The (Ir)Responsibility of (Under)Estimating Missing Data

García, Paula Fernández; Vallejo, Guillermo; Livacic-Rojas, Pablo; Herrero, Ellián Tuero

doi:10.3389/fpsyg.2018.00556

Cited by 16 publications

(11 citation statements)

References 38 publications

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“…The results, although robust, are preliminary and have been obtained from a cross-sectional study; it is necessary to apply the questionnaire in the clinic to establish cut-off points and see its evolution after vocal rehabilitation or psychological therapy, as other authors have already pointed out [ 12 , 36 ]. A longitudinal study is currently being undertaken to observe the evolution of patients as they progress in their rehabilitation, with special emphasis being placed on planning the sample size to consider the loss of data, a common problem in this type of research [ 37 , 38 ]. All these limitations generate new perspectives for future studies, such as increasing the number of participants, establishing variations of the questionnaire for different laryngectomy modalities, or making comparisons between different countries.…”

Section: Discussionmentioning

confidence: 99%

Validation of “Self-Evaluation of Communication Experiences after Laryngectomy” (SECEL) Questionnaire for Spanish-Speaking Laryngectomized Patients

Villanueva

García

Arena

et al. 2022

Cancers

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Background: Laryngectomized patients have communicative limitations when they lose their phonatory function after surgery. The scale “Self-Evaluation of Communication Experiences after Laryngectomy” (SECEL) assesses the impact of total laryngectomy on quality of life related to communication. The aim of this study was to translate and adapt the SECEL from English to Spanish and to apply this first version to a group of patients to check its reliability and validity. Materials and Methods: One-hundred-and-one laryngectomized patients completed the SECEL scale adjusted to Spanish, the Voice Handicap Index (VHI-30), the Hospital Anxiety and Depression Scale (HADS) and the EORTC QLQ-H&N35 questionnaire. Results: The Spanish version of the SECEL questionnaire has the same format and conceptual, semantic and idiomatic meaning as the original. The resulting model is composed of 21 items dimensioned in two highly correlated factors that are clear, meaningful, and replicable leading to measures that are reliable enough to be used in clinical evaluation. Conclusion: The Spanish translation and validation of the SECEL questionnaire were performed. It can be considered an appropriate tool to assess communication skills from laryngectomized people.

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Section: Discussionmentioning

confidence: 99%

Validation of “Self-Evaluation of Communication Experiences after Laryngectomy” (SECEL) Questionnaire for Spanish-Speaking Laryngectomized Patients

Villanueva

García

Arena

et al. 2022

Cancers

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“…Missing at random (MAR): the missing values depend on observed data only. Missing not at random (MNAR): the missing values the missing values depend on missing values themselves [15] . Dexin Shi.…”

Section: Discussionmentioning

confidence: 99%

A novel method for handling missing data in health care real-world study: Optimal Intact Subset Method

Chang

Tong

et al. 2022

Preprint

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Background Handling missing data is indispensable in health care real-world data processing. Deleting or imputing missing data may introduce error or lead to multicollinearity. Therefore, we tried to explore a novel missing data processing method to avoid the above issues. Method By exploring an optimal deleting way of columns and rows with missing data, we developed a missing data processing method which can retain most information of original datasets. Traditionally, the goal can be realized by traversing all possible deleting combinations. But the computational cost is too high to use in large datasets. Therefore, we established an Optimal Intact Subset Method (OIS.Method) by using an indicator containing missing information of both columns and rows to determine an optimal deleting order of columns. OIS.Method can ascertain the optimal deleting way and simplify computing meanwhile. In order to validate the effectiveness of OIS.Method, we compared OIS.Method with five other data-imputation methods in 700 classification datasets (simulated datasets 1) generated by computer. In order to simulate real-world datasets, we generated simulated datasets 2: introducing redundant variables in simulated datasets 1. We also compared OIS.Method with control methods on that. Finally, we validated OIS.Method in two real-world classification tasks: 1. predict the risk of hypotension during dialysis, 2. predict the risk of drug adverse reaction in elderly patients with type 2 diabetes. Results In simulated datasets 1, we found that OIS.Method performed well when the distribution of missing data was unbalanced among columns. In simulated datasets 2, the comprehensive performance of OIS.Method was better in all evaluating dimensions. In two real-world datasets, OIS.Method could acquire better classification performance. We used the area under ROC curve (AUC) to evaluate it: OIS.Method VS Simple Impute VS Random Forest VS Modified Random Forest, 0.8179 ± 0.0005VS0.8116 ± 0.0002VS0.8087 ± 0.0009VS0.8093 ± 0.0014 in task1, and 0.7028VS0.6963VS0.6957VS0.6699 in task2. Conclusions Our study provided a novel method for handing missing data in real-world study. Compared with other existing missing data processing methods, the calculation of OIS.Method is smaller, and OIS.Method can reflect the true data situation of original datasets. Moreover, OIS.Method is well-suited for real-world datasets with large sample size and multiple variables.

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“…These two sets of deletions amounted to 9% of our sample. Scholars have recommended that techniques for handling missing data be implemented if more than 10% of the data are missing (Fernández-García, Vallejo-Seco, Livácic-Rosas, & Tuero-Herrero, 2018; Little, Jorgensen, Lang, & Moore, 2014). Of note, no substantial differences were found when missing data were imputed and those results can be requested from the first author.…”

Section: Methodsmentioning

confidence: 99%

Expectations, experiences, and desires: Mothers’ perceptions of the division of caregiving and their postnatal adaptation.

Powell¹,

Karraker²

2019

Journal of Family Psychology

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First-time and experienced mothers’ prenatal expectations, postnatal experiences, and postnatal desires regarding the division of infant caregiving responsibilities with her partner were examined to explore whether postnatal desires may be a better indicator of mothers’ postnatal adaptation (self-efficacy, satisfaction, stress, depressive symptoms, and state anxiety) than prenatal expectations and postnatal experiences. Mothers (N = 132) completed a prenatal questionnaire during their third trimester and a postnatal questionnaire at 8 weeks postpartum. Women’s parental status (first-time vs. experienced) and their postnatal work status were associated with their expectations, experiences, and desires for the division of caregiving. Women whose partners were involved in the caregiving tasks reported more positive postnatal adaptation, whereas women, and particularly first-time mothers, who desired more partner involvement had poorer postnatal adaptation. The results suggest that mothers’ postnatal desires concerning the division of caregiving is important to consider in examining mothers’ postnatal adaptation.

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The (Ir)Responsibility of (Under)Estimating Missing Data

Cited by 16 publications

References 38 publications

Validation of “Self-Evaluation of Communication Experiences after Laryngectomy” (SECEL) Questionnaire for Spanish-Speaking Laryngectomized Patients

Validation of “Self-Evaluation of Communication Experiences after Laryngectomy” (SECEL) Questionnaire for Spanish-Speaking Laryngectomized Patients

A novel method for handling missing data in health care real-world study: Optimal Intact Subset Method

Expectations, experiences, and desires: Mothers’ perceptions of the division of caregiving and their postnatal adaptation.

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