Acute bronchitis is one of the commonest medical
problems managed by health services, and one of the important
clinical questions is whether antibiotics do any good. This précis
of a BMJ article earlier this year looks at the topic.
Fittingly, for such a common problem, there have
been four systematic reviews comparing antibiotics with placebo for
treating bronchitis. All, however, have reached clinically unhelpful
conclusions, which simply exposes the perennial problem for all
systematic reviews that demonstrate no or only marginal benefits
from the intervention: is there a subgroup that might derive
benefit? It also exposes the procrustean nature of our definitions
of acute bronchitis.
Three of the reviews included meta-analyses and
one was a qualitative systematic review of the literature. They
include almost all the same studies, although Fahey et al called
their review a systematic review of acute cough in adults and
included unpublished data from Stephenson. They all came to similar
ambiguous and clinically unhelpful conclusions, the most negative
being, “the current literature does not support antibiotic treatment
for acute bronchitis” while the most positive concluded,
“antibiotics may be modestly effective for a minority of patients
with acute bronchitis”.
It would be far better to have a review that
contained data from only a few studies but was analysed in a way
that clinicians could be reasonably sure that they were not dealing
with some cases of pneumonia.
What can the practising clinician do while
awaiting such analysis? The use of antibiotics may be justified in
those with lower respiratory tract signs – confirmed by 256 patients
in four studies – or in those who are aged 55 or older and either
“feel ill” or have a “frequent day-time cough” – confirmed by 27
patients in one study. For other patients there is more evidence for
benefit from bronchodilators than from antibiotics - shown in 80
patients in two studies.
Ref (editorial so not much online)
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Randomised controlled trials and observational
studies are often seen as mutually exclusive, if not opposing,
methods of clinical research. Two recent reports, however,
identified clinical questions (19 in one report, five in the other)
where both randomised trials and observational methods had been used
to evaluate the same question, and performed a head to head
comparison of them.
In contrast to the belief that randomised controlled trials are
more reliable estimators of how much a treatment works, both reports
found that observational studies did not overestimate the size of
the treatment effect compared with their randomised counterparts.
The authors say that the merits of well designed observational
studies may need to be re-evaluated: case-control and cohort studies
may
need to assume more respect in assessing medical
therapies and large scale observational databases should be better
exploited. The first claim flies in the face of half a century of
thinking, so are these authors right?
The combined results from the two reports show a
striking concordance between the estimates obtained with the two
research designs. A correlation analysis performed on their combined
databases found that the correlation coefficient between the odds
ratio of randomised trials and the odds ratio of observational
designs was 0.84. This represents excellent concordance. In fact, it
is better than that observed when the results of small randomised
trials and their meta-analyses were compared with the results of
large randomised trials. To complicate matters, the concordance has
been worse when the results of specific large randomised trials on
the same topic were compared among themselves.
Popular wisdom has it that a “gold standard”
method should give more or less the same results when repeated
several times, while a poor method would suffer from lots of
variability. So should observational studies be the gold standard
instead of randomised trials?
Perhaps more importantly, Benson and Hartz and
Concato et al are still dealing with only a very small portion of
randomised and observational research. Their sampling failed to
capture some prodigious discrepancies between the two methods.
Interventions such as
b
carotene and a-tocopherol,
which have brought fame to observational epidemiologists, crashed
when they were tested in rigorous randomised controlled trials.
Given the hundreds of thousands of trials and observational studies
that have been conducted and are still being reports is limited and
subject to strong selection biases.
But this study looked only at very selected
clinical questions in that both designs were concurrently used, and
investigators were willing to compare the designs in an even smaller
minority. In further work about 50 topics where both randomised and
observational evidence were compared in the same meta-analsysis.
These were taken from over 2000 meta-analyses performed in the past
25 years. Despite some overlap, the two types of designs are mainly
used in different settings.
For interventions that show very large harmful
effects in observational studies, randomised trials may be
justifiably discouraged and never performed. For interventions that
have already shown large beneficial treatment effects in
observational trials the ethics of randomisation may also be
questioned. Interventions with modest postulated effects (risk
ratios in the range 0.40-0.90) are likely to be targeted by
randomised trials; in this setting, observational studies may not be
given comparable credit and may be unjustifiably discarded once
randomised trials have been performed.
Finally, for interventions with very small
postulated effects (risk ratios 0.90-1.00) adequately powered
randomised trials may be difficult to perform given the sample size
requirements, and thus only observational evidence may be generated.
Another important selection force is the
frequency of the outcome of interest. Rare yet important outcomes
are unlikely to be studied in trials, given the extreme requirements
of sample size and follow up. In contrast, when the outcomes of
interest are common, trials are convenient.
More empirical evidence is needed on the merits of various
research designs. We need more quantitative evidence to understand
what exactly each design can tell us and how often and why each
design may go wrong. Discarding observational evidence when
randomised trials are available is missing an opportunity.
Conversely, abandoning plans for randomised trials in favour of
quick and dirty observational designs is poor science
Ref: (web)
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