David Freedman wrote:
Frank, would you feel comfortable giving us the reference to the NEJM article
with the 'missing vs <' error ? I'm sure that things like this happen
fairly often, and I'd like to use this example in teaching
thanks, david freedman
@ARTICLE{gus93int,
author = {{The GUSTO Investigators}},
year = 1993,
title = {An international randomized trial comparing four thrombolytic
strategies for acute myocardial infarction},
journal = NEJM,
volume = 329,
pages = {673-682},
annote = {GUSTO; t-PA; mega-trials}
}
The error was in the incidence of the secondary endpoint of death or
stroke (the union of the two). The incidence is slightly wrong because
the secondary endpoint was computed by setting the event indicator to
one if the time until stroke or death was less than the follow-up time.
Some patients had time until stroke or death missing. Although the
statistical team was alerted to this error after publication, no
correction was issued.
Frank
Frank E Harrell Jr wrote:
Dieter Menne wrote:
P.Dalgaard wrote:
IF TYPE='TRUCK' and count=12 THEN VEHICLES=TRUCK+((CAR+BIKE)/2.2);
vehicles <- ifelse(TYPE=='TRUCK' & count=12, TRUCK+((CAR+BIKE)/2.2), NA)
Read both versions to an audience, and you will have to admit that this
is
one of the cases where SAS is superior.
Here's a case where SAS is clearly not superior:
IF type='TRUCK' AND count<12 THEN vehicles=truck+(car+bike)/2.2;
If count is missing, the statement is considered TRUE and the THEN is
executed. This is because SAS considers a missing as less than any
number. This resulted in a significant error, never corrected, in a
widely cited New England Journal of Medicine paper.
Frank
Dieter
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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