Sequential stopping for high-throughput experiments

Biostatistics

Volumn 14, Issue 1, 2013, Pages 75-86

  Rossell, David ^a   Müller, Peter ^b  

^a INSTITUTE FOR RESEARCH IN BIOMEDICINE   (Spain)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Decision theory; Forward simulation; High throughput experiments; multiple testing; Optimal design; Sample size; Sequential design

Indexed keywords

ARTICLE; COMPUTER SIMULATION; HIGH THROUGHPUT SEQUENCING; HUMAN; METHODOLOGY; MICROARRAY ANALYSIS; PROTEIN MICROARRAY; SAMPLE SIZE; SEQUENCE ANALYSIS; STATISTICAL ANALYSIS;

COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MICROARRAY ANALYSIS; PROTEIN ARRAY ANALYSIS; RESEARCH DESIGN; SAMPLE SIZE; SEQUENCE ANALYSIS, RNA;

EID: 84871241556     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxs026     Document Type: Article

References (26)

1. ARMSTRONG, S. A., STAUNTON, J. E., SILVERMAN, L. B., PIETERS, R., BOER, M. L., MINDEN, M. D., SALLAN, E. S., LANDER, E. S., GOLUB, T. R. AND KORSMEYER, S. J. (2002). MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nature Genetics 30, 41-47.
2. BENJAMINI, Y. AND YEKUTIELI, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165-1188.
3. BERRY, D. A. AND FRISTEDT, B. (1985). Bandit Problems. London: Chapman & Hall.
4. BERRY, D. A., M̈ULLER, P., GRIEVE, A. P., SMITH, M., PARKE, T., BLAZED, R., MITCHARD, N. AND KRAMS, M. (2001). Adaptive Bayesian Designs for Dose-Ranging Drug Trials, Volume V. New York: Springer.
5. BROCKWELL, A. E. AND KADANE, J. B. (2003). A gridding method for Bayesian sequential decision problems. Journal of Computational and Graphical Statistics 12, 566-584.
6. CAMPO DELL'ORTO, M., ZANGRANDO, A., TRENTIN, L., LI, R., LIU, W. M., TE KRONNIE, G., BASSO, G. AND KOHLMANN, A. (2007). New data on robustness of gene expression signatures in leukemia: comparison of three distinct total rna preparation procedures. BMC Genomics 8, 188.
7. CARLIN, B., KADANE, J. AND GELFAND, A. (1998). Approaches for optimal sequential decision analysis in clinical trials. Biometrics 54, 964-975.
8. DEGROOT, M. (2004). Optimal Statistical Decisions. Hoboken, NJ: Wiley-Interscience.
9. DOBBIN, K. AND SIMON, R. M. (2007). Sample size planning for developing classifiers using high-dimensional DNA microarray data. Biostatistics 8, 101-117.
10. DURRIEU, G. AND BRIOLLAIS, L. (2009). Sequential design for microarray experiments. Journal of the American Statistical Association 104, 650-660.
11. FERREIRA, J. A. AND ZWINDERMAN, A. (2006). Approximate power and sample size calculations with the Benjamini-Hochberg method. The International Journal of Biostatistics 2, 8.
12. GIBBONS, R. D., BHAUMIK, D. K., COX, D. R., GRAYSON, D. R., DAVIS, J. M. AND SHARMA, R. P. (2005). Sequential prediction bounds for identifying differentially expressed genes in replicated microarray experiments. Journal of Statistical Planning and Inference 129, 19-37.
13. KENNETH M. R., THOMAS, L. AND SZPIRO, A. A. (2008). Trading bias for precision: decision theory for intervals and sets. Technical Report, Working Paper 336, UW Biostatistics, http://www. bepress.com/uwbiostat/paper336.
14. LEE, M. L. AND WHITMORE, G. (2004). Power and sample size for microarray studies. Statistics in Medicine 11, 3543-3570.
15. MANNSPERGER, H. A., GADE, S., HENJES, F., BEISSBARTH, T. AND KORF, U. (2010). RPPanalyzer: analysis of reversephase protein array data. Bioinformatics 26, 2202-2203.
16. M̈ULLER, P., BERRY, D., GRIEVE, A., SMITH, M. AND KRAMS, M. (2006). Simulation-based sequential Bayesian design. Journal of Statistical Planning and Inference 137, 3140-3150.
17. M̈ULLER, P., PARMIGIANI, G., ROBERT, C. AND ROUSSEAU, J. (2004). Optimal sample size for multiple testing: the case of gene expression microarrays. Journal of the American Statistical Association 99, 990-1001.
18. PAN, W., LIN, J. AND LE, C. T. (2002). How many replicates of arrays are required to detect gene expression changes in microarray experiments? Genome Biology 3, research0022. 1-0022. 10.
19. ROSSELL, D. (2009). GaGa: a simple and flexible hierarchical model for differential expression analysis. Annals of Applied Statistics 3, 1035-1051.
20. ROSSELL, D., M̈ULLER, P. AND ROSNER, G. (2006). Screening designs for drug development. Biostatistics 8, 595-608.
21. RUPPERT, D., NETTLETON, D. AND HWANG, J. T. G. (2007). Exploring the information in p-values for the analysis and planning of multiple-test experiments. Biometrics 63, 483-495.
22. SMYTH, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, Number 1, Article 3.
23. TIBSHIRANI, R. (2006). A simplemethod for assessing sample sizes in microarray experiments. BMC Bioinformatics 7, 106.
24. YANG, Y. H. AND SPEED, T. (2002). Design issues for cDNA microarray experiments. Nature Reviews Genetics 3, 579-588.
25. YUAN, M. AND KENDZIORSKI, C. (2006). A unified approach for simultaneous gene clustering and differential expression identification. Biometrics 62, 1089-1098.
26. ZIEN, A., FLUCK, J., ZIMMER, R. AND LENGAUER, T. (2003). Microarrays: how many do you need? Journal of Computational Biology 10, 653-667.