Sequential stopping for high-throughput experiments.
In high-throughput experiments, the sample size is typically chosen informally. Most formal sample-size calculations depend critically on prior knowledge. We propose a sequential strategy that, by updating knowledge when new data are available, depends less critically on prior assumptions. Experiments are stopped or continued based on the potential benefits in obtaining additional data. The underlying decision-theoretic framework guarantees the design to proceed in a coherent fashion. We propose intuitively appealing, easy-to-implement utility functions. As in most sequential design problems, an exact solution is prohibitive. We propose a simulation-based approximation that uses decision boundaries. We apply the method to RNA-seq, microarray, and reverse-phase protein array studies and show its potential advantages. The approach has been added to the Bioconductor package gaga.
Biostatistics and Bioinformatics Unit, Institute for Research in Biomedicine of Barcelona, Barcelona 08028, Spain. david.rossell@irbbarcelona.org
David
D
Rossell
Peter
P
Müller
D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D046228:Microarray Analysis / Q000379:methods; D040081:Protein Array Analysis; D012107:Research Design; D018401:Sample Size; D017423:Sequence Analysis, RNA / Q000379:methods