RCTs are most often analysed according the intention-to-treat (ITT) principle. An ITT analysis includes all randomized patients in the groups to which they were randomly assigned, regardless of the treatment they actually received and regardless of early withdrawal from the study or deviation from the protocol.2, 52, 53 In the Bio-CAPTURE study,
patients are not randomized. However, the term ITT analysis is used in chapter 4-6, as all patients were analysed for the full period of analysis. This has also been done in other observational studies.17, 54
As the duration of a study increases, the number of patients continuing in the study usually declines, leading to missing data.52 Data can be missing for a variety of reasons, including
withdrawal from the study due to lack or loss of efficacy or adverse events.2, 52, 53, 55
Different approaches for providing an estimated value for missing data exist, commonly referred to as ‘imputation’ of missing data (Table 1). In our study, the efficacy outcome measures are PASI 50, PASI 75 and PASI 90. Imputation methods commonly used in RCTs are last observation carried forward (LOCF) and nonresponder imputation (NRI).
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Chapter 10
With LOCF, the previous available value is used for the subsequent missing value(s). Although criticized by statisticians, LOCF is the most commonly used imputation method in RCTs. It is generally considered to provide a conservative estimate of efficacy.52
The most conservative approach is NRI. With NRI, a patient with a missing value is regarded as a nonresponder, also referred to as ‘missing equals failure’ (MEF). In an as- treated analysis, missing values are excluded from the analysis, which is also referred to as ‘missing equals excluded’ (MEX).2, 53, 56 The as-treated analysis is sometimes also called
observed values analysis (chapter 2) and is mainly being used in observational studies and open-label extension studies from RCTs.4, 56, 57
Based on the research question, different analytical methods can be chosen. The as- treated analysis gives an idea of maximum efficacy. However, if one wants to know what treatment efficacy is under less ideal conditions, including patients who discontinue treatment due to insufficient efficacy, an ITT analysis will provide better information.2
As shown in chapter 6, the methodological approach used has a major influence on the efficacy results. In our study, efficacy could double when the as-treated approach was used instead of the modified NRI approach.
Using NRI in an observational cohort study is problematic, as the inclusion of patients is continuously ongoing. Applying NRI for patients who were still using etanercept at the time of analysis, but did not reach all time points of analysis due to an insufficient duration
Table 1. Description of different approaches for the analysis of data in clinical studies.52
Approach Abbreviation Description Equivalent terminology Populations
Intention-to-treat ITT All randomized patients in the groups to which they were randomly assigned
Per-protocol PP All patients who did not deviate from the protocol Adherers only
Intention-to-observe ITO All patients entering the observational phase of a long- term study
Maintenance ITT
Imputation of missing data
Missing equals succes MES Missing values are assigned as a success
Missing equals failure MEF Missing values are assigned as a failure Nonresponder imputation
Missing equals excluded MEX Missing values are excluded from the analysis As-treated
Missing completely at random
MCAR The missingness of data does not depend on the
previously observed or current unobserved outcomes
Missing at random MAR The missingness of data depends on the previously observed values, but not the current unobserved values
Missing not at random MNAR The missingness of data depends on the current unobserved outcomes
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of follow-up, was considered inappropriate. Therefore, a less conservative modification of the NRI approach, named the modified NRI approach, was used in chapter 6.56
The influence of missing data on the efficacy outcomes of a study depends on the reasons for missing data. As shown in Figure 1 in chapter 6, the most frequent reason for discontinuing etanercept treatment in our study was loss of efficacy or a combination of loss of efficacy and adverse events.55 For this reason and for reason of the prolonged
treatment duration of responders, the as-treated analysis gives a too positive view of the efficacy of etanercept. On the other hand, nonresponder imputation would probably give a too negative view of the efficacy of etanercept, as a substantial number of treatment episodes had missing data for other reasons than loss of efficacy or a combination of loss of efficacy and adverse events. The modified NRI method may underestimate the efficacy of etanercept as well, as some patients who discontinued etanercept due to lack of efficacy or a combination of lack of efficacy and adverse events, actually were PASI 75 responders (Figure 3). This means that a PASI 75 response is not always a sufficient response for patients and/or dermatologists.
A way to overcome the bias introduced by each statistical method, is the use and development of other outcome measures. A possible outcome measure could be the amount of time patients continue to take a particular drug, which is also referred to as ‘drug survival’. Drug survival is an indirect measure of drug efficacy. However, drug survival is also dependent on side effects, general satisfaction with the treatment and the availability of other therapies.36, 55 An alternative outcome measure could be represented by a biomarker
that measures psoriasis activity instead of an outcome measure that measures psoriasis severity like the PASI. However, reliable biomarkers are not available at this moment.
In conclusion: every method of analysis has its advantages and disadvantages and can introduce a bias. As the method of analysis used has a large influence, we support the use of different methods of analysis.