Dataset Information

Comparison of results from different imputation techniques for missing data from an anti-obesity drug trial.

ABSTRACT:

Background

In randomised trials of medical interventions, the most reliable analysis follows the intention-to-treat (ITT) principle. However, the ITT analysis requires that missing outcome data have to be imputed. Different imputation techniques may give different results and some may lead to bias. In anti-obesity drug trials, many data are usually missing, and the most used imputation method is last observation carried forward (LOCF). LOCF is generally considered conservative, but there are more reliable methods such as multiple imputation (MI).

Objectives

To compare four different methods of handling missing data in a 60-week placebo controlled anti-obesity drug trial on topiramate.

Methods

We compared an analysis of complete cases with datasets where missing body weight measurements had been replaced using three different imputation methods: LOCF, baseline carried forward (BOCF) and MI.

Results

561 participants were randomised. Compared to placebo, there was a significantly greater weight loss with topiramate in all analyses: 9.5 kg (SE 1.17) in the complete case analysis (N?=?86), 6.8 kg (SE 0.66) using LOCF (N?=?561), 6.4 kg (SE 0.90) using MI (N?=?561) and 1.5 kg (SE 0.28) using BOCF (N?=?561).

Conclusions

The different imputation methods gave very different results. Contrary to widely stated claims, LOCF did not produce a conservative (i.e., lower) efficacy estimate compared to MI. Also, LOCF had a lower SE than MI.

SUBMITTER: Jorgensen AW

PROVIDER: S-EPMC4237333 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Comparison of results from different imputation techniques for missing data from an anti-obesity drug trial.

Jørgensen Anders W AW Lundstrøm Lars H LH Wetterslev Jørn J Astrup Arne A Gøtzsche Peter C PC

PloS one 20141119 11

<h4>Background</h4>In randomised trials of medical interventions, the most reliable analysis follows the intention-to-treat (ITT) principle. However, the ITT analysis requires that missing outcome data have to be imputed. Different imputation techniques may give different results and some may lead to bias. In anti-obesity drug trials, many data are usually missing, and the most used imputation method is last observation carried forward (LOCF). LOCF is generally considered conservative, but there ...[more]

PMID: 25409438

Similar Datasets

Project description:BackgroundMissing participant outcome data (MOD) are ubiquitous in systematic reviews with network meta-analysis (NMA) as they invade from the inclusion of clinical trials with reported participant losses. There are available strategies to address aggregate MOD, and in particular binary MOD, while considering the missing at random (MAR) assumption as a starting point. Little is known about their performance though regarding the meta-analytic parameters of a random-effects model for aggregate binary outcome data as obtained from trial-reports (i.e. the number of events and number of MOD out of the total randomised per arm).MethodsWe used four strategies to handle binary MOD under MAR and we classified these strategies to those modelling versus excluding/imputing MOD and to those accounting for versus ignoring uncertainty about MAR. We investigated the performance of these strategies in terms of core NMA estimates by performing both an empirical and simulation study using random-effects NMA based on electrical network theory. We used Bland-Altman plots to illustrate the agreement between the compared strategies, and we considered the mean bias, coverage probability and width of the confidence interval to be the frequentist measures of performance.ResultsModelling MOD under MAR agreed with exclusion and imputation under MAR in terms of estimated log odds ratios and inconsistency factor, whereas accountability or not of the uncertainty regarding MOD affected intervention hierarchy and precision around the NMA estimates: strategies that ignore uncertainty about MOD led to more precise NMA estimates, and increased between-trial variance. All strategies showed good performance for low MOD (<5%), consistent evidence and low between-trial variance, whereas performance was compromised for large informative MOD (>?20%), inconsistent evidence and substantial between-trial variance, especially for strategies that ignore uncertainty due to MOD.ConclusionsThe analysts should avoid applying strategies that manipulate MOD before analysis (i.e. exclusion and imputation) as they implicate the inferences negatively. Modelling MOD, on the other hand, via a pattern-mixture model to propagate the uncertainty about MAR assumption constitutes both conceptually and statistically proper strategy to address MOD in a systematic review.

Dataset Information

Comparison of results from different imputation techniques for missing data from an anti-obesity drug trial.

Background

Objectives

Methods

Results

Conclusions

Publications

Comparison of results from different imputation techniques for missing data from an anti-obesity drug trial.

Similar Datasets

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