Subtypes of the missing not at random missing data mechanism.

Gomer, Brenna; Yuan, Ke‐Hai

doi:10.1037/met0000377

Cited by 17 publications

(17 citation statements)

References 27 publications

(48 reference statements)

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“…Another interesting development related to Rubin's framework is the use of graphical models-directed acyclic graphs and missingness graphs (m-graphs)-to represent and understand missing data mechanisms. To illustrate, the m-graphs in Figure 1 depict the focused and diffuse missing not at random processes described by (Gomer & Yuan, 2021). Schafer and Graham (2002) used a similar diagram to represent Rubin's mechanisms, and others have since developed the framework as tool for understanding and clarifying the conditions under which valid estimates are possible (R. M. Daniel et al, 2012;Mohan et al, 2013;Mohan & Pearl, 2021;Moreno-Betancur et al, 2018;Thoemmes & Mohan, 2015;Thoemmes & Rose, 2014).…”

Section: Missing Data Mechanismsmentioning

confidence: 99%

Missing data: An update on the state of the art.

Enders¹

2023

Psychological Methods

106

102

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The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of Psychological Methods. Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of applications that are possible with modern missing data techniques has increased dramatically, and software options are light years ahead of where they were. This article provides an update on the state of the art that catalogs important innovations from the past two decades of missing data research. The paper addresses topics described in the original paper, including developments related to missing data theory, full information maximum likelihood, Bayesian estimation, multiple imputation, and models for missing not at random processes. The paper also describes newer factored regression specifications and missing data handling for multilevel models, both of which have been a focus of recent research. The paper concludes with a summary of the current software landscape and a discussion of several practical issues.

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Section: Missing Data Mechanismsmentioning

confidence: 99%

Missing data: An update on the state of the art.

Enders¹

2023

Psychological Methods

106

102

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“…Other approaches should not go unmentioned. For instance, research has suggested distinguishing different subtypes of the MNAR missing data mechanism [57]. Sensitivity analyses may also be implemented using multiple imputation [58][59][60] or Bayesian statistics [61][62][63].…”

Section: Discussionmentioning

confidence: 99%

Sensitivity analyses for data missing at random versus missing not at random using latent growth modelling: a practical guide for randomised controlled trials

Staudt

Freyer‐Adam

Ittermann

et al. 2022

BMC Med Res Methodol

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Background Missing data are ubiquitous in randomised controlled trials. Although sensitivity analyses for different missing data mechanisms (missing at random vs. missing not at random) are widely recommended, they are rarely conducted in practice. The aim of the present study was to demonstrate sensitivity analyses for different assumptions regarding the missing data mechanism for randomised controlled trials using latent growth modelling (LGM). Methods Data from a randomised controlled brief alcohol intervention trial was used. The sample included 1646 adults (56% female; mean age = 31.0 years) from the general population who had received up to three individualized alcohol feedback letters or assessment-only. Follow-up interviews were conducted after 12 and 36 months via telephone. The main outcome for the analysis was change in alcohol use over time. A three-step LGM approach was used. First, evidence about the process that generated the missing data was accumulated by analysing the extent of missing values in both study conditions, missing data patterns, and baseline variables that predicted participation in the two follow-up assessments using logistic regression. Second, growth models were calculated to analyse intervention effects over time. These models assumed that data were missing at random and applied full-information maximum likelihood estimation. Third, the findings were safeguarded by incorporating model components to account for the possibility that data were missing not at random. For that purpose, Diggle-Kenward selection, Wu-Carroll shared parameter and pattern mixture models were implemented. Results Although the true data generating process remained unknown, the evidence was unequivocal: both the intervention and control group reduced their alcohol use over time, but no significant group differences emerged. There was no clear evidence for intervention efficacy, neither in the growth models that assumed the missing data to be at random nor those that assumed the missing data to be not at random. Conclusion The illustrated approach allows the assessment of how sensitive conclusions about the efficacy of an intervention are to different assumptions regarding the missing data mechanism. For researchers familiar with LGM, it is a valuable statistical supplement to safeguard their findings against the possibility of nonignorable missingness. Trial registration The PRINT trial was prospectively registered at the German Clinical Trials Register (DRKS00014274, date of registration: 12th March 2018).

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“…In general, both pairwise and listwise deletion result in lower statistical power, under any missing data mechanism (Enders & Bandalos, 2001). Moreover, if the missing data come from MAR or MNAR, then a model’s parameter estimates may be biased (Gomer & Yuan, in press).…”

Section: Missing Datamentioning

confidence: 99%

Data Exclusion in Policy Survey and Questionnaire Data: Aberrant Responses and Missingness

Hong

Carter

Kim

et al. 2023

Policy Insights from the Behavioral and Brain Sciences

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Data preprocessing is an integral step prior to analyzing data in psychological science, with implications for its potentially guiding policy. This article reports how psychological researchers address data preprocessing or quality concerns, with a focus on aberrant responses and missing data in self-report measures. 240 articles were sampled from four journals: Psychological Science, Journal of Personality and Social Psychology, Developmental Psychology, and Abnormal Psychology from 2012 to 2018. Nearly half of the studies did not report any missing data treatment (111/240; 46.25%), and if they did, the most common approach was listwise deletion (71/240; 29.6%). Studies that remove data due to missingness removed, on average, 12% of the sample. Likewise, most studies do not report any aberrant responses (194/240; 80%), but if they did, they classified 4% of the sample as suspect. Most studies are either not transparent enough about their data preprocessing steps or may be leveraging suboptimal procedures. Recommendations can improve transparency and data quality.

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Subtypes of the missing not at random missing data mechanism.

Cited by 17 publications

References 27 publications

Missing data: An update on the state of the art.

Missing data: An update on the state of the art.

Sensitivity analyses for data missing at random versus missing not at random using latent growth modelling: a practical guide for randomised controlled trials

Data Exclusion in Policy Survey and Questionnaire Data: Aberrant Responses and Missingness

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