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Department of Medicine, Queens University, Kingston, Ontario, Canada
| ABSTRACT |
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KEY WORDS: immunonutrition randomized clinical trials
| INTRODUCTION |
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To best answer these questions of efficacy, studies that compare the
outcomes of two or more interventions are needed. A variety of
methodologies can be used to compare the outcomes (Fig. 1
). Each of these methodologies has its strengths, weaknesses and
limitations but generally speaking, systematic reviews of randomized
trials (and meta-analyses) and individual randomized trials tend to
produce more robust results than cohort studies or case-control
studies. The conclusion that more robust methods generate stronger
inferences has led to the notion of levels of evidence and grades of
recommendations (Sackett 1989
). Strong clinical
recommendations can be made (i.e., grade A recommendations) when
supported by rigorous randomized trials or systematic reviews with a
low chance of error (level I evidence). Moderately strong
recommendation (grade B) can be made from randomized trials or
systematic reviews with a high risk of error (level II evidence).
Weaker recommendations (grade C) are based on less rigorous studies or
randomized trials in different patient populations or randomized trials
focusing on surrogate outcomes. Finally, no recommendations can be made
from evidence that comes from nonrandomized studies of patient
populations other than the one of interest, from animal studies or from
studies based on biological rationale. A version of the relationship
between levels of evidence and grades of recommendations is outlined in
Table 1
.
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| The use of surrogate outcomes |
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| Underpowered studies |
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| The methodological quality of primary studies |
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Unblinded studies are susceptible to bias and weaken or invalidate
inferences from clinical studies. For example, in a recent randomized,
unblinded, multicenter trial of trauma patients, the efficacy of an
"immune-enhancing" formula was compared with a standard enteral
product (Moore et al. 1994
). Study feedings were started
within 24 h of admission to the Intensive Care Unit (ICU). On
average, patients receiving the "immune-enhancing" feeding were on
the ventilator for 1.9 ± 0.4 d vs. 5.3 ± 2.9 d
for patients receiving the control feeding. It is not likely that a
nutrition intervention initiated on day 1 in the ICU would result in
patients coming off the ventilator 1 day later. Perhaps the
baseline characteristics of these patients were somehow systematically
different or they were managed differently compared with control
patients. Unfortunately, the manuscript does not provide any details on
other cointerventions, especially how patients were weaned from
mechanical ventilators. The likelihood of such a bias weakens (if not
invalidates) any inferences that can be made from this study.
Admittedly, in some cases, blinding is not possible. In such
situations, to minimize bias, investigators must make extra efforts to
standardize patient care and/or use third-party (blinded)
adjudicators to determine study outcomes.
Increasingly, studies of enteral nutrition are basing clinical
recommendations on the results of one or more "compliance" analyses
(Atkinson et al. 1998
, Bower et al. 1995
,
Kenler et al. 1996
). For example, Atkinson et al. (1998)
reported a large, multicenter trial that compared an
immune-enhancing formula with a standard formula in critically ill
patients. Three hundred ninety-eight patients were entered into the
study. On an intention-to-treat basis, 48% of the patients who
received the immune-enhancing formula died compared with 44% in
the control group (P > 0.05). There was no significant
difference in length of stay. When the investigators restricted the
data analysis to the 25% of the randomized patients who received a
specific amount of enteral nutrition within the first 72 h, they
found a significant reduction in duration of mechanical ventilation and
length of ICU stay in patients receiving the immune-enhancing
formula. However, patients receiving immunonutrition also tended to
have an increased mortality and to die earlier (which may explain why
length of stay was reduced!). The authors concluded, "... those
patients in whom it was possible to achieve early enteral
nutrition ... had a significant reduction in the morbidity of
their critical illness." An editorial accompanying the publication of
this study (Zaloga 1998
) stated, "... the analysis
of subgroups who received critical amounts of formula is valid.
Intention-to-treat analysis is less valid since it would also fail to
demonstrate the beneficial effects of drugs in patients who fail to
receive adequate amounts."
This notion is inconsistent with current teaching in clinical
epidemiology (Guyatt et al. 1993
). An intention-to-treat
analysis requires that all patients originally randomized in the trial
be accounted for in the group to which they were randomized and is
generally considered to be the most valid estimate of
treatment effect. The goal of randomization is to equally distribute
known and unknown prognostic variables between study groups so that
observed differences can be attributed to the study intervention and
not differences in patient populations. When randomized patients are
dropped or excluded from the analysis, the distribution of these
prognostic variables may be disturbed and the study results biased.
Because they may include patients who do not receive the intervention,
intention-to-treat analyses can be considered to be less
sensitive in detecting a treatment effect.
Another approach to analyzing the data that represents a compromise between validity and sensitivity is known as an "efficacy" analysis. In an efficacy analysis, the investigator states a priori that certain groups of patients will be omitted from the analysis on the basis of scientific rationale. For example, patients who were not "truly eligible" or patients who never received the feedings might be excluded. Compared with the intention-to-treat analysis, this approach is more sensitive to detecting a treatment effect but is more likely to be biased. The burden rests with the investigators to convince the scientific community that the analysis has not been biased.
A third approach to analyzing data is to group patients on the basis of
events that occur after randomization, such as the amount of enteral
feedings received. Analyses classified by variables measured
after baseline (such as compliance or tolerance) are more
likely to mislead than to inform (Oxman and Guyatt 1992
,
Yusuf et al. 1991
). There may be an interaction between
compliance or tolerance to enteral feedings and the study intervention,
resulting in a bias that cannot be compensated. For example, most
immune-enhancing formulas contain arginine. Arginine
supplementation increases nitric oxide production. Nitric oxide plays a
role in gastric emptying (Zaloga and Marik 2000
).
Therefore, arginine supplementation may decrease gastric emptying,
resulting in decreased tolerance of enteral feedings in the
experimental group. Patients in the experimental group may not tolerate
enteral feeding for reasons that are different from those of patients
in the control group, resulting in different patient populations in the
"compliance" analysis. For this reason, it is likely that these
analyses are biased. It is recommended that no study subjects be
withdrawn from the analysis because of compliance reasons
(Friedman et al. 1985
).
Returning to our clinical example (Atkinson et al. 1998
), the investigators stated, a priori, that only patients
who tolerated >2.5 L within 72 h would be included in the
analysis (just because the investigators state a priori their decision
rule does not eliminate the risk of bias). If only 25% of randomized
patients are compliant with this amount, it raises questions concerning
the utility of the intervention. Furthermore, patients purported to
benefit cannot be identified a priori. Therefore, if this benefit is to
be gained, all of the patients would have to be treated. However, we
already know that when all of them were treated, no advantage ensued.
Therefore, there must be some subgroup in whom the treatment was
disadvantageous. Whatever gain is achieved in one subgroup would be
lost in another. The good would be negated by harm.
| Limited generalizability |
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| Believability of industry-sponsored research |
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Although many physicians implicitly mistrust the results of studies
sponsored by industry, there is a growing body of empirical evidence to
support this notion. It is clear that interactions between physicians
and the pharmaceutical industry influence prescribing and
professional behavior (Wazana 2000
). Considerable
difficulties are encountered in attempts to access original data for
the purposes of conducting a meta-analyses when those data reside
with pharmaceutical companies (Johansen and Gotzsche 1999
). Other studies suggest that physicians with financial
ties to manufacturers were much less likely to criticize the safety or
efficacy of these agents (Barnes and Bero 1998
,
Rochon et al. 1994
, Stelfox et al. 1998
).
Similarly, in a study of publications of clinical trials pertinent to
the practice of general internal medicine, there was a significant
association between positive results and funding from pharmaceutical
manufacturers (Davidson 1986
). Negative results
sometimes are not even published while product continues to be sold
(Ross Products 1996
). Finally, in a recent review of
economic evaluations, Friedberg et al. (1999)
found that
pharmaceutical company sponsorship of economic analyses was associated
with a reduced likelihood of reporting unfavorable results.
Collectively, these findings raise serious concerns about the
believability of industry sponsored trials.
These concerns can have an adverse effect on physician practice in the following two ways: 1) physicians can adopt new practices based on misleading information (false positive) from industry trials; or 2) because of their inherent disbelief of industry-sponsored research, some physicians may not adopt new technology that is shown (true positive) to improve patient outcomes (and economic outcomes). Alternative sources of funding, either alone or in combination with industry support would likely enhance the believability of randomized trials, although empirical evidence for this notion is lacking.
| CONCLUSIONS |
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| FOOTNOTES |
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2 Abbreviations used: CI, confidence interval;
ICU, Intensive Care Unit; RCT, randomized clinical trials; RR, risk
ratio. ![]()
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