Tests of Auditory Perception in Children with OME
Description
Experiments were performed on children on their ability to
differentiate a signal in broadband 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).
Usage
OME
Format
The OME
data frame has 1129 rows and 7 columns:
ID

Subject ID (1 to 99, with some IDs missing). A few subjects were
measured at different ages.
OME

"low"
or "high"
or "N/A"
(at ages other than
30 and 60 months).
Age

Age of the subject (months).
Loud

Loudness of stimulus, in decibels.
Noise

Whether the signal in the stimulus was "coherent"
or
"incoherent"
.
Correct

Number of correct responses from Trials
trials.
Trials

Number of trials performed.
Background
The experiment was to study otitis media with effusion (OME), a very
common childhood condition where the middle ear space, which is
normally airfilled, 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 ‘binaural
hearing’ is used to describe the listening conditions in which the
brain is processing information from both ears at the same time. The
brain computes differences in the intensity and/or timing of signals
arriving at each ear which contributes to sound localisation and also
to our ability to hear in background noise.
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.
Source
Sarah Hogan, Dept of Physiology, University of Oxford, via
Dept of Statistics Consulting Service
Examples
fp1 < deriv(~ 0.5 + 0.5/(1 + exp((xL75)/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))
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((xL75)/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$L75 > 30,]
summary(lm(L75 ~ Noise/Age, data = OMEif, na.action = na.omit))
summary(lm(L75 ~ Noise/(Age + OME), data = OMEif,
subset = (Age >= 30 & Age <= 60),
na.action = na.omit), correlation = FALSE)
fpl75 < deriv(~ sqrt(n)*(r/n  0.5  0.5/(1 + exp((xL75)/scal))),
c("L75", "scal"),
function(r,n,x,L75,scal) NULL)
nls(0 ~ fpl75(Correct, Trials, Loud, L75, scal),
data = OME[OME$Noise == "coherent",],
start = c(L75=45, scal=3))
nls(0 ~ fpl75(Correct, Trials, Loud, L75, scal),
data = OME[OME$Noise == "incoherent",],
start = c(L75=45, scal=3))
fpl75age < deriv(~sqrt(n)*(r/n  0.5  0.5/(1 +
exp((xL75slope*age)/scal))),
c("L75", "slope", "scal"),
function(r,n,x,age,L75,slope,scal) NULL)
OME.nls1 <
nls(0 ~ fpl75age(Correct, Trials, Loud, Age, L75, slope, scal),
data = OME[OME$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$Noise == "incoherent",],
start = c(L75=45, slope=0, scal=2))
sqrt(diag(vcov(OME.nls2)))
OMEf < OME[rep(1:nrow(OME), OME$Trials),]
OMEf$Resp < with(OME, rep(rep(c(1,0), length(Trials)),
t(cbind(Correct, TrialsCorrect))))
OMEf < OMEf[, match(c("Correct", "Trials"), names(OMEf))]
fp2 < deriv(~ 0.5 + 0.5/(1 + exp((xL75)/exp(lsc))),
c("L75", "lsc"),
function(x, L75, lsc) NULL)
try(summary(nlme(Resp ~ fp2(Loud, L75, lsc),
fixed = list(L75 ~ Age, lsc ~ 1),
random = L75 + lsc ~ 1  UID,
data = OMEf[OMEf$Noise == "coherent",], method = "ML",
start = list(fixed=c(L75=c(48.7, 0.03), lsc=0.24)), verbose = TRUE)))
try(summary(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)))