gluten


Evidence of gluten sensitivity

This notebook contains an exploration of results from this paper:

http://onlinelibrary.wiley.com/doi/10.1111/apt.13372/epdf

which reports results of a study of gluten sensitivity.

Copyright 2015 Allen Downey

MIT License: http://opensource.org/licenses/MIT


In [1]:
from __future__ import print_function, division

import thinkbayes2
import thinkplot

from scipy import stats

%matplotlib inline

The following class defines the function that computes the likelihood of the data given the hypothetical number of subjects who are gluten sensitive.

I assume that a subject who is gluten sensitive has a 95% chance of successfully identifying gluten flour based on resumption of symptoms, and that a subject who is not gluten sensitive has only a 40% chance of identifying gluten flour (and a 20% chance of detecting no difference).

The function works by computing the PMF of the number of gluten identifications conditioned on gs, and then selecting the actual number of identifications from the PMF.


In [2]:
class Gluten(thinkbayes2.Suite):
    
    def Likelihood(self, data, hypo):
        """
        hypo: int hypothetical # who are gluten sensitive
        data: (int, int) # who correctly identify gluten, others
        """
        gs = hypo
        yes, no = data
        n = yes + no
        ngs = n - gs
        
        pmf1 = thinkbayes2.MakeBinomialPmf(gs, 0.95)
        pmf2 = thinkbayes2.MakeBinomialPmf(ngs, 0.1)
        pmf = pmf1 + pmf2
        return pmf[yes]

The prior is uniform from 0 to 35; that is, there are equally likely to be any number of GS subjects in the study.


In [3]:
data = 12, 23
prior = Gluten(range(0, 35+1))
thinkplot.Pdf(prior)


Here's the update.

In [4]:
posterior = prior.Copy()
posterior.Update(data)


Out[4]:
0.032678923853482846

Here's what the posterior looks like.


In [5]:
thinkplot.PrePlot(1)
thinkplot.Pdf(posterior)
thinkplot.Config(xlabel='# who are gluten sensitive', 
                 ylabel='PMF', legend=False)
#thinkplot.Save(root='gluten1', formats=['png'])


And the posterior credible intervals.


In [6]:
posterior.CredibleInterval(90)


Out[6]:
(6, 13)

In [7]:
posterior.CredibleInterval(95)


Out[7]:
(6, 13)

We can also compare to a null hypothesis in which none of the respondents are gluten sensitive.


In [8]:
prior2 = Gluten([0])
prior2.Update(data)


Out[8]:
7.3956946672325594e-05

The Bayes factor in favor of the null hypothesis is about 8, which is moderate evidence. If you started thinking the probability of the null was 50%, you should now believe it is about 90%.


In [9]:
0.11 / 0.013


Out[9]:
8.461538461538462

In [10]:
8.5/9.5


Out[10]:
0.8947368421052632

In [10]: