Dear all:
The reason why one cannot shift groups when you have randomized a treatment is because of the
statistical assumptions around the randomization process. You can have "drop out", but that must
be reported and explained in the study. If the drop out is "biased" --- that is there is some
reason for the drop out that coincides with the treatment effect that can negate the apparent
findings of the study. In some cases, particularly when one is evaluating "effectiveness of an
intervention" rather than the "efficacy of a treatment" drop outs can be important in terms of what
they tell you about how to apply the intervention.
To go back to my original example of an unintended contamination - the Bolivia study had
contamination of the control group, not from any deliberate mixing up of placebos and iodine
capsules or anyone buying iodine capsules on the market because they realized that they were
beneficial. The cause of the "contamination" that improved the iodine levels of all the children is
unknown. The endocrinologists I've talked to speculate that the levels of iodine were so high
(most of the iodine is excreted out of the body within a few days of ingestion - the rest is retained
in body fat stores) that iodine may have filtered into the water system through the children's urine.
No proof of this speculation.
The Bolivia study authors did something creative with their data. They analyzed the Change in
iodine level with the Change in cognitive tests. This showed an association. Such an analysis,
however, does not allow one to make a "probability" statement because you no longer have
randomly assigned groups. You can only make a statement of association, otherwise known as
"plausibility". While such a statement is suggestive, it cannot really determine causality. You can
always find spurious associations that just happen by chance and have no real relationship to each
other.
So, in the Schanler study, the case of the of the 17 infants(20%) in the DM (pasteurized human
donor milk) most certainly could have washed out the effect of DM. I might have handled this
differently and considered them drop outs, BUT that would have diminished the sample size even
further. There are some really excellent new statistical tools that were developed when AIDS
became a problem. These tools are beyond my skills. The tools were designed to anticipate a
treatment that might actually be harmful - one that might increase the death rate even higher
than the AIDS it was intended to treat. So, there are rules for picking up on a significant enough
climb in a serious risk and stopping the treatment at that point. "Stopping a treatment" which is
essentially what happened in the DM group. A significant number failed to thrive and because of
the risks the treatment was stopped. Had this potential problem been anticipated, a sophisticated
statistician would have taken this into account prior to the study. However, I think not one of us
would have ever anticipated this as a potential outcome.
What is interesting, however, is that despite the contamination, the DM group had similar levels of
NEC as the MM group. We just don't have the power to figure out if this really means anything or
if more infants had been included whether this would hold up.
Also, one cannot make a valid statistical comparison on a "probability" level between the mother's
milk (MM) group and the combined preterm formula (PF) and pasteurized donor milk (DM) groups
because the ability to have enough milk to be in the MM group was NOT randomly assigned. This
drops the level of analysis to association and sources of confounding (eg. mother's age,
education) should be included in the analysis. This is a frequent occurence with breastfeeding
studies that two randomly assigned groups such as DM and PF or two different types of formula
are compared against a group that is not randomly assigned. The two randomly assigned groups
should be compared against each other. The other comparison should be reported in a separate
table and should never be treated as if it were a comparison of a randomized treatment.
All-in-all I found that the study was excellently designed. I'm impressed that Pediatrics published
a negative study, but probably did because of the overall quality. There is always a criticism of
journals that don't publish negative findings. I am unhappy about the extrapolation of the results
beyond what can be concluded statistically. In this case, I think the author's discussion section
should have been far more cautious and I think they did make statements that cannot be
supported statistically.
Susan Burger, MHS, PhD, IBCLC
PS. Probably have bored you all to death by now.
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