According to this website: https://datayze.com/labor-probability-calculator

One can model the probability distribution function of spontaneous labour given two pieces of information.

a) the due date of the pregnant women b) the current date

I have a set of information that provides the due date of labour, and actual date of labour for hundred's of women. It looks like this:

Est. Due Date Actual Labour Date
11th Sep 9th Sep
12th Oct 18th Oct
... ...
15th Aug 26th July

I would like to be able to generate a model similar to the above linked website that allows me to make predictions similar to the website. I.e. I can make a statement like

Your due date is 14th November 2022, given you haven't already given birth there is a x% chance you will give birth today.

or more generally a function that takes a due date as input and outputs the probability of the labour happening over the next number of days (until that probability hits 0 or close enough)

I.e. if the due date is 14th Nov and labour hasn't occurred yet it might produce:

Date Probability of labour
11th Nov 3.1%
12th Nov 3.6%
... ...
15th Dec 0.00003%

The website suggests a skewed normal distribution, but I am not sure how to generate these given the data I have.

Does anyone know how to model such a distribution given what I have and how to use that to make predictions?


1 Answer 1

  1. I disagree with the website author that, based on the information given, these data necessarily follow a skewed normal distribution. I agree the data most likely exhibit skewness (non-equal median and mean - for some reason the author discusses the mode on this point, perhaps a Freudian slip?), and a skewed normal may provide a reasonable approximation. What would be most useful is a continuous histogram of the data based on a kernal density estimator, and based day-by-day labor probabilities on that.
  2. Whereas you often see univariate statistics such as the mean and standard deviation being reported on key variables in a dataset, this does not imply that the data are normal, nor are these measures invalidated when the data do not follow normal rules. As a method of moment estimator, the mean and SD are a robust measure of location and scale.
  3. Estimation of a skewed normal distribution is discussed widely. The wikipedia article, even, gives a nice overview. Typically, you identify the power law that optimally transforms the data by a Box-Cox transformation, and after applying the inverse transform, estimating the mean and standard deviation to identify the 3 parameter normal curve.
  • $\begingroup$ Okay thank you for the reply. In light of what you have said do you think there is a better way to achieve what I am asking then? Do you have any suggestions? $\endgroup$
    – Tom
    Nov 11, 2022 at 17:31

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