TY - JOUR
T1 - A CD-based mapping method for combining multiple related parameters from heterogeneous intervention trials
AU - Jiao, Yang
AU - Mun, Eun Young
AU - Trikalinos, Thomas A.
AU - Xie, Minge
N1 - Funding Information:
We would like to thank the following contributors to Project INTEGRATE in alphabetical order: John S. Baer, Department of Psychology, The University of Washington, and Veterans' Affairs Puget Sound Health Care System; Nancy P. Barnett, Center for Alcohol and Addiction Studies, Brown University; M. Dolores Cimini, University Counseling Center, The University at Albany, State University of New York; William R. Corbin, Department of Psychology, Arizona State University; Kim Fromme, Department of Psychology, The University of Texas, Austin; JosephW. LaBrie, Department of Psychology, Loyola Marymount University; Mary E. Larimer, Department of Psychiatry and Behavioral Sciences, The University of Washington; Matthew P. Martens, Department of Educational, School, and Counseling Psychology, The University of Missouri; James G. Murphy, Department of Psychology, The University of Memphis; Scott T. Walters, Department of Health Behavior and Health Systems, The University of North Texas Health Science Center; Helene R. White, Center of Alcohol Studies, Rutgers, The State University of New Jersey; and the late Mark D. Wood, Department of Psychology, The University of Rhode Island.
Publisher Copyright:
© 2020 International Press of Boston, Inc.
PY - 2020
Y1 - 2020
N2 - Effect size can differ as a function of the elapsed time since treatment or as a function of other key covariates, such as sex or age. In evidence synthesis, a better understanding of the precise conditions under which treatment does work or does not work well has been highly valued. With increasingly accessible individual patient or participant data (IPD), more precise and informative inference can be within our reach. However, simultaneously combining multiple related parameters across heterogeneous studies is challenging because each parameter from each study has a specific interpretation within the context of the study and other covariates in the model. This paper proposes a novel mapping method to combine study-specific estimates of multiple related parameters across heterogeneous studies, which ensures valid inference at all inference levels by combining sample-dependent functions known as Confidence Distributions (CD). We describe the "CD-based mapping method" and provide a data application example for a multivariate random-effects meta-analysis model. We estimated up to 13 study-specific regression parameters for each of 14 individual studies using IPD in the first step, and subsequently combined the study-specific vectors of parameters, yielding a full vector of hyperparameters in the second step of metaanalysis. Sensitivity analysis indicated that the CD-based mapping method is robust to model misspecification. This novel approach to multi-parameter synthesis provides a reasonable methodological solution when combining complex evidence using IPD.
AB - Effect size can differ as a function of the elapsed time since treatment or as a function of other key covariates, such as sex or age. In evidence synthesis, a better understanding of the precise conditions under which treatment does work or does not work well has been highly valued. With increasingly accessible individual patient or participant data (IPD), more precise and informative inference can be within our reach. However, simultaneously combining multiple related parameters across heterogeneous studies is challenging because each parameter from each study has a specific interpretation within the context of the study and other covariates in the model. This paper proposes a novel mapping method to combine study-specific estimates of multiple related parameters across heterogeneous studies, which ensures valid inference at all inference levels by combining sample-dependent functions known as Confidence Distributions (CD). We describe the "CD-based mapping method" and provide a data application example for a multivariate random-effects meta-analysis model. We estimated up to 13 study-specific regression parameters for each of 14 individual studies using IPD in the first step, and subsequently combined the study-specific vectors of parameters, yielding a full vector of hyperparameters in the second step of metaanalysis. Sensitivity analysis indicated that the CD-based mapping method is robust to model misspecification. This novel approach to multi-parameter synthesis provides a reasonable methodological solution when combining complex evidence using IPD.
KW - Combining confidence density functions
KW - Individual participant data
KW - Individual patient data
KW - Mapping matrix
KW - Multi-parameter synthesis
KW - Multivariate random-effects meta-analysis
UR - http://www.scopus.com/inward/record.url?scp=85089542364&partnerID=8YFLogxK
U2 - 10.4310/SII.2020.V13.N4.A10
DO - 10.4310/SII.2020.V13.N4.A10
M3 - Article
AN - SCOPUS:85089542364
SN - 1938-7989
VL - 13
SP - 533
EP - 549
JO - Statistics and its Interface
JF - Statistics and its Interface
IS - 4
ER -