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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: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.

Project description:Randomized controlled trials are considered the "gold standard" for scientific rigor in the assessment of benefits and harms of interventions in healthcare. They may not always be feasible, however, when evaluating quality improvement interventions in real-world healthcare settings. Non-randomized controlled trials (NCTs) are designed to answer questions of effectiveness of interventions in routine clinical practice to inform a decision or process. The on-off NCT design is a relatively new design where participant allocation is by alternation. In alternation, eligible patients are allocated to the intervention "on" or control "off?" groups in time series dependent sequential clusters.We used two quality improvement studies undertaken in a Canadian primary care setting to illustrate the features of the on-off design. We also explored the perceptions and experiences of healthcare providers tasked with implementing the on-off study design.The on-off design successfully allocated patients to intervention and control groups. Imbalances between baseline variables were attributed to chance, with no detectable biases. However, healthcare providers' perspectives and experiences with the design in practice reveal some conflict. Specifically, providers described the process of allocating patients to the off group as unethical and immoral, feeling it was in direct conflict with their professional principle of providing care for all. The degree of dissatisfaction seemed exacerbated by: 1) the patient population involved (e.g., patient population viewed as high-risk (e.g., depressed or suicidal)), 2) conducting assessments without taking action (e.g., administering the PHQ-9 and not acting on the results), and 3) the (non-blinded) allocation process.Alternation, as in the on-off design, is a credible form of allocation. The conflict reported by healthcare providers in implementing the design, while not unique to the on-off design, may be alleviated by greater emphasis on the purpose of the research and having research assistants allocate patients and collect data instead of the healthcare providers implementing the trial. In addition, consultation with front-line staff implementing the trials with an on-off design on appropriateness to the setting (e.g., alignment with professional values and the patient population served) may be beneficial.Health Eating and Active Living with Diabetes: ClinicalTrials.gov identifier: NCT00991380. Date registered: 7 October 2009. Controlled trial of a collaborative primary care team model for patients with diabetes and depression: Clintrials.gov Identifier: NCT01328639 Date registered: 30 March 2011.

Project description:Ecological and evolutionary research questions are increasingly requiring the integration of research fields along with larger data sets to address fundamental local- and global-scale problems. Unfortunately, these agendas are often in conflict with limited funding and a need to balance animal welfare concerns. Planned missing data design (PMDD), where data are randomly and deliberately missed during data collection, combined with missing data procedures, can be useful tools when working under greater research constraints. Here, we review how PMDD can be incorporated into existing experimental designs by discussing alternative design approaches and demonstrate with simulated data sets how missing data procedures work with incomplete data. PMDDs can provide researchers with a unique toolkit that can be applied during the experimental design stage. Planning and thinking about missing data early can (1) reduce research costs by allowing for the collection of less expensive measurement variables; (2) provide opportunities to distinguish predictions from alternative hypotheses by allowing more measurement variables to be collected; and (3) minimize distress caused by experimentation by reducing the reliance on invasive procedures or allowing data to be collected on fewer subjects (or less often on a given subject). PMDDs and missing data methods can even provide statistical benefits under certain situations by improving statistical power relative to a complete case design. The impacts of unplanned missing data, which can cause biases in parameter estimates and their uncertainty, can also be ameliorated using missing data procedures. PMDDs are still in their infancy. We discuss some of the difficulties in their implementation and provide tentative solutions. While PMDDs may not always be the best option, missing data procedures are becoming more sophisticated and more easily implemented and it is likely that PMDDs will be effective tools for a wide range of experimental designs, data types and problems in ecology and evolution.

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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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