Missing data: Discussion points from the PSI missing data expert group

Burzykowski, Tomasz; Carpenter, James; Coens, Corneel; Evans, Daniel J.; France, Lesley; Kenward, Mike; Lane, P. W.; Matcham, James; Morgan, D. D.; Phillips, Alan; Roger, James; Sullivan, Brian; White, Ian R.; Yu, Ly-Mee

doi:10.1002/pst.391

Cited by 22 publications

(17 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, a recent report on prevention and treatment of missing data, with a focus on clinical trials, produced by the US National Academy of Sciences ,p. 5, made the following recommendation: ‘Recommendation 15: Sensitivity analyses should be part of the primary reporting of findings from clinical trials. Examining sensitivity to the assumptions about the missing data mechanism should be a mandatory component of reporting.’ Other guidelines issued lately also stressed the need to perform sensitivity analyses that assess the impact of missing data on reported inferences and conclusions.…”

Section: Sensitivity Analysis For Studies With Missing Datamentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity analysis for a partially missing binary outcome in a two-arm randomized clinical trial

Liublinska

Rubin

2014

Statist. Med.

View full text Add to dashboard Cite

Although recent guidelines for dealing with missing data emphasize the need for sensitivity analyses, and such analyses have a long history in statistics, universal recommendations for conducting and displaying these analyses are scarce. We propose graphical displays that help formalize and visualize the results of sensitivity analyses, building upon the idea of ‘tipping-point’ analysis for randomized experiments with a binary outcome and a dichotomous treatment. The resulting ‘enhanced tipping-point displays’ are convenient summaries of conclusions obtained from making different modeling assumptions about missingness mechanisms. The primary goal of the displays is to make formal sensitivity analyses more comprehensible to practitioners, thereby helping them assess the robustness of the experiment’s conclusions to plausible missingness mechanisms. We also present a recent example of these enhanced displays in a medical device clinical trial that helped lead to FDA approval.

show abstract

Section: Sensitivity Analysis For Studies With Missing Datamentioning

confidence: 99%

“…Examining sensitivity to the assumptions about the missing data mechanism should be a mandatory component of reporting.' Other guidelines issued lately [16][17][18] also stressed the need to perform sensitivity analyses that assess the impact of missing data on reported inferences and conclusions.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis for a partially missing binary outcome in a two-arm randomized clinical trial

Liublinska

Rubin

2014

Statist. Med.

View full text Add to dashboard Cite

show abstract

“…We believe that if trialists follow this framework then there is scope for considerable improvement in the appropriateness, consistency, and reporting of ITT analyses when outcomes are missing. However, the best approach to missing data is always to design and conduct the trial to maximise data collection [2, 3]. A careful ITT analysis strategy, and in particular an appropriate sensitivity analysis, recognises the increase in uncertainty that arises from missing outcomes, and therefore increases the incentive for researchers to maximise their data completeness.…”

Section: Discussionmentioning

confidence: 99%

Including all individuals is not enough: Lessons for intention-to-treat analysis

2012

Self Cite

View full text Add to dashboard Cite

Background Intention-to-treat analysis requires all randomised individuals to be included in the analysis in the groups to which they were randomised. However, there is confusion about how intention-to-treat analysis should be performed in the presence of missing outcome data. Purpose To explain, justify and illustrate an intention-to-treat analysis strategy for randomised trials with incomplete outcome data. Methods We consider several methods of analysis and compare their underlying assumptions, plausibility, and numbers of individuals included. We illustrate the intention-to-treat analysis strategy using data from the UK700 trial in the management of severe mental illness. Results Depending on the assumptions made about the missing data, some methods of analysis that include all randomised individuals may be less valid than methods that do not include all randomised individuals. Further, some methods of analysis that include all randomised individuals are essentially equivalent to methods that do not include all randomised individuals. Limitations This work assumes that the aim of analysis is to obtain an accurate estimate of the difference in outcome between randomised groups, not to obtain a conservative estimate with bias against the experimental intervention. Conclusions Clinical trials should employ an intention-to-treat analysis strategy, comprising a design that attempts to follow up all randomised individuals, a main analysis that is valid under a stated plausible assumption about the missing data, and sensitivity analyses which include all randomised individuals in order to explore the impact of departures from the assumption underlying the main analysis. Following this strategy recognises the extra uncertainty arising from missing outcomes and increases the incentive for researchers to minimise the extent of missing data.

show abstract

“…Several guidelines have been published in recent years on the issue of missing data in clinical trials (National Research Council, ; Committee for Medicinal Products for Human Use (CHMP), ; Burzykowski et al, ; Carpenter & Kenward, ) and for CEA in particular (Briggs, Clark, Wolstenholme, & Clarke, ; Burton, Billingham, & Bryan, ; Faria, Gomes, Epstein, & White, ; Manca & Palmer, ; Marshall, Billingham, & Bryan, ). Key recommendations include: taking practical steps to limit the number of missing observations; avoiding methods whose validity rests on contextually implausible assumptions, and using methods that incorporate all available information under reasonable assumptions; and assessing the sensitivity of the results to departures from these assumptions. …”

Section: Introductionmentioning

confidence: 99%

“…Several guidelines have been published in recent years on the issue of missing data in clinical trials (National Research Council, 2010; Committee for Medicinal Products for Human Use (CHMP), 2011; Burzykowski et al, 2010;Carpenter & Kenward, 2007) and for CEA in particular (Briggs, Clark, Wolstenholme, & Clarke, 2003;Burton, Billingham, & Bryan, 2007;Faria, Gomes, Epstein, & White, 2014;Manca & Palmer, 2005;Marshall, Billingham, & Bryan, 2009). Key recommendations include:…”

Section: Introductionmentioning

confidence: 99%

Missing data in trial‐based cost‐effectiveness analysis: An incomplete journey

2018

View full text Add to dashboard Cite

SUMMARYCost‐effectiveness analyses (CEA) conducted alongside randomised trials provide key evidence for informing healthcare decision making, but missing data pose substantive challenges. Recently, there have been a number of developments in methods and guidelines addressing missing data in trials. However, it is unclear whether these developments have permeated CEA practice. This paper critically reviews the extent of and methods used to address missing data in recently published trial‐based CEA.Issues of the Health Technology Assessment journal from 2013 to 2015 were searched. Fifty‐two eligible studies were identified. Missing data were very common; the median proportion of trial participants with complete cost‐effectiveness data was 63% (interquartile range: 47%–81%). The most common approach for the primary analysis was to restrict analysis to those with complete data (43%), followed by multiple imputation (30%). Half of the studies conducted some sort of sensitivity analyses, but only 2 (4%) considered possible departures from the missing‐at‐random assumption.Further improvements are needed to address missing data in cost‐effectiveness analyses conducted alongside randomised trials. These should focus on limiting the extent of missing data, choosing an appropriate method for the primary analysis that is valid under contextually plausible assumptions, and conducting sensitivity analyses to departures from the missing‐at‐random assumption.

show abstract

Missing data: Discussion points from the PSI missing data expert group

Cited by 22 publications

References 5 publications

Sensitivity analysis for a partially missing binary outcome in a two-arm randomized clinical trial

Sensitivity analysis for a partially missing binary outcome in a two-arm randomized clinical trial

Including all individuals is not enough: Lessons for intention-to-treat analysis

Missing data in trial‐based cost‐effectiveness analysis: An incomplete journey

Contact Info

Product

Resources

About