Evaluating Joint Effects of Induction-Salvage Treatment Regimes on Overall Survival in Acute Leukemia

Abstract

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.

Keywords: Causal inference; Clinical trial; Dynamic treatment regime; Treatment policy.

Figure 1

Possible pathways of successive states, transition times and salvage therapy following induction treatment in acute leukemia patients.

Figure 2

Overall mean survival estimates and 90% non-parametric bootstrap confidence intervals for estimators under the likelihood method, excluding outliers. Strategies such as (FAI,HDAC,HDAC) stand for “Give FAI as induction, but if the patient’s disease is resistant to therapy, or if relapse occurs after achieving complete remission, then give HDAC as salvage.” For each strategy, the effective sample size n is the total number of patients in the study whose treatment regime was consistent with the strategy.

Figure 3

Overall mean survival estimates and 90% non-parametric bootstrap confidence intervals for estimators under the likelihood method based on all observations in the dataset including putative outliers. Strategies such as (FAI,HDAC,HDAC) stand for “Give FAI as induction, but if the patient’s disease is resistant to therapy, or if relapse occurs after achieving complete remission, then give HDAC as salvage.” For each strategy, the effective sample size n is the total number of patients in the study whose treatment regime was consistent with the strategy.