Intention To Treat Vs Per Protocol

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Intention To Treat Vs Per Protocol
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 difference between per protocol and intention-to-treat?

By using the ITT approach, investigators aim to assess the effect of assigning a drug whereas by adopting the PP analysis, researchers investigate the effect of receiving the assigned treatment, as specified in the protocol.

What is on protocol or intention to treat analysis?

Abstract – Clinicians, institutions, and policy makers use results from randomized controlled trials to make decisions regarding therapeutic interventions for their patients and populations. Knowing the effect the intervention has on patients in clinical trials is critical for making both individual patient as well as population-based decisions.

However, patients in clinical trials do not always adhere to the protocol. 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. 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.

This method allows the investigator (or consumer of the medical literature) to draw accurate (unbiased) conclusions regarding the effectiveness of an intervention. This method preserves the benefits of randomization, which cannot be assumed when using other methods of analysis.

The risk of bias is increased whenever treatment groups are not analyzed according to the group to which they were originally assigned. 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.

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. The most effective way to establish a causal relationship between an intervention and outcome is through a randomized controlled trial (RCT) study design.1 – 3 Randomization affords an unbiased comparison between groups as it controls for both known and unknown confounding variables.

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). 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.

Although 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. Incorrect analysis of the data can introduce bias even in the setting of the correct implementation of a valid random allocation sequence.

  • It is therefore important to preserve the integrity of randomization during the implementation of the study and in analysis.
  • 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.

Anything that disrupts the prognostic balance afforded by randomization introduces bias into the study and analysis. 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.

What is meaning per protocol?

Journal List CMAJ v.183(6); 2011 Apr 5 PMC3071397

As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more about our disclaimer. CMAJ.2011 Apr 5; 183(6): 696.

I congratulate CMAJ and Boutis and colleagues for a brilliant research paper.1 Intention-to-treat analysis is a comparison of the treatment groups that includes all patients as originally allocated after randomization. This is the recommended method in superiority trials to avoid any bias. For missing observations, “last value carried forward” is the recommended method.

Per-protocol analysis is a comparison of treatment groups that includes only those patients who completed the treatment originally allocated. If done alone, this analysis leads to bias. In noninferiority trials, both intention to treat and per-protocol analysis are recommended; both approaches should support noninferiority.

In the article by Boutin and colleagues, intention to treat should have included 50 patients in either group as per randomization or at least 45 in the group with splints (in 4 patients, the diagnosis was wrong) and 50 in the group with casts; this may change the results to indicate a borderline effect.

In that article, the analysis was done with 43 patients in the splint group and 49 in the cast group, which appears to be a per-protocol analysis, though it was called an intention-to-treat analysis. Hence, noninferiority can be concluded only after analysis by both approaches.

When should you use a per protocol analysis?

Per protocol analysis is particularly useful for interpreting non-inferiority trials and, under given conditions, for analysing the adverse effects of treatments.

What is the difference between ITT and PPP?

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.

  1. Once the participants are vaccinated, researchers study each group’s outcomes to determine if the vaccine is safe and effective.
  2. 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 advantage of per protocol?

Discussion – Table 1 illustrates the kind of information provided to the general public on breast cancer screening: three plain language syntheses or fact boxes produced for individual decision-making on breast screening based on recent systematic reviews.

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These figures were produced by the UK Independent Panel, by the Euroscreen Working Group, and by the Harding Centre for Risk Literacy, All three provide ITT estimates on the benefits of screening; surprisingly, the full papers by The UK Independent Panel and by Euroscreen also report per protocol estimates (women who actually responded).

As for the harms of screening, the UK Independent Panel fact sheet clearly states that the estimate of overdiagnosis is calculated for women actually screened “as a proportion of cancers detected during their screening period”. Understanding the fact sheets is difficult, which defeats their purpose.

  1. Further, their plain language/narrative presentation does not clarify which estimates are used and to which population, invited or screened, they apply.
  2. Individuals may believe they have understood the information and have made an informed choice, but have they? Table 1 Comparison of three plain language syntheses or fact boxes summarising benefits and harms of mammographic screening for breast cancer as reported by the authors (intention-to-treat, ITT) and re-computed according to an individual decision making perspective (per protocol, PP) To illustrate the difference between intention-to-treat analysis results and per protocol estimates, we can use the same approach as the Harding Centre for Risk Literacy for mammography, applied to colorectal screening.

With ITT, we obtain an estimate for colorectal cancer screening with faecal occult blood test of 85 deaths out of 10,000 persons screened for ten years and 100 deaths for the same population in the absence of screening. With a per protocol analysis, there would be 74 deaths out of 10,000 persons screened with at least one test in 10 years.

Which should the individual take into consideration in the decision-making process, 85 deaths per 10,000 screened or 74 deaths per 10,000 screened? The Cochrane Review on colorectal screening does take these two calculations into account, however; both the mortality reduction for the whole randomised population and adjusted for participation are reported,

The first issue to be addressed concerns using ITT analysis results for the purpose of helping individuals invited to screening to decide whether or not to participate. Indeed, doing so would result in the paradox that the invitee would apparently be affected by any of the benefits and/or harms observed in the ITT analysis thanks solely to having received the invitation, regardless of actual participation.

  • Clearly, however, both harms and benefits are downstreams of screening participation.
  • Therefore, the correct numbers to use to help inform individual decision-making are those regarding the subjects who actually participated, which do not come from the ITT analysis but from PP.
  • Intention-to-treat remains the first and only analysis to test the hypothesis for the overall effectiveness of any intervention; rejection of the null hypothesis in the intention-to-treat analysis is a prerequisite to proceed to per protocol analysis.

In the case of a therapeutic intervention in the clinical setting, most of the causes of therapy interruption or non-compliance to the protocol are both prognostic factors and causally linked to the therapy itself (intolerance, side effects, etc.). The best prospective estimate of therapy efficacy that we can therefore give patients who must chose between therapeutic options is based on intention-to-treat analysis, given that it cannot be known a priori whether a therapy will be tolerated and/ or finished.

  • An individual’s decision to participate in screening, however, is not usually determined by potential negative or positive prognostic factors, and there is no causal link between participation and screening results.
  • Therefore, if participants and non-participants differ in their baseline mortality or incidence this will be due to a self-selection bias.

Further, the target screening population is healthy; in the absence of other interventions, it is thus absolutely unlikely that a large number of deaths or cancer diagnoses will occur in the time elapsing between invitation and participation. Therefore, any potential bias that per protocol analysis may introduce due to the exclusion of outcomes that occurred between randomization and screening test should not be relevant, while it can be quite relevant when the mortality rate in the study population is high, i.e.

  1. In many therapeutic trials,
  2. Given the absence of any causal link between participation and screening results (the decision is made before the first screening test) and given the data produced by the trials, the self-selection bias mentioned above can be adjusted for.
  3. In fact, we can measure incidence and mortality in the controls and compare them with those of the non-participants, thereby obtaining a direct measure of the self-selection bias (Figure 1 ).

This measure is based on the outcomes themselves and thus includes all the possible effects of detected and undetected confounding variables. Figure 1 Theoretical framework of the intention-to-treat and the proposed per protocol analysis applied to cancer screening trials that randomised to invitation to screening or no intervention. In conclusion, a correct per protocol analysis can produce data useful to support individual decision-making providing it meets the following conditions: 1) it must include all the randomised subjects who presented for the test; 2) it must not exclude any subject for any reason after presentation; 3) it must compare the results in this cohort with those of the control arm; 4) it must appropriately adjust for self-selection bias.

What is the difference between ITT and tot?

Intent to treat versus treatment on the treated – Many RCTs experience difficulty with recruitment or attrition of study subjects. Not all subjects that are offered the treatment will accept it or complete it. Therefore, the effect on the whole group may differ from the effect on only those who received the full treatment.

When designing an RCT, the researcher must decide whether to estimate the intent to treat (ITT), the treatment on the treated (TOT), or both. The ITT estimates the average effect of offering the treatment on outcomes, or the effect on everyone who was offered the treatment, whether or not they received it.

The TOT estimates the average effect of the actual treatment on outcomes, or the effect only on those who received the full treatment. In some cases where program participation is voluntary, the ITT may be the more policy-relevant effect. In others, researchers may be interested in understanding the effect of the intervention on everyone in the population.

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)

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

What are the disadvantages of per protocol analysis?

Per protocol analysis excludes patients who deviated from the protocol. It can introduce a form of bias called attrition bias, in which the groups of patients being compared no longer have similar characteristics.

What is the per protocol effect?

Background – In randomized trials, the per-protocol effect is the effect that would have been estimated if all participants had adhered to their randomly assigned treatment strategies during the entire follow-up, However, because adherence to the assigned treatment strategy is not in itself randomized, a naive comparison that excludes trial participants who fail to adhere to their assigned strategies will generally be biased,

For example, in a trial of a new treatment versus standard of care to treat coronary heart disease, adherers to the treatment may be individuals who also tend to take antihypertensive treatment. Thus, a lower rate of disease among adherers may simply reflect their higher uptake of antihypertensives rather than a benefit of the treatment under study.

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Therefore, analyses that attempt to estimate the per-protocol effect typically need to adjust for prognostic factors that, like antihypertensive use in our example, are also associated with adherence. That is, per-protocol analyses are observational analyses of the randomized trial data and therefore need to adjust for confounders.

In randomized trials of point interventions that are administered shortly after randomization (e.g., a one-dose vaccination, a one-time screening test), adherence to the assigned intervention is fully determined at baseline and therefore can only be affected by baseline factors. The implication is that per-protocol analyses of point interventions only need to adjust for baseline confounders.

On the other hand, in randomized trials of treatment strategies that are sustained during the follow-up (e.g., treatment for coronary heart disease, antiretroviral treatment for HIV-positive patients), adherence to the treatment strategy must also be sustained during the follow-up.

The implication of this potentially time-varying adherence is that per-protocol analyses of sustained strategies need to adjust for time-varying confounders — time-varying prognostic factors that affect treatment decisions — as well as for baseline confounders — baseline prognostic factors that affect treatment decisions,

For example, in a randomized trial to estimate the effect of two antiretroviral therapies on mortality, an increased alcohol intake during the follow-up is a time-varying confounder because it affects both the risk of death and of non-adherence to the assigned treatment.

It follows that valid estimation of the per-protocol effect of sustained treatment strategies requires adequate data collection of treatment and confounders after randomization. Many randomized trials collect such post-randomization data, but most only do so at pre-specified intervals (e.g., every 12 months).

Because non-adherence may occur at any time during the follow-up, the confounders measured at the pre-specified times may not be sufficient or relevant to adjust for non-adherence that took place at an unknown time between the pre-specified measurement times.

What is an ITT population?

In a randomised trial, the set of all randomised patients is known as the ‘intention to treat population’, or the ITT population.

What is a naive per protocol analysis?

8.2.2 Specifying the nature of the effect of interest: ‘intention-to-treat’ effects versus ‘per-protocol’ effects – Assessments for one of the RoB 2 domains, ‘Bias due to deviations from intended interventions’, differ according to whether review authors are interested in quantifying:

  1. the effect of assignment to the interventions at baseline, regardless of whether the interventions are received as intended (the ‘intention-to-treat effect’); or
  2. the effect of adhering to the interventions as specified in the trial protocol (the ‘per-protocol effect’) (Hernán and Robins 2017).

If some patients do not receive their assigned intervention or deviate from the assigned intervention after baseline, these effects will differ, and will each be of interest. For example, the estimated effect of assignment to intervention would be the most appropriate to inform a health policy question about whether to recommend an intervention in a particular health system (e.g.

  • Whether to instigate a screening programme, or whether to prescribe a new cholesterol-lowering drug), whereas the estimated effect of adhering to the intervention as specified in the trial protocol would be the most appropriate to inform a care decision by an individual patient (e.g.
  • Whether to be screened, or whether to take the new drug).

Review authors should define the intervention effect in which they are interested, and apply the risk-of-bias tool appropriately to this effect. The effect of principal interest should be specified in the review protocol: most systematic reviews are likely to address the question of assignment rather than adherence to intervention.

  1. analyse participants in the intervention groups to which they were randomized, regardless of the interventions they actually received; and
  2. include all randomized participants in the analysis, which requires measuring all participants’ outcomes.

An ITT analysis maintains the benefit of randomization: that, on average, the intervention groups do not differ at baseline with respect to measured or unmeasured prognostic factors. Note that the term ‘intention-to-treat’ does not have a consistent definition and is used inconsistently in study reports (Hollis and Campbell 1999, Gravel et al 2007, Bell et al 2014).

  1. Patients and other stakeholders are often interested in the effect of adhering to the intervention as described in the trial protocol (the ‘per-protocol effect’), because it relates most closely to the implications of their choice between the interventions.
  2. However, two approaches to estimation of per-protocol effects that are commonly used in randomized trials may be seriously biased.

These are:

  • ‘as-treated’ analyses in which participants are analysed according to the intervention they actually received, even if their randomized allocation was to a different treatment group; and
  • naïve ‘per-protocol’ analyses restricted to individuals who adhered to their assigned interventions.

Each of these analyses is problematic because prognostic factors may influence whether individuals adhere to their assigned intervention. If deviations are present, it is still possible to use data from a randomised trial to derive an unbiased estimate of the effect of adhering to intervention (Hernán and Robins 2017).

  • However, appropriate methods require strong assumptions and published applications of such methods are relatively rare to date.
  • When authors wish to assess the risk of bias in the estimated effect of adhering to intervention, use of results based on modern statistical methods may be at lower risk of bias than results based on ‘as-treated’ or naïve per-protocol analyses.

Trial authors often estimate the effect of intervention using more than one approach. They may not explain the reasons for their choice of analysis approach, or whether their aim is to estimate the effect of assignment or adherence to intervention. We recommend that when the effect of interest is that of assignment to intervention, the trial result included in meta-analyses, and assessed for risk of bias, should be chosen according to the following order of preference:

  1. the result corresponding to a full ITT analysis, as defined above;
  2. the result corresponding to an analysis (sometimes described as a ‘modified intention-to-treat’ (mITT) analysis) that adheres to ITT principles except that participants with missing outcome data are excluded (see Section 8.4.2 ; such an analysis does not prevent bias due to missing outcome data, which is addressed in the corresponding domain of the risk-of-bias assessment);
  3. a result corresponding to an ‘as-treated’ or naïve ‘per-protocol’ analysis, or an analysis from which eligible trial participants were excluded.

Why use intent to treat analysis?

Rationale – Randomized clinical trials analyzed by the intention-to-treat (ITT) approach provide unbiased comparisons among the treatment groups. Intention to treat analyses are done to avoid the effects of crossover and dropout, which may break the random assignment to the treatment groups in a study.

Does intention-to-treat reduce attrition bias?

Discussion – Biased results from RCTs ultimately put the patients at risk for being treated with pharmaceuticals with questionable efficacy and which may cause harm. Taking into account the expenses of accompanying RA treatment, this study is not only biomedical but also a socioeconomic necessity.

The term mITT is used to describe different methods for excluding participants postrandomised from analysis, thereby affecting and disregarding not only the ITT principle but also—and more importantly—the overriding purpose of ITT. Postrandomisation exclusions are known to induce bias, and theoretically mITT will introduce bias.23, 24 Our study aim to establish if the bias is of practical concern, and focuses on the direction and magnitude of bias associated with mITT analyses.

This study will present arguments as to why mITT approximates ITT or point to the problems concerning the use of mITT. As the term mITT embraces a broad notion of trials, we will delve into how the different types of modification influence effect size.

  1. This study may come out with neutral findings—which would not imply that overall bias associated with mITT analyses can be excluded, but may indicate that our study lacks the statistical power necessary to detect the bias.
  2. If some form of mITT can substitute ITT, guidelines regarding the use of mITT should be issued.

In general this study examines many determinants, and therefore a risk of type I errors due to multiple comparisons exists and results must be interpreted carefully regardless of statistical significance.61 This study is limited by the lack of agreement in how ITT and mITT are defined.

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Our mITT definition and categorisation is based on deviations described in the literature but have some shortcomings; for example, in cases where only one postbaseline visit is required the mITT category postbaseline assessment will correspond to a completer’s analysis. As in other meta-epidemiological studies, we are limited by the many sources of heterogeneity, for example, differences in disease duration, type of RA population and intervention dose.

As meta-epidemiological studies concerns methodology and do not aim at establishing the empirical evidence for an intervention effect, this underlying premise of heterogeneity can be viewed as acceptable. However, heterogeneity should always be borne in mind when interpreting results.

  • Our primary objective is to examine whether mITT is associated with different effect sizes, implying empirical evidence for bias in treatment effects.
  • ITT prevents attrition bias when evaluating treatment assignment but may not provide a true estimate of treatment effect if some patients are non-adherent.17 As the term ‘bias’ comprises deviation from the true intervention effect, it can be perceived as misleading to regard systematic errors in treatment effect between mITT and ITT analyses as ‘bias,’ given that ITT analysis may fail to provide a true evaluation of the intervention effect.

However, ITT analysis is recommended as the least biased way to estimate intervention effects 7 and concerns regarding the systematic errors between mITT and ITT remain, regardless of terminology. This project builds on the premise that the trials included are otherwise less prone to bias, although there is no guarantee that recent trials on biologics and targeted interventions will be at low risk of bias.

  • This study may point to potential bias and disadvantages in the handling of missing data in RCTs, otherwise known for having a low RoB compared with other study designs.62 SI has been criticised on a theoretical level, but its implication on efficacy outcomes in RA trials is uncharted.
  • Accordingly, this study may provide empirical evidence that can support or contradict existing critics.

Regardless of our findings one should always be careful when interpreting results from trials where data are missing and consider the reasons for missing data and potential impact on effect estimates.7, 63 The study examines potential bias associated with industry funding.

What is per protocol population 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 is the difference between ITT and mITT?

Selection of Studies – To compare methodological quality, industry sponsorship, the presence of authors’ conflicts of interest, and findings among trials based on the type of intention-to-treat reporting, we sought to identify the top medical journals and specialty journals published in 2006 that were more likely to report the use of an mITT approach according to our previous survey,

  1. The three high-ranking medical journals were the Journal of American Medical Association, the New England Journal of Medicine and the Lancet; and the three specialty journals were Antimicrobial Agents and Chemotherapy, the American Heart Journal and the Journal of Clinical Oncology.
  2. We carried out a computerized search in Medline (via PubMed) to identify RCTs published in these six journals using the publication type “randomized controlled trials”.

The results were then cross-checked against a search of the Cochrane Central Register of Controlled Trials. Excluded reports included: reports of phase I trials; pharmacokinetic, pharmacodymanic or dose-comparing studies; cluster RCTs; post-hoc studies; research letters; cost-effectiveness studies; study protocols; and prognostic and diagnostic studies. Study Screening Process, AAC: Antimicrobial Agents and Chemotherapy; AHJ: American Heart Journal; JAMA: Journal of American Medical Association; JCO: Journal of Clinical Oncology; NEJM: New England Journal of Medicine. Two other reviewers independently and in duplicate, extracted the following information from the journal articles: characteristics of the trial, primary outcomes, number of allocation groups, number of patients, main outcome measure, P -values, and number of subjects excluded from the analysis.

  1. Moreover, reporting of flow-charts, sample size calculations, and information regarding missing data, withdrawals or patients lost to follow-up were recorded.
  2. The relevant RCTs were subsequently classified according to the type of intention-to-treat analyses used as follows: ITT, trials reporting the use of standard ITT analyses; mITT, trials reporting the use of “modified intention-to-treat” analyses; or “no ITT” trials not reporting the use of any intention-to-treat analyses.

This classification was independent of the reporting of any post-randomisation exclusion. Trials reporting the use of ITT with descriptions or conditions different from the standard intention-to-treat definition were classified as mITT. Descriptions of mITT were retrieved to evaluate the type and deviation from a true intention-to-treat analysis according to our previous classification.

What does pre protocol mean?

​noun uncountable ​legal. UK /ˌpriː ækʃ(ə)n ˈprəʊtəkɒl/ DEFINITIONS1. in England and Wales, actions that a court usually expects parties to take before they start a case at court.

What is the difference between safety population and ITT?

Re: Difference between itt and safety populations So ITT is those randomized based on randomized treatment not what they were actually given. Safety is all randomized with any treatment.

What does pre protocol mean?

​noun uncountable ​legal. UK /ˌpriː ækʃ(ə)n ˈprəʊtəkɒl/ DEFINITIONS1. in England and Wales, actions that a court usually expects parties to take before they start a case at court.

What is the difference between ITT and mITT?

Selection of Studies – To compare methodological quality, industry sponsorship, the presence of authors’ conflicts of interest, and findings among trials based on the type of intention-to-treat reporting, we sought to identify the top medical journals and specialty journals published in 2006 that were more likely to report the use of an mITT approach according to our previous survey,

The three high-ranking medical journals were the Journal of American Medical Association, the New England Journal of Medicine and the Lancet; and the three specialty journals were Antimicrobial Agents and Chemotherapy, the American Heart Journal and the Journal of Clinical Oncology. We carried out a computerized search in Medline (via PubMed) to identify RCTs published in these six journals using the publication type “randomized controlled trials”.

The results were then cross-checked against a search of the Cochrane Central Register of Controlled Trials. Excluded reports included: reports of phase I trials; pharmacokinetic, pharmacodymanic or dose-comparing studies; cluster RCTs; post-hoc studies; research letters; cost-effectiveness studies; study protocols; and prognostic and diagnostic studies. Study Screening Process, AAC: Antimicrobial Agents and Chemotherapy; AHJ: American Heart Journal; JAMA: Journal of American Medical Association; JCO: Journal of Clinical Oncology; NEJM: New England Journal of Medicine. Two other reviewers independently and in duplicate, extracted the following information from the journal articles: characteristics of the trial, primary outcomes, number of allocation groups, number of patients, main outcome measure, P -values, and number of subjects excluded from the analysis.

Moreover, reporting of flow-charts, sample size calculations, and information regarding missing data, withdrawals or patients lost to follow-up were recorded. The relevant RCTs were subsequently classified according to the type of intention-to-treat analyses used as follows: ITT, trials reporting the use of standard ITT analyses; mITT, trials reporting the use of “modified intention-to-treat” analyses; or “no ITT” trials not reporting the use of any intention-to-treat analyses.

This classification was independent of the reporting of any post-randomisation exclusion. Trials reporting the use of ITT with descriptions or conditions different from the standard intention-to-treat definition were classified as mITT. Descriptions of mITT were retrieved to evaluate the type and deviation from a true intention-to-treat analysis according to our previous classification.

What are protocols in clinical trials?

Abstract – Trial protocols are documents that describe the objectives, design, methodology, statistical considerations and aspects related to the organization of clinical trials. Trial protocols provide the background and rationale for conducting a study, highlighting specific research questions that are addressed, and taking into consideration ethical issues.