Statistical methodologies to pool across multiple intervention studies

Translational Behavioral Medicine

Volumn 6, Issue 2, 2016, Pages 228-235

  Bangdiwala, Shrikant I ^a,b   Bhargava, Alok ^c   O’Connor, Daniel P ^d   Robinson, Thomas N ^e   Michie, Susan ^f   Murray, David M ^g   Stevens, June ^a   Belle, Steven H ^h   Templin, Thomas N ⁱ   Pratt, Charlotte A ^j  

^a IL   (United States)

^b UNIVERSITY OF SOUTH AFRICA   (South Africa)

^c UNIVERSITY OF MARYLAND   (United States)

^d UNIVERSITY OF HOUSTON   (United States)

^e STANFORD UNIVERSITY   (United States)

^f UNIVERSITY COLLEGE LONDON   (United Kingdom)

^g EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT   (United States)

^h UNIVERSITY OF PITTSBURGH GRADUATE SCHOOL OF PUBLIC HEALTH   (United States)

ⁱ WAYNE STATE UNIVERSITY   (United States)

^j NATIONAL HEART LUNG AND BLOOD INSTITUTE   (United States)

more..

Author keywords

Multilevel meta regression; Multilevel structural models; Random effects meta analysis; Statistical pooling of studies; Study level meta regression

Indexed keywords

ANALYSIS OF VARIANCE; ARTICLE; BEHAVIOR ASSESSMENT; DATA ANALYSIS; EXPLORATORY RESEARCH; INTERVENTION STUDY; METHODOLOGY; PRIORITY JOURNAL; QUALITY CONTROL; RANDOM SAMPLE; STATISTICAL ANALYSIS; TAXONOMY; FEMALE; HUMAN; MALE; META ANALYSIS (TOPIC); STATISTICAL MODEL;

DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; MALE; META-ANALYSIS AS TOPIC; MODELS, STATISTICAL; RESEARCH DESIGN;

EID: 84976578829     PISSN: 18696716     EISSN: 16139860     Source Type: Journal    
DOI: 10.1007/s13142-016-0386-8     Document Type: Article

References (26)

1. DeMets DL. Methods for combining randomized clinical trials: strengths and limitations. Stat Med. 1987; 6: 341-348.
2. Belle SH, Czaja SJ, Schulz R, et al. Using a new taxonomy to combine the uncombinable: integrating results across diverse interventions. Psychol Aging. 2003; 18(3): 396-405.
3. Spinks A, Turner C, Nixon J, McClure RJ (2009) The ‘WHO Safe Communities’ model for the prevention of injury in whole populations. Cochrane Database of Systematic Reviews Issue 3. Art. No.: CD004445. DOI:10.1002/14651858.CD004445.pub3.
4. Pratt CA, Boyington J, Esposito L, et al. Childhood obesity prevention and treatment research (COPTR): interventions addressing multiple influences in childhood and adolescent obesity. Contemp Clin Trials. 2013; 36(2): 406-413.
5. Lytle LA, Svetkey LP, Patrick K, et al. The EARLY trials: a consortium of studies targeting weight control in young adults. Translat Behav Med. 2014; 4(3): 304-313.
6. Czajkowski SM, Powell LH, Adler N, et al. (2015) From ideas to efficacy: The ORBIT model for developing behavioral treatments for chronic diseases. Health Psychology (to appear in print)
7. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959; 22: 719-748.
8. Bangdiwala SI, Villaveces A, Garrettson M, et al. Statistical methods for designing and assessing the effectiveness of community-based interventions with small numbers. Int J Inj Control Saf Promot. 2012; 19(3): 242-248.
9. Morton SC, Adams JL, Suttorp MJ, Shekelle PG (2004) Meta-regression approaches: what, why, when, and how?, Technical Review 8, Agency for Healthcare Research and Quality Publication No. 04–0033. Rockville.
10. O’Connor DP, Lee RE, Mehta P, et al. Childhood obesity research demonstration project: cross-site evaluation methods. Childhood Obes. 2015; 11: 92-103.
11. Schulz R, Czaja SJ, McKay JR, et al. Intervention taxonomy (ITAX): describing essential features of interventions. Am J Health Behav. 2010; 34(6): 811-821.
12. Czaja SJ, Schulz R, Lee CC, et al. A methodology for describing and decomposing complex psychosocial and behavioral interventions. Psychol Aging. 2003; 18(3): 385-395.
13. Bhargava A. Randomized controlled experiments in health and social sciences: some conceptual issues. Econ Hum Biol. 2008; 6: 293-298.
14. Bhargava A, Hays J. Behavioral variables and education are predictors of dietary change in the women’s health trial: feasibility study in minority populations. Prev Med. 2004; 38(4): 442-51.
15. Jöreskog KG. Simultaneous factor analysis in several populations. Psychometrika. 1971; 36: 409-426.
16. Sörbom D. A general method for studying differences in factor means and factor structure between groups. Br J Math Stat Psychol. 1974; 27: 229-239.
17. Duncan TE, Duncan SC, Strycker LA. An introduction to latent variable growth curve modeling: concepts, issues, and application. 2nd ed. Mahwah: Lawrence Erlbaum Associates, Inc.; 2006.
18. Rabe-Hesketh S, Skrondal A, Pickles A. Generalized multilevel structural equation modeling. Psychometrika. 2004; 69: 167-190.
19. Cox D. Planning of experiments. New York: John Wiley & Sons; 1958.
20. Fisher RA. The design of experiments. Edinburgh: Oliver and Boyd; 1935.
21. Bhargava A, Guthrie J. Unhealthy eating habits, physical exercise and macronutrient intakes are predictors of anthropometric indicators in the women’s health trial: feasibility study in minority populations. Br J Nutr. 2002; 88(6): 719-28.
22. Rao CR. Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation. Proc Camb Philos Soc. 1948; 44: 50-57.
23. Bhargava A. Wald tests and systems of stochastic equations. Int Econ Rev. 1987; 28: 789-808.
24. Sargan JD. Some tests of dynamic specification for a single equation. Econometrica. 1980; 48: 879-898.
25. Weiner BJ, Lewis MA, Clauser SB, et al. In search of synergy: strategies for combining interventions at multiple levels. J Natl Cancer Inst Monogr. 2012; 44: 34-41.
26. Wald A. Sequential analysis. New York: Dover Publications; 1947.