Intention To Treat

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Intention To Treat
From Wikipedia, the free encyclopedia In medicine an intention-to-treat ( ITT ) analysis of the results of a randomized controlled trial is based on the initial treatment assignment and not on the treatment eventually received. ITT analysis is intended to avoid various misleading artifacts that can arise in intervention research such as non-random attrition of participants from the study or crossover,

ITT is also simpler than other forms of study design and analysis, because it does not require observation of compliance status for units assigned to different treatments or incorporation of compliance into the analysis. Although ITT analysis is widely employed in published clinical trials, it can be incorrectly described and there are some issues with its application.

Furthermore, there is no consensus on how to carry out an ITT analysis in the presence of missing outcome data.

What is the difference between ITT and PP?

Terminology breakdown – Let’s start with some definitions. The World Health Organization (WHO) defines vaccine efficacy as “a measure of how much the vaccine lowered the risk of getting sick.” In randomized controlled trials, researchers randomly assign participants to a group that receives the vaccine being studied or a group that receives a placebo.

Once the participants are vaccinated, researchers study each group’s outcomes to determine if the vaccine is safe and effective. In an ITT analysis, participants are studied in their randomized groups regardless of whether they complete the study vaccination or receive another intervention instead of the assigned treatment.

Participants may drop out of studies for myriad reasons. For example, they may have moved away from the study location. ITT considers all randomized participants in the analysis, whether they drop out or not. Conversely, in a PP analysis, researchers only analyze data from those who strictly adhered to the study protocol.

What is the purpose of the intention-to-treat principle?

JAMA Guide to Statistics and Methods July 2, 2014 JAMA.2014;312(1):85-86. doi:10.1001/jama.2014.7523

Original Investigation Erythropoietin for Traumatic Brain Injury Claudia S. Robertson, MD; H. Julia Hannay, PhD; José-Miguel Yamal, PhD; Shankar Gopinath, MD; J. Clay Goodman, MD; Barbara C. Tilley, PhD; and the Epo Severe TBI Trial Investigators; Athena Baldwin, PAC; Lucia Rivera Lara, MD; Hector Saucedo-Crespo, MD; Osama Ahmed, MD; Santhosh Sadasivan, MD; Luciano Ponce, MD; Jovanny Cruz-Navarro, MD; Hazem Shahin, MD; Imoigele P. Aisiku, MD; Pratik Doshi, MD; Alex Valadka, MD; Leslie Neipert, PhD; Jace M. Waguspack, MS; M. Laura Rubin, MS; Julia S. Benoit, PhD; Paul Swank, PhD JAMA Guide to Statistics and Methods Interpreting the Results of Intention-to-Treat, Per-Protocol, and As-Treated Analysis Valerie A. Smith, DrPH; Cynthia J. Coffman, PhD; Michael G. Hudgens, PhD

Full Text The intention-to-treat (ITT) principle is a cornerstone in the interpretation of randomized clinical trials (RCTs) conducted with the goal of influencing the selection of medical therapy for well-defined groups of patients. The ITT principle defines both the study population included in the primary efficacy analysis and how the outcomes are analyzed.

Under ITT, study participants are analyzed as members of the treatment group to which they were randomized regardless of their adherence to, or whether they received, the intended treatment.1 – 3 For example, in a trial in which patients are randomized to receive either treatment A or treatment B, a patient may be randomized to receive treatment A but erroneously receive treatment B, or never receive any treatment, or not adhere to treatment A.

In all of these situations, the patient would be included in group A when comparing treatment outcomes using an ITT analysis. Eliminating study participants who were randomized but not treated or moving participants between treatment groups according to the treatment they received would violate the ITT principle.

What is the difference between treatment and intention-to-treat?

Intention-to-Treat Analysis – Intention-to-treat analyses are counterintuitive. 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.

  1. Although as-treated analyses may seem more intuitive, they have the potential to introduce significant biases.
  2. 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.
  3. 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.

What is the ITT effect?

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 difference between per protocol and as treated analysis?

ITT, PP, and AT Approaches to Analyses – The ITT principle is the most commonly used approach for the primary analysis of RCTs. It measures the effect of assigning patients to treatment, which includes differences in individuals’ adherence.2 With the ITT approach, all randomized patients are included in the analysis, based on the groups to which they were initially randomly assigned.

The PP and AT analyses estimate the effect of receiving a treatment.3, 4 Per-protocol only analyzes data from participants who follow the protocol, excluding their data after they become nonadherent. AT analyses consider the treatment actually received by the participant, without regard to adherence to their randomization assignment.

As-treated and PP analyses are not simple to interpret because of the potential loss of an important benefit of randomization: the elimination of systematic bias in treatment assignment. Selection bias and confounding in the treatment effect estimate arises if patients who are more adherent with the assigned treatment differ in ways that also influence their outcomes compared with those who are less adherent.

What is PP in clinical trials?

What is a PP Population, or ‘Per Protocol’ Population in a Clinical Trial? The per protocol population, or PP population is usually defined as all patients completing the study without major protocol deviations – that is, those who followed the rules of the study.

What are the disadvantages of intention-to-treat?

Intention to Treat Epidemiological studies are usually analysed on the basis of intention to treat because this reduces due to non-random dropout from the study. The alternative approach is known as on treatment analysis. Pros of intention to treat analysis:

reduces confounding due to non-random drop out or non-compliance with the intervention studied more pragmatic for answering real world questions because it is the effect of offering the treatment that is analysed as opposed to the effect of taking it

Cons of intention to treat analysis:

reduces statistical power and may thus fail to demonstrate a real effect (wash-in periods are sometimes used to minimise power loss by ensuring tolerability of the intervention and thus reducing non-compliance)

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Copyright © 2000-2018 StatsDirect Limited, all rights reserved., : Intention to Treat

How many types of treatment do we have?

Theoretically, there are three classifications of medical treatment : Curative – to cure a patient of an illness. Palliative – to relieve symptoms from an illness. Preventative – to avoid the onset of an illness.

Is treatment an intervention?

In medicine, a treatment, procedure, or other action taken to prevent or treat disease, or improve health in other ways.

What is the difference between ATT and ATE?

Notation and g-computation steps – In the remaining, we will use capital letters to refer to random variables and lowercase letters to represent the specific realizations of the corresponding random variables. Let A denote the treatment, with a and a * as its index and reference values, Y the outcome, C a set of covariates sufficient for confounding control, and Y a the potential outcome that would have occurred had treatment A, perhaps contrary to fact, been set to a, Each subject in the population has a pair of potential outcomes, one being observed and the other being counterfactual. Y a is the observed outcome had the subject received the treatment A  =  a whereas Y a* is the counterfactual outcome. Conversely, for subjects who receive placebo (control), Y a* is the observed outcome while Y a is the counterfactual outcome. The ATE, defined as E ( Y a − Y a * ), is the average marginal treatment effect in the total population. The ATT, defined as E ( Y a − Y a * | A = a ) and the ATU, defined as E ( Y a − Y a * | A = a * ), measure the marginal treatment effect in the subpopulation that received the treatment and the subpopulation that did not, respectively. When the assumptions of consistency, conditional exchangeability given C, and positivity are met, the target causal parameters ATE, ATT and ATU on the risk difference scale can be estimated using observational data and the following estimators: ATE = Σ c What is the ITT?

An invitation to tender (ITT) is the initial step in competitive tendering, in which suppliers and contractors are invited to provide offers for supply or service contracts, the ITT is one process in IT procurement.

Is intention-to-treat analysis good?

Intention-to-Treat Analysis – Intention-to-treat analyses are counterintuitive. 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.

  1. Although as-treated analyses may seem more intuitive, they have the potential to introduce significant biases.
  2. 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.
  3. 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.

What is the difference between intention to diagnose and per protocol?

The Per-Protocol (PP) principle – 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.

any major protocol deviations (e.g. intake of a concomitant medication affecting the primary endpoint) non-availability of measurements of the primary endpoint non-sufficient exposure to study treatment

There might be further criteria for selecting a PP population; however, the following approaches are essential:

The assignment to the PP analysis set needs to take place prior to the analysis (if possible in a blinded manner). Deviations that might be affected by the actual treatment should not be used as exclusion criteria: e.g., “premature discontinuation from the study” might not be a good choice of criterion for exclusion from the PP analysis, if this discontinuation was due to lack of efficacy (and therefore associated with the treatment received).

Both approaches, the ITT and the PP approach, are valid but have different roles in the analysis of clinical studies. Let’s come back to the question at the beginning of this article: What is worse, scenario A (claim a non-existing effect) or B (neglect an existing effect)? To answer this, consider the essential difference between the two cases: Case A means that a statistically proven result is actually wrong – a result that might cause dangerous effects.

  1. Based on such a proof, an inefficacious treatment might be approved and patients put into danger.
  2. Situation B on the other hand means that efficacy was not proven but also not refused,
  3. However, the non-proven efficacy does not equal a proven inefficacy! From a scientific perspective, such a non-decision has less implications than a wrong proof.

Therefore, in clinical trials situation A (also known as type I error ) is strictly controlled via a low pre-defined level of significance: a level of 5% e.g. says that (if there is actually no effect) the probability of situation A is only 5% or less.

Situation B (known as type II error ) on the other hand, is controlled via a meaningful sample size calculation, but usually with a less strict criterion (e.g.20%). Concluding, it is more essential to avoid a wrong proof than to avoid a wrong non-decision (which is also bad, but A is worse). Consequently, it is essential to keep the probability of situation A below the level of significance (e.g.5%).

Thus, the common rule for clinical trial analyses is: be conservative! While “conservative” means: do not increase the probability of a type I error !

What is the difference between SAP and protocol?

Statistical Analysis Plan: What is it & How to Develop it Regardless of the research design, statistics are a crucial component of research since it allows the researchers to summarize the collected data and give it to others for interpretation. So, when drafting a plan for data analysis, we must consider it.

  • We need a defined analytic plan before we start collecting data.
  • The SAP (statistical analysis plan) will direct us from the beginning to the conclusion, help us summarize and describe the data, and test our hypotheses.
  • The statistical analysis plan (SAP) describes the intended clinical trial analysis.

The SAP is a technical document that describes the statistical methods of research analysis, as opposed to the protocol, which represents the analysis. The clinical trial report will contain all the statistical outputs that are defined in the SAP. The most popular documents utilized by statistical programmers to build their deliverables are the SAP and annotated CRFs.

What is PK and PD in clinical trials?

16.1. INTRODUCTION – In this chapter we seek to describe the stages of drug discovery, focusing on the utility of animal pain models and of pharmacokinetic/pharmacodynamic relationships (PK/PD) at each stage. In simple terms, the study of PK and PD in drug discovery is often paired and described in reciprocal terms, where PK is the analysis of how the body affects a drug, while PD is the analysis of how a drug affects the body.

  1. PK is defined by how a compound is absorbed, distributed, metabolized, and excreted; and PD is the measure of a compound’s ability to interact with its intended target leading to a biologic effect.
  2. In this chapter, we briefly describe the stages of drug discovery and the process of defining structure-activity relationship (SAR) both in vitro, through optimization of several key characteristics collectively referred to as pharmaceutical profiling, and in vivo, through the combined use of PK assessment and animal pain models to assess compound efficacy, noting the types and endpoints employed, and why it is important to pair these efficacy models with models of side effects.
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We follow this with data investigating penetration of compounds into the brain versus that in spinal cord and also correlate efficacy in rodent pain models with clinical efficacy. We hope to convey the importance of PK and of PK/PD relationships in the process of developing pain drugs,

What is the difference between efficacy and effectiveness?

A Primer on Effectiveness and Efficacy Trials 1 Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA 2 Department of Clinical Sciences, University of Texas Southwestern, Dallas, Texas, USA Find articles by 3 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA Find articles by 3 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA 4 Veterans Affairs Center for Clinical Management Research, Ann Arbor, Michigan, USA Find articles by

1 Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA 2 Department of Clinical Sciences, University of Texas Southwestern, Dallas, Texas, USA 3 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA 4 Veterans Affairs Center for Clinical Management Research, Ann Arbor, Michigan, USA

* Dedman Scholar of Clinical Care, Division of Digestive and Liver Diseases, University of Texas Southwestern Medical Center, 5959 Harry Hines Blvd, POB 1, Suite 420, Dallas, Texas 75390-8887, USA. E-mail: Received 2013 Jun 19; Accepted 2013 Aug 19. © 2014 American College of Gastroenterology This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/ Although efficacy and effectiveness studies are both important when evaluating interventions, they serve distinct purposes and have different study designs. Unfortunately, the distinction between these two types of trials is often poorly understood.

In this primer, we highlight several differences between these two types of trials including study design, patient populations, intervention design, data analysis, and result reporting. Intervention studies can be placed on a continuum, with a progression from efficacy trials to effectiveness trials.

  • Efficacy can be defined as the performance of an intervention under ideal and controlled circumstances, whereas effectiveness refers to its performance under ‘real-world’ conditions.
  • However, the distinction between the two types of trial is a continuum rather than a dichotomy, as it is likely impossible to perform a pure efficacy study or pure effectiveness study.

There are several steps that must occur for an efficacious intervention to be effective in clinical practice; therefore, an efficacy trial can often overestimate an intervention’s effect when implemented in clinical practice. An efficacious intervention must be readily available, providers must identify the target population and recommend the intervention, and patients must accept and adhere to the intervention.

For example, several studies highlight how underutilization of colorectal cancer and hepatocellular carcinoma screening contribute to poor effectiveness in clinical practice.,,,, In fact, poor access, recommendation, acceptance, and adherence rates can lead to highly efficacious interventions being less effective in practice than less-efficacious interventions.

For example, ultrasound has a sensitivity of 63% for detecting hepatocellular carcinoma at an early stage in prospective efficacy studies and is regarded as being more efficacious that alpha fetoprotein. However, in a recent effectiveness study, ultrasound only had a sensitivity of 32%, comparable to that of alpha fetoprotein (sensitivity 46%).

  • This gap was related to the low utilization rates of ultrasound and its operator-dependent nature.
  • Similarly, hepatitis C and hepatocellular carcinoma therapy can also be highly efficacious in reducing morbidity and mortality but are limited by low rates of access, recommendation, and acceptance.
  • Although efficacy research maximizes the likelihood of observing an intervention effect if one exists, effectiveness research accounts for external patient-, provider-, and system-level factors that may moderate an intervention’s effect.

Therefore, effectiveness research can be more relevant for health-care decisions by both providers in practice and policy-makers. The distinction between these two types of trials is important but often poorly understood. In fact, an analysis of product evaluations for Health Technology Assessments found that efficacy data is often assumed to be effectiveness data.

Efficacy study Effectiveness study
Question Does the intervention work under ideal circumstance? Does the intervention work in real-world practice?
Setting Resource-intensive ‘ideal setting’ Real-world everyday clinical setting
Study population Highly selected, homogenous population Several exclusion criteria Heterogeneous population Few to no exclusion criteria
Providers Highly experienced and trained Representative usual providers
Intervention Strictly enforced and standardized No concurrent interventions Applied with flexibility Concurrent interventions and cross-over permitted

Efficacy studies investigate the benefits and harms of an intervention under highly controlled conditions. Although this has multiple methodologic advantages and creates high internal validity, it requires substantial deviations from clinical practice, including restrictions on the patient sample, control of the provider skill set and limitations on provider actions, and elimination of multimodal treatments.

  • A placebo-controlled randomized controlled trial (RCT) design is ideal for efficacy evaluation because it minimizes bias through multiple mechanisms, such as standardization of the intervention and double blinding.
  • RCTs generally eliminate issues of access (intervention is provided free), provider recommendation, and patient acceptance and adherence.

Effectiveness studies (also known as pragmatic studies) examine interventions under circumstances that more closely approach real-world practice, with more heterogeneous patient populations, less-standardized treatment protocols, and delivery in routine clinical settings.

Effectiveness studies may also use a RCT design; however, the intervention is more often compared with usual care, rather than placebo. Minimal restrictions are placed on the provider actions in modifying dose, the dosing regimen, or co-therapy, allowing tailored therapy for each subject. Although effectiveness studies sacrifice some internal validity, they have higher external validity than efficacy studies.

Effectiveness trials without a witnessed effect may be related to one of several factors including an ineffective intervention, poor implementation, lack of provider acceptance, or lack of patient acceptance and adherence. Efficacy trials use strict inclusion and exclusion criteria to enroll a defined, homogenous patient population.

Inclusion criteria confirm that patients truly have the disease of interest, whereas exclusion criteria exclude those who are unlikely to respond to the intervention. For example, efficacy studies may exclude patients who are at low risk for the primary outcome, those who are deemed likely to be non-compliant, or those with significant comorbid medical conditions.

However, these strict inclusion and exclusion criteria can limit the generalizability of the results to patients seen in clinical practice. Effectiveness trials typically have limited exclusion criteria and involve a more heterogeneous population, including higher rates of non-compliant patients and more subjects with significant comorbid conditions.

  1. However, effectiveness trials can still exclude patients for safety concerns, as these patients would not be expected to get the intervention in usual practice.
  2. For example, a recent RCT demonstrated that rectal indomethacin significantly reduced the risk of post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis; however, only high-risk patients, such as those with sphincter of Oddi dysfunction, were included.

Effectiveness studies would help clarify if these results can be generalized to low-risk and medium-risk patients undergoing ERCP in everyday practice. In efficacy trials, interventions are delivered in a highly standardized way, including timing and dosage of medications and perhaps even the associated patient education.

  • The use of concurrent medications or interventions is often restricted, so any witnessed effect can be attributed to the intervention of interest.
  • Furthermore, efficacy trials are conducted with top-quality equipment and highly experienced providers, who are often provided training in the intervention and measurement of outcomes prior to the study.

Finally, intensive resources are often dedicated to maximize provider uptake and patient compliance with the intervention. Research assistants can provide intense counseling, education, and even reminders for scheduled medications or clinic appointments.

  1. This intensive attention can in part explain the high placebo effect seen in some trials, such as those in irritable bowel syndrome and inflammatory bowel disease.
  2. Effectiveness trials standardize the availability of the intervention in the study sample but do not go to extremes to reinforce implementation by providers or participation by patients.

There are no requirements regarding provider expertise, and equipment quality may be variable. Similarly, providers are not restricted in terms of offering concurrent therapies or crossing over patients on-and-off therapy, which can lead to higher rates of drug–drug interactions and make it less clear if any effect was truly related to the intervention of interest.

  1. Finally, additional study resources, such as reminder phone calls or study coordinators, are not available to augment provider and/or patient compliance.
  2. Both efficacy and effectiveness trials typically use an intention-to-treat approach for statistical analysis.
  3. However, given that efficacy trials aim to address if interventions work under ideal circumstances, secondary analyses using a per-protocol approach may be informative.

Alternative techniques that have been proposed to account for differences between efficacy and effectiveness include contaminated adjustment intention to treat and voting with their feet analyses.,, Effectiveness trials often have higher rates of missing data than efficacy trials.

  • There are several methods for handing missing data, with details beyond the scope of this primer.
  • The applicability of results from both efficacy and effectiveness studies depend on the context of the trial and the situation to which the data are being applied.
  • It is crucial for any study to provide sufficient data regarding the trial’s setting, participants, and intervention.

A trial with an insufficient description regarding the intervention is effectively rendered useless, as external implementation and validation is impossible. Guidelines for reporting results of efficacy and effectiveness studies should be followed to standardize reporting of results.

Clinicians have historically been frustrated by the lack of consideration of external validity in RCTs, other efficacy studies, and guidelines. Accordingly, there has been a call for studies whose results can be more readily applied to everyday clinical practice. This culminated in the American Recovery and Reinvestment Act, which allotted more than $1 billion to support comparative effectiveness research (CER).

The Institute of Medicine has defined CER as “the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition, or to improve the delivery of care.” The purpose of CER is to assist patients, providers, and policy-makers in making informed decisions that can improve health care both at the individual and population levels.

  1. As suggested by the name, CER places an emphasis on effectiveness studies, conducted in settings similar to real-world clinical practice, to maximize external validity of any results.
  2. With increased funding support for effectiveness research, the number of effectiveness studies will likely increase over the next several years.

An understanding of the distinction between efficacy and effectiveness research is not only crucial when conducting research but also interpreting results from studies and deciding how applicable it may be to clinical practice and patients who may have less access and less adherence to medications.

  1. Given a growing focus on evidence-based medicine and pay-for-performance measures, providers must base clinical decisions on the best available evidence.
  2. However, defining the best available evidence may not always be clear.
  3. Although some prioritize efficacy data from RCTs, others view effectiveness data as more pertinent to real-world clinical practice decisions.

There are at least two tools, which can help clinicians judge where a trial may lie on the efficacy–effectiveness continuum., Gartlehner and colleagues identified criteria to distinguish efficacy and effectiveness studies, with a sensitivity and specificity of 72% and 83%, respectively. Guarantors of the article : Amit G. Singal, MD, MS and Akbar K. Waljee, MD, MS. Specific author contributions: Study concept and design, drafting of the manuscript, critical revision of the manuscript for important intellectual content, and study supervision: Amit G.

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: A Primer on Effectiveness and Efficacy Trials