What is the intent to treat approach in clinical trials?
Randomized clinical trials analyzed by the intent-to-treat approach provide unbiased comparisons among treatment groups. To avoid dilution of treatment effect, many people also perform an analysis by treatment actually received, although this method may introduce bias into the results.
What is an example of intent to treat?
For example, if a person in a study is randomized to a medical treatment but ends up getting surgery—or no treatment at all—their outcomes are still considered as part of the medical treatment group. In an ideal world, of course, intent to treat and actual treatment would be the same.
Do randomized clinical trials with intent-to-treat approaches provide unbiased comparisons?
Randomized clinical trials analyzed by the intent-to-treat approach provide unbiased comparisons among treatment groups. To avoid dilution of treatment effect, many people also perform an analysis by treatment actually received, although this method may introduce bias into the results. This paper pr …
What is the difference between as treated analysis and intention-to-treat analysis?
In an analysis by treatment received (as-treated analysis), the effect of a therapy is judged only in patients who actually receive the therapy; in an intention-to-treat analysis, patients are evaluated on the basis of the group to which they were randomly assigned, regardless of whether they actually received the therapy.
What is treatment on the treated effect?
the treatment effect on the treated group equals the treatment effect on the control group (layman terms: people in the control group would do as good as the treatment group if they were treated).
What is average treatment effect on untreated?
For instance, one might wish to project the effect of the smoking cessation intervention in a city that did not receive the intervention in order to gauge its potential impact when such intervention is actually implemented. This latter quantity is referred to as the average treatment effect on the untreated (ATU).
What is ITT ToT?
ITT (Intent to Treat) = People made eligible for treatment / intervention. TOT (Treatment on the Treated) = People who actually took the. treatment / intervention.
Is ToT the same as late?
The ToT relies on the same assumptions as the LATE and is estimated in the same way: using an instrumental variables (IV) approach; the only difference is that for the ToT none of the comparison group members received the treatment.
What are heterogeneous treatment effects?
Heterogeneity of treatment effect (HTE) is the nonrandom, explainable variability in the direction and magnitude of treatment effects for individuals within a population.
What is the sample average treatment effect?
In contrast, the sample average treatment effect (SATE) is the mean difference in the counterfactual outcomes for the study units. The sample parameter is easily interpretable and arguably the most relevant when the study units are not sampled from some specific super-population of interest.
What is the difference between ATE and ITT?
ITT -- effect of ASSIGNMENT on outcome. LATE -- effect of treatment on outcome FOR COMPLIERS. ATE -- effect of treatment on outcome for EVERYBODY.
What is the difference between ITT and PP?
While an analysis according to the ITT principle aims to preserve the original randomization and to avoid potential bias due to exclusion of patients, the aim of a per-protocol (PP) analysis is to identify a treatment effect which would occur under optimal conditions; i.e. to answer the question: what is the effect if ...
What is the difference between ITT and mITT?
Trials were categorized based on the "type" of intention-to-treat reporting as follows: ITT, trials reporting the use of standard ITT approach; mITT, trials reporting the use of a "modified intention-to-treat" approach; and "no ITT", trials not reporting the use of any intention-to-treat approach.
How is ITT effect calculated?
Estimating the ITT effect is straightforward. The ITT estimate is essentially the difference between the treatment group and control group mean (often adjusted for baseline differences), regardless of the degree of compliance.
What is the monotonicity assumption?
The monotonicity assumption implies there are no “defiers”, i.e., no patients who would be prescribed Treatment A when seen by a physician who usually prefers B and would be prescribed Treatment B when seen by a physician who usually prefers A (a rigorous and more general definition is provided below).
When is late equal to ATT?
The LATE equals the ATT in the case of an experiment with "one-sided non-compliance". That is, everyone not eligible (Z=0) cannot take the treatment D, but those assigned (Z=1) may or may not.
What is the intent to treat principle?
Intent-to-treat analysis aims to estimate the effect of treatment as offered, or as assigned. This analysis entails comparisons of randomized groups and include outcome data for all randomized participants regardless of their status regarding non-adherence to assigned treatment protocols and missed assessment encounters. Petkova and Teresi (5) attributed the term “intent-to-treat” to Hill (6) with a common refrain “once randomized, always analyzed.” FDA regulations emphasize this point in more formal language: “The intention-to-treat principle implies that the primary analysis should include all randomized subjects. Compliance with this principle would necessitate complete follow-up of all randomized subjects for study outcomes.” (4).
What is non-ITT approach?
A number of Non-ITT approaches that aim to estimate the as-received treatment effect through adjustments for non-adherence have been used in the medical literature in general and mental health research literature in particular. These methods are vulnerable to selection bias due to confounders, both measured and unmeasured, that might affect both the adherence status and outcome. Such selection bias may be classified into two categories, overt and hidden bias (21). Overt bias is attributable to observed confounders, and therefore can be explicitly adjusted for with statistical methods such as covariate adjustment or propensity scores analysis (e.g., Marcus paper in this volume). Such adjustments are made with the Non-ITT approaches. In contrast, hidden bias arises from unobserved confounders, and therefore cannot be explained entirely by covariate or propensity score adjustments of the Non-ITT approaches. Nonetheless, we consider below the instrumental variable approach as one Non-ITT method that attempts to account for hidden bias under several assumptions.
How does ITT differ from non-ITT?
It is important to recognize that the ITT and Non-ITT strategies differ not only in terms of the estimation procedure, but also in terms of the underlying research goal. Given the distinction between the effect of treatment “as assigned” corresponding to the ITT approach and the effect of treatment “as received” addressed by the Non-ITT strategies, the investigator needs to choose carefully which treatment effect is the primary research goal for a specific study. The as-received treatment effect of the Non-ITT approaches attempts to measure the effect of the experimental treatment relative to the control condition when all patients adhere to the assigned treatment condition. Such an effect is usually the primary research goal for the development of new treatments. In contrast, the as-assigned treatment effect of the ITT analysis is usually more pertinent for the evaluation of the effectiveness of the treatment in terms of the public health benefits of administering the treatment in the community in light of inevitable treatment non-adherence. A treatment with a high as-received treatment effect might not yield a high as-assigned treatment effect if the adherence rate is low when the treatment is offered. Such a distinction has implications for the relationships among data-based estimates of these effects for specific studies, which are addressed below in the sections on the ITT and non-ITT strategies. Related distinctions of ITT and non-ITT treatment effects are made in terms of treatment efficacy versus effectiveness (13). In the ensuing discussion, we refer to the treatment effects of Non-ITT analyses as “as-received treatment effects” and the effects of ITT analyses as “as-assigned treatment effects.”.
What is A01 in a randomized controlled trial?
A01 = observed average for participants who do receive the treatment in the randomized to control group (e.g., take the medication in the usual care group)
What is non-ITT analysis?
While the ITT principle has been the dominant design and analysis paradigm for clinical trials in a variety of contexts, other approaches, which we refer to as “Non-ITT analyses,” aim to estimate the effect of treatment as delivered or as received(as opposed to “as assigned” under the ITT approach) to account for treatment non-adherence. These Non- ITT analyses are commonly presented as secondary analyses in terms of as-treated or per-protocol treatment effects along with ITT results (7–9). Indeed, the FDA allows for such supplementary results: “Under many circumstances, it (use of the full analysis set) may also provide estimates of treatment effects that are more likely to mirror those observed in subsequent practice.” (4). Such sentiments have been voiced not only about data analysis, but also the need to collect adherence data as outcomes in addition to clinical outcomes (10–12,7).
Does ITT test for treatment non-adherence?
Furthermore, the ITT approach does not necessarily provide a valid test and estimate of the as-received treatment effect (5,7,11), especially when treatment non-adherence rate is high. Hence, in the presence of treatment non-adherence, the common assertion that the ITT approach under-estimates the true treatment effect only applies if the goal is evaluating the as-received treatment effect but not necessarily when the focus is on the as-assigned treatment effect, as discussed above. In contrast, the Non-ITT methods discussed next in the context of estimating the as-received treatment effect may be biased for both the as-received and as-assigned treatment effects.
Does treatment non-adherence continue?
In terms of timing, treatment non-adherence as defined by the study investigators may occur intermittently or continue until the end of study follow-up. In any case, it is important that the schedule for outcome data collection continue regardless of the type or timing of the treatment non-adherence. Finally, in one of the examples studied in this paper, treatment adherence was not defined with respect to the experimental treatment but instead with respect to physicians following guidelines for treating depressed patients. Here adherence was measured in both the treatment and control groups.
What is the FDA guidelines for clinical trials?
The FDA guideline further explains that the results of a clinical trial should be assessed not only for the subset of patients who completed the study , but also for the entire patient population randomized (the ITT analysis).[22,23]
Why is ITT analysis important?
ITT analysis reflects the practical clinical scenario because it admits noncompliance and protocol deviations. ITT analysis maintains prognostic balance generated from the original random treatment allocation. It gives an unbiased estimate of treatment effect.[3,4,14] If noncompliant subjects and dropouts are excluded from the final analysis, it might create important prognostic differences among treatment groups. Moreover, subjects may be noncompliant or may drop out from the study due to their response of treatment.[3]
Why does ITT analysis preserve sample size?
ITT analysis preserves the sample size because if noncompliant subjects and dropouts are excluded from the final analysis, it might significantly reduce the sample size, leading to reduced statistical power.[3]
What is the purpose of ITT analysis?
Randomized controlled trials often suffer from two major complications, i.e., noncompliance and missing outcomes. One potential solution to this problem is a statistical concept called intention-to-treat (ITT) analysis. ITT analysis includes every subject who is randomized according to randomized treatment assignment. It ignores noncompliance, protocol deviations, withdrawal, and anything that happens after randomization. ITT analysis maintains prognostic balance generated from the original random treatment allocation. In ITT analysis, estimate of treatment effect is generally conservative. A better application of the ITT approach is possible if complete outcome data are available for all randomized subjects. Per-protocol population is defined as a subset of the ITT population who completed the study without any major protocol violations.
Why are RCTs important?
RCTs are the ideal design in assessing the efficacy and safety of medicine. In an RCT, the study subjects is randomly allocated to receive one of the treatments under study after assessment of eligibility but before the intervention is administered. Randomization in clinical trials reduces bias. The purpose of the RCT is to ensure that the groups differ only with respect to the interventions being compared.[8]
Why is ITT analysis conservative?
In ITT analysis, estimate of treatment effect is generally conservative because of dilution due to noncompliance. Also, heterogeneity might be introduced if noncompliants, dropouts and compliant subjects are mixed together in the final analysis. Moreover, end-point data will differ markedly among noncompliant, dropouts and compliant subjects, and interpretation might become difficult if a large proportion of participants cross over to opposite treatment arms.[3,4,12,16,17] ITT analysis has been criticized for being too cautious and thus being more susceptible to type II error.[12,15]
What is ITT analysis?
ITT analysis limits inferences based on arbitrary or ad hocsubgroups of patients in the trial and emphasizes greater accountability for all patients enrolled in the study. Also, it minimizes type I error due to cautious approach and allows for the greatest generalizability.[15]
What is intention to treat analysis?
In an analysis by treatment received ( as-treated analysis ), the effect of a therapy is judged only in patients who actually receive the therapy; in an intention-to-treat analysis, patients are evaluated on the basis of the group to which they were randomly assigned, regardless of whether they actually received the therapy. Although as-treated analyses may seem more intuitive, they have the potential to introduce significant biases. Patients who do not adhere to a given therapy may differ significantly from those who do and often have higher event rates than do adherent patients. In addition, compliance may not be balanced between groups, particularly for therapies with significant side effects. Thus, the exclusion of subjects who do not continue the assigned therapy for whatever reason tends to bias the interpretation toward a conclusion of greater efficacy of the therapy being evaluated because only compliant patients are studied. Such an approach may confirm biological efficacy but does not establish real-world effectiveness; in clinical practice, the overall performance of a given therapy must take into account patients who cannot or will not adhere. 56 Intention-to-treat analysis provides an estimate of treatment effect that tends to be more conservative, and it remains the “gold standard” for the interpretation of RCTs. In studies with high rates of noncompliance, as-treated analyses may be performed as a secondary analysis in order to investigate biological efficacy, but such analyses should be seen as supplementary to the intention-to-treat analysis.
What is missing data in a clinical trial?
Missing data is, to some extent, unavoidable in any clinical trial with long-term follow-up. Subjects may withdraw consent for any number of reasons; subjects may move out of the state, transition between care providers, etc., thus becoming lost to follow-up (LTFU). These missing outcome data can be addressed through imputation procedures as described above; however, too much missing data can jeopardize the validity of a clinical trial. It is best to both prespecify a plan for handling missing data in the analysis and make extensive efforts to avoid its occurrence. During protocol development, investigators should consider the patient population, the treatment regimen and the follow-up requirements very carefully, to identify elements which might impact adherence. The protocol should clearly specify the difference between discontinuation from study treatment and discontinuation from the study; all randomized subjects should be followed until the primary outcome has been obtained regardless of whether the study treatment has been discontinued, unless consent has been withdrawn. 48 The primary outcome of TBI trials is generally ascertained at 6 months postinjury; much can happen between hospital discharge, which might occur as early as a few days after injury, and the 6-month visit. Frequent, perhaps monthly, contact with subjects, to maintain updated contact information and as a reminder of scheduled visits, may help to minimize LTFU. The selection of the primary endpoint may also be a factor. The GOSE includes death as a category, which means that death is not a cause of missing data on the primary outcome, nor is such poor neurological functioning as to be unable to cooperate with testing. This is of course not true for all endpoints, as discussed in the section Outcome Measures. An outcome such as the GOSE, which can be administered over the phone and can be answered by a proxy if necessary, might result in less missing outcome data than a neuropsychological outcome, which requires a clinic visit by the subject. One might consider obtaining the outcome over the phone when the visit is scheduled or when a reminder call is placed; if the subject fails to appear for the clinic visit, the outcome data would be available, and if the subject does appear, the data could be updated to reflect their current status. Simple measures to assist with travel could make a dramatic impact on follow-up rates associated with clinic visits.
How many eyes were treated with bevasiranib?
The intent-to-treat population consisted of 129 eyes, the modified intent-to-treat population consisted of 126 eyes, and the per-protocol analysis consisted of 110 eyes which received both intravitreal bevasiranib injections and at least one follow-up visit. The safety analyses were conducted on the 127 eyes which received at least one bevasiranib injection (one eye was treated with bevasiranib but did not return for required follow-up visits). The efficacy analyses were based on the 110 eyes in the per-protocol analysis. The three groups in the per-protocol analysis were well balanced with respect to visual acuity and lesion type. The numbers of eyes with previous treatment for CNV were also similar between groups. Bevasiranib had an excellent safety profile with no cases of endophthalmitis and only one eye in the 3.0-mg group developed uveitis, which resolved with topical steroids. There were no unexpected systemic adverse events related to the drug and prior pharmacokinetic studies showed no detectable systemic absorption of bevasiranib following intravitreal injection.
What is a single imputation?
Single imputation [e.g., Last Observation Carried Forward (LOCF), best case, worst case) approaches create a single complete data set. LOCF can be applied in longitudinal studies; the last observed outcome data are carried forward to replace the missing outcome. LOCF is not appropriate for degenerative disorders where the outcome state is expected to decline steadily over time. The LOCF imputation may be more reliable at the extremes of the outcome distribution. A subject who is deceased at 3 months will of course remain so at 6 months, and a subject who has achieved a 3-month GOSE of Good Recovery will likely remain so at 6 months. In the middle of the distribution, however, the 3 months lapsing between outcome assessments may leave considerable room for improvement across outcome states. Alternatively, the best- and worst-case approaches impute missing outcomes using the best and worst possible outcomes, respectively. A combination of these is also possible, where the best possible outcome is imputed for subjects in the control arm and the worst possible outcome is imputed for subjects in the treatment arm, resulting in a conservative estimate of treatment efficacy. The single imputation approaches ignore the uncertainty associated with the imputation procedure, resulting in P -values that are biased downward, i.e., that will reject the hypothesis of no treatment effect too often. 48
What is modified ITT analysis?
A modified ITT analysis includes all randomized patients that meet a specific minimum standard or simple set of criteria. The criteria should be simple, objective, and very straightforward. The criteria should not be related to the outcome (e.g., if you know that patients with lower socioeconomic status will be less likely to respond to your intervention, you cannot use socioeconomic status as a criterion). Using subjective criteria defeats the purpose of a modified ITT. The more common modified ITT will include all patients who received at least one dose of the study intervention (regardless of what happened to the patient after the initial dose). The study protocol should pre-specify a modified ITT analysis and its criteria. Otherwise, selection bias can occur (e.g., if you find that a certain characteristic correlates with poor intervention response, you could later exclude patients with that characteristic from the analysis.) Another common modified ITT analysis excludes all patients who had major protocol violations (e.g., did not meet the study selection criteria). Usually the primary analysis should be an ITT analysis, but in some rare cases (e.g., noninferiority or equivalence trials), a modified ITT may be appropriate. Modified ITT analyses are relatively common for secondary or exploratory analysis. Determining whether to include sites with protocol violations can be a challenge. ( Figure 14.4 :.
How does paradoxical intention help insomnia?
Turner and Michael Asher in the 1970s. This technique is designed mainly to address the excessive performance anxiety which contributes to sleep-onset difficulties. This treatment instructs the insomnia sufferer to attempt to stay awake as long as possible after retiring while lying passively in bed. As can be surmised, the insomnia sufferer is placed in the paradoxical position of having to perform the activity of not sleeping when in bed. By doing so, it eliminates the performance anxiety and challenge of trying to fall asleep.
When designing a trial, the sample size should be inflated to account for the anticipated missing data rate?
When designing a trial, the sample size should be inflated to account for the anticipated missing data rate to avoid low power. If subjects with missing data are planned to be excluded from the analysis, then an inflation factor equal to the anticipated proportion of subjects with missing data is sufficient. However, that same inflation factor is insufficient if the trial will be analyzed under the Intention-to-Treat principle. Instead the inflation factor should account for both the anticipated missingness rate and the associated dilution of the treatment effect. 48
What is intent to treat?
When used in medical research studies, the phrase intent to treat refers to a type of study design. In this type of study, scientists analyze the results of their study based on what the patients were told to do. In other words, doctors look at patient results based on how they were supposed to be treated, rather than what actually happened.
Why use intent to treat model?
Intent to treat explicitly acknowledges the fact that how drugs work in the lab may have very little to do with how they work in the field. In fact, one of the reasons that promising drugs are often so disappointing ...
Why is intent to treat study less promising than earlier?
When an intent to treat study is less promising than earlier, more closely observed studies, scientists will often ask why. This may be an attempt to salvage what had been considered to be a promising treatment.
Why are there drawbacks to treating trials?
Drawbacks. Not all people like intent to treat trials. One reason is that they can underestimate a medication's potential effectiveness. For example, early trials of pre-exposure prophylaxis for HIV in gay men showed that the treatment seemed relatively effective... but only in individuals who took it regularly.
Why are promising drugs so disappointing?
In fact, one of the reasons that promising drugs are often so disappointing when they're released is that people don't take them the way they do in the studies. (There are also often other differences between real-world patients and research patients.)
Do scientists want to know how drugs work?
The biggest one is that, from a practical standpoint, they simply make sense. Scientists want to know how drugs or treatments will work in the real world. In the real world, not everyone takes drugs as prescribed. Not everyone ends up getting the surgery they are recommended.
Can you judge a drug if you don't take it?
Some people say that a drug doesn't work if patients won't take it. Others say that you can't judge a medication if patients aren't taking it as prescribed. Both sides have a point. There is no perfect answer.
What is intention to treat analysis?
In an analysis by treatment received ( as-treated analysis ), the effect of a therapy is judged only in patients who actually receive the therapy; in an intention-to-treat analysis, patients are evaluated on the basis of the group to which they were randomly assigned, regardless of whether they actually received the therapy. Although as-treated analyses may seem more intuitive, they have the potential to introduce significant biases. Patients who do not adhere to a given therapy may differ significantly from those who do and often have higher event rates than do adherent patients. In addition, compliance may not be balanced between groups, particularly for therapies with significant side effects. Thus, the exclusion of subjects who do not continue the assigned therapy for whatever reason tends to bias the interpretation toward a conclusion of greater efficacy of the therapy being evaluated because only compliant patients are studied. Such an approach may confirm biological efficacy but does not establish real-world effectiveness; in clinical practice, the overall performance of a given therapy must take into account patients who cannot or will not adhere. 56 Intention-to-treat analysis provides an estimate of treatment effect that tends to be more conservative, and it remains the “gold standard” for the interpretation of RCTs. In studies with high rates of noncompliance, as-treated analyses may be performed as a secondary analysis in order to investigate biological efficacy, but such analyses should be seen as supplementary to the intention-to-treat analysis.
What is missing data in a clinical trial?
Missing data is, to some extent, unavoidable in any clinical trial with long-term follow-up. Subjects may withdraw consent for any number of reasons; subjects may move out of the state, transition between care providers, etc., thus becoming lost to follow-up (LTFU). These missing outcome data can be addressed through imputation procedures as described above; however, too much missing data can jeopardize the validity of a clinical trial. It is best to both prespecify a plan for handling missing data in the analysis and make extensive efforts to avoid its occurrence. During protocol development, investigators should consider the patient population, the treatment regimen and the follow-up requirements very carefully, to identify elements which might impact adherence. The protocol should clearly specify the difference between discontinuation from study treatment and discontinuation from the study; all randomized subjects should be followed until the primary outcome has been obtained regardless of whether the study treatment has been discontinued, unless consent has been withdrawn. 48 The primary outcome of TBI trials is generally ascertained at 6 months postinjury; much can happen between hospital discharge, which might occur as early as a few days after injury, and the 6-month visit. Frequent, perhaps monthly, contact with subjects, to maintain updated contact information and as a reminder of scheduled visits, may help to minimize LTFU. The selection of the primary endpoint may also be a factor. The GOSE includes death as a category, which means that death is not a cause of missing data on the primary outcome, nor is such poor neurological functioning as to be unable to cooperate with testing. This is of course not true for all endpoints, as discussed in the section Outcome Measures. An outcome such as the GOSE, which can be administered over the phone and can be answered by a proxy if necessary, might result in less missing outcome data than a neuropsychological outcome, which requires a clinic visit by the subject. One might consider obtaining the outcome over the phone when the visit is scheduled or when a reminder call is placed; if the subject fails to appear for the clinic visit, the outcome data would be available, and if the subject does appear, the data could be updated to reflect their current status. Simple measures to assist with travel could make a dramatic impact on follow-up rates associated with clinic visits.
How many eyes were treated with bevasiranib?
The intent-to-treat population consisted of 129 eyes, the modified intent-to-treat population consisted of 126 eyes, and the per-protocol analysis consisted of 110 eyes which received both intravitreal bevasiranib injections and at least one follow-up visit. The safety analyses were conducted on the 127 eyes which received at least one bevasiranib injection (one eye was treated with bevasiranib but did not return for required follow-up visits). The efficacy analyses were based on the 110 eyes in the per-protocol analysis. The three groups in the per-protocol analysis were well balanced with respect to visual acuity and lesion type. The numbers of eyes with previous treatment for CNV were also similar between groups. Bevasiranib had an excellent safety profile with no cases of endophthalmitis and only one eye in the 3.0-mg group developed uveitis, which resolved with topical steroids. There were no unexpected systemic adverse events related to the drug and prior pharmacokinetic studies showed no detectable systemic absorption of bevasiranib following intravitreal injection.
What is a single imputation?
Single imputation [e.g., Last Observation Carried Forward (LOCF), best case, worst case) approaches create a single complete data set. LOCF can be applied in longitudinal studies; the last observed outcome data are carried forward to replace the missing outcome. LOCF is not appropriate for degenerative disorders where the outcome state is expected to decline steadily over time. The LOCF imputation may be more reliable at the extremes of the outcome distribution. A subject who is deceased at 3 months will of course remain so at 6 months, and a subject who has achieved a 3-month GOSE of Good Recovery will likely remain so at 6 months. In the middle of the distribution, however, the 3 months lapsing between outcome assessments may leave considerable room for improvement across outcome states. Alternatively, the best- and worst-case approaches impute missing outcomes using the best and worst possible outcomes, respectively. A combination of these is also possible, where the best possible outcome is imputed for subjects in the control arm and the worst possible outcome is imputed for subjects in the treatment arm, resulting in a conservative estimate of treatment efficacy. The single imputation approaches ignore the uncertainty associated with the imputation procedure, resulting in P -values that are biased downward, i.e., that will reject the hypothesis of no treatment effect too often. 48
What is modified ITT analysis?
A modified ITT analysis includes all randomized patients that meet a specific minimum standard or simple set of criteria. The criteria should be simple, objective, and very straightforward. The criteria should not be related to the outcome (e.g., if you know that patients with lower socioeconomic status will be less likely to respond to your intervention, you cannot use socioeconomic status as a criterion). Using subjective criteria defeats the purpose of a modified ITT. The more common modified ITT will include all patients who received at least one dose of the study intervention (regardless of what happened to the patient after the initial dose). The study protocol should pre-specify a modified ITT analysis and its criteria. Otherwise, selection bias can occur (e.g., if you find that a certain characteristic correlates with poor intervention response, you could later exclude patients with that characteristic from the analysis.) Another common modified ITT analysis excludes all patients who had major protocol violations (e.g., did not meet the study selection criteria). Usually the primary analysis should be an ITT analysis, but in some rare cases (e.g., noninferiority or equivalence trials), a modified ITT may be appropriate. Modified ITT analyses are relatively common for secondary or exploratory analysis. Determining whether to include sites with protocol violations can be a challenge. ( Figure 14.4 :.
How does paradoxical intention help insomnia?
Turner and Michael Asher in the 1970s. This technique is designed mainly to address the excessive performance anxiety which contributes to sleep-onset difficulties. This treatment instructs the insomnia sufferer to attempt to stay awake as long as possible after retiring while lying passively in bed. As can be surmised, the insomnia sufferer is placed in the paradoxical position of having to perform the activity of not sleeping when in bed. By doing so, it eliminates the performance anxiety and challenge of trying to fall asleep.
When designing a trial, the sample size should be inflated to account for the anticipated missing data rate?
When designing a trial, the sample size should be inflated to account for the anticipated missing data rate to avoid low power. If subjects with missing data are planned to be excluded from the analysis, then an inflation factor equal to the anticipated proportion of subjects with missing data is sufficient. However, that same inflation factor is insufficient if the trial will be analyzed under the Intention-to-Treat principle. Instead the inflation factor should account for both the anticipated missingness rate and the associated dilution of the treatment effect. 48
What is intention to treat?
Typically, ‘Intention-to-Treat’ population can be simply defined as all patients who are randomized. However, the formal definition of the “Intention-to-Treat” population contains several other concepts: The formal definition of the "Intention to Treat" is usually referred to the one suggested by Fisher, LD et al.
What does "as treated" mean in medical terminology?
The term “as treated” means that when we do analysis/summaries, the treatment assignment is based on the actual treatment the patients receive, not the treatment the patients are supposed to receive.
What happens if some randomized subjects do not receive the randomly alloccated treatment?
If some randomized subjects do not receive the randomly alloccated treatment and if there are randomization errors , ‘ as treated’ population will be different from ITT population or 'as randomized' population.
What is ITT analysis?
ITT analysis is usually described as “once randomized, always analyzed”. ITT analysis is referred as ‘as randomized’ - the opposite of the term ‘as treated’. In majority of cases, if all randomized subjects receive their allocated treatments and if there is no randomization error, the ‘as treated’ analysis will be the same as 'as randomized' ...
Why should a safety analysis be based on the as treated population?
Typically, the safety analysis should always be based on the 'as treated' population since it really reflects the safety of patients under each treatment.
When we design a non-inferiority trial to support the product registration, the regulatory agencies may suggest?
When we design a non-inferiority trial to support the product registration, the regulatory agencies may suggest the statistical analysis using ‘as treated’ population. In FDA’s Guidance for Industry Non-Inferiority Clinical Trials, it suggested the ‘as-treated’ analysis for primary efficacy endpoint:
Is ITT analysis based on randomization?
In this case, to strictly follow the ITT principle, the ITT analysis will include all 121 subjects in r-proUK group and all 59 subjects in Control group - purely based on the randomization, not based on whether or not the randomized subject receive the actual treatment or receive the wrong treatment. The ITT analysis was indeed used in the paper. However, since the PROACT II study was a proof of concept study, the analysis based on 'as treated' population should be used as well.