[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: implementation of boschloo's test: very slow execution |

Date |
Fri, 22 Feb 2008 18:18:52 -0000 |

I don't see that you need hold constants in variables. Use scalars not variables to hold constants. This refers to -current- and -PHOsum-. I have only scanned this quickly, but at first sight it appears that your variable -p- is never used elsewhere in the code. Converting some of this to Mata is perhaps the most obvious speed-up. Nick n.j.cox@durham.ac.uk Eva Poen I am currently trying to implement the unconditional test for comparing two binomial proportions by Boschloo. The test is described e.g. here: http://www.west.asu.edu/rlberge1/papers/comp2x2.pdf (page 3). The problem with my implementation is that it takes a long time to execute: for n1=148 and n2=132, it takes roughly 30 minutes on a reasonably modern machine. I am using Stata 9.2 for this. I was wondering if anyone could suggest improvements to make it faster. Here is how the test works: Say you have two samples of size n1 and n2. In sample 1, there are x1 successes whereas in sample 2 there are x2. a) compute fisher's exact test for the two proportions and record the two-sided p-value. b) for every possible combination of successes in the two samples (which is n1Xn2 combinations): b1) compute fisher's exact test b2) check whether the p-value is less than or equal to the p-value from the original sample in step a). b3) if it is <= the result from a), compute the product of the binomial probability mass functions for this combination. Do this for (a lot) of values for 0 <= p <= 1. If it is > the result from step a) , do nothing. c) The result of step b3) is added up over all n1Xn2 iterations. d) Search for the maximum of c) over all values of p. This maximum is the p-value for this test. Here is what I do in every step. I first create a variable that holds all probability values of p that I want to do the calculations for: set obs 10001 qui gen double p = (_n-1)/10001 The higher the "resolution" of p, the more accurate is the result going to be. I also create a variable that holds the current calculations, and one that holds the (running) sum over all iterations: qui gen double PH0sum = 0 qui gen double current = . a) qui tabi `=n1-x1' `=n2-x2' \ `=x1' `=x2', exact scalar fisher = r(p_exact) b) and c) forvalues xx1 = 0/`=n1' { forvalues xx2 = 0/`=n2' { qui tabi `=n1-`xx1'' `=n2-`xx2'' \ `xx1' `xx2', exact scalar fishnew = r(p_exact) if fishnew <= fisher { qui replace current = Binomial(n1,`xx1',theta)-Binomial(n1,`=`xx1'+1',theta))*(Binomial(n2,`xx 2',theta)-Binomial(n2,`=`xx2'+1',theta)) qui replace PH0sum = PH0sum + current } } } d) qui sum PH0sum scalar p_boschloo = r(max) ******************************************************** I can replicate an example I found in a Biometrics(2003) paper by Mehrotra et al., so I believe that the calculations are correct. Is there a way to speed things up inside the nested loop? I'd be greatful for any hints, as I have to run quite a lot of these tests; time does matter here. Best regards, Eva * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: implementation of boschloo's test: very slow execution***From:*"Eva Poen" <eva.poen@gmail.com>

**References**:**implementation of boschloo's test: very slow execution***From:*"Eva Poen" <eva.poen@gmail.com>

- Prev by Date:
**st: RE: Strange problem with forvalues** - Next by Date:
**Re: implementation of boschloo's test: very slow execution** - Previous by thread:
**Re: implementation of boschloo's test: very slow execution** - Next by thread:
**Re: implementation of boschloo's test: very slow execution** - Index(es):

© Copyright 1996–2016 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |