Treatment of cancer involves a series of decisions made over time regarding selection of first-line therapy, of maintenance and salvage treatments, and of dosing and timing, and regarding treatment modification in the face of toxicity. Most cancer clinical trials focus on effects of treatments at a single decision point, e.g., selection of first-line chemotherapy. Usually, conclusions on the best overall strategy are drawn from several separate single decision point studies. This approach cannot take into account the effects of prior and subsequent decisions. For example, therapy administered at one decision point may have delayed effects on the efficacy of future treatments. Thus, for the purpose of developing strategies or algorithms for treatment across a series of decisions, there has been growing recognition in chronic disease and disorder research that the entire sequential decision process should be studied as a whole.
This recognition has led to considerable interest in methodology for the development of dynamic treatment regimes. A dynamic treatment regime is a set of sequential decision rules that use the accrued information on a patient to dictate the next treatment action from among the available options, hereby personalizing each step of treatment to the patient and operationalizing clinical practice. With several treatment options at each decision point and a myriad of possibilities for synthesizing the accrued information at each decision point into rules, numerous regimes may be conceived. Thus, it is of great interest to identify an optimal regime, i.e., the regime that leads to the greatest benefit if followed by the patient population. Clearly, formulation of optimal dynamic treatment regimes to guide cancer therapy would represent a major step toward personalized cancer medicine.
During the previous project period, we developed fundamental methodology for the discovery and evaluation of optimal dynamic treatment regimes. These advances form a critical foundation for tackling key challenges arising in the cancer context, the resolution of which will enable the discovery and implementation of optimal regimes for personalized cancer treatment. We will undertake four specific aims toward this objective:
Aim 1: Develop regimes that balance efficacy, toxicity, cost, patient preference, and clinical judgement.
Aim 2: Develop clinical trial designs for discovery of dynamic treatment regimes.
Aim 3: Advance methods for estimation and inference for optimal treatment regimes.
Aim 4: Develop methods for discovery of optimal regimes in a restricted, feasible class.
The results of this research have the potential to significantly enhance our understanding of the genetic basis of inter-individual variability in drug response and in discovering effective new individualized therapies to improve the quality and longevity of cancer patients.