Statistical Meta-Analysis with Applications

Statistical Meta-Analysis with Applications

Volumn , Issue , 2008, Pages 1-247

  Hartung, Joachim ^a   Knapp, Guido ^a   Sinha, Bimal K ^b  

^a TU DORTMUND UNIVERSITY   (Germany)

^b UNIVERSITY OF MARYLAND BALTIMORE COUNTY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; DATA ACQUISITION; PUBLIC HEALTH; STATISTICAL TESTS;

BAYESIAN APPROACHES; CATEGORICAL DATA; DATA EVALUATION; ENVIRONMENTAL STUDIES; PROBLEM FORMULATION; PUBLICATION BIAS; STATISTICAL METHODOLOGIES; WORKED-OUT EXAMPLES;

STATISTICS;

EID: 84947762347     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470386347     Document Type: Book

References (230)

1. Abrams, K. R., Lambert, P. C, Sanso, B., Shaw, C, and Marteau, T. M. (2000). Meta-analysis of heterogeneously reported study results: A Bayesian approach. In Meta-Analysis in Medicine and Health Policy, D. Stangl and D. A. Berry (Eds.). New York: Marcel Dekker.
2. Abrams, K. and Sanso, B. (1998). Approximate Bayesian inference for random effects metaanalysis. Statistics in Medicine, 17, 201-218.
3. Agresti, A. (1980). Generalized odds ratio for ordinal data. Biometrics, 36, 59-67.
4. Allescher, H.-D., Böckenhoff, A., Knapp, G., Wienbeck, M., and Hartung, J. (2001). Treatment of non-ulcer dyspepsia: a meta-analysis on placebo-controlled prospective studies. Scandinavian Journal of Gastroenterology, 36, 934-941.
5. Ananda, M. M. A. and Weerahandi, S. (1997). Two-way anova with unequal cell frequencies and unequal variances. Statistica Sinica, 7, 631-646.
6. Anderson, T. W. (1984). An Introduction to Multivariate Statistical Analysis. New York: Wiley.
7. Antiplatelet Trialists' Collaboration (1988). Secondary prevention of vascular disease by prolonged antiplatelet treatment. British Medical Journal, 296, 320-332.
8. Argac, D., Makambi, K. H., and Hartung, J. (2001). A note on testing the nullity of the between group variance in the one-way random effects model under variance heterogeneity. Journal of Applied Statistics, 28, 215-222.
9. Asiribo, O. and Gurland, J. (1990). Coping with variance heterogeneity. Communications in Statistics-Theory and Methods, 19, 4029-4048.
10. Bashore, T. R., Osman, A., and Heffley, E. F. (1989). Mental slowing in elderly persons: a cognitive psychophysiological analysis. Psychology & Aging, 4, 235-244.
11. Becker, B. J. (1990). Coaching for the scholastic aptitude test: further synthesis appraisal. Review of Educational Research, 60, 373-417.
12. Becker, B. J. (1994). Combining significance levels. In The Handbook of Research Synthesis, H. Cooper and L. V. Hedges (Eds.). New York: Russell Sage Foundation.
13. Becker, B. J. (2000). Multivariate meta-analysis. In Handbook of Applied Statistics and Mathematical Modeling, H. E. A. Tinsley and S. G. Brown (Eds.) San Diego: Academic Press.
14. Begg, C. B. (1994). Publication bias. In The Handbook of Research Synthesis, H. Cooper and L.V. Hedges (Eds.). New York: Russell Sage Foundation.
15. Begg, C. B. and Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 50, 1088-1101.
16. Begg, C. B. and Pilote L. (1991). A model for incorporating historical controls into a metaanalysis. Biometrics, 47, 899-906.
17. Berger, J. O. (1980). Statistical Decision Theory and Bayesian Analysis (2nd ed.). Berlin: Springer.
18. Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis. New York: Springer.
19. Berkey, C. S., Anderson, J. J., and Hoaglin, D. C. (1996). Multiple-outcome meta-analysis of clinical trials. Statistics in Medicine, 15, 537-557.
20. Berkey, C. S., Hoaglin, D. C, Antezak-Bouckoms, A., Mosteller, R, and Colditz, G. A. (1998). Meta-analysis of multiple outcomes by regression with random effects. Statistics in Medicine, 17, 2537-2550.
21. Berkey, C. S., Hoaglin, D. C, Mosteller, R, and Colditz, G. A. (1995). A random effects regression model for meta-analysis. Statistics in Medicine, 14, 395-411.
22. Berlin, J. A. and Antman, E. M. (1994). Advantages and limitations of meta-analytic regressions of clinical trials data. Online Journal of Current Clinical Trials, Doc No 134.
23. Bernardo, J. M. and Smith, A. P. (1993). Bayesian Theory. Chichester: Wiley.
24. Berry, S. C. (2000). Meta-analysis versus large trials: Resolving the controversy. In Meta-Analysis in Medicine and Health Policy, D. Stangl and D. A. Berry (Eds.). New York:Marcel Dekker.
25. Bhattacharya, C. G. (1980). Estimation of a common mean and recovery of interblock information. Annals of Statistics, 8, 205-211.
26. Biggerstaff, B. J. and Tweedie, R. L. (1997). Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis. Statistics in Medicine 16, 753-768.
27. Birge, R. T. (1932). The calculation of errors by the method of least squares. Physical Review, 40, 207-227.
28. Birnbaum, A. (1954). Combining independent tests of significance. Journal of the American Statistical Association, 49, 559-575.
29. Bishop, Y. M. M., Fienberg, S. E., and Holland, P. W. (1975). Discrete Multivariate Analysis:Theory and Practice. Cambridge, MA: MIT Press.
30. Boardman, T. J. (1974). Confidence intervals for variance components - A comparative Monte Carlo study. Biometrics, 30, 251-262.
31. Brophy, J. and Joseph, L. (2000). A Bayesian meta-analysis of randomized mega-trials for the choice of thrombolytic agent in acute myocardial infarction. In: Meta-Analysis in Medicine and Health Policy, D. Stangl and D. A. Berry (Eds.). New York: Marcel Dekker.
32. Brown, L. D. and Cohen, A. (1974). Point and confidence estimation of a common mean and recovery of interblock information. Annals of Statistics, 2, 963-976.
33. Brown, M. B. andForsythe, A. B. (1974). The small sample behavior of some statistics which test the equality of several means. Technometrics, 16, 129-132.
34. Burdick, R. K. and Eickman, J. (1986). Confidence intervals on the among group variance component in the unbalanced one-fold nested design. Journal of Statistical Computation and Simulation, 26, 205-219.
35. Burdick, R. K. and Graybill, F. A. (1992). Confidence Intervals on Variance Components. New York: Marcel Dekker.
36. Burdick, R. K., Maqsood, F., and Graybill, F. A. (1986). Confidence intervals on the intraclass correlation in the unbalanced one-way classification. Communications in Statistics -Theory and Methods, 15, 3353-3378.
37. Chiu, C. W. T. (1997). Synthesizing multiple outcome measures with missing data: A sensitivity analysis on the effect of metacognitive reading intervention. Unpublished manuscript, Department of Counseling, Educational Psychology, and Special Education, Michigan State University, East Lansing, MI.
38. Churchill, G. A., Ford, N. M., Hartley, S. W., and Walker, O. C. (1985). The determinants of salesperson performance: a meta-analysis. Journal of Marketing Research, 22, 103-118.
39. Cochran, W. G. (1937). Problems arising in the analysis of a series of similar experiments. Journal of the Royal Statistical Society (Supplement), 4, 102-118.
40. Cochran, W. G. (1954). The combination of estimates from different experiments. Biometrics, 10, 101-129.
41. Cohen, A. and Sackrowitz, H. B. (1974). On estimating the common mean of two normal distributions. Annals of Statistics, 2, 1274-1282.
42. Cohen, A. and Sackrowitz, H. B. (1984). Testing hypotheses about the common mean of normal distributions. Journal of Statistical Planning and Inference, 9, 207-227.
43. Cohen, A., and Sackrowitz, H. B. (1989). Exact tests that recover inter-block information in balanced incomplete block designs. Journal of the American Statistical Association, 84, 556-559.
44. Cohen, J. (1969). Statistical Power Analysis for the Behavioral Sciences. New York: Academic Press.
45. Cohen, J. (1977). Statistical Power Analysis for the Behavioral Sciences (rev. ed.). New York: Academic Press.
46. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
47. Cohen, P. A. (1983). Comment on "A selective review of the validity of student ratings of teaching." Journal of Higher Education, 54, 449-458.
48. Colditz, G. A., Brewer, F. B., Berkey, C. S., Wilson, E. M., Burdick, E., Fineberg, H. V., and Mosteller, F. (1994). Efficacy of BCG vaccine in the prevention of tuberculosis. Journal of the American Medical Association 271, 698-702.
49. Collins, R., Gray, R., Godwin, J., and Peto, R. (1987). Avoidances of large biases and large random errors in the assessment of moderate treatment effects: The need for systematic overviews. Statistics in Medicine, 6, 245-250.
50. Cooper, H. and Hedges, L. V. (1994). The Handbook of Research Synthesis. New York:Russell Sage Foundation.
51. Das, R. and Sinha, B. K. (1987). Robust optimum invariant unbiased tests for variance components. Proceedings of 2nd International Tampere Conference in Statistics, University of Tampere, Finland.
52. Dasgupta, A. and Sinha, B. K. (2006). On some statistical aspects of combining gallup poll results. Technical Report, Department of Mathematics and Statistics, University of Maryland, Baltimore County.
53. DerSimonian, R. and Laird, N. M. (1983). Evaluating the effect of coaching on SAT scores:a meta-analysis. Harvard Educational Review, 53, 1-15.
54. DerSimonian, R. and Laird, N. M. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials, 1, 177-188.
55. Dominici, F. and Parmigiani, G. (2000). Combining studies with continuous and dichotomous responses: A latent variables approach. In: Meta-Analysis in Medicine and Health Policy, D. Stangl and D. A. Berry (Eds.). New York: Marcel Dekker.
56. Draper, D., Gaver, D. P., Jr., Goel, P. K., Greenhouse, J. B., Hedges, L. V., Morris, C. N., Tucker, J. R., and Waternaux, C. M. (1992). Combining Information: Statistical Issues and Opportunities for Research. American Statistical Association, National Academy Press, Washington, D.C.
57. DuMouchel, W. and Normand, S. L. (2000). Computer-modeling and graphical strategies for meta-analysis. In: Meta-Analysis in Medicine and Health Policy, D. Stangl and D. A. Berry (Eds.). New York: Marcel Dekker.
58. Eberhardt, K. R., Reeve, C. P., and Spiegelman, C. H. (1989). A minimax approach to combining means, with practical examples. Chemometrics and Intelligent Laboratory Systems, 5, 129-148.
59. Egger, M., Davey Smith, G., Schneider, M., and Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315, 629-634.
60. Fairweather, W. R. (1972). A method of obtaining an exact confidence interval for the common mean of several normal populations. Applied Statistics, 21, 229-233.
61. Farley, J. U. and Lehmann, D. R. (1986). Meta-Analysis in Marketing: Generalization of Response Models. Lexington, MA: Lexington Books.
62. Farley, J. U. and Lehmann, D. R. (2001). The important role of meta-analysis in international research in marketing. International Marketing Review, 18, 70-79.
63. Farley, J. U., Lehmann, D. R., and Sawyer, A. (1995). Empirical marketing generalization using meta-analysis. Marketing Science, 14, G36-G46.
64. Feingold, M. (1985). A test statistic for combined intra- and inter-block estimates. Journal of Statistical Planning and Inference, 12, 103-114.
65. Feingold, M. (1988). A more powerful test for incomplete block designs. Communications in Statistics-Theory and Methods, 17, 3107-3119.
66. Fisher, R. A. (1932). Statistical Methods for Research Workers (4th ed.) London: Oliver and Boyd.
67. Fleiss, J.L. (1994). Measures of effect size for categorical data. In: The Handbook of Research Synthesis, H. Cooper and L. V. Hedges (Eds.). New York: Russell Sage Foundation.
68. Gamage, J. and Weerahandi, S. (1998). Size performance of some tests in one-way anova. Communications in Statistics - Simulation and Computation, 27, 625-640.
69. Gart, J. J. and Zweifel, J. R. (1967). On the bias of various estimators of the logit and its variance with application to quantal bioassay. Biometrika, 54, 181-187.
70. Gauss, C. F. (1809). Theoria motus corporum coelestium in sectionis conicis solem ambien- tum. Hamburg: Perthes and Besser.
71. Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2004). Bayesian Data Analysis. Boca Raton, FL: Chapman & Hall/CRC Press.
72. George, E. O. (1977). Combining independent one-sided and two-sided statistical tests -Some theory and applications. Doctoral dissertation, University of Rochester.
73. Givens, G. H., Smith, D. D., and Tweedie, R. L. (1997). Publication bias in meta-analysis:A Bayesian data-augmentation approach to account for issues exemplified in the passive smoking debate. Statistical Science, 12, 221-247.
74. Glass, G. V. (1976). Primary, secondary, and meta-analysis. Educational Researcher, 5, 3-8.
75. Glass, G. V., McGaw, B., and Smith, M. L. (1981). Meta-Analysis in Social Research. Beverly Hills, CA: Sage.
76. Gleser, L. J. and Olkin, I. (1994). Stochastically dependent effect sizes. In The Handbook of Research Synthesis, H. Cooper and L. V. Hedges (Eds.). New York: Russell Sage Foundation.
77. Graybill, F. A. (1976). Theory and Application of the Linear Model. North Scituate, MA: Duxbury Press.
78. Graybill, F. A. and Deal, R. B. (1959). Combining unbiased estimators. Biometrics, 15, 543-550.
79. Greenhouse, J. B. and Iyengar, S. (1994). Sensitivity analysis and diagnostics. In The Handbook of Research Synthesis, H. Cooper and L. V. Hedges (Eds.). New York: Russell Sage Foundation.
80. Greenland, S. (1994). A critical look at some popular meta-analytical methods. American Journal of Epidemiology, 140, 290-296.
81. Greenwald, R., Hedges, L. V., and Laine, R. D. (1996). The effect of school resources on student-achievement. Review of Educational Research, 66, 361-396.
82. Griffiths, W. and Judge, G. (1992). Testing and estimating location vectors when the error covariance matrix is unknown. Journal of Econometrics, 54, 121-138.
83. Haff, L. R. (1979). An identity for the Wishart distribution with applications. Journal of Multivariate Analysis, 9, 531-544.
84. Haldane, J. B. S. (1955). The estimation and significance of the logarithm of a ratio of frequencies. Annals of Human Genetics, 20, 309-311.
85. Hardy, R. J. and Thompson, S. G. (1996). A likelihood approach to meta-analysis with random effects. Statistics in Medicine 15, 619-629.
86. Hartung, J. (1981). Nonnegative minimum biased invariant estimation in variance component models. Annals of Statistics, 9, 278-292.
87. Hartung, J. (1999a). A note on combining dependent tests of significance. Biometrical Journal, 41, 849-855.
88. Hartung, J. (1999b). An alternative method for meta-analysis. Biometrical Journal, 41, 901-916.
89. Hartung, J. and Argac, D. (2002). Generalizing the Welch test to nonzero hypotheses on the variance component in the one-way random effects model under variance heterogeneity. Statistics, 36, 89-99.
90. Hartung, J., Argac, D., and Makambi, K. H. (2002). Small sample properties of tests on homogeneity in one-way anova and meta-analysis. Statistical Papers, 43, 197-235.
91. Hartung, J., Böckenhoff, A., and Knapp, G. (2003). Generalized Cochran-Wald statistics in combining of experiments. Journal of Statistical Planning and Inference, 113, 215-237.
92. Hartung, J. and Knapp, G. (2000). Confidence intervals for the between group variance in the unbalanced one-way random effects model of analysis of variance. Journal of Statistical Computation and Simulation 65, 4, 311-324.
93. Hartung, J. and Knapp, G. (2001a). On tests of the overall treatment effect in the meta-analysis with normally distributed responses. Statistics in Medicine, 20, 1771-1782.
94. Hartung, J.and Knapp, G. (2001b) A refined method for the meta-analysis of controlled clinical trials with binary outcome. Statistics in Medicine, 20, 3875-3889.
95. Hartung, J. and Knapp, G. (2004). Improved tests of homogeneity in randomized controlled multi-center trials with binary outcome. Far East Journal of Theoretical Statistics, 13, 101-126.
96. Hartung, J. and Knapp, G. (2005a). On confidence intervals for the among-group variance in the one-way random effects model with unequal error variances. Journal of Statistical Planning and Inference, 127, 157-177.
97. Hartung, J. and Knapp, G. (2005b). Models for combining results of different experiments: retrospective and prospective. American Journal of Mathematical and Management Sciences, 25, 149-188.
98. Hartung, J. and Makambi, K. H. (2002). Positive estimation of the between-study variance in meta-analysis. South African Statistical Journal, 36, 55-76.
99. Hartung, J., Makambi, K. H., and Argac, D. (2001). An extended ANOVA F-test with applications to the heterogeneity problem in meta-analysis. Biometrical Journal, 43, 135-146.
100. Hedges, L. V. (1981). Distribution theory for Glass's estimator of effect size and related estimators. Journal of Educational Statistics, 6, 107-128.
101. Hedges, L. V. (1982). Estimating effect size from a series of independent experiments. Psychological Bulletin, 92, 490-499.
102. Hedges, L. V. (1994). Fixed effects models. In: The Handbook of Research Synthesis, H. Cooper and L.V. Hedges (Eds.). New York: Russell Sage Foundation.
103. Hedges, L. V., Gurevitch, J., and Curtis, P. S. (1999). The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150-1156.
104. Hedges, L. V. and Olkin, I. (1985). Statistical Methods for Meta-Analysis. Boston: Academic Press.
105. Heine, B. (1993). Nonnegative estimation of variance components in an unbalanced oneway random effects model. Communications in Statistics - Theory and Methods, 22, 2351-2371.
106. Higgins, J. P. T. and Thompson, S. G. (2004). Controlling the risk of spurious findings from meta-regression. Statistics in Medicine, 23, 1663-1682.
107. Hunter, J. E., Schmidt, F. L., and Jackson, G. B. (1982). Meta-analysis: Cumulating Research Finding across Studies. Beverly Hills, CA: Sage.
108. Iyengar, S. and Greenhouse, J. B. (1988). Selection models and the file drawer problem. Statistical Science, 3, 109-135.
109. Iyer, H., Wang, J. M., and Mathew, T. (2004). Models and confidence intervals for true values in interlaboratory trials. Journal of the American Statistical Association, 99, 1060-1071.
110. Johnson, M. (1993). Comparative efficacy of NaF and SMFP dentifrices in caries prevention: a meta-analysis overview. Caries Research, 27, 328-336.
111. Johnson, N. L. and Kotz, S. (1970). Distributions in Statistics: Continuous Univariate Distributions. New York: Wiley.
112. Jones, D. R. (1992). Meta-analysis of observational epidemiological studies: a review. Journal of the Royal Society of Medicine, 85, 165-168.
113. Jordan, S. M. and Krishnamoorthy, K. (1996). Exact confidence intervals for the common mean of several normal populations. Biometrics, 52, 77-86.
114. Kalaian, H. and Becker, B. J. (1986). The effects of coaching on Scholastic Aptitude Test performance: A multivariate meta-analysis approach. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.
115. Kempthorne, O., Mukhopadhyay, N., Sen, P. K., and Zacks, S. (1991). Research-How to do it: A panel discussion. Statistical Science, 6, 149-162.
116. Khatri, C. G. and Shah, K. R. (1974). Estimation of location parameters from two linear models under normality. Communications in Statistics, 3, 647-663.
117. Khuri, A. I., Mathew, T., and Sinha, B. K. (1998). Statistical Tests for Mixed Linear Models. New York: Wiley.
118. Kim, J. P. (1998). Meta-analysis of equivalence of computerized and pencil-and-paper testing for ability measures. Unpublished manuscript, Department of Counseling, Educational Psychology, and Special Education, Michigan State University, East Lansing, MI.
119. Knapp, G. and Hartung, J. (2003). Improved tests for a random effects meta-regression with a single covariate. Statistics in Medicine, 22, 2693-2710.
120. Kubokawa, T. (1990). Minimax estimation of common coefficients of several regression models under quadratic loss. Journal of Statistical Planning and Inference, 24, 337-345.
121. LaMotte, L.R. (1973). On nonnegative quadratic unbiased estimation of variance components. Journal of the American Statistical Association, 68, 728-730.
122. Larose, D.T. (2000). A Bayesian meta-analysis of the relationship between duration of estrogen exposure and occurrence of endometrial cancer. In: Meta-Analysis in Medicine and Health Policy, D. Stangl and D. A. Berry (Eds.). New York: Marcel Dekker.
123. Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Paris: Courcier.
124. Lentner, M. and Bishop, T. (1986). Experimental Design and Analysis. Blacksburg, VA: Valley Book.
125. Li, X. (2000). Statistical Issues in Environmental Problems. Ph.D. dissertation, University of Maryland, Baltimore County, MD.
126. Li, X., Nussbaum, B., and Sinha, B. K. (2000). A statistical analysis of hillsdale lake data. Calcutta Statistical Association Bulletin, 50, 293-305.
127. Li, Y, Shi, L., and Roth, H. D. (1994). The bias of the commonly-used estimate of variance in meta-analysis. Communications in Statistics - Theory and Methods, 23, 1063-1085.
128. Li, Z. and Begg, C. B. (1994). Random effects models for combining results from controlled and uncontrolled studies in a meta-analysis. Journal of the American Statistical Association, 89, 1523-1527.
129. Liang, K. Y. and Self, S. G. (1985). Test for homogeneity of odds ratio when the data are sparse. Biometrika, 72, 353-358.
130. Light, R J. and Pillemer, D. B. (1984). Summing up: The Science of Reviewing Research. Cambridge, MA: Harvard University Press.
131. Lipsitz, S. R., Dear, K. B. G., Laird, N. M., andMolenberghs, G. (1998). Tests for homogeneity of the risk difference when data are sparse. Biometrics, 54, 148-160.
132. Little, R. J. A, and Rubin, D. B. (1987). Statistical Analysis with Missing Data. New York: Wiley.
133. Lodish, L. M., Abraham, M., Kalmenson, S., Livelsberger, J., Lubetkin, B., Richardson, B., and Stevens, M. E. (1995). How T.V. advertising works: a meta-analysis of 389 real world split cable T.V. advertising experiments. Journal of Marketing Research, 32, 125-139.
134. Makambi, K. H. (2003). Weighted inverse chi-square method for correlated significance tests. Journal of Applied Statistics, 30, 225-234.
135. Mandel, J. and Paule, R. C. (1970). Interlaboratory evaluation of a material with unequal number of replicates. Analytical Chemistry, 42, 1194-1197.
136. Mantel, N. and Haenszel, W. (1959). Statistical aspects of the analysis of data from retrospective studies of disease. Journal of the National Cancer Institute, 22, 719-748.
137. Marden, J. I. (1991). Sensitive and sturdy p-values. The Annals of Statistics, 19, 918-934.
138. Mathew, T, Sinha, B. K., and Zhou, L. (1993). Some statistical procedures for combining independent tests. Journal of the American Statistical Association, 88, 912-919.
139. McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society, Series B, 42, 109-142.
140. Mehrotra, D. V. (1997). Improving the Brown-Forsythe solution to the generalized Behrens-Fisher problem. Communications in Statistics - Simulation and Computation, 26, 1139-1145.
141. Mehta, J. S. and Gurland, J. (1969). Combination of unbiased estimates of the mean which consider inequality of unknown variances. Journal of the American Statistical Association, 64, 1042-1055.
142. Meier, P. (1953). Variance of a weighted mean. Biometrics, 9, 59-73.
143. Mitra, P. K. and Sinha, B. K. (2007). On some aspects of estimation of a common mean of two independent normal populations. Journal of Statistical Planning and Inference 137, 184-193.
144. Montgomery, D. (1991). Design and Analysis of Experiments (3rd ed.). New York: Wiley.
145. Mosteller, F. and Bush, R. (1954). Selected quantitative techniques. In Handbook of Social Psychology: Theory and Method, Vol. 1, G. Lindsey (Ed.). Cambridge, MA: Addison-Wesley.
146. Nair, K. A. (1980). Distribution of an estimator of the common mean of two normal populations. Annals of Statistics, 8, 212-216.
147. Norwood, T. E. and Hinkelmann, K. (1977). Estimating the common mean of several normal populations. Annals of Statistics, 5, 1047-1050.
148. Patil, G. P. and Rao, C. R. (1977). The weighted distributions: A survey of their applications. In Applications of Statistics, Krishnaiah (Ed.). Amsterdam: North-Holland.
149. Pauler, D. K. and Wakefield, J. (2000). Modeling and implementation issues in Bayesian meta-analysis. In: Meta-Analysis in Medicine and Health Policy, D. Stangl and D. A. Berry (Eds.). New York: Marcel Dekker.
150. Pearson, K. (1904). Report on certain enteric fever inoculation statistics. British Medical Journal, 2, 1243-1246.
151. Pearson, K. (1933). On a method of determining whether a sample of given size n supposed to have been drawn from a parent population having a known probability integral has probably been drawn at random. Biometrika, 25, 379-410.
152. Petitti, D. B. (1994). Meta-Analysis, Decision Analysis, and Cost-Effectiveness Analysis. New York: Oxford University Press.
153. Pettigrew, H. M., Gart, J. J., and Thomas. D. G. (1986). The bias and higher cumulants of the logarithm of a binomial variate. Biometrika, 73, 425-435.
154. Piggott, T. D. (1994). Methods for handling missing data in research synthesis. In: The Handbook of Research Synthesis, H. Cooper and L. V. Hedges (Eds.). New York: Russell Sage Foundation.
155. Rao, C. R. (1947). General methods of analysis for incomplete block designs. Journal of the American Statistical Association, 42, 541-561.
156. Rao, C. R. (1972). Estimation of variance and covariance components in linear models. Journal of the American Statistical Association, 67, 122-115.
157. Rao, C. R. (1973). Linear Statistical Inference and Its Applications. New York: Wiley.
158. Rao, P. S. R. S, Kaplan, J., and Cochran, W. G. (1981). Estimators for the one-way random effects model with unequal error variances. Journal of the American Statistical Association, 76, 89-97.
159. Raudenbush, S. W. (1984). Magnitude of teacher expectancy effects on pupil IQ as a function of the credibility of expectancy induction: A synthesis of findings from 18 experiments. Journal of Educational Psychology, 76, 85-97.
160. Raudenbush, S. W., Becker, B. J., and Kalaian, H. (1988). Modeling multivariate effect sizes. Psychological Bulletin, 103, 111-120.
161. Raudenbush, S. W. and Bryk, A. S. (1985). Empirical Bayes meta-analysis. Journal of Educational Statistics, 10, 75-98.
162. Riley, R. D., Abrams, K. R., Lambert, P. C, Sutton, A. J., and Thompson, J. R. (2007a). An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes. Statistics in Medicine, 26, 78-97.
163. Riley, R. D., Abrams, K. R., Lambert, P. C, Sutton, A. J., and Thompson, J. R. (2007b). Bivariate random-effects meta-analysis and the estimation of between-study correlation. BMC Medical Research Methodology, 7:3, 1471-2288/7/3.
164. Riley, R. D., Heney, D., Jones, D. R., Sutton, A. J., Lambert, P. C, Abrams, K. R., Young, B., and Wailoo, A. J. (2004). A systematic review of molecular and biological tumor markers in neuroblastoma. Clinical Cancer Research, 10, 4-12.
165. Rohatgi, V. K. (1976). An Introduction to Probability Theory and Mathematical Statistics. New York: Wiley.
166. Rosenthal, R. (1978). Combining results of independent studies. Psychological Bulletin, 85, 185-193.
167. Rosenthal, R. (1979). The "file drawer problem" and tolerance for null results. Psychological Bulletin, 86, 638-641.
168. Rosenthal, R. (1984). Meta-Analytic Procedures for Social Research. Beverly Hills, CA:Sage.
169. Rosenthal, R. (1994). Parametric measures of effect size. In: The Handbook of Research Synthesis, H. Cooper and L. V. Hedges (Eds.). New York: Russell Sage Foundation.
170. Rosenthal, R. and Rubin, D. B. (1986). Meta-analytical procedures for combining studies with multiple effect sizes. Psychological Bulletin, 99, 400-406.
171. Rubin, D. B. (1981). Estimation in parallel randomized experiments. Journal of Educational Statistics, 6, 337-401.
172. Rukhin, A. L. (2007). Conservative confidence intervals based on weighted means statistics. Statistics & Probability Letters, 77, 853-861.
173. Rukhin, A. L., Biggerstaff, B. J., and Vangel, M. G. (2000). Restricted maximum likelihood estimation of a common mean and the Mandel-Paule algorithm. Journal of Statistical Planning & Inference, 83, 319-330.
174. Rukhin, A. L. and Vangel, M. G. (1998). Estimation of a common mean and weighted mean statistics. Journal of the American Statistical Association, 93, 303-309.
175. Ryan, E. D., Blakeslee, T., and Furst, D. M. (1986). Mental practice and motor skill learning:an indirect test of the neuromuscular feedback hypothesis. International Journal of Sport Psychology, 17, 60-70.
176. Satterthwaite, F. E. (1946). An approximate distribution of estimates of variance components. Biometrics Bulletin, 2, 110-114.
177. Searle, S. R. (1971). Linear Models. New York: Wiley.
178. Searle, S. R., Casella, G., and McCulloch, C. E. (1992). Variance Components. New York: Wiley.
179. Sethuraman, R. (1995). A meta-analysis of national brand and store brand cross-promotional price elasticities. Marketing Letters, 6, 275-286.
180. Shah, K. R. (1975). Analysis of block designs. Gujarat Statistical Review, 2, 1-11.
181. Shah, K. R. (1992). Recovery of inter-block information: an update. Journal of Statistical Planning and Inference, 30, 163-172.
182. Shinozaki, N. (1978). A note on estimating the common mean of k normal populations and the Stein Problem. Communications in Statistics A, 7, 1421-1432.
183. Sidik, K. and Jonkman, J. N. (2002). A simple confidence interval for meta-analysis. Statistics in Medicine 21, 3153-3159.
184. Sinha, B. K. (1979). Is the maximum likelihood estimate of the common mean of several normal populations admissible? Sankhyā, B, 40, 192-196.
185. Sinha, B. K. (1985). Unbiased estimation of the variance of the Graybill-Deal estimator of the common mean of several normal distributions. Canadian Journal of Statistics, 13, 243-247.
186. Sinha, B. K. and Mouqadem, O. (1982). Estimation of the common mean of two univariate normal populations. Communications in Statistics - Theory and Methods, 11, 1603-1614.
187. Sinha, B. K., O'Brien, R., and Smith, W. (1991). A statistical procedure to evaluate cleanup standards. Journal of Chemometrics, 5, 249-261.
188. Sinha, B. K. and Sinha, B. K. (1995). An application of bivariate exponential models and related inference. Journal of Statistical Planning and Inference, 44, 181-191.
189. Skinner, J. B. (1991). On combining studies. Drug Information Journal, 25, 395-403.
190. Smith, T. C., Spiegelhalter, D. J., and Thomas, A. (1995). Bayesian approaches to random-effects meta-analysis: a comparative study. Statistics in Medicine, 14, 2685-2699.
191. Snedecor, G. W. and Cochran, W. G. (1967). Statistical Methods (6th ed.). Ames, IA: The Iowa State University Press.
192. Steinkamp, M. W. and Maehr, M. L. (1983). Affect, ability and science achievement: A quantitative synthesis of correlation research. Review of Educational Research, 53, 369-396.
193. Sterne, J. A. C, Egger, M., and Sutton, A. J. (2001). Meta-analysis software. In: Systematic Reviews in Health Care: Meta-Analysis in Context (2nd ed.), M. Egger, G. D. Smith, and D. G Altman (Eds.). London: BMJ Books.
194. Stigler, S. M. (1986). The History of Statistics - The Measurement of Uncertainty before 1900. Cambridge, MA: Harvard University Press.
195. Stouffer, S. A., Suchman, E. A., DeVinney, L. C, Star, S. A., and Williams, R. M., Jr. (1949). The American Soldier, Volume I. Adjustment during Army Life. Princeton, NJ: Princeton University Press.
196. Tellis, G. J. (1988). The price elasticity of selective demand: a meta-analysis of econometric models of sales. Journal of Marketing Research, 25, 331-341.
197. Thomas, J. D. and Hultquist, R. A. (1978). Interval estimation for the unbalanced case of the one-way random effects model. Annals of Statistics, 6, 582-587.
198. Thursby, J. G. (1992). A comparison of several exact and approximate tests for structural shift under heteroscedasticity. Journal of Econometrics, 53, 363-386.
199. Timm, N. H. (1975). Multivariate Analysis with Applications in Education and Psychology. Belmont, CA: Brooks/Cole.
200. Timm, N. H. (1999). A note on testing for multivariate effect sizes. Journal of Educational and Behavioral Statistics, 24, 132-145.
201. Tippett, L. H. C. (1931). The Methods of Statistics. London: Williams & Norgate.
202. Tsui, K. and Weerahandi, S. (1989). Generalized p-values in significance testing of hypotheses in the presence of nuisance parameters. Journal of the American Statistical Association, 84, 602-607.
203. Tukey, J.W. (1951). Components in regression. Biometrics, 7, 33-69.
204. United States Environmental Protection Agency (1990). Health Effects of Passive Smoking:Assessment of Lung Cancer in Adults and Respiratory Disorders in Children. US EPA Publication NO. EPA-600-90-006A, Washington DC.
205. Van Houwelingen, H. C, Arends, L. R., and Stijnen, T. (2002). Advanced methods in metaanalysis: multivariate approach and meta-regression. Statistics in Medicine, 21, 589-624.
206. Verbeke, G. and Molenberghs, G. (1997). Linear Mixed Models in Practice. New York: Springer.
207. Viechtbauer, W. (2007). Confidence intervals for the amount of heterogeneity in meta-analysis. Statistics in Medicine, 26, 37-52.
208. Wald, A. (1940). A note on the analysis of variance with unequal class frequencies. Annals of Mathematical Statistics, 11, 96-100.
209. Wang, C. M. (1990). On the lower bound of confidence coefficients for a confidence interval on variance components. Biometrics, 46, 187-192.
210. Warn, D. E., Thompson, S. G., and Spiegelhalter, D. J. (2002). Bayesian random effects meta-analysis of trials with binary outcomes: methods for the absolute risk difference and relative risk scales. Statistics in Medicine, 21, 1601-1623.
211. Weerahandi, S. (1993). Generalized confidence intervals. Journal of the American Statistical Association, 88, 899-905.
212. Weerahandi, S. (1995). Exact Statistical Methods for Data Analysis. New York: Springer.
213. Weerahandi, S. (2004). Generalized Inference in Repeated Measures. New York: Wiley.
214. Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38, 330-336.
215. White, H. D. (1994). Scientific communication and literature retrieval. In The Handbook of Research Synthesis, H. Cooper and L.V. Hedges (Eds.). New York: Russell Sage Foundation.
216. Whitehead, A. (2002). Meta-Analysis of Controlled Clinical Trials. Chichester: Wiley.
217. Whitehead, A. and Jones, N. M. B. (1994). A meta-analysis of clinical trials involving different classifications of response into ordered categories. Statistics in Medicine, 13, 2503-2515.
218. Whitehead, A., Omar, R. Z., Higgins, J. R T., Savaluny, E., Turner, R. M., and Thompson, S. G. (2001). Meta-analysis of ordinal outcomes using individual patient data. Statistics in Medicine, 20, 2243-2260.
219. Wilkinson, B. (1951). A statistical consideration in psychological research. Psychological Bulletin, 48, 156-158.
220. Williams, J. S. (1962). A confidence interval for variance components. Biometrika, 49, 278-281.
221. Witte, S. and Victor, N. (2004). Some problems with the investigation of noninferiority in meta-analysis. Methods of Information in Medicine, 43, 470-474.
222. Woolf, B. (1955). On estimating the relation between blood group and disease. Annals of Human Genetics, 19, 251-253.
223. Yates, F. (1939). The recovery of inter-block information in varietal trials arranged in three dimensional lattice. Annals of Eugenics, 9, 136-156.
224. Yates, F. (1940). The recovery of inter-block information in balanced incomplete block designs. Annals of Eugenics, 10, 317-325.
225. Yates, F. and Cochran, W. G. (1938). The analysis of groups of experiments. Journal of Agricultural Science, 28, 556-580.
226. Yu, R, Sun, Y, and Sinha, B. K. (2002). Estimation of the common mean of a bivariate normal population. Annals of the Institute of Statistical Mathematics, 54, 861-878.
227. Zacks, S. (1966). Unbiased estimation of the common mean of the two normal distributions based on small samples of equal size. Journal of American Statistical Association, 61, 467-476.
228. Zacks, S. (1970). Bayes and fiducial equivariant estimators of the common mean of two populations. Annals of Mathematical Statistics, 41, 59-69.
229. Zhang, Z. (1992). Recovery tests in BIBD's with very small degrees of freedom for inter-block errors. Statistics and Probability Letters, 15, 197-202.
230. Zhou, L. and Mathew, T. (1993). Combining independent tests in linear models. Journal of the American Statistical Association, 88, 650-655.