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A novel item-allocation procedure for the three-form planned missing data design.

ABSTRACT: We propose a new method of constructing questionnaire forms in the three-form planned missing data design (PMDD). The random item allocation (RIA) procedure that we propose promises to dramatically simplify the process of implementing three-form PMDDs without compromising statistical performance. Our method is a stochastic approximation to the currently recommended approach of deterministically spreading a scale's items across the X-, A-, B-, and C-blocks when allocating the items in a three-form design. Direct empirical support for the performance of our method is only available for scales containing at least 12 items, so we also propose a modified approach for use with scales containing fewer than 12 items. We also discuss the limitations of our procedure and several nuances for researchers to consider when implementing three-form PMDDs using our method. ? The RIA procedure allows researchers to implement statistically sound three-form planned missing data designs without the need for expert knowledge or results from prior statistical modeling. ? The RIA procedure can be used to construct both "paper-and-pencil" questionnaires and questionnaires administered through online survey software. ? The RIA procedure is a simple framework to aid in designing three-form PMDDs; implementing the RIA method does not require any specialized software or technical expertise.

SUBMITTER: Lang KM

PROVIDER: S-EPMC7327835 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A novel item-allocation procedure for the three-form planned missing data design.

Lang Kyle M KM Moore E Whitney G EWG Grandfield Elizabeth M EM

MethodsX 20200528

We propose a new method of constructing questionnaire forms in the three-form planned missing data design (PMDD). The <i>random item allocation</i> (RIA) procedure that we propose promises to dramatically simplify the process of implementing three-form PMDDs without compromising statistical performance. Our method is a stochastic approximation to the currently recommended approach of deterministically spreading a scale's items across the X-, A-, B-, and C-blocks when allocating the items in a th ...[more]

PMID: 32637328

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Project description:BackgroundYin deficiency (YD) is a pathological condition characterized by emaciation, afternoon fever, dry mouth, and night sweats. The incidence of YD is 23.3%. A 27-item Yin Deficiency Scale (YDS) was developed to estimate the clinical severity of YD. This study aimed to develop three short-form YDS versions to reduce the burden of response time, using three item-reduction approaches: Rasch, equidiscriminatory item-total correlation (EITC), and factor-based analyses.MethodsTwo datasets were analyzed from previous studies (169 outpatients from May to June 2009 and 237 healthy college students from January to April 2016). The optimal response category was examined using Rasch analysis. Items with higher item-total correlations were determined using the EITC. Using a factor-based approach, the items were reduced, while maintaining the original YDS construct. Reliability was estimated using the person separation index (PSI) and Cronbach's α values. The predictive accuracy was examined using the area under the curve (AUC). Finally, the relationship between YD and dysfunctional breathing (DB) was examined using factor scores from the YDS and the Korean version of the Nijmegen Questionnaire (KNQ).ResultsWe developed two 14-item YDS versions using the Rasch and EITC approaches, and a 16-item YDS version using a factor-based approach. Rasch analysis suggested an optimal response category of five points. The PSI of Rasch and Cronbach's α of the EITC and factor-based versions were 2.19, 0.855, and 0.827. The AUCs of the three short-form YDS were 0.812, 0.811, and 0.818. The sensitivity of the EITC-YDS was 0.632, which was lower than its specificity of 0.875. The fatigue-related scores of the factor-based YDS were fairly correlated with the factor scores of the KNQ estimating the DB (r = 0.349-0.499).ConclusionThe 14-item Rasch- and 16-item factor-based YDS may replace the original YDS during YD's primary screening, epidemiological surveys, and health checkups.

Project description:BackgroundMissing data are an inevitable challenge in Randomised Controlled Trials (RCTs), particularly those with Patient Reported Outcome Measures. Methodological guidance suggests that to avoid incorrect conclusions, studies should undertake sensitivity analyses which recognise that data may be 'missing not at random' (MNAR). A recommended approach is to elicit expert opinion about the likely outcome differences for those with missing versus observed data. However, few published trials plan and undertake these elicitation exercises, and so lack the external information required for these sensitivity analyses. The aim of this paper is to provide a framework that anticipates and allows for MNAR data in the design and analysis of clinical trials.MethodsWe developed a framework for performing and using expert elicitation to frame sensitivity analysis in RCTs with missing outcome data. The framework includes the following steps: first defining the scope of the elicitation exercise, second developing the elicitation tool, third eliciting expert opinion about the missing outcomes, fourth evaluating the elicitation results, and fifth analysing the trial data. We provide guidance on key practical challenges that arise when adopting this approach in trials: the criteria for identifying relevant experts, the outcome scale for presenting data to experts, the appropriate representation of expert opinion, and the evaluation of the elicitation results.The framework was developed within the POPPI trial, which investigated whether a preventive, complex psychological intervention, commenced early in ICU, would reduce the development of patient-reported post-traumatic stress disorder symptom severity, and improve health-related quality of life. We illustrate the key aspects of the proposed framework using the POPPI trial.ResultsFor the POPPI trial, 113 experts were identified with potentially suitable knowledge and asked to participate in the elicitation exercise. The 113 experts provided 59 usable elicitation questionnaires. The sensitivity analysis found that the results from the primary analysis were robust to alternative MNAR mechanisms.ConclusionsFuture studies can adopt this framework to embed expert elicitation within the design of clinical trials. This will provide the information required for MNAR sensitivity analyses that examine the robustness of the trial conclusions to alternative, but realistic assumptions about the missing data.

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A novel item-allocation procedure for the three-form planned missing data design.

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A novel item-allocation procedure for the three-form planned missing data design.

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