Dataset Information

Potential impact of missing outcome data on treatment effects in systematic reviews: imputation study.

ABSTRACT:

Objective

To assess the risk of bias associated with missing outcome data in systematic reviews.

Design

Imputation study.

Setting

Systematic reviews.

Population

100 systematic reviews that included a group level meta-analysis with a statistically significant effect on a patient important dichotomous efficacy outcome.

Main outcome measures

Median percentage change in the relative effect estimate when applying each of the following assumption (four commonly discussed but implausible assumptions (best case scenario, none had the event, all had the event, and worst case scenario) and four plausible assumptions for missing data based on the informative missingness odds ratio (IMOR) approach (IMOR 1.5 (least stringent), IMOR 2, IMOR 3, IMOR 5 (most stringent)); percentage of meta-analyses that crossed the threshold of the null effect for each method; and percentage of meta-analyses that qualitatively changed direction of effect for each method. Sensitivity analyses based on the eight different methods of handling missing data were conducted.

Results

100 systematic reviews with 653 randomised controlled trials were included. When applying the implausible but commonly discussed assumptions, the median change in the relative effect estimate varied from 0% to 30.4%. The percentage of meta-analyses crossing the threshold of the null effect varied from 1% (best case scenario) to 60% (worst case scenario), and 26% changed direction with the worst case scenario. When applying the plausible assumptions, the median percentage change in relative effect estimate varied from 1.4% to 7.0%. The percentage of meta-analyses crossing the threshold of the null effect varied from 6% (IMOR 1.5) to 22% (IMOR 5) of meta-analyses, and 2% changed direction with the most stringent (IMOR 5).

Conclusion

Even when applying plausible assumptions to the outcomes of participants with definite missing data, the average change in pooled relative effect estimate is substantive, and almost a quarter (22%) of meta-analyses crossed the threshold of the null effect. Systematic review authors should present the potential impact of missing outcome data on their effect estimates and use this to inform their overall GRADE (grading of recommendations assessment, development, and evaluation) ratings of risk of bias and their interpretation of the results.

SUBMITTER: Kahale LA

PROVIDER: S-EPMC7448113 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

Potential impact of missing outcome data on treatment effects in systematic reviews: imputation study.

Kahale Lara A LA Khamis Assem M AM Diab Batoul B Chang Yaping Y Lopes Luciane Cruz LC Agarwal Arnav A Li Ling L Mustafa Reem A RA Koujanian Serge S Waziry Reem R Busse Jason W JW Dakik Abeer A Schünemann Holger J HJ Hooft Lotty L Scholten Rob Jpm RJ Guyatt Gordon H GH Akl Elie A EA

BMJ (Clinical research ed.) 20200826

<h4>Objective</h4>To assess the risk of bias associated with missing outcome data in systematic reviews.<h4>Design</h4>Imputation study.<h4>Setting</h4>Systematic reviews.<h4>Population</h4>100 systematic reviews that included a group level meta-analysis with a statistically significant effect on a patient important dichotomous efficacy outcome.<h4>Main outcome measures</h4>Median percentage change in the relative effect estimate when applying each of the following assumption (four commonly disc ...[more]

PMID: 32847800

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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.

Project description:BackgroundMissing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robustness of results. Controlled Multiple Imputation (MI) procedures, including delta-based and reference-based approaches, have been developed for analysis under missing-not-at-random assumptions. However, it is unclear how often these methods are used, how they are reported, and what their impact is on trial results. This review evaluates the current use and reporting of MI and controlled MI in RCTs.MethodsA targeted review of phase II-IV RCTs (non-cluster randomised) published in two leading general medical journals (The Lancet and New England Journal of Medicine) between January 2014 and December 2019 using MI. Data was extracted on imputation methods, analysis status, and reporting of results. Results of primary and sensitivity analyses for trials using controlled MI analyses were compared.ResultsA total of 118 RCTs (9% of published RCTs) used some form of MI. MI under missing-at-random was used in 110 trials; this was for primary analysis in 43/118 (36%), and in sensitivity analysis for 70/118 (59%) (3 used in both). Sixteen studies performed controlled MI (1.3% of published RCTs), either with a delta-based (n = 9) or reference-based approach (n = 7). Controlled MI was mostly used in sensitivity analysis (n = 14/16). Two trials used controlled MI for primary analysis, including one reporting no sensitivity analysis whilst the other reported similar results without imputation. Of the 14 trials using controlled MI in sensitivity analysis, 12 yielded comparable results to the primary analysis whereas 2 demonstrated contradicting results. Only 5/110 (5%) trials using missing-at-random MI and 5/16 (31%) trials using controlled MI reported complete details on MI methods.ConclusionsControlled MI enabled the impact of accessible contextually relevant missing data assumptions to be examined on trial results. The use of controlled MI is increasing but is still infrequent and poorly reported where used. There is a need for improved reporting on the implementation of MI analyses and choice of controlled MI parameters.

Project description:BackgroundIncomplete categorical variables with more than two categories are common in public health data. However, most of the existing missing-data methods do not use the information from nonresponse (missingness) probabilities.MethodsWe propose a nearest-neighbour multiple imputation approach to impute a missing at random categorical outcome and to estimate the proportion of each category. The donor set for imputation is formed by measuring distances between each missing value with other non-missing values. The distance function is calculated based on a predictive score, which is derived from two working models: one fits a multinomial logistic regression for predicting the missing categorical outcome (the outcome model) and the other fits a logistic regression for predicting missingness probabilities (the missingness model). A weighting scheme is used to accommodate contributions from two working models when generating the predictive score. A missing value is imputed by randomly selecting one of the non-missing values with the smallest distances. We conduct a simulation to evaluate the performance of the proposed method and compare it with several alternative methods. A real-data application is also presented.ResultsThe simulation study suggests that the proposed method performs well when missingness probabilities are not extreme under some misspecifications of the working models. However, the calibration estimator, which is also based on two working models, can be highly unstable when missingness probabilities for some observations are extremely high. In this scenario, the proposed method produces more stable and better estimates. In addition, proper weights need to be chosen to balance the contributions from the two working models and achieve optimal results for the proposed method.ConclusionsWe conclude that the proposed multiple imputation method is a reasonable approach to dealing with missing categorical outcome data with more than two levels for assessing the distribution of the outcome. In terms of the choices for the working models, we suggest a multinomial logistic regression for predicting the missing outcome and a binary logistic regression for predicting the missingness probability.

Project description:BackgroundThere is no consensus on how authors conducting meta-analysis should deal with trial participants with missing outcome data. The objectives of this study are to assess in Cochrane and non-Cochrane systematic reviews: (1) which categories of trial participants the systematic review authors consider as having missing participant data (MPD), (2) how trialists reported on participants with missing outcome data in trials, (3) whether systematic reviewer authors actually dealt with MPD in their meta-analyses of dichotomous outcomes consistently with their reported methods, and (4) the impact of different methods of dealing with MPD on pooled effect estimates in meta-analyses of dichotomous outcomes.Methods/designWe will conduct a methodological study of Cochrane and non-Cochrane systematic reviews. Eligible systematic reviews will include a group-level meta-analysis of a patient-important dichotomous efficacy outcome, with a statistically significant effect estimate. Teams of two reviewers will determine eligibility and subsequently extract information from each eligible systematic review in duplicate and independently, using standardized, pre-piloted forms. The teams will then use a similar process to extract information from the trials included in the meta-analyses of interest. We will assess first which categories of trial participants the systematic reviewers consider as having MPD. Second, we will assess how trialists reported on participants with missing outcome data in trials. Third, we will compare what systematic reviewers report having done, and what they actually did, in dealing with MPD in their meta-analysis. Fourth, we will conduct imputation studies to assess the effects of different methods of dealing with MPD on the pooled effect estimates of meta-analyses. We will specifically calculate for each method (1) the percentage of systematic reviews that lose statistical significance and (2) the mean change of effect estimates across systematic reviews.DiscussionThe impact of different methods of dealing with MPD on pooled effect estimates will help judge the associated risk of bias in systematic reviews. Our findings will inform recommendations regarding what assumptions for MPD should be used to test the robustness of meta-analytical results.

Dataset Information

Potential impact of missing outcome data on treatment effects in systematic reviews: imputation study.

Objective

Design

Setting

Population

Main outcome measures

Results

Conclusion

Publications

Potential impact of missing outcome data on treatment effects in systematic reviews: imputation study.

Similar Datasets

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