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 addres
To the Editor Drs Newgard and Lewis provided an overview of many important issues to consider when dealing with missing data in clinical research. In
Missing data are data that we planned to collect to answer a research question, such as participant characteristics at the start of the study or their health outcomes after receiving some treatments, but for some reason we were not able to. In practice there are various ways in which missin...
In this case, my dependent variable was a series of estimates for net real GDP change from 2019 to 2021 across a wide range of advanced and developing countries (I used data from the Economist Intelligence Unit). Although GDP data for 2021 are still estimates for many countries, most of ...
This study examined whether or not activity monitor data collected as part of a typical 7-day physical activity (PA) measurement protocol can be expected to be missing at random. A total of 315 participants (9-18 years) each wore a SenseWear Armband monitor for 7 consecutive days. ...
Missing data in covariates can result in biased estimates and loss of power to detect associations. It can also lead to other challenges in time-to-event analyses including the handling of time-varying effects of covariates, selection of covariates and t
16, where DL is investigated as a general-purpose solution to image-to-image translation problems, we apply DL to missing data reconstruction with the aim of transforming an input incomplete data set into a corresponding complete data set, which is an important ongoing research topic in ...
primary research focus is on analytic issues related to missing data analyses, and he leads the research team responsible for developing the Blimp software application for missing data analyses. He also conducts research in the areas of multilevel modeling and structural equation modeling and is an ...
Missing data is a pervasive problem in clinical research. Generative adversarial imputation nets (GAIN), a novel machine learning data imputation approach, has the potential to substitute missing data accurately and efficiently but has not yet been evalu
in various ways: dropping patients with missing data, imputing with the mean, or using automatic techniques (e.g., machine learning) to handle or impute the data. Here, we systematically reviewed the methods used to handle missing data in EM research. A systematic review was performed after ...