual distribution, rather than assuming a particular theoretical parametric distribution.

# KDE for residuals

Here are the KDEs for the log serum creatinine residuals for each gender and race/ethnicity combination.

    par(mfcol=c(5,2),
        mar=c(2,2,2.5,2)+0.1,
        oma=c(4,4,4,0),
        mgp=c(1,1,0))

    for (gendr in all.gendr){
      for (r in all.reth){
         grsub <- subset(nhanes_mec_svy, riagendr==gendr & ridreth1==r & !is.na(ridexagm) & !is.na(loglbxscr))
        grsub <- update(grsub, logscresid=loglbxscr-
                          predict(spline_serumcreat[gender==gendr &
                                                        reth==r,
                                                      sc_spline[[1]]],
                                  grsub$variables[,ridexagm])$y)
         tmp.kde <- ks::kde(x=grsub$variables[, logscresid],
                       w=grsub$variables[, wtmec6yr])
        plot(tmp.kde, xlab=NA, ylab=NA, xlim=c(-1.72,2.7), drawpoints=TRUE, col.pt="black")
        title(paste(gendr, r), line=0.5, cex.main=1.5)
        }
      }
    title(xlab='Log serum creatinine residual',
         ylab='KDE density',
         outer=TRUE,
         line=2,
         cex.lab=2)