# OME

##### Tests of Auditory Perception in Children with OME

Experiments were performed on children on their ability to differentiate a signal in broad-band noise. The noise was played from a pair of speakers and a signal was added to just one channel; the subject had to turn his/her head to the channel with the added signal. The signal was either coherent (the amplitude of the noise was increased for a period) or incoherent (independent noise was added for the same period to form the same increase in power).

The threshold used in the original analysis was the stimulus loudness needs to get 75% correct responses. Some of the children had suffered from otitis media with effusion (OME).

- Keywords
- datasets

##### Usage

`OME`

##### source

Sarah Hogan, Dept of Physiology, University of Oxford, via Dept of Statistics Consulting Service

##### Background

The experiment was to study otitis media with effusion (OME), a very
common childhood condition where the middle ear space, which is
normally air-filled, becomes congested by a fluid. There is a
concomitant fluctuating, conductive hearing loss which can result in
various language, cognitive and social deficits. The term

Some years ago, it was found that children of 7--8 years with a history of significant OME had significantly worse binaural hearing than children without such a history, despite having equivalent sensitivity. The question remained as to whether it was the timing, the duration, or the degree of severity of the otitis media episodes during critical periods, which affected later binaural hearing. In an attempt to begin to answer this question, 95 children were monitored for the presence of effusion every month since birth. On the basis of OME experience in their first two years, the test population was split into one group of high OME prevalence and one of low prevalence.

##### Examples

```
# Fit logistic curve from p = 0.5 to p = 1.0
fp1 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/scal)),
c("L75", "scal"),
function(x,L75,scal)NULL)
nls(Correct/Trials ~ fp1(Loud, L75, scal), data = OME,
start = c(L75=45, scal=3))
nls(Correct/Trials ~ fp1(Loud, L75, scal),
data = OME[OME$Noise == "coherent",],
start=c(L75=45, scal=3))
nls(Correct/Trials ~ fp1(Loud, L75, scal),
data = OME[OME$Noise == "incoherent",],
start = c(L75=45, scal=3))
# individual fits for each experiment
aa <- factor(OME$Age)
ab <- 10*OME$ID + unclass(aa)
ac <- unclass(factor(ab))
OME$UID <- as.vector(ac)
OME$UIDn <- OME$UID + 0.1*(OME$Noise == "incoherent")
rm(aa, ab, ac)
OMEi <- OME
library(nlme)
fp2 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/2)),
"L75", function(x,L75) NULL)
dec <- getOption("OutDec")
options(show.error.messages = FALSE, OutDec=".")
OMEi.nls <- nlsList(Correct/Trials ~ fp2(Loud, L75) | UIDn,
data = OMEi, start = list(L75=45), control = list(maxiter=100))
options(show.error.messages = TRUE, OutDec=dec)
tmp <- sapply(OMEi.nls, function(X)
{if(is.null(X)) NA else as.vector(coef(X))})
OMEif <- data.frame(UID = round(as.numeric((names(tmp)))),
Noise = rep(c("coherent", "incoherent"), 110),
L75 = as.vector(tmp), stringsAsFactors = TRUE)
OMEif$Age <- OME$Age[match(OMEif$UID, OME$UID)]
OMEif$OME <- OME$OME[match(OMEif$UID, OME$UID)]
OMEif <- OMEif[OMEif$L75 > 30,]
summary(lm(L75 ~ Noise/Age, data = OMEif, na.action = na.omit))
summary(lm(L75 ~ Noise/(Age + OME), data = OMEif,
subset = (Age >= 30 & Age <= 60),
na.action = na.omit), cor = FALSE)
# Or fit by weighted least squares
fpl75 <- deriv(~ sqrt(n)*(r/n - 0.5 - 0.5/(1 + exp(-(x-L75)/scal))),
c("L75", "scal"),
function(r,n,x,L75,scal) NULL)
nls(0 ~ fpl75(Correct, Trials, Loud, L75, scal),
data = OME[OME$Noise == "coherent",],
start = c(L75=45, scal=3))
nls(0 ~ fpl75(Correct, Trials, Loud, L75, scal),
data = OME[OME$Noise == "incoherent",],
start = c(L75=45, scal=3))
# Test to see if the curves shift with age
fpl75age <- deriv(~sqrt(n)*(r/n - 0.5 - 0.5/(1 +
exp(-(x-L75-slope*age)/scal))),
c("L75", "slope", "scal"),
function(r,n,x,age,L75,slope,scal) NULL)
OME.nls1 <-
nls(0 ~ fpl75age(Correct, Trials, Loud, Age, L75, slope, scal),
data = OME[OME$Noise == "coherent",],
start = c(L75=45, slope=0, scal=2))
sqrt(diag(vcov(OME.nls1)))
OME.nls2 <-
nls(0 ~ fpl75age(Correct, Trials, Loud, Age, L75, slope, scal),
data = OME[OME$Noise == "incoherent",],
start = c(L75=45, slope=0, scal=2))
sqrt(diag(vcov(OME.nls2)))
# Now allow random effects by using NLME
OMEf <- OME[rep(1:nrow(OME), OME$Trials),]
OMEf$Resp <- with(OME, rep(rep(c(1,0), length(Trials)),
t(cbind(Correct, Trials-Correct))))
OMEf <- OMEf[, -match(c("Correct", "Trials"), names(OMEf))]
## this fails in R on some platforms
fp2 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/exp(lsc))),
c("L75", "lsc"),
function(x, L75, lsc) NULL)
G1.nlme <- nlme(Resp ~ fp2(Loud, L75, lsc),
fixed = list(L75 ~ Age, lsc ~ 1),
random = L75 + lsc ~ 1 | UID,
data = OMEf[OMEf$Noise == "coherent",], method = "ML",
start = list(fixed=c(L75=c(48.7, -0.03), lsc=0.24)), verbose = TRUE)
summary(G1.nlme)
G2.nlme <- nlme(Resp ~ fp2(Loud, L75, lsc),
fixed = list(L75 ~ Age, lsc ~ 1),
random = L75 + lsc ~ 1 | UID,
data = OMEf[OMEf$Noise == "incoherent",], method="ML",
start = list(fixed=c(L75=c(41.5, -0.1), lsc=0)), verbose = TRUE)
summary(G2.nlme)
```

*Documentation reproduced from package MASS, version 7.3-19, License: GPL-2 | GPL-3*