The patients in group B have a similar outcome: 30 total patients have died at the 12-month follow-up. Of these 30 patients, 15 died within three weeks after enrollment and the remaining 15 died between six weeks and 12 months. In group A, 30 total patients have died (the primary outcome of the trial) at the 12-month follow-up. In this example, there is a six-week waiting period between randomization and surgery. R, randomization RR, relative risk RRR, relative risk reduction. control (B = medical management only) in patients with cardiovascular disease.
#Emergenyc math 2017 trial#
Hypothetical prospective randomized controlled trial evaluating effectiveness of intervention (A = medical management + surgery) vs. 4, 5 In this article, the author presents and reviews a hypothetical example to illustrate how failure to apply this concept when interpreting results from a randomized trial can lead to misleading conclusions. The concept of analyzing patients according to which group they were originally assigned is called intention-to-treat analysis (or the intention-to-treat principle). Therefore, the goal of the investigator is to preserve this prognostic balance throughout the entire study, including the analysis phase after all data and outcomes have been recorded. One such way investigators and consumers of the medical literature may arrive at an incorrect and biased assessment of results is by failing to evaluate patients according to the group to which they were originally assigned.Īnything that disrupts the prognostic balance afforded by randomization introduces bias into the study and analysis. It is therefore important to preserve the integrity of randomization during the implementation of the study and in analysis. Incorrect analysis of the data can introduce bias even in the setting of the correct implementation of a valid random allocation sequence. If two (or more) groups are prognostically balanced, with the exception of the intervention, and an investigator observes a difference in outcomes, a sound argument can be made attributing the difference in result to the intervention under study.Īlthough recognized as the “gold standard” study design for establishing a causal relationship between intervention and outcome, the process of randomization alone does not wholly guard against bias. If done correctly, randomization yields groups that are balanced with regard to prognostic variables (variables that have an impact or an influence on developing the outcome under study). 1 – 3 Randomization affords an unbiased comparison between groups as it controls for both known and unknown confounding variables. The most effective way to establish a causal relationship between an intervention and outcome is through a randomized controlled trial (RCT) study design. This article will review the “intention-to-treat” principle and its converse, “per-protocol” analysis, and illustrate how using the wrong method of analysis can lead to a significantly biased assessment of the effectiveness of an intervention. If an intervention is truly effective (truth), an intention-to-treat analysis will provide an unbiased estimate of the efficacy of the intervention at the level of adherence in the study. The risk of bias is increased whenever treatment groups are not analyzed according to the group to which they were originally assigned.
This method preserves the benefits of randomization, which cannot be assumed when using other methods of analysis. This method allows the investigator (or consumer of the medical literature) to draw accurate (unbiased) conclusions regarding the effectiveness of an intervention. Intention-to-treat analysis is a method for analyzing results in a prospective randomized study where all participants who are randomized are included in the statistical analysis and analyzed according to the group they were originally assigned, regardless of what treatment (if any) they received. Excluding patients from the analysis who violated the research protocol (did not get their intended treatment) can have significant implications that impact the results and analysis of a study. However, patients in clinical trials do not always adhere to the protocol. Knowing the effect the intervention has on patients in clinical trials is critical for making both individual patient as well as population-based decisions. Clinicians, institutions, and policy makers use results from randomized controlled trials to make decisions regarding therapeutic interventions for their patients and populations.
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