Small sample sequential multiple assignment randomized trials (snSMARTs) are multistage trial designs to identify the best overall treatment. In snSMARTs, binary response/nonresponse outcomes are measured at intermediate and final timepoints. If the patient is responding at the intermediate timepoint, they continue on the same treatment. Otherwise, they are re-randomized to one of the remaining treatments. We expand the snSMART design to allow for continuous outcomes. The probability of staying on the same treatment is proportional to the intermediate outcome eliminating the need for a categorical tailoring variable defining response/nonresponse. This re-randomization scheme allows for trials to continue without requiring a dichotomous variable. Additionally, this increases the probability of observing patients in all treatment regimens which is critical in small sample studies. Here we present simulation results from continuous snSMART trials and estimated treatment effects using Bayesian analysis. Bias, root mean square error, coverage probability of credible intervals, and credible interval width of treatment effects are examined in this design setting.