You have just completed your registration at OpenAire.
Before you can login to the site, you will need to activate your account.
An e-mail will be sent to you with the proper instructions.
Important!
Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version
of the site upon release.
The term meta-analysis refers to a set of statistical techniques for combining findings from\ud different studies in order to draw more definitive conclusions about some treatment or exposure\ud effect of interest in a particular context. Recently, meta-analyses which aim to combine the\ud individual observations collected in each study, instead of simple summary measures, have been\ud gaining in popularity in medical research. The main advantage of this so-called Individual\ud Patient Data Meta-Analyses (IPD-MA) is that they have much more statistical power to\ud investigate heterogeneity of the contributing studies and to explore treatment covariate effects.\ud Unfortunately, missing data are a common problem that affects nearly every dataset in clinical\ud or epidemiological studies and therefore also the meta-analyses of such datasets. When not\ud handled properly, missing data can lead to invalid inferences and therefore a lot of research\ud work has focussed on deriving, implementing and disseminating appropriate methods.\ud The motivation for this thesis comes from two IPD-MA, called INDANA and MAGGIC. Some\ud challenges introduced by missing data in these projects include the presence of wholly missing\ud variables in some studies, the variety of types of partially observed variables and the presence\ud of interactions and non-linearities in the substantive models of interest.\ud In this thesis we propose a Joint Modelling Multiple Imputation (JM-MI) approach to overcome\ud these issues. Motivated by the lack of available software, in the first part of this thesis we\ud develop and describe jomo, a new R package for Multilevel MI. A key feature of jomo compared\ud to other packages for MI, is that it allows for the presence of random, or fixed, study-specific\ud covariance matrices in the imputation model, therefore allowing for heteroscedasticity when\ud imputing.\ud Successively we use this package to prove how our proposed method can be as good as standard\ud methods used nowadays to treat missing data in IPD-MA with partially observed continuous\ud variables. Furthermore we show how it performs in more challenging situations, i.e. to impute\ud missing data in studies with few observations or even with systematically missing variables.\ud We then extend the method to include partially observed variables that are not continuous,\ud developing and evaluating a strategy based on latent normal variables to impute categorical\ud data.\ud Finally we use the methods introduced to impute missing data in the two motivating metaanalyses,\ud INDANA and MAGGIC.
Easterbrook, P. J., Berlin, J. A., Gopalan, R. and Matthews, D. R. (1991) Publication bias in clinical research. Lancet, 337(8746), 867{872.
Eddelbuettel, D. and Francois, R. (2011) Rcpp: Seamless r and c++ integration. Journal of Statistical Software, 40(1), 1{18.
Fibrinogen Studies Collaboration (2009) Systematically missing confounders in individual participant data meta-analysis of observational cohort studies. Statistics in Medicine., 28(8), 1218{1237. DOI: 10.1002/sim.3540.
Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (2014) Bayesian data analysis, volume 2. Chapman & Hall/CRC.
Geman, S. and Geman, D. (1984) Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721{741.
Goldstein, H. (2011) Multilevel Statistical Models. Wiley. 4th Edition.
Goldstein, H. (2014) Heteroscedasticity and Complex Variation. John Wiley & Sons, Ltd.
Goldstein, H., Carpenter, J., Kenward, M. and Levin, K. (2009) Multilevel models with multivariate mixed response types. Statistical Modelling., 9(3), 173{197. DOI: 10.1177/1471082X0800900301.
Goldstein, H., Carpenter, J. and Browne, W. (2014) Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and nonlinear terms. Journal of the Royal Statistical Society: Series A., 177(2), 553{564. DOI: 10.1111/rssa.12022.
Guey er, F., Boutitie, F., Boissel, J. P., Coope, J., Cutler, J., Ekbom, T., Fagard, R., Friedman, L., Perry, H. M. and Pocock, S. (1995) INDANA: a meta-analysis on individual patient data in hypertension. Protocol and preliminary results. Therapie, 50(4), 353{362.
Hardy, R. J. and Thompson, S. G. (1996) A likelihood approach to meta-analysis with random e ects. Statistics in Medicine, 15(6), 619{629.
Hartung, J. and Knapp, G. (2001) A re ned method for the meta-analysis of controlled clinical trials with binary outcome. Statistics in Medicine, 20(24), 3875{3889.
Higgins, J. P., Thompson, S. G., Deeks, J. J. and Altman, D. G. (2003) Measuring inconsistency in meta-analyses. BMJ, 327(7414), 557{560.
Higgins, J. P. T. and Green, S. (eds) (2008) Cochrane Handbook for Systematic Reviews of Interventions. The Cochrane Collaboration, fth edition.
Higgins, J. P. T. and Thompson, S. G. (2002) Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539{1558.
Holford, T. R. (1980) The analysis of rates and of survivorship using log-linear models. Biometrics, 36(2), pp. 299{305.
Hunter, J. and Schmidt, F. (1990) Methods of meta-analysis: correcting error and bias in research ndings. Sage Publications.
Jolani, S., Debray, T. P., Ko jberg, H., van Buuren, S. and Moons, K. G. (2015) Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE. Stat Med, 34(11), 1841{1863.
Laird, N. and Olivier, D. (1981) Covariance analysis of censored survival data using log-linear analysis techniques. Journal of the American Statistical Association, 76(374), 231{240.
Laird, N. M. and Ware, J. H. (1982) Random-e ects models for longitudinal data. Biometrics, 38(4), 963{974.
Lee, K. J. and Thompson, S. G. (2008) Flexible parametric models for random-e ects distributions. Statistics in Medicine, 27(3), 418{434.
Liu, J., Gelman, A., Hill, J., Su, Y.-S. and Kropko, J. (2013) On the stationary distribution of iterative imputations. Biometrika.
Louis, T. A. (1982) Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 44(2), pp. 226{233.
Mason, A., Richardson, S. and Best, N. (2012) Strategy for Modelling Nonrandom Missing Data Mechanisms in Observational Studies Using Bayesian Methods. Journal of O cial Statistics., 28(2), 279{302.
McCullagh, P. (1980) Regression models for ordinal data. Journal of the Royal Statistical Society. Series B (Methodological), 42(2), pp. 109{142.
Meinfelder, F. (2011) BaBooN: Bayesian Bootstrap Predictive Mean Matching - Multiple and single imputation for discrete data. R package version 0.1-6.
Meng, X.-L. (1994) Multiple Imputation inferences with uncongenial sources of input. Statist. Sci., 9(4), 566{573.
Molenberghs, G., Michiels, B., Kenward, M. G. and Diggle, P. J. (1998) Monotone missing data and pattern-mixture models. Statistica Neerlandica, 52(2), 153{161.
Molenberghs, G., Fitzmaurice, G., Kenward, M., Tsiatis, A. and Verbeke, G. (2014) Handbook of Missing Data Methodology. Chapman & Hall/CRC Handbooks of Modern Statistical Methods. Taylor & Francis.
Pocock, S. J., Ariti, C. A., McMurray, J. J., Maggioni, A., K ber, L., Squire, I. B., Swedberg, K., Dobson, J., Poppe, K. K., Whalley, G. A. and Doughty, R. N. (2013) Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur. Heart J., 34(19), 1404{1413.
Quartagno, M. and Carpenter, J. (2014) jomo: A package for Multilevel Joint Modelling Multiple Imputation.
R Core Team (2014) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
Resche-Rigon, M., White, I., Bartlett, J., Peters, S. and Thompson, S. (2013) Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data. Statistics in Medicine., 32(28), 4890{905. DOI: 10.1002/sim.5894.
Riley, R. D., Lambert, P. C., Staessen, J. A., Wang, J., Guey er, F., Thijs, L. and Boutitie, F. (2008) Meta-analysis of continuous outcomes combining individual patient data and aggregate data. Statistics in Medicine, 27(11), 1870{1893.
Riley, R. D., Lambert, P. C. and Abo-Zaid, G. (2010) Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ, 340, c221.
Rubin, D. (1976) Inference and DOI:10.1093/biomet/63.3.581.
Seaman, S., Bartlett, J. and White, I. (2012) Multiple imputation of missing covariates with non-linear e ects and interactions: an evaluation of statistical methods. BMC Medical Research Methodology, 12(1), 46.
Shrier, I., Boivin, J.-F., Steele, R. J., Platt, R. W., Furlan, A., Kakuma, R., Brophy, J. and Rossignol, M. (2007) Should meta-analyses of interventions include observational studies in addition to randomized controlled trials? a critical examination of underlying principles. American Journal of Epidemiology, 166(10), 1203{1209.
Sidik, K. and Jonkman, J. N. (2005) Simple heterogeneity variance estimation for metaanalysis. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(2), 367{384.
Simmonds, M. C., Higgins, J. P., Stewart, L. A., Tierney, J. F., Clarke, M. J. and Thompson, S. G. (2005) Meta-analysis of individual patient data from randomized trials: a review of methods used in practice. Clin Trials, 2(3), 209{217.
Spratt, M., Carpenter, J., Sterne, J. A. C., Carlin, J. B., Heron, J., Henderson, J. and Tilling, K. (2010) Strategies for multiple imputation in longitudinal studies. American Journal of Epidemiology, 172(4), 478{487.
Stanley, T. D. and Jarrell, S. B. (1989) Meta-regression analysis: A quantitative method of literature surveys. Journal of Economic Surveys, 3(2), 161{170.
Stewart, G. B., Altman, D. G., Askie, L. M., Duley, L., Simmonds, M. C. and Stewart, L. A. (2012) Statistical analysis of individual participant data meta-analyses: a comparison of methods and recommendations for practice. PLoS ONE, 7(10), e46042.
Stewart, L. A. and Parmar, M. K. (1993) Meta-analysis of the literature or of individual patient data: is there a di erence? Lancet, 341(8842), 418{422.
Ted Harding, F. T. and Schafer, J. L. (2012) cat: Analysis of categorical-variable datasets with missing values. R package version 0.0-6.5.
van Buuren, S. (2011) The Handbook of Advanced Multilevel Analysis., chapter Multiple imputation of multilevel data, pp. 173{196. Milton Park,UK: Routledge.
van Buuren, S. and Groothuis-Oudshoorn, K. (2011) mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1{67.
Von Hippel, P. T. (2009) How to impute interactions, squared and other transformed variables. Sociological Methodology, 39(1), 265{291.
Zhao, J. and Schafer, J. (2013) pan: Multiple Imputation for multivariate panel or clustered data. R Foundation for Statistical Computing. R Package, version 0.9.