Clinical trial quality and sources of bias

There are a large number of potential sources of bias in clinical trials, and many types of bias result in overestimation of treatment effects. In addition, some clinical trial reports draw conclusions that are not justified by the data. For example, publications describing prospective cohort trials sometimes attribute benefits to the treatment when the absence of a control group makes such conclusions unwarranted. RCTs are the optimal clinical trial design for establishing efficacy, yet all RCTs have limitations and some are biased in ways that make the conclusions potentially misleading. Clinical trial reports must therefore be scrutinized carefully to determine if the conclusions are justified given the study design and data analysis, and a systematic method of evaluating RCTs for sources of bias can be employed [7]. In this section we will discuss factors that can decrease the validity of clinical trials, focusing on those that can reduce the internal and external validity of an RCT.

Internal validity

There are a large number of potential sources of bias in clinical trials. Unfortunately, many tend to make treatments appear better than they truly are, and incorporating biased trial results into clinical decision making can result in failure to adequately treat pain, the development of side effects, inconvenience, and unnecessary costs.

The adequacy of randomization is vitally important to the internal validity of an RCT. Some interventions that are consistently effective in nonrandomized trials can be consistently found to be ineffective in randomized trials [6]. Moreover, studies that provide unclear descriptions of the randomization process have also been found to consistently overestimate treatment effects when compared to studies that clearly describe randomization methods [6]. As noted above, treatment allocation methods that are not based on a valid approach to generating random numbers are not considered adequate methods of randomization [27].

Studies with large numbers of subjects who drop out from one or more arms of the trial should be viewed critically because drop-outs can change the composition of the original randomized treatment groups and also make the study sample no longer representative of the intended population. A large number of drop-outs can also indicate that the trial was not carefully designed or conducted. Studies that do not clearly describe the disposition of all study subjects, especially those who drop out, should be viewed critically.

The adequacy of blinding is also very important in the interpretation of clinical trials. Interventions that appear highly efficacious in unblinded studies are sometimes shown to be ineffective in blinded studies [6]. Given the subjective nature of pain measurements and the large placebo effects that are found in pain trials, lack of blinding or unintentional unblinding of either subjects or investigators can lead to substantial bias. Subjects' guesses about their treatment assignment should be assessed when they complete their participation in a study and these results should be described in the published reports of RCTs.

Inappropriate statistical analyses can also compromise internal validity and potentially lead to erroneous conclusions. Failure to state the prespecified primary endpoint raises the possibility that endpoints showing statistically significant effects have been selected for emphasis on the basis of the analyses, which makes interpretation of the trial results hazardous if not impossible. Occasionally, the primary endpoint is specified in the methods section, but the results and conclusions emphasize other endpoints, presumably because analyses of the primary endpoint were not favorable.

Sometimes, it is erroneously concluded that two interventions are equivalent when an RCT designed to test for superiority fails to show it. This is not an appropriate conclusion for a superiority trial showing no group differences; such trials should include a detailed description of the sample size assumptions and statistical power calculations, which will make it possible for readers to determine if assumptions used about the treatment effect size may have accounted for the lack of significant group differences. Equivalence and noninferiority trials require a different statistical analysis plan than superiority trials, including a prespecified equivalence or non-inferiority margin for the primary endpoint, which should be based on clinical judgment as well as statistical considerations. Studies that are specifically designed to test for equivalence or noninferiority should state this.

As described above, the sample used in the data analyses should be clearly specified, and, for a superiority trial, the primary analysis should typically be based on an ITT sample. The method of handling missing data should also be specified in advance of the data analyses, and ideally, alternative methods (e.g. both BOCF and LOCF) would be reported.

External validity

An RCT can have high internal validity yet produce biased estimates of treatment response due to problems of external validity; that is, the results of the trial may not apply to the patients treated in clinical practice. Extrapolation of clinical trial results to patient care can be challenging and potentially lead to patient harm, if, for example, study results from one patient population are inappropriately applied to a different population [50]. There are two aspects of RCTs that commonly reduce external validity: the representativeness of the study sample and the dosing strategies used in the trial.

There are a number of factors that can affect the study sample in ways that limit the generalizability of the subjects' treatment response to other patients. For example, similar RCTs performed in different countries sometimes have very different results [6, 51]. In addition, the mere fact that a subject is willing and able to participate in a clinical trial distinguishes him or her from the broader pool of patients for whom the treatment might be appropriate. Moreover, the recruitment methods investigators employ - for example, identifying patients from clinics or advertising in newspapers - can have a large impact on the types of patients enrolled in an RCT [32, 52].

The inclusion and exclusion criteria are used to define the study sample in an RCT, yet they frequently result in samples that differ substantially from the population for which the treatment is intended [53]. Strictly speaking, the conclusions from a placebo-controlled trial should describe how an intervention compares with placebo in the sample studied. The study sample is presumed to represent the population of all patients who meet the inclusion and exclusion criteria; however, the published conclusions in RCTs typically extrapolate the results to the entire population of patients with a particular disorder, not just those who would have been eligible to participate in the trial.

For example, most recent RCTs of treatments for chronic pain have required subjects to have an average pain score of 4 or greater on 0-10 daily diaries rated during a baseline week preceding randomization (a criterion of 5 or greater has also occasionally been used). When efficacy has been demonstrated in such studies, however, it is typically not concluded that the study's results may only apply to patients with moderate or severe pain. Although designing an RCT so that enrollment is limited to patients with moderate or greater pain may increase the likelihood that an active treatment will be superior to placebo, the results of the study may not extrapolate to patients with mild pain.

Another important limitation in interpreting the results of clinical trials involves the dosing strategy, which can have a substantial effect on trial outcome, including evaluations of both efficacy and tolerabil-ity. The rate at which the dosage of a medication is titrated, the maximum dosage administered, and whether the dosing strategy involves titration to a fixed dosage or flexible dosing adjusted on the basis of beneficial effects and tolerability can all have a major impact on the generalizability of the trial's results to clinical practice. In RCTs designed to compare the efficacy and tolerability of different medications, the dosing regimens used can be a major determinant of the results [47]. For example, if one medication is titrated more slowly and to a lower dosage than another, it is likely to be better tolerated; similarly, if one medication is titrated to relatively higher dosages than another, it could show greater efficacy when no differences would exist if equianalgesic dosages of the two medications had been used.

Other potential sources of bias

Reports of clinical trials should clearly identify the funding source and any potential conflicts of interest of the investigators. Industry-sponsored trials are typically designed with the objectives of demonstrating the efficacy, superiority, or greater tolerability and safety of the sponsor's product, and it is important to carefully consider potential biases in the study design and data collection, analysis, and interpretation of such trials from this perspective [47, 54, 55]. For example, a recent study found that trials sponsored by for-profit organizations were much more likely to recommend an intervention as the "treatment of choice" than trials sponsored by nonprofit organizations, and that neither the magnitude of the treatment effect nor the occurrence of adverse events explained the association between sponsorship and positive recommendations [56]. Although considerable attention has been paid to various biases associated with industry-sponsored trials, it should be recognized that such trials undergo a high level of scrutiny when they are submitted to regulatory agencies for product approval. It has been observed that academic investigators are also invested in the outcome of the research they conduct, but it is more difficult to evaluate these "nonfinancial conflicts of interest" [57, 58] and the role they play in clinical trials than the more obvious conflicts represented by industry sponsorship. Knowledgeable reviewers are the major defense against bias, and all readers should be cautious in accepting what they read, "remembering that the ultimate validation for any scientific observation is replication" [58].

Publication bias is another important source of bias in the literature. Unfavorable RCT results are sometimes not published or are pooled with favorable RCTs to produce a favorable publication, and the hazards of interpreting the results of clinical trials when negative results remain undisclosed have received increasing attention in both the medical and lay literature. In addition, favorable RCT results are sometimes published multiple times. In one example, a total of 15 RCTs were conducted to assess the efficacy of an antidepressant for major depression; three of these were never published, yet a total of 20 publications describing the RCTs appeared in the literature, including duplicate publications of the same trial but with different authors [43]. Various efforts are underway to encourage investigators, from both industry and academia, to register trials at their inception so that a public record is available of all clinical trials that have been conducted [59]. Although clinical trial registration is a very positive development, its effectiveness remains to be established. To ensure that the development of improved clinical trial research methods and the identification of efficacious treatments are not impeded, the publication of negative trials by sponsors, investigators, and journal editors must therefore be strongly encouraged.

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