Our approach to the acceptability of post-randomisation exclusions focuses on two primary goals: to avoid bias and to minimise random error. The best way to achieve these goals depends on whether investigators wish to address an explanatory (efficacy) or management (effectiveness) question. Ideally, investigators will avoid post-randomisation exclusions through rigorous design and pretesting of the study protocol. We address four situations, illustrated by real or hypothetical studies, that are unusual and ideally should not arise during the conduct of most clinical trials.
Patients mistakenly included who do not meet inclusion criteria
Patients may be inappropriately randomised into clinical trials as a result of human error. Many clinical trials involve acutely ill patients who require urgent interventions. Determination of patients' eligibility for inclusion in these studies must be made quickly and consent and randomisation arranged expediently. Often study personnel work in chaotic clinical environments. Time constraints may result in patients who do not meet predetermined eligibility criteria being mistakenly included (box ).
Ineligible patients mistakenly included
When ineligible patients are mistakenly included, investigators could remove these patients from both study arms without risking bias. However, so that the decision to remove such patients is unbiased and not influenced by events that occurred after randomisation (and may therefore be affected by whether patients received experimental or control treatment), an independent adjudication committee blinded to treatment and outcome must systematically review each patient. Also, the adjudication committee must base its decisions solely on information that reflects the patient's status before randomisation. Investigators should clearly state the number of patients randomised but not included in the primary analysis of data and explain the circumstances under which such patients were enrolled but excluded from the analysis.
Although excluding a large number of patients may not introduce bias, it may weaken any inferences from the study, because of the decreased sample size (that is, decrease the precision of the estimates of effect). If ineligible patients have a similar response to treatment to that of eligible patients, their exclusion will reduce the power of the study. If the reason for exclusion was that they were expected to have a reduced or no response to treatment, and the expectation is correct, their inclusion will introduce random error and reduce the power of the study and the precision of the estimate of treatment effect. Furthermore, the most informative analysis will depend on whether clinicians ultimately intend to apply the study results to patients represented by those who were mistakenly randomised.
Poor or excessively broad eligibility criteria
Poorly defined or excessively broad eligibility criteria can lead to the inclusion of patients who do not have the condition of interest and are therefore unlikely to benefit from treatment. For example, studies of severe infections resulting in sepsis syndrome are often beset by difficulties in defining the condition of interest and the eligibility criteria.11,12
The diversity of clinical presentations often results in the enrolment of patients who meet eligibility criteria and receive treatment but are unlikely to benefit (box ).
Excessively broad eligibility criteria
Under such circumstances the primary analysis should include all randomised patients. A secondary analysis that includes only patients who had the condition of interest and that is based on data collected before randomisation can also be informative and unbiased (see Discussion).
Patients randomised before eligibility for inclusion can be confirmed
If investigators expect delays in obtaining clinical or laboratory information on patients' eligibility, they should ideally postpone randomisation until this information is available. However, even with sound methods and procedures and the best of intentions, instances when patients must be randomised before all the data needed to confirm eligibility are available will occur (box ).
Randomisation of patients before data are available to confirm their eligibility
Excluding such patients has serious potential implications. For example, one study of an anti-influenza drug randomised 629 patients, of whom 255 (40%) were later found to not have influenza.14
The study reported that, in the 374 patients who were infected, the study drug reduced the duration of illness by 30% (P<0.001). However, analysis of all 629 randomised patients shows a less dramatic but still significant effect of the study drug, with a reduced duration of 22% (P=0.004). Although in this particular case the result of the intention to treat analysis was significant, exclusion of 40% of randomised patients in many trials could have a more dramatic impact on results and could transform a null result into a positive one, reflecting the biological effect of the treatment in patients with the target condition.
On the other hand, retrospective exclusion of a large number of patients who would not be expected to benefit from the treatment creates a potentially misleading impression of the overall effect (positive and negative) of the treatment on the population to whom it will be applied. For example, the antiviral drug in this study caused nausea or vomiting in 19% of all randomised patients. Presumably the 255 patients who received the drug but did not have influenza experienced the same degree of side effects, without any benefit.
This clinical scenario mirrors real life clinical situations where doctors need to treat patients before all information is available. The major issue in the interpretation of results becomes one of effectiveness versus efficacy or explanatory versus pragmatic approaches. One would want to be sure that the benefit of the study drug to patients with the underlying condition outweighs the harm to patients exposed to the drug without possibility of benefit. Therefore, the primary presentation of the results should include all the patients randomised into the study. Exclusion or failure to report outcomes of patients without the condition of interest, but whom doctors must necessarily treat, risks underestimating the negative sides of the intervention. Investigators should also conduct a secondary analysis of efficacy, particularly when the intention to treat analysis leaves uncertainty as to whether the treatment is effective. This analysis, if it adheres to the rules of blinded adjudication we described above, will lead to an unbiased estimate of treatment effect in patients who truly had the underlying condition of interest.
Patients prematurely randomised into a clinical trial
Premature randomisation occurs when clinical circumstances evolve so that the patient never receives the intervention (an issue of methodology). Trials evaluating universal prestorage leucoreduction of red blood cells before surgery show the effect of premature randomisation (box ).
Excluding all randomised patients who did not receive a unit of red blood cells will not bias the analysis, as long as allocation to treatment or control arm could not influence the likelihood that patients receive a transfusion. We believe this is a secure inference. The only impact of excluding patients who did not receive a transfusion will be to enhance the precision of the estimate—and the meaningfulness of the estimate of relative risk reduction for the clinician. To ensure that allocation could not have influenced whether patients received a transfusion, investigators should report an analysis of all randomised patients, as well as baseline characteristics for all patients excluded from the analysis.
In studies in which only patients allocated to one of two arms will receive the target intervention, excluding such patients will lead to biased results. For example, in a clinical trial of epidural anaesthesia in childbirth, some women randomised to the epidural treatment arm did not need an epidural because their pain levels did not rise above their personal thresholds.16
Investigators should not exclude these patients from the analysis, as they cannot identify similar patients in the control arm.