Concealing the sequence from recruiters

• Adequate: if investigators and patients cannot foresee the assignment to intervention groups (i.e. numbered and coded identical sealed boxes prepared by central pharmacy, sealed opaque envelopes)

• Inadequate: allocation schedule open for recruiting physician to view beforehand, unsealed envelopes

Concealing the allocation of interventions from those recruiting participants is a crucial step in the progress of an RCT. The randomisation list is usually kept away from enrolment sites (for example in a central clinical trials office or pharmacy). Less ideally, sealed opaque envelopes are used - a method that is still susceptible to tampering by opening the envelopes or holding them up against a bright light.5 Failure to conceal such allocation means that those recruiting patients can foresee which treatment a patient is about to have. Such lack of concealment can result in selective enrolment of patients on the basis of prognostic factors,6 and loss of the "even playing field" that randomisation was designed to achieve.

Motives for interfering with the randomisation schedule include a desire on the part of investigators to ensure that their new treatment is successful by deliberately allocating patients in a better prognostic group to that treatment. Another reason may be that a doctor wants to ensure that particular patients are not allocated to a control or placebo group. Such selective recruitment is a form of selection bias, resulting in an unfair comparison of the interventions under evaluation. Trials in which concealment of allocation was judged to have been inadequate were found to have inflated the estimates of benefit by about 30% when compared with studies reporting adequate concealment.3

Blinding (masking) the intervention

Blinding or masking is the extent to which trial participants are kept unaware of treatment allocation. Blinding can refer to at least four groups of people: those recruiting patients, the study participants themselves, those assessing the outcomes in study participants, and those analysing the results.7 The term "double blind" traditionally refers to a study in which both the participants and the investigators are "blind" to the study intervention allocation, but the term is ambiguous unless qualified by a statement as to who exactly was blinded.

Blinding is less of an issue with objective outcomes such as death but is very important with subjective outcomes such as the opinion of participants or assessment of disease activity, as in most dermatology trials. Blinding may be achieved by a range of techniques such as ensuring that placebo tablets look, feel, smell and taste the same as the active tablets,8 or, in the case of ointments, by using as a placebo the same vehicle or base in which the active ingredient is formulated.9

Issues of blinding may seem superficially similar to allocation concealment in that both refer to concealing the interventions. The distinction is important in the sense that failure to conceal the randomisation sequence may result in unequal groups, (i.e. a form of selection bias) whereas failure to mask the intervention once a fair randomisation has taken a place represents a form of detection or information bias. Both can result in an incorrect estimate of the effects of a treatment. Studies that are not double blind typically overestimate treatment effects by about 14% when compared with studies that are double blind.3

Accounting for all those randomised

The whole point of randomisation is to create two or more groups that are as similar to each other as possible, the only exception being the intervention under study. In this way the additional effects of the intervention can be assessed.10 A potentially serious violation of this principle is the failure to take into account all those who were randomised when conducting the final main analysis, for example participants who deviate from the study protocol, those who do not adhere to the interventions and those who subsequently drop out for other reasons. People who drop out of trials differ from those who remain in them in several ways.11 People may drop out because they die, encounter adverse events, get worse (or no better), or simply because the proposed regimen is too complicated for a busy person to follow. They may even drop out because the treatment works so well. Ignoring participants who have dropped out in the analysis is not acceptable. Excluding participants who drop out after randomisation potentially biases the results. One way to reduce bias is to perform an intention-to-treat (ITT) analysis, in which all those initially randomised in the final analysis are included.11,12

Unless one has detailed information on why participants dropped out of a study, it cannot be assumed that an analysis of those remaining in the study to the end are representative of those randomised to the groups at the beginning. Failure to perform an ITT analysis may inflate or deflate estimates of treatment effect.4 Performing an ITT analysis is often regarded as a major criterion by which the quality of an RCT is assessed.

It is entirely appropriate to conduct an analysis of all those who remained at the end of a study (a "per protocol" analysis) alongside the ITT analysis.12 Discrepancies between results of ITT and per protocol analyses may indicate the potential benefit of the intervention under ideal compliance conditions and the need to explore ways of reformulating the intervention so that fewer participants drop out of the trial. Discrepancies may also indicate serious flaws in the study design.

Quality scales

Faulty reporting generally reflects faulty trial methods.3,5 A number of scales have been developed for assessing study trial quality over the past 15 years. These vary in the dimensions covered and complexity.2 Generally, the recent trend has been to use the few quality criteria given in Box 9.1, plus a few more that the appraiser considers important in relation to the condition being studied.3 It is now considered unwise to use summary quality scores in an attempt to "adjust" the potentially biased treatment estimate because this varies with the scale used and how the components of each scale are weighted.13 Instead, greater emphasis is placed on using the components of the scale as a check list and considering how each may affect the results.3

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