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Intention-to-treat analysis and its application in orthodontics
Journal of the World Federation of Orthodontists 1 (2012) e45
Contents lists available at SciVerse ScienceDirect
Journal of the World Federation of Orthodontists journal homepage: www.jwfo.org
Editorial
Intention-to-treat analysis and its application in orthodontics
Over the years I have often witnessed orthodontists blame “noncompliant patients” for poor treatment outcomes. Ideally, however, patients should not be expected to fit a given therapy, but rather we, the orthodontists, should develop therapies suitable to each patient. Only thus can we further extend the benefits of orthodontic treatment to people around the world. One way to address this issue by thinking outside the box is to employ an example of treatment in the field of healthcare but outside the realm of orthodontics. Let us examine the use of chemotherapy to treat cancer. Some tumors are treated with chemotherapy whose side effects vary in intensity among individuals. Sometimes, the treatment regimen carries on for as long as the patient can tolerate the medication. However, some patients are forced to interrupt the treatment due to the drug’s side effects. If patients discontinue treatment, should they be branded as “noncompliant”? Obviously not. The same rationale could easily apply to many conditions in the health sciences, such as bipolar disorders and mood stabilizers in psychiatry, obstructive sleep apnea and CPAP devices in sleep medicine, and clearly some orthodontic appliances in orthodontics. Understanding this phenomenon affords an insight into two practical implications, one clinical and one methodological. Certain patients find it hard to wear some of the appliances that we prescribe in orthodontics. For example, the use of headgear might negatively impact on the sleep of some individuals and interfering with the use of appliances since some patients refuse to wear them during their daily activities. Hence, we should bear in mind such shortcomings and offer viable alternatives for those patients with specific limitations. The impact on adherence to treatment can also have methodological implications. As long as there are drop-outs from clinical trials there is a natural increase in treatment effects. Let me give you an example. Let us imagine that researchers are interested in assessing the efficacy of a new intraoral appliance for the correction of Angle Class II malocclusion. After sample size calculation, they concluded that a minimum of 109 individuals should be randomly assigned to three groups. They estimated a drop-out rate of up to 10%. Then, 120 patients were included in the Control, Kloehn, and New Appliance groups. Our theoretical researchers completed the treatment of all patients that remained in the study and assessed the outcomes. Table 1 summarizes the results of our randomized clinical trial. The control group was removed from data presentation and the level of significance for the Chi-square test was set at 5%.
2212-4438/$ e see front matter Ó 2012 World Federation of Orthodontists. http://dx.doi.org/10.1016/j.ejwf.2012.08.002
Table 1 Results of a hypothetical RCT that compares control individuals with those using the Kloehn appliance, and a New Appliance for the correction of Class II malocclusion. For theoretical purposes only, the outcome was simplified as Success and Failure, and the Control group removed from the data presentation Group
Success (%)
Failure (%)
Kloehn (n ¼ 114) New Appliance (n ¼ 92)
99 (88.1) 90 (97.8)
13 (11.8) 2 (2.2)
Success and failure differed between groups (X2 ¼ 6.59; P ¼ .01).
The results show a significant difference (P ¼ .01), and a success rate of 97.8% for the New Appliance vs. 88.1% for the Kloehn appliance. So, according to the data of this hypothetical study, which appliance yielded the best results? The answer seems obvious. The Kloehn appliance is better than the New Appliance. No, this is no mistake. The results show that the Kloehn is superior. One should be concerned when the frequency of, or reasons for, dropping out differ between intervention groups. The point in question here is that drop-outs must be part of the results of most, if not all, clinical trials. A higher frequency of patients abandoning treatment in one group may suggest that the treatment is too painful, or produces a negative aesthetic impact, or has some other characteristic that undermines the compliance rate of patients. If a patient does not conclude a given treatment, such treatment should be deemed a failure. A proper reading of the results in Table 1 shows that out of the original 120 patients included in each group, 99 achieved success in the Kloehn group while 90 were successful in the New Appliance Group. One way to mitigate such deficiency is to apply intention-totreat analysis (ITA), or a clear, accurate description of who was included in each analysis. ITA is not a statistical approach to data. As a matter of fact, it corresponds to analyzing the groups exactly as randomized, regardless of whether they received, or adhered to, the allocated intervention. This approach minimizes biases. Unfortunately, it is still seldom mentioned in the orthodontic literature. I recommend that you pay attention to this relevant detail while reading clinical trials. Numbers should be interpreted, not read. Jorge Faber, Editor-in-Chief Brasilia, Brazil
Contents lists available at SciVerse ScienceDirect
Journal of the World Federation of Orthodontists journal homepage: www.jwfo.org
Editorial
Intention-to-treat analysis and its application in orthodontics
Over the years I have often witnessed orthodontists blame “noncompliant patients” for poor treatment outcomes. Ideally, however, patients should not be expected to fit a given therapy, but rather we, the orthodontists, should develop therapies suitable to each patient. Only thus can we further extend the benefits of orthodontic treatment to people around the world. One way to address this issue by thinking outside the box is to employ an example of treatment in the field of healthcare but outside the realm of orthodontics. Let us examine the use of chemotherapy to treat cancer. Some tumors are treated with chemotherapy whose side effects vary in intensity among individuals. Sometimes, the treatment regimen carries on for as long as the patient can tolerate the medication. However, some patients are forced to interrupt the treatment due to the drug’s side effects. If patients discontinue treatment, should they be branded as “noncompliant”? Obviously not. The same rationale could easily apply to many conditions in the health sciences, such as bipolar disorders and mood stabilizers in psychiatry, obstructive sleep apnea and CPAP devices in sleep medicine, and clearly some orthodontic appliances in orthodontics. Understanding this phenomenon affords an insight into two practical implications, one clinical and one methodological. Certain patients find it hard to wear some of the appliances that we prescribe in orthodontics. For example, the use of headgear might negatively impact on the sleep of some individuals and interfering with the use of appliances since some patients refuse to wear them during their daily activities. Hence, we should bear in mind such shortcomings and offer viable alternatives for those patients with specific limitations. The impact on adherence to treatment can also have methodological implications. As long as there are drop-outs from clinical trials there is a natural increase in treatment effects. Let me give you an example. Let us imagine that researchers are interested in assessing the efficacy of a new intraoral appliance for the correction of Angle Class II malocclusion. After sample size calculation, they concluded that a minimum of 109 individuals should be randomly assigned to three groups. They estimated a drop-out rate of up to 10%. Then, 120 patients were included in the Control, Kloehn, and New Appliance groups. Our theoretical researchers completed the treatment of all patients that remained in the study and assessed the outcomes. Table 1 summarizes the results of our randomized clinical trial. The control group was removed from data presentation and the level of significance for the Chi-square test was set at 5%.
2212-4438/$ e see front matter Ó 2012 World Federation of Orthodontists. http://dx.doi.org/10.1016/j.ejwf.2012.08.002
Table 1 Results of a hypothetical RCT that compares control individuals with those using the Kloehn appliance, and a New Appliance for the correction of Class II malocclusion. For theoretical purposes only, the outcome was simplified as Success and Failure, and the Control group removed from the data presentation Group
Success (%)
Failure (%)
Kloehn (n ¼ 114) New Appliance (n ¼ 92)
99 (88.1) 90 (97.8)
13 (11.8) 2 (2.2)
Success and failure differed between groups (X2 ¼ 6.59; P ¼ .01).
The results show a significant difference (P ¼ .01), and a success rate of 97.8% for the New Appliance vs. 88.1% for the Kloehn appliance. So, according to the data of this hypothetical study, which appliance yielded the best results? The answer seems obvious. The Kloehn appliance is better than the New Appliance. No, this is no mistake. The results show that the Kloehn is superior. One should be concerned when the frequency of, or reasons for, dropping out differ between intervention groups. The point in question here is that drop-outs must be part of the results of most, if not all, clinical trials. A higher frequency of patients abandoning treatment in one group may suggest that the treatment is too painful, or produces a negative aesthetic impact, or has some other characteristic that undermines the compliance rate of patients. If a patient does not conclude a given treatment, such treatment should be deemed a failure. A proper reading of the results in Table 1 shows that out of the original 120 patients included in each group, 99 achieved success in the Kloehn group while 90 were successful in the New Appliance Group. One way to mitigate such deficiency is to apply intention-totreat analysis (ITA), or a clear, accurate description of who was included in each analysis. ITA is not a statistical approach to data. As a matter of fact, it corresponds to analyzing the groups exactly as randomized, regardless of whether they received, or adhered to, the allocated intervention. This approach minimizes biases. Unfortunately, it is still seldom mentioned in the orthodontic literature. I recommend that you pay attention to this relevant detail while reading clinical trials. Numbers should be interpreted, not read. Jorge Faber, Editor-in-Chief Brasilia, Brazil