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Treatment Sequencing, Asymmetry, and Uncertainty: Protocol Strategies for Combination Chemotherapy¹

Department of Biostatistics, Harvard School of Public Health, and Dana-Farber Cancer Institute, Boston, Massachusetts 02115

This paper summarizes a large number of simulations which relax the "symmetry" assumptions in the Goldie-Coldman treatment model, which was symmetrical with respect to two drugs considered for use together. The results are that, under relevant violations of the assumptions, non-alternating treatment schedules frequently outperform alternation and combination substantially. The magnitudes of these effects are at least as large as the improvements made by shortening the time periods between treatment changes. Three protocol design strategies derived from these results are described: (a) when there is no knowledge of parameters, empirical trials in search of the best schedule can be contemplated, if patients in such trials have fundamentally "similar" tumors, in a special sense given the name "pattern homogeneity"; (b) when minimal knowledge of cell kill parameters is available, the "worst drug rule" could perform remarkably well. This strategy is contrary to much of current practice, but a clear rationale is proposed; and (c) when detailed knowledge of tumor parameters is available for each individual, detailed modeling to predict optimal schedule promises great improvements in treatment outcome.

Additional considerations addressed include factors determining the merit of these strategies, suggestions for new laboratory research, and implications for future clinical chemotherapy research.

¹ Supported by USPHS Research Fellowship T32-ES07142-03, by National Cancer Institute Research Grant CA-39640, and by a Biomedical Research Support Grant administered through Dana-Farber Cancer Institute.

Received 9/23/85. Revised 3/12/86. Accepted 5/ 1/86.

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