6,284,044 research outputs found
One-sample aggregate data meta-analysis of medians
An aggregate data meta-analysis is a statistical method that pools the
summary statistics of several selected studies to estimate the outcome of
interest. When considering a continuous outcome, typically each study must
report the same measure of the outcome variable and its spread (e.g., the
sample mean and its standard error). However, some studies may instead report
the median along with various measures of spread. Recently, the task of
incorporating medians in meta-analysis has been achieved by estimating the
sample mean and its standard error from each study that reports a median in
order to meta-analyze the means. In this paper, we propose two alternative
approaches to meta-analyze data that instead rely on medians. We systematically
compare these approaches via simulation study to each other and to methods that
transform the study-specific medians and spread into sample means and their
standard errors. We demonstrate that the proposed median-based approaches
perform better than the transformation-based approaches, especially when
applied to skewed data and data with high inter-study variance. In addition,
when meta-analyzing data that consists of medians, we show that the
median-based approaches perform considerably better than or comparably to the
best-case scenario for a transformation approach: conducting a meta-analysis
using the actual sample mean and standard error of the mean of each study.
Finally, we illustrate these approaches in a meta-analysis of patient delay in
tuberculosis diagnosis
Exact balanced random imputation for sample survey data
Surveys usually suffer from non-response, which decreases the effective
sample size. Item non-response is typically handled by means of some form of
random imputation if we wish to preserve the distribution of the imputed
variable. This leads to an increased variability due to the imputation
variance, and several approaches have been proposed for reducing this
variability. Balanced imputation consists in selecting residuals at random at
the imputation stage, in such a way that the imputation variance of the
estimated total is eliminated or at least significantly reduced. In this work,
we propose an implementation of balanced random imputation which enables to
fully eliminate the imputation variance. Following the approach in Cardot et
al. (2013), we consider a regularized imputed estimator of a total and of a
distribution function, and we prove that they are consistent under the proposed
imputation method. Some simulation results support our findings
- …