17 papers · ranked by Valyu relevance
Tlamelo Emmanuel, Thabiso Maupong, Dimane Mpoeleng, Thabo Semong + 2 more
'Banyatsang Mphago' 'Oteng Tabona'] Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these…
Yiran Dong, Chao-Ying Joanne Peng
The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods…
Peter C. Austin, Ian R. White, Douglas S. Lee, Stef van Buuren
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the variables of interest are not measured or recorded for all subjects in the sample. Common approaches to addressing the presence of missing data include complete-case analyses, where subjects with missing data are…
Janus Christian Jakobsen, Christian Gluud, Jørn Wetterslev, Per Winkel
'Per Winkel'] Background Missing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the analytical methods applied to amend the…
Shahidul Islam Khan, Abu Sayed Md Latiful Hoque
In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these data is a major concern to the stakeholders, efficiently handling…
Monica Casella, Nicola Milano, Pasquale Dolce, Davide Marocco
Introduction Missing data in psychometric research presents a substantial challenge, impacting the reliability and validity of study outcomes. Various factors contribute to this issue, including participant non-response, dropout, or technical errors during data collection. Traditional methods like mean imputation or…
María P. Fernández-García, Guillermo Vallejo-Seco, Pablo Livácic-Rojas, Ellian Tuero-Herrero
'Pablo Livácic-Rojas' 'Ellian Tuero-Herrero'] It is practically impossible to avoid losing data in the course of an investigation, and it has been proven that the consequences can reach such magnitude that they could even invalidate the results of the study. This paper describes some of the most likely causes of…
Gunther Eysenbach, Filip Smit, David Streiner, Matthijs Blankers + 2 more
'Maarten W J Koeter' 'Gerard M Schippers'] Background Missing data is a common nuisance in eHealth research: it is hard to prevent and may invalidate research findings. Objective In this paper several statistical approaches to data “missingness” are discussed and tested in a simulation study. Basic approaches (complete…
Melanie L Bell, Mallorie Fiero, Nicholas J Horton, Chiu-Hsieh Hsu
Background Missing outcome data is a threat to the validity of treatment effect estimates in randomized controlled trials. We aimed to evaluate the extent, handling, and sensitivity analysis of missing data and intention-to-treat (ITT) analysis of randomized controlled trials (RCTs) in top tier medical journals, and…
Anny K. G. Rodrigues, Raydonal Ospina, Marcelo R. P. Ferreira, Afnizanfaizal Abdullah
'Afnizanfaizal Abdullah'] Many machine learning procedures, including clustering analysis are often affected by missing values. This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive distances (VKFCM-K-LP) under three types of…
Shona Fielding, Peter M Fayers, Alison McDonald, Gladys McPherson + 1 more
Objective QoL data were routinely collected in a randomised controlled trial (RCT), which employed a reminder system, retrieving about 50% of data originally missing. The objective was to use this unique feature to evaluate possible missingness mechanisms and to assess the accuracy of simple imputation methods. Methods…
Muhammad Salar Khan, Larissa-Margareta Batrancea
Within the national innovation system literature, the low- and middle-income countries (LMICs) eligible for the World Bank’s International Development Association (IDA) support, are rarely part of empirical discourses on growth, development, and innovation. One major issue hindering empirical analyses in LMICs is the…
Daniel W. A. Noble, Shinichi Nakagawa
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…
Ahmad R. Alsaber, Jiazhu Pan, Adeeba Al-Hurban
In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five…
Adrienne Kline, Yuan Luo
Most datasets suffer from partial or complete missing values, which has downstream limitations on the available models on which to test the data and on any statistical inferences that can be made from the data. Several imputation techniques have been designed to replace missing data with stand in values. The various…
Katya L Masconi, Tandi E Matsha, Justin B Echouffo-Tcheugui, Rajiv T Erasmus + 1 more
Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this…
Karl Schweizer, Andreas Gold, Dorothea Krampen
In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlations as input and at…