Comparative effectiveness research (CER) relies fundamentally on accurate assessment of treatment efficacy and safety that, ideally, can be tailored to specific patients. The growing number of treatment options for a given condition, as well as the rapid escalation in their costs, has generated an increasing need for scientifically rigorous simultaneous comparisons of multiple treatments in clinical practice. Also called mixed or multiple treatments meta-analysis, network meta-analysis (NMA) expands the scope of a conventional pairwise meta-analysis by simultaneously analyzing both direct comparisons of interventions within randomized controlled trials and indirect comparisons across trials .... Compared to traditional meta-analysis of just two treatments, NMA presents many additional statistical challenges. In particular, a typical randomized trial compares only a few (typically tw) treatments, which intrinsically creates a large amount of missing data when, say, a dozen treatments must be compared simultaneously, since the outcomes for treatments not studied in a particular trial are missing by design. Currently available statistical methods, which are based on treatment contrasts, focus only on relative treatment effect estimates and have other serious limitations. The overall goal of this proposal is to develop cutting-edge statistical methods, and to integrate them into publicly available, easy-to-use software, to enhance patient-centered NMA. Specifically, we will develop multivariate Bayesian hierarchical models for binary outcomes from the perspective of missing data methods with the following three specific aims: 1) to extend our preliminary work on estimating patient-centered parameters (e.g., absolute risk, risk difference and relative risk) with a single endpoint to allow non-ignorable missingness;2) to simultaneously model multiple endpoints (e.g. outcomes for efficacy and safety) with proper consideration of non-ignorable missingness;and 3) to incorporate individual patient characteristics. In addition, we propose new methods to measure and detect inconsistency between the direct and indirect evidence, and to borrow strength cautiously from less reliable data sources. We propose to perform empirical assessment of the strengths and weaknesses of these methods through many real data applications and simulations. Completion of the three aims will substantially advance CER analytical methods for comparing multiple treatments across multiple endpoints and tailored to patient characteristics.

Public Health Relevance

The primary goal of the proposed project is to advance patient-centered network meta-analytical methods. Specifically, the overarching goal of this project is to develop statistical methods and related software for patient-centered network meta-analysis (NMA) of randomized clinical trials with binary endpoints. The proposed statistical methodology will be broadly applicable to statistical analysis and interpretation of complex data sets arising in NMA, with multiple endpoints and individual patient covariates. It will improve patient- centered outcomes research and public health by facilitating the integrated comparison of multiple treatments for cancer, cardiovascular, infectious and many other diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21AI103012-02
Application #
8661112
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Gezmu, Misrak
Project Start
2013-05-15
Project End
2015-04-30
Budget Start
2014-05-01
Budget End
2015-04-30
Support Year
2
Fiscal Year
2014
Total Cost
$216,464
Indirect Cost
$66,464
Name
University of Minnesota Twin Cities
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
555917996
City
Minneapolis
State
MN
Country
United States
Zip Code
55455
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Lin, Lifeng; Chu, Haitao; Hodges, James S (2016) Sensitivity to Excluding Treatments in Network Meta-analysis. Epidemiology 27:562-9
Hong, Hwanhee; Chu, Haitao; Zhang, Jing et al. (2016) A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons. Res Synth Methods 7:6-22
Hong, Hwanhee; Chu, Haitao; Zhang, Jing et al. (2016) Rejoinder to the discussion of ""a Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons,"" by S. Dias and A. E. Ades. Res Synth Methods 7:29-33
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Ma, Xiaoye; Nie, Lei; Cole, Stephen R et al. (2016) Statistical methods for multivariate meta-analysis of diagnostic tests: An overview and tutorial. Stat Methods Med Res 25:1596-619
Lin, Lifeng; Chu, Haitao; Hodges, James S (2016) Alternative measures of between-study heterogeneity in meta-analysis: Reducing the impact of outlying studies. Biometrics :
Zhang, Jing; Fu, Haoda; Carlin, Bradley P (2015) Detecting outlying trials in network meta-analysis. Stat Med 34:2695-707
Chen, Yong; Liu, Yulun; Ning, Jing et al. (2015) A hybrid model for combining case-control and cohort studies in systematic reviews of diagnostic tests. J R Stat Soc Ser C Appl Stat 64:469-489
Liu, Yulun; Chen, Yong; Chu, Haitao (2015) A unification of models for meta-analysis of diagnostic accuracy studies without a gold standard. Biometrics 71:538-47

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