The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Informative Drop-Out in Longitudinal Data Analysis
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Citations
Cites
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Hsiao, C. (2014) · doi:10.1017/cbo9781139839327 · Cited by 7,869
Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
Smith, A. F. M., & Roberts, G. O. (1993) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1993.tb01466.x · Cited by 1,713
Experimental designs balanced for the estimation of residual effects of treatments
Williams, E. (1949) · Australian Journal of Scientific Research Series A Physical Sciences · doi:10.1071/ch9490149 · Cited by 1,104
The AIDS Epidemic: Estimating Survival After AIDS Diagnosis From Surveillance Data
Tu, X. M., Meng, X.-L., & Pagano, M. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594285 · Cited by 36
Cited by
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Gueorguieva, R., & Krystal, J. H. (2004) · Archives of General Psychiatry · doi:10.1001/archpsyc.61.3.310 · Cited by 1,529
Modeling the Drop-Out Mechanism in Repeated-Measures Studies
Little, R. J. A. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476615 · Cited by 1,522
Yancy, W. S., Olsen, M. K., Guyton, J. R., Bakst, R. P., & Westman, E. C. (2004) · Annals of Internal Medicine · doi:10.7326/0003-4819-140-10-200405180-00006 · Cited by 946
The use of multiple imputation for the analysis of missing data.
Sinharay, S., Stern, H. S., & Russell, D. (2001) · Psychological Methods · doi:10.1037/1082-989x.6.4.317 · Cited by 560
Metadata
- Authors
- Diggle, P., & Kenward, M. G.
- Venue
- Applied Statistics
- Publisher
- JSTOR
- Published
- 1994
- Volume
- 43
- Issue
- 1
- Pages
- 49
- ISSN
- 0035-9254 (ISSN-L)
- OpenAlex
- W1591638896
- Cited by
- 1,468
- References
- 6
- FWCI
- 20.77
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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- CoChat. (2026). Citation check: Informative Drop-Out in Longitudinal Data Analysis. https://app.cochat.ai/papers/10.2307/2986113
- The paper
- Diggle, P., & Kenward, M. G. (1994). Informative Drop-Out in Longitudinal Data Analysis. Applied Statistics. https://doi.org/10.2307/2986113
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
