In Defense of Intent-To-Treat Analyses in Randomized Trials Group 7: Wenchao Li, Shuang Liang, Stephen Smela, Tien Vo, Fan Wang & Michelle Warren

Proposition The primary analysis of randomized trials should always be intention-to-treat. To ensure that this analysis can be carried out, data collection following treatment discontinuation through the end of the trial should be a protocol requirement.

Intent-to-Treat An analysis in which patients are included in the group to which they were randomized irrespective of compliance, administrative errors (e.g., error in eligibility), or other protocol deviations. International Conference on Harmonization (ICH) guidelines use the term "full analysis" set to refer to the set of all patients randomized. Can be viewed as a comparison of treatment policies.

Per-Protocol / As-Treated An analysis in which patients are included in the group corresponding to the treatment they actually received. Patient compliance and "switchovers" are considered in the analysis. Typically, in a "per protocol" analysis, patients who do not meet all of the eligibility criteria or do not adhere to the protocol are excluded, and events that occur after treatment discontinuation are excluded. ICH guidelines use the term "per protocol" to define a group of patients who were "adherent" to the protocol. The alternative to ITT analysis

Why Do ITT?

Generalizability In “real life” uptake of an intervention will never be “per-protocol” It’s important to know that the intervention will still work, despite human error! PP analyses are based on a subset of participants Results may not generalize to the population from which the original sample was taken Mimics actual practice. ITT is a “does it work?” approach.

Preserving Randomization Reasons for attrition/non-adherence may be different across arms Excluding people from the analysis accordingly may introduce confounding effects The effect obtained via PP analysis reflects some combination of treatment effect and subset selection bias[2] It’s impossible to disentangle the two The randomization process ensures that treatment and control arms are comparable; removing people who have been randomized violates the integrity of the randomization process

Loss of Power PP compares those who received the various treatments as intended. ITT yields a “diluted effect” by including those who effectively are not receiving treatment

Is Loss of Power Really an Issue? PP analyses can also result in loss of power by reducing the number of observations available Whether ITT results in loss of power is an empirical question In fact, studies have found that ITT analyses can result in larger treatment effects being observed than PP analyses[1]

Combatting Loss of Power Improve adherence protocols Increase sample size Let’s say you’re still concerned about loss of power. What can you do? In other words, if you’re planning an ITT analysis, precisely because you’ll be concerned about diluting the treatment effect, you’ll take protocol adherence more seriously. In fact, you’ll have better protocol adherence than if you simply plan to exclude those who go off the treatment. Increasing sample size may cost more, but at the end of the day, it is better to know that the observed effect is conservative than it is to not know whether the effect your seeing is due to bias

Missing Data You cannot include people who have become lost to follow-up in an ITT analysis You need a full dataset with endpoints for all participants, regardless of whether they have completed the study

Combatting Missing Data Planning to not collect the data is not a solution As we have seen in class, halting data collection at treatment discontinuation may result in missing long-term effects of the treatment that arise even after treatment stops[2] Improve retention protocols Develop a plan for missing data[1,2] [Multiple] Imputation Carry last observation forward Instrumental variables, other statistical techniques You can’t analyze data you don’t collect. As with protocol adherence, ITT forces you to take retention more seriously.

But Can the Treatment Work? ITT focuses on “does it work” not “can it work”

20/20 Hindsight You can always conduct secondary analyses If data collection is stopped because of protocol non-adherence, then only PP analyses are possible[2, 4] If participants contribute follow-up data irrespective of any protocol violation, both ITT and PP analyses can be performed Moral of the story: everybody wins when you plan for and implement ITT!!!

References Have TRT, Normand ST, Marcus SM, Brown CH, Lavori PL, Duan N. Intent-to-treat vs. non-intent-to-treat analyses under treatment non-adherence in mental health randomized trials. Psychiatr Ann 2008; 38(12): 772-783. Lachin JM. Statistical considerations in the intent-to-treat principle. Controlled Clinical Trials 2000; 21: 167-189 Little R, Yao L. Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics 1996; 52(4): 1324-1333. Nich C, Carroll KM. ‘Intention-to-treat’ meets ‘missing data’: implications of alternate strategies for analyzing clinical trials data. Drug Alcohol Depend 2002; 68: 121-130.

Intent To Treat “The primary analysis of randomized trials should always be intention-to- treat and to ensure this analysis can be carried out, data collection following treatment discontinuation through the end of the trial should be a protocol requirement.” Group 8: Zhiyuan Xu, Yiwen Zhang, Chelsea Nichols, Lu Wang, Nadir Demirel, Travis Myers

Arguments Against Intent to Treat In Intent to Treat analysis there is no way to efficiently statistically evaluate compliance Evaluation of trials of efficacy (Pragmatic trials) versus trials of method (exploratory trials) The distinction is probably most heated in Pharmaceutical Trials where the desired information is whether or not the drug is effective in the population taking/able to take the drug.

Non Compliance and Intent to Treat The impact of non-compliance on the intent-to-treat estimate in a comparison of active drugs is more complex. For example Let Drug A be equivalent or inferior to Drug B. Then assume that Drug A is very unpleasant to take, leading to lower compliance to Drug A than to Drug B. In this case, the intent-to-treat will overestimate the ‘pure’ treatment benefit of Drug B (although one can argue that this is appropriate given B’s superior tolerability). The ITT estimate of treatment effect in a placebo-controlled trial is clearly diluted by non-compliance, if there is no effective alternative therapy available.

Against Intent to Treat Cont’d Argued in Non-inferiority Trials To demonstrate that a new treatment is not inferior to a control, one should determine an amount of inferiority, frequently called the margin, that is unacceptable.

Non-inferiority Trials To ensure that a successful trial demonstrates that the study treatment has at least some efficacy, this margin must be no greater than the smallest effect one is confident that the active control has, for example, the top of the confidence limit in bar A. One then sets a null hypothesis of inferiority by that amount or more and designs a trial to reject the null hypothesis.

Non-inferiority Trials An intent-to-treat analysis may tend to lead to a conclusion of non- inferiority for a drug that is truly inferior to the active control among compliers. This fear has led to some reliance on per-protocol analyses for non- inferiority designs.

Alternatives to Intent to Treat Method-effectiveness Models The most popular is “per-protocol” method The patients are evaluated for the treatment they complete The per protocol alternative to the intention-to-treat method may be practical where evidence-based medicine and individual patient care converge.

Examples of Applications Subgroup and Per-Protocol Analysis of the Randomized European Trial on Isolated Systolic Hypertension in the Elderly In 1989, the European Working Party on High Blood Pressure in the Elderly started the double-blind, placebo-controlled, Systolic Hypertension in Europe Trial to test the hypothesis that antihypertensive drug treatment would reduce the incidence of fatal and nonfatal stroke in older patients with isolated systolic hypertension. This report addresses whether the benefit of antihypertensive treatment varied according to sex, previous cardiovascular complications, age, initial blood pressure (BP), and smoking or drinking habits in an intention-to-treat analysis and explores whether the morbidity and mortality results were consistent in a per- protocol analysis.

Other Recommendations The European Committee for Proprietary Medicinal Products Points to Consider document indicated that for a superiority trial, the intent-to-treat analysis should be considered primary and the per- protocol supportive; but for a non- inferiority trial, the analyses are equally important.

A Recap of the ITT Lecture Points Primary analysis should usually be ITT (need to continue collecting data to do this right) – it addresses a pragmatic policy/management question which is always relevant. ITT analysis requires excellent trial conduct. It is appropriate to carry out secondary “per protocol” or “as treated” analyses but these have to be interpreted with caution. For analyses which are not intent-to-treat it is often difficult/impossible to quantify bias resulting from not comparing like with like If exclusions after randomization are to be made as part of secondary “per protocol” analyses, they should be specified in the protocol Think about what you want to estimate in advance.

Sources CPMP. Points to Consider on switching between superiority and non-inferiority July 2000. http://www.emea.eu.int/pdfs/human/ewp/048299en.pdf Brittain E. et Lin, D. Statist. Med. 2005; 24:1–10 Prescrire Int. 2012 Dec;21(133):304-6. http://www.ncbi.nlm.nih.gov/pubmed/23373104 Staessen JA, Fagard R, Thijs L, et al. Subgroup and Per- Protocol Analysis of the Randomized European Trial on Isolated Systolic Hypertension in the Elderly. Arch Intern Med. 1998;158(15):1681-1691. doi:10.1001/archinte.158.15.1681. Lachin, John M. "Statistical considerations in the intent-to- treat principle."Controlled clinical trials 21.3 (2000): 167-189. Sheiner, Lewis B., and Donald B. Rubin. "Intention-to-treat analysis and the goals of clinical trials*." Clinical Pharmacology & Therapeutics 57.1 (1995): 6-15.