| Literature DB >> 23166608 |
Mohamad Alshurafa1, Matthias Briel, Elie A Akl, Ted Haines, Paul Moayyedi, Stephen J Gentles, Lorena Rios, Chau Tran, Neera Bhatnagar, Francois Lamontagne, Stephen D Walter, Gordon H Guyatt.
Abstract
BACKGROUND: Authors of randomized trial reports seem to hold a variety of views regarding the relationship between missing outcome data (MOD) and intention to treat (ITT). The objectives of this study were to systematically investigate how authors of methodology articles define ITT in the presence of MOD, how they recommend handling MOD under ITT, and to make a proposal for potential improvement in the definition and use of ITT in relation to MOD. METHODS ANDEntities:
Mesh:
Year: 2012 PMID: 23166608 PMCID: PMC3499557 DOI: 10.1371/journal.pone.0049163
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Figure 1Flow diagram for articles included in this review.
ITT, intention to treat; MOD, missing outcome data.
Definitions of intention to treat (ITT) in relation to missing outcome data (MOD).
| No (%) of articles (n = 66) | |
|
| 5 (8) |
|
| 25 (38) |
|
| 36 (55) |
| Provided one definition of ITT | 19 |
|
| 7 |
|
| 1 |
|
| 11 |
| Provided multiple definitions of ITT | 17 |
|
| 14 |
|
| 5 |
|
| 17 |
For details please see , Table 1.
Strategies to deal with missing outcome data (MOD) under intention to treat (ITT).
| ITT involves a specific strategy for MOD | No (%) of articles (n = 28) |
| Complete case analysis | 3 (11) |
| Worst case scenario | 9 (32) |
| Best case scenario | 6 (21) |
| Assumption all experienced outcome of interest | 8 (29) |
| Assumption none experienced outcome of interest | 5 (18) |
| Last observation carried forward | 14 (50) |
| Censored at the time lost to follow-up in a survival analysis | 12 (43) |
| Multiple imputation strategy | 3 (11) |
| Sensitivity analysis (2 or more strategies should be used) | 14 (50) |
| Other | 11 (39) |
Most articles suggested several strategies; for details see Appendix S4, Table 2.
Details about „other strategies“ are summarized in Appendix S4, Table 3.
Essential components to report in randomized clinical trials with respect to the analysis.
|
|
| Claim of ITT: if individuals were analyzed in the groups to which they were randomized with details about any post-randomization exclusions |
| No claim of ITT, e.g. if analysis exclusively focused on individuals who complied with the study protocol (‘per protocol’ or ‘as treated’ analysis) |
|
|
| A) No MOD (complete follow-up) |
| B) Individuals with MOD were not considered in the analysis (complete/available case analysis) |
| C) Imputation with explicit description. Options include individuals with MOD were considered in the analysis: |
| i) assuming all experienced the outcome of interest, |
| ii) assuming none experienced the outcome of interest, |
| iii) assuming a worst case scenario (i.e. individuals with MOD in the experimental group experienced the outcome of interest and those in the control group did not), |
| iv) assuming a best case scenario (i.e. individuals with MOD in the experimental group did not experienced the outcome of interest and those in the control group did), |
| v) last observation carried forward, |
| vi) censored at the time lost to follow-up in a survival analysis, |
| vii) multiple imputation, |
| viii) any other imputation/modelling that needs to be specified. |
| D) Two or more of the options in B & C (sensitivity analysis) |
It may be appropriate to exclude randomized patients in order to achieve efficiencies while preserving prognostic balance between groups if two conditions are met [23]: (1) allocation to treatment or control could not possibly influence whether a particular randomized individual met criteria for post-randomization exclusion, (2) the decision about post-randomization is made without possible bias (commonly achieved through review blinded to allocation).
There are various ways of handling missing data; we provide illustrative examples for reporting purposes.
For dichotomous outcome data.