Analysis of Censored Data from Highly Fractionated Experiments

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Abstract
Although censored (including interval-censored) data may be easier or less costly to collect than complete data, they contain less information and are harder to analyze. Existing methodology is inadequate for analyzing such data from highly fractionated experiments. We propose an iterative method that provides a simple and flexible way to consider many models simultaneously. The method's simplicity makes it easy to implement with existing software, results in computational savings, and promotes experimenter involvement. We demonstrate the procedure by reanalyzing data from two real experiments. A simulation study also demonstrates its superiority over some existing methods, including Taguchi's minute accumulating analysis.

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