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Introduction
Published in Anastasios A. Tsiatis, Marie Davidian, Shannon T. Holloway, Eric B. Laber, Dynamic Treatment Regimes, 2019
Anastasios A. Tsiatis, Marie Davidian, Shannon T. Holloway, Eric B. Laber
In the context of treatment of a chronic disease or disorder, a dynamic treatment regime is a set of sequential decision rules, each corresponding to a key point in the disease or disorder progression at which a decision on the next treatment action for a patient must be made. Each rule takes as input information on the patient to that point and returns the treatment he/she should receive from among the available, feasible options. A dynamic treatment regime thus formalizes the process by which a clinician treating a patient synthesizes information and selects treatments in practice. Dynamic treatment regimes are also referred to as adaptive treatment strategies or adaptive interventions, notably in the literature on treatment of mental health and behavioral disorders.
Less is More? First Impressions From COSMIC-313
Published in Cancer Investigation, 2023
OS is a key secondary endpoint and its estimates will need to be carefully evaluated. Intermediate endpoints such as PFS are nowadays more reliable as primary endpoints for oncology phase 3 RCTs because they more directly benefit from the random treatment assignment whereas OS estimates are more likely to be biased by mediator-outcome confounding (19–22). However, barring extreme scenarios such as crossover (22), we have not yet reached the point in most oncology phase 3 settings whereby the biases in OS estimation due to the effect of subsequent therapies would be expected to reverse the conclusions between PFS and OS signals. Therefore, we should expect the OS estimates of COSMIC-313 to either show a positive signal in favor of the triplet combination or at least be inconclusive. It would certainly be worrisome if the OS signal instead favors the control group. Future pivotal RCTs should carefully collect data not only on the number and type of subsequent therapies but also on mediator-outcome confounders that may influence how these therapies were chosen. This will facilitate the more reliable estimation of OS as part of dynamic treatment regimes tailored towards improving survival, preserving quality of life, and minimizing logistical, financial, and other costs for each individual patient (23–25).
TAM kinase inhibition and immune checkpoint blockade– a winning combination in cancer treatment?
Published in Expert Opinion on Therapeutic Targets, 2021
Pavlos Msaouel, Giannicola Genovese, Jianjun Gao, Suvajit Sen, Nizar M. Tannir
The majority of ongoing trials testing the combination of TAM inhibitors with ICIs lack control arms of ICI alone or single-agent TAM inhibition. Such controls are necessary to properly estimate the added benefit of combining TAM inhibition with immunomodulation compared with either strategy alone. Furthermore, an argument can be made that combining these strategies may not produce any meaningful difference in the OS of patients compared with sequentially administering each of these therapies alone. Such questions can be addressed by dynamic treatment regime models, although such models are very complicated and can require substantial resources [167]. One way to address this question within the context of a typical-randomized clinical trial design may be to focus on other clinically meaningful endpoints such as the CR rate. In a similar manner, the combination of the ICI drugs nivolumab and ipilimumab became a widely accepted strategy for metastatic clear cell RCC because it was found to produce previously unprecedented CR rates in the range of 8–11%. These considerations can also be addressed by incorporating high-resolution pharmacodynamic and clinical efficacy endpoints within trial designs with the aim of detecting the synergistic effects of combination strategies versus the simple additive activity expected from multimodal therapies.