# [R] Statistical question about logistic regression simulatio

(Ted Harding) Ted.Harding at manchester.ac.uk
Wed Aug 26 17:03:57 CEST 2009

```On 26-Aug-09 14:17:40, Denis Aydin wrote:
> Hi R help list
> I'm simulating logistic regression data with a specified odds ratio
> (beta) and have a problem/unexpected behaviour that occurs.
>
> The datasets includes a lognormal exposure and diseased and healthy
> subjects.
>
> Here is my loop:
>
> ors <- vector()
> for(i in 1:200){
>
># First, I create a vector with a lognormally distributed exposure:
> n <- 10000 # number of study subjects
> mean <- 6
> sd <- 1
> expo <- rlnorm(n, mean, sd)
>
># Then I assign each study subject a probability of disease with a
># specified Odds ratio (or beta coefficient) according to a logistic
># model:
> inter <- 0.01 # intercept
> or <- log(1.5) # an odds ratio of 1.5 or a beta of ln(1.5)
> p <- exp(inter + or * expo)/(1 + exp(inter + or * expo))
>
># Then I use the probability to decide who is having the disease and who
># is not:
> disease <- rbinom(length(p), 1, p) # 1 = disease, 0 = healthy
>
># Then I calculate the logistic regression and extract the odds ratio
> model <- glm(disease ~ expo, family = binomial)
> ors[i] <- exp(summary(model)\$coef[2]) # exponentiated beta = OR
> }
>
> Now to my questions:
>
> 1. I was expecting the mean of the odds ratios over all simulations to
> be close to the specified one (1.5 in this case). This is not the case
> if the mean of the lognormal distribution is, say 6.
> If I reduce the mean of the exposure distribution to say 3, the mean of
> the simulated ORs is very close to the specified one. So the simulation
> seems to be quite sensitive to the parameters of the exposure
> distribution.
>
> 2. Is it somehow possible to "stabilize" the simulation so that it is
> not that sensitive to the parameters of the lognormal exposure
> distribution? I can't make up the parameters of the exposure
> distribution, they are estimations from real data.
>
> 3. Are there general flaws or errors in my approach?
>
>
> Thanks a lot for any help on this!
>
> All the best,
> Denis
>
> --
> Denis Aydin

You need to look at the probabilities 'p' being generated by your code.

Taking first your case "mean <- 6" (and sorting 'expo', and using
whatever seed my system had current at the time):

n <- 10000 # number of study subjects
mean <- 6
sd <- 1
expo <- sort(rlnorm(n, mean, sd))
p <- exp(inter + or * expo)/(1 + exp(inter + or * expo))

p[1:20]
#  [1] 0.9763438 0.9918962 0.9924002 0.9965314 0.9980887 0.9984698
#  [7] 0.9993116 0.9993167 0.9994007 0.9994243 0.9996288 0.9997037
# [13] 0.9998728 0.9998832 0.9999284 0.9999346 0.9999446 0.9999528
# [19] 0.9999561 0.9999645

so that almost all of your 'p's are very close to 1.0, which means
that almost all or even all) of your responses will be "1". Indeed,
continuiung from the above:

disease <- rbinom(length(p), 1, p)
disease[1:20]
#  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
sum(disease)
# [1] NaN
sum(is.nan(disease))
# [1] 710

What has happened here is that the higher values of 'expo' are so
large (in the 1000s) that the calculation of 'p' gives NA, because
the value of exp(inter + or * expo) is +Inf, so the calculation of
'p' is in terms of (+Inf)/(+Inf), which is NA.

Now compare with what happens when "mean <- 3":

mean <- 3
sd <- 1
expo <- sort(rlnorm(n, mean, sd))
p <- exp(inter + or * expo)/(1 + exp(inter + or * expo))
p[1:20]
#  [1] 0.5514112 0.5543155 0.5702318 0.5830025 0.5885994 0.5889078
#  [7] 0.5908860 0.6004657 0.6029123 0.6042805 0.6048688 0.6122290
# [13] 0.6123407 0.6135233 0.6137499 0.6139299 0.6153900 0.6181017
# [19] 0.6184093 0.6203757
sum(is.na(p))
#  [1] 0
max(expo)
#  [1] 728.0519

So now no NAs (max(expo), though large, is now not large enough to
make the calculation of 'p' yield NA).

These smaller probabilities are now well away from 1.0, so a good mix
of "0" and "1" responses can be expected, although a good number of
the 'p's will still be very close to 1 or will be set equal to 1.

disease <- rbinom(length(p), 1, p)
disease[1:20]
#  [1] 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0 1 1 0 0 1

(as expected), and

sum(disease)
# [1] 9740

As well as the problem with p = NA ---> disease = NaN, when you
have all the probabiltiies close to 1 and (as above) get all the
'disease' outcomes = 1, the resulting attempt to fit the glm will
yield nonsense.

In summary: do not use silly paramater values for the model you
are simulating. It will almost always not work (for reasons
illustrated above), and even if it appreas to work the result
will be highly unreliable. If in doubt, have a look at what you
are getting, along the line, as illustrated above!

The above reasons almost certainly underlie your finding that the
mean of simulated OR estimates is markedly different from the value
which you set when you run the case "mean <- 6", and the much
better finding when you run the case "mean <- 3".

Hoping this helps,
Ted.

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Date: 26-Aug-09                                       Time: 16:03:49
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