Analysis of clinical trials
Failure to include all participants in the analysis may bias the trial results. Most trials do not yield perfect data, however. "Protocol violations" may occur, such as when the patients do not receive the full intervention or the correct intervention or a few ineligible patients are randomly allocated in error. Despite the fact that the most clinical trials are carefully planned, many problems can occur during the conduct of the study. Some examples are as follows:
* The patients who do not satisfy the inclusion and/or exclusion criteria are included in the trial,
* A patient is randomized to the Treatment A but has been treated with the Treatment B,
* Some patients drop out from the study, or
* Some patients are not compliant, that is, do not take their medication as instructed, and so on.
The as-Treated analysis has the general idea to compare the subjects with their treatment regimen that they received. It does not consider the fact which treatment they were assigned for the treatment.
The randomized clinical trials analyzed by the intention-to-treat (ITT) approach provide the unbiased comparisons among the treatment groups. Since it came up in the 1960s, the principle of the ITT has become widely accepted for the analysis of the controlled clinical trials. In the ITT population, none of the patients is excluded and the patients are analyzed according to the randomization scheme. Although the medical investigators have often difficulties in accepting the ITT analysis, it is the pivotal analysis for the FDA and EMEA. The ITT analysis is generally favoured because it avoids the bias associated with the non-random loss of the participants. The ITT analysis is not appropriate for examining the adverse effects. Although the statistical techniques employed in the clinical trials are often quite simple, recent statistical research tackled specific and difficult the clinical trial issue, like the dropouts, compliance, non-inferiority studies, and so on. Probably the most important problem is the occurrence of the dropout in a clinical trial. For instance, when the patients drop out before a response can be obtained they cannot be included in the analysis, even not in an ITT analysis.
The basic ITT principle is that participants in the trials should be analysed in the groups to which they were randomized, regardless of whether they received or adhered to the allocated intervention. Two issues are involved here. The first issue is that the participants who strayed from the protocol (for example by not adhering to the prescribed intervention, or by being withdrawn from active treatment) should still be kept in the analysis. An extreme variation of this is the participants who receive the treatment from the group they were not allocated to, who should be kept in their original group for the analysis. This issue causes no problems provided that, as a systematic reviewer, you can extract the appropriate data from the trial reports.
Ø The rationale for this approach is that, in the first instance, we want to estimate the effects of allocating an intervention in practice, not the effects in the subgroup of the participants who adhere to it.
Ø The second issue in the ITT analyses is the problem of loss to follow-up. The people are lost from the clinical trials for many reasons. They may die, or move away; they may withdraw themselves or be withdrawn by their clinician, perhaps due to the adverse effects of the intervention being studied.
If the participants are lost to follow-up then the outcome may not be measured on them. But the strict ITT principle suggests that they should still be included in the analysis. There is an obvious problem - we often do not have the data that we need for these participants. In order to include such participants in an analysis, we must either find out whether the outcome data are available for them by contacting the trial lists, or we must impute (i.e. make up) their outcomes. This involves making assumptions about the outcomes in the 'lost' participants.
The analysis can only be restricted to the participants who fulfil the protocol in the terms of the eligibility, interventions, and outcome assessment. This analysis is known as an "on-treatment" or "per protocol" analysis. Also, the per-protocol restricts the comparison of the treatments to the ideal patients, that is, those who adhered perfectly to the clinical trial instructions as stipulated in the protocol. This population is classically called the per-protocol population and the analysis is called the per-protocol-analysis. A per-protocol analysis envisages determining the biological effect of the new drug. However, by restricting the analysis to a selected patient population, it does not show the practical value of the new drug.
The most important problem during the performance of the clinical trial is the occurrence of the dropout. For instance, when the patients drop out before a response can be obtained they cannot be included in the analysis, even not in an ITT analysis. When the patients are examined on a regular basis, a series of the measurements is obtained. In that case, the measurements obtained before the patient dropped out can be used to establish the unknown measurement at the end of the study. The Last-Observation-Carried-Forward (LOCF) method allows to analysis the data. But, the recent research shows that this method gives a biased estimate of the treatment effect and underestimates the variability of the estimated result. Let's assume that there are 8 weekly assessments after the baseline observation. If a patient drops out of the study after the third week, then this value is "carried forward" and assumed to be his or her score for the 5 missing data points. The assumption is that the patients improve gradually from the start of the study until the end, so that carrying forward an intermediate value is a conservative estimate of how well the person would have done had he or she remained in the study. The advantages to this approach are that
* It minimises the number of the subjects who are eliminated from the analysis, and
* It allows the analysis to examine the trends over time, rather than focusing simply on the endpoint.
AR Waladkhani. (2008). Conducting clinical trials. A theoretical and practical guide. ISBN: 9783940934000
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