Year: 2010
Type: article
Abstract: When analyzing data from randomized clinical trials, an intention-to-treat (ITT) analysis gives the highest level of evidence.However, this type of analysis is frequently the subject of both confusion and controversy.This article will review the ITT principle, why it is important, how it is applied and misapplied, and alternative strategies and their limitations. CASE STUDYThe Spine Patient Outcomes Research Trial (SPORT) on disk herniation will be used as a case study throughout this article [1].In this trial, 501 patients with disk herniation were randomized to receive surgery (lumbar diskectomy) or standard nonoperative care.The ITT analysis showed no effect for surgery, but both a non-ITT reanalysis of the data and a parallel observational cohort study [2] showed a strong benefit for surgery.These conflicting results incited much discussion and debate about the ITT approach (see Sidebar for more details). WHAT IS ITT?The ITT principle stipulates that all participants who are randomized must be included in the statistical analysis and analyzed according to the treatment group to which they were randomly assigned, regardless of what treatment, if any, they actually received.Thus, patients will be analyzed as a member of their initial randomization group even if they refuse or discontinue the intervention, "cross over" to another randomization group, miss follow-up measurements, or otherwise violate study protocol.In SPORT, only 60% of 245 patients randomized to surgery actually received surgery, but the ITT analysis counted all 245 as members of the surgery group.Similarly, 45% of 256 patients randomized to nonoperative care decided to undergo surgery sometime during the study, but the ITT analysis retained all 256 as members of the nonoperative care group. WHY ITT?The ITT analysis may seem, on first reflection, to run counter to common sense.If the patients didn't receive surgery, it seems unfair to count them as if they did.However, ITT has 2 strong rationales.First, it is necessary to preserve the strengths of randomization.The purpose of randomization is to ensure that potential prognostic factors are balanced between the treatment groups.This balance may be lost if some patients are excluded from the analysis or analyzed according to how they self-selected rather than how they were randomized.For example, excluding nonadherent patients may introduce bias because patients who adhere to treatments tend to have better outcomes regardless of whether the treatment is effective.In one study [3], those who adhered to the trial drug (clofibrate) had reduced mortality; but those who adhered to the placebo pill had the same reduction in mortality.Analyzing patients according to the treatment they actually received is also likely to bias the results; for example, if patients who self-select to surgery have a stronger belief in the efficacy of surgery this may create a spurious association between surgery and positive outcomes.It may seem justifiable to exclude patients who are nonadherent for reasons outside of their control, such as those who die before receiving treatment; but this can also create bias.Take a hypothetical example presented by Montori and Guyatt [4].In a stroke trial, patients are randomized to receive surgery or nonoperative care.Some patients in both study arms die early in the trial; because there is a lag between randomization and surgery, some patients in
Source: PM&R
Author: Kristin L. Sainani
Institution: Stanford University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2097918448