Suchergebnisse

This book provides a comprehensive introduction to performing meta-analysis using the statistical software R. It is intended for quantitative researchers and students in the medical and social sciences who wish to learn how to perform meta-analysis with R. As such, the book introduces the key concepts and models used in meta-analysis. It also includes chapters on the following advanced topics: publication bias and small study effects; missing data; multivariate meta-analysis, network meta-analysis; and meta-analysis of diagnostic studies.

Abstract
The inverse probability weighting (IPW) method is commonly used to deal with missing-at-random outcome (response) data collected by surveys with complex sampling designs. However, IPW methods generally assume that fully observed predictor variables are available for all sampled units, and it is unclear how to appropriately implement these methods when one or more independent variables are subject to missing values. Multiple imputation (MI) methods are well suited for a variety of missingness patterns but are not as easily adapted to complex sampling designs. In this case study, we consider the National Survey of Morbidity and Risk Factors (EMENO), a multistage probability sample survey. To understand the strengths and limitations of using either missing data treatment method for the EMENO, we present an extensive simulation study modeled on the EMENO health survey, with the target analysis being the estimation of population prevalence of hypertension as well as the association between hypertension and income. Both variables are subject to missingness. We test a variety of IPW and MI methods in simulation and on empirical data from the survey, assessing robustness by varying missingness mechanisms, proportions of missingness, and strengths of fitted response propensity models.

The primary analysis of time‐to‐event data typically makes the censoring at random assumption, that is, that—conditional on covariates in the model—the distribution of event times is the same, whether they are observed or unobserved. In such cases, we need to explore the robustness of inference to more pragmatic assumptions about patients post‐censoring insensitivity analyses. Reference‐based multiple imputation, which avoids analysts explicitly specifying the parameters of the unobserved data distribution, has proved attractive to researchers. Building on results for longitudinal continuous data, we show that inference using a Tobit regression imputation model for reference‐based sensitivity analysis with right censored log normal data isinformation anchored, meaning the proportion of information lost due to missing data under the primary analysis is held constant across the sensitivity analyses. We illustrate our theoretical results using simulation and a clinical trial case study.