Typical oncology practice often includes not only an initial,
frontline treatment, but also subsequent treatments given if the
initial treatment fails. The physician chooses a treatment at each
stage based on the patient's baseline covariates and history of
previous treatments and outcomes. Such sequentially adaptive medical
decision-making processes are known as dynamic treatment regimes,
treatment policies, or multi-stage adaptive treatment
strategies. Conventional analyses in terms of frontline treatments
that ignore subsequent treatments may be misleading, because they
actually are an evaluation of more than front-line treatment
effects on outcome. We are motivated by data from a randomized trial
of four combination chemotherapies given as frontline treatments to
patients with acute leukemia. Most patients in the trial also
received a second-line treatment, chosen adaptively and subjectively
rather than by randomization, either because the initial
treatment was ineffective or the patient's cancer later recurred. We
evaluate effects on overall survival time of the 16 two-stage
strategies that actually were used. Our methods include a
likelihood-based regression approach in which the transition times
of all possible multi-stage outcome paths are modeled, and
estimating equations with inverse probability of treatment weighting
to correct for bias. While the two approaches give different
numerical estimates of mean survival time, they lead to the same
substantive conclusions when comparing the two-stage regimes.
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