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Bias in Clinical Trial
It is a challenging task for researchers to conduct a study that will be credible, mainly when it refers to large clinical trials. In this case, scholars should overcome various kinds of bias to make their work free from possible errors. Thus, Effect of Behavioral Interventions on Inappropriate Antibiotic Prescribing Among Primary Care Practices: A Randomized Clinical Trial by Meeker et al. (2016) has a few potential sources of bias. However, the researchers have devoted their energies to protect against bias and achieve reliable results of the study.
Social desirability bias has the potential to influence the clinical trial under consideration. According to Althubaiti (2016), this bias denotes that participants of a study do not provide exact information when confidentiality is not guaranteed to them. As for the trial by Meeker et al. (2016), they did not provide anonymity to the clinicians they considered. This bias tends to lead the study against a significant result because it will present inadequate information. Recall bias has little impact on the study under consideration because the clinicians should report their immediate actions but after a particular time.
Thus, this bias can influence research in an unclear way. Hotter et al. (2017) state that selection bias is a common problem for extensive studies. It has the potential to influence the clinical trial by Meeker et al. (2016) since it included 248 randomized clinicians. This bias is likely to lead the study to a generalized result. Performance bias can be a problem for a significant finding because it focused on subjective outcomes that were prescribing antibiotics (Meeker et al., 2016).
According to Higgins et al. (2011), detection bias can lead to a study against a significant result because researchers assess the findings according to their knowledge rather than objective information. For example, Meeker et al. (2016) could be biased toward the changes in antibiotics prescribing depending on the number of interventions. Attrition bias has the potential to result in less credible outcomes because a certain number of clinicians who refused to participate in the study can influence the findings (Meeker et al. 2016).
According to Page et al. (2016), different sources of bias have an impact on trials with subjective outcomes, and expectation bias is among them. It has the potential that the obtained result will be against the researchers expectations, which will make them doubt the findings. Finally, volunteer bias is said to have an impact on the clinical trial under consideration because the clinicians who agreed to participate do not represent a balanced state of affairs. Thus, Meeker et al. (2016) mention that most of their participants were women (565). It leads the study against the significant outcome because a greater part of men can change the findings.
The clinical trial by Meeker et al. (2016) has protected against different kinds of bias. Firstly, the researchers took their efforts to overcome detection bias by introducing blinding to the study. The scholars analyzed the rate of antibiotic prescription without knowing how many interventions a clinician has experienced. Secondly, attrition bias was overcome since the researchers managed to obtain feedback from every clinician who had started participating in the clinical trial. Finally, the scholars protected against volunteer bias because they chose and invited 353 clinicians to contribute to a randomized selection (Meeker et al., 2016, p. 565). Thus, various types of bias are present in clinical trials, but it is possible to overcome them.
Pragmatic and Explanatory Trials
In the world of science, there are two main types of clinical trials, and it refers to pragmatic and explanatory ones. The two variations provide researchers with various possibilities to evaluate particular phenomena with different objectives. Thus, it is necessary to understand the peculiarities of pragmatic and explanatory trials to understand the true nature of the results obtained. Furthermore, one can use the Pragmascope tool to determine a type of study.
To begin with, one should note that pragmatic trials focus on the general nature of studies. Williams, Burden-Teh, and Nunn (2015) argue that pragmatic clinical trials seek to determine the effectiveness of an intervention in a real-world setting to inform clinical decision making (p. 1). Such studies pay great attention to participants health conditions, age, sex, and others. At the same time, Merali and Wilson (2017) state that explanatory trials are optimized to demonstrate the efficacy of an intervention in a highly selected patient group (p. 404). Thus, it is said that pragmatic trials focus on external validity, while explanatory ones pay attention to internal validity.
At this point, it is reasonable to discuss the tradeoffs between the two types of research. Porzsolt et al. (2015) mention that the successful results of an explanatory study do not guarantee that a pragmatic trial will witness the same outcome. The different peculiarities of these approaches denote that decent work is between the two extremes of explanatory and pragmatic studies (Porzsolt et al., 2015, p.47). However, the use of methodological and statistical controls can change this equilibrium. Thus, the introduction of many additional elements will result in artificial conditions, which is a characteristic feature of explanatory trials.
As has already been mentioned, the Pragmascope tool can be used to determine the type of a particular study. In this case, Monthly High-Dose Vitamin D Treatment for the Prevention of Functional Decline: A Randomized Clinical Trial by Bischoff-Ferrari et al. (2016) is going to be analyzed. According to Tosh, Soares-Weiser, and Adams (2011), the tool includes ten domains that should be evaluated from 0 to 5. The eligibility criteria get four since the researchers base their study on the target population, but the round figure of 200 participants indicates a particular selection bias (Bischoff-Ferrari et al., 2016, p. 176).
Three points are given to the flexibility of experimental intervention since the study offers variations of the intervention, but it monitors the application. As for practitioner expertise, for both experimental and comparison interventions, these scales get two points each because the trial by Bischoff-Ferrari et al. (2016) makes the practitioners delve into many vitamin D treatment variations. The flexibility of comparison gets five points because the study does not offer any comparison of the intervention with everyday practice.
Five points for outcomes indicate that it is a pragmatic trial since they are essential for the health of participants, and the study analyzes the findings in the long term. Participant compliance was not considered in the study, which results in zero points. Practitioner adherence results in five points because there were no efforts to improve it. No data were excluded from the outcomes, which results in five points for the analysis of the outcomes scale. Follow-up intensity gets four points because Bischoff-Ferrari et al. (2016) state that follow-up is needed to confirm the findings (p. 182). A total of 35 points denotes that the study is a balance of pragmatic and explanatory trials.
References
Bischoff-Ferrari, H. A., Dawson-Hughes, B., Oray, J., Staehelin, H. B., Meyer, O. W., Theiler, R., & Egli, A. (2016). Monthly high-dose vitamin D treatment for the prevention of functional decline: A randomized clinical trial. JAMA, 176(2), 175-183.
Merali, Z., & Wilson J. R. (2017). Explanatory versus pragmatic trials: An essential concept in study design and interpretation. Clinical Spine Surgery, 30(9), 404-406.
Porzsolt, F., Galito Rocha, N. G., Toledo-Arruda, A., Thomaz, T. G., Moraes, C., Bessa-Guerra, T. R., & Weiss, C. (2015). Efficacy and effectiveness trials have different goals, use different tools, and generate different messages. Pragmatic and Observational Research, 6, 47-54.
Tosh, G., Soares-Weiser, K., & Adams, C. E. (2011). Pragmatic vs explanatory trials: The Pragmascope tool to help measure differences in protocols of mental health randomized controlled trials. Dialogues in Clinical Neuroscience, 13(2), 209-215.
Williams, H. C., Burden-Teh, E., & Nunn, A. J. (2015). What is a pragmatic clinical trial? Journal of Investigative Dermatology, 135, 1-3.
Althubaiti, A. (2016). Information bias in health research: Definition, pitfalls, and adjustment methods. Journal of Multidisciplinary Healthcare, 9, 211-217.
Higgins, J. P. T., Altman, D. G., Gotzsche, P. C., Juni, P., Moher, D., Oxman, A. D., & Sterne, J. A. C. (2011). The Cochrane Collaborations tool for assessing risk of bias in randomized trials. BMJ, 343, 1-9.
Hotter, B., Ulm, L., Hoffmann, S., Katan, M., Montaner, J., Bustamante, A., & Meisel, A. (2017). Selection bias in clinical stroke trials depending on ability to consent. BMC Neurology, 17(206), 1-7.
Meeker, D., Linder, J. A., Fox, C. R., Friedberg, M. W., Persell, S. D., Goldstein, N. J., & Doctor, J. N. (2016). Effect of behavioral interventions on inappropriate antibiotic prescribing among primary care practices: A randomized clinical trial. Jama, 315(6), 562-570.
Page, M. J., Higgins, J. P. T., Clayton, G., Sterne, J. A. C., Hróbjartsson, A., & Savovi, J. (2016). Empirical evidence of study design biases in randomized trials: Systematic review of meta-epidemiological studies. Plos One, 11(7). Web.
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