Multiple imputation for non-response when estimating HIV prevalence using survey data
Bibliographic Data
| ID | 15379259 |
|---|---|
| Authors | Amos Chinomona (Rhodes University, corresponding author), Henry Mwambi (0000-0001-9654-400X, University of KwaZulu-Natal) |
| Year | 2015 |
| Volume | 15 |
| Issue | 1 |
| Pages | 1059-1059 |
| Publication date | 2015-10-16 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | BMC Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1471-2458 • E-ISSN: 1471-2458 |
| Publisher | BioMed Central (PUBLISHER • GB) |
| DOI | 10.1186/s12889-015-2390-1 |
| PMID | 26475303 |
| OpenAlex | W1948465458 |
| Language | EN |
| Citations received | 4 |
| References cited | 22 |
There is considerable variation between estimates obtained between the two approaches. The use of multiple imputations allows the uncertainty brought about by the imputation process to be measured. This consequently yields more reliable estimates of the parameters of interest and reduce the chances of declaring significant effects unnecessarily (type I error). In addition, the utilization of the powerful and flexible statistical computing packages in R enhances the computations
Biostatistics · Data collection · Data mining · Econometrics · Imputation (statistics · Logistic regression · Missing data · Public health · Statistics · Survey data collection · Bayesian Methods and Mixture Models · Computer Science · Mathematics · Medicine · Statistical Methods and Bayesian Inference · Survey Sampling and Estimation Techniques
Statistical analysis with missing data
Analysis of Incomplete Multivariate Data
Generalized Linear Models
Applied Logistic Regression
A weakly informative default prior distribution for logistic and other regression models
Multiple imputation
The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement
Generalized Linear Models
The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement
Handling missing data in survey research
Multiple imputation for missing data in epidemiological and clinical research
Multiple imputation
An introduction to modern missing data analyses
Mice
Complex Surveys
Multiple Imputation for Nonresponse in Surveys
Applied Survey Data Analysis
| Citations per year | 0.4 |
|---|---|
| Citation span | 2016 - 2025 (10) |
| Highly cited | No |