isht osuentqi smkea em wtna ot aet an e iolc coeoik adn hpoe i bedle otu
dDi on neo coniet taht hte dsdO atrio on teh pto elft is o?rwng mA I simings t?ohngmsie If you lctueaalc ,ti 'ist 6 sjut leik teh pot rgthi ..e.o.n
RO 1g;&t atsnciide nicedraes cocrnruece of eetn.v heT olny RO trgaeer ntha 1 swa in hte alebt htat tieadcnid atth the jtcbesu tae oskcoie tub intd'd knird .klim ,hTus thta si het oynl oen twhi a ficastngiin croeccuern
For a more systematic approach. First look at cookies p-val is sig when not stratified, the top table is stratified the OR > 1 => sig => cookies have association.
Then look at milk p-val is sig when not stratified, the bottom table stratified the OR = 1 => loss of significance => milk have no association.
Uworld ID 1173 has a good explanation for how to look at stratified analysis.
ehT cfta ttah eth odsd triao ni the opt telf si tercrinoc kmsea sith uteoinqs vrey lfcutdf.ii It masek it arepap sa if teh eocisok aer veutaiasc ubt eht mkli adh omes icveetoprt tr.faoc oS snbu.ooixo
iIyilatnl mkli rngidnik was idoatsasce tiwh E.lcio rbuoktae whit =9O3R. nad &0tlP00;.1 fingii(c..nSat). eftAr ttirsiaicoftan oint tae sooekic nad did otn tea koeicso OR eabmce 1 senatid fo 39. meginan eht ainotcossia eapie.adsdpr eofrher,Te eaignt cseiook swa a nuocnerofd nad erhet si on arel casoniitosa wbneeet rngkindi iklm nda ,dis.Eicnl.t.eoa.. 'imskl (het cnodo)rfneu rttbcnoiinuo was pelisrnoebs rof hte OR fo 9.3 ni hte risft eclp.a Tish swa rreetdfhu enmdrotasdte hwit RO fo 6 in hte soociek naloe urpog.
For people who generally had trouble reading the two charts:
First chart: We separated the entire population into two smaller populations to test for the cookies affect. In Population A (drank milk) there was an odds ratio of 6 (typo in the actual chart). In Population B (did not drink milk) there was an odds ratio of 6. Since the odds ratios are not 1, we can conclude that the cookies have an effect regardless of the population (ie drank milk people versus didn't drink milk people).
Second chart: New set of populations to test for the effect of milk. In Population C (ate cookies) there was an odds ratio of 1. In Population D (did not eat cookies) there was also an odds ratio of 1. This means that milk did not have an effect ever and didn't contribute to the disease.
"Only cookies are independently associated with E. coli cases" means that only the cookies cause the disease without the effects of something else.
Tihs eon htree weer ofur sdod rtao,si neo idrdvpeo dnure hace tae.bl Teh olny one ttha adh an sodd atior arretge tnah 10. was teh elatb in eth top tihgr (
Odsd tRiao = 6, I iveee),bl chwih ehnw oyu odlkoe ta eth ,allebs dle to het higtr narsew.
n"A osdd iatro fo 1 ntdciseai ttah eht itcnndoio ro tenve ndreu sytdu si lalyqeu ekllyi ot uoccr in othb g.sroup nA odsd riaot reegtar tnah 1 iieatcnds tath het ntiondoci or tveen si moer eylkil to crouc ni hte tfrsi p"ro.ug (Ohektiaodripd//swe/gnortk:_i./watiipid.s)
The OR in the upper left 22 table is incorrect, which should be 6 (726/36*2 =6), not 1. This means the OR of "ate cookies" does not change after stratification by "drank milk", so "drank milk" is not a confounder, and "ate cookies" is independently asso w/ EHEC outbreak.
On the other hand, OR for "drank milk" changed a lot (from 3.9 to 1.0), which indicates "drank milk" might be a confounder and, therefore, is not independently asso w/ EHEc outbreak.
we can make things simple like this way: if we want to know whether X1,or X2 correlates Y, we just separately test X1 and Y, and X2 and Y accordingly. When test X1 with Y, we require no X2 exposure; When test X2 with Y, we require no X1 exposure;
We test cookie with diarrhea, when milk was not drunk (top right): positive We test milk with diarrhea, when no cookie was eaten (lower right): negative
conclusion: only cookie correlates to the diarrhea
A question i more generally have is...
Is it possible that when you stratify the data, (i.e. comparing the effect that eating cookies has, in people who drink milk or people who do not drink milk) that the odds ratio will show significance for one but not the other?
Said differently, in the example above, could eating cookies in people who drink milk lead to a significant increase the risk of infection, but not in people who who didn't drink milk?
I've looked around in this comment thread, and have seen people mention the term "effect modification"; is that what my example above would show?
In other words, if eating cookies + drinking milk leads to a significant risk, but eating cookies + not drinking milk has no associated risk, would that mean that the milk has an "effect modification" on the risk of getting infection in people who eat cookies?
heT ywkerdo si aoEs"Ea"de.NtDPsDc(LITENiNY) cihWh ni hnmau aaggenul msaen NOT" TAC"E.DAIOSS