Joint modeling of longitudinal health-related quality of life data and survival.

Abstract

In cancer research, outcome measures may co-vary. Treatment and treatment related impairment of health-related quality of life (HRQoL) may affect survival. When these effects are analyzed separately, bias may arise. Therefore, we investigated the combined effect of treatment and longitudinally measured HRQoL on survival. Patients with anaplastic oligodendrogliomas (n = 288) who were randomized (EORTC 26951) to radiotherapy (RT) alone or RT plus procarbazine, lomustine, and vincristine (PCV) chemotherapy were analyzed. HRQoL [appetite loss (AP)] was assessed with the EORTC QLQ-C30. We compared survival results from different analysis strategies: Cox model with treatment only [model 1 (M1)] or with treatment and time-dependent AP score [model 2 (M2)] and the joint model combining longitudinal AP score and survival [model 3 (M3)]. The estimated hazard ratio (HR) for RT plus PCV was 0.76 (95 % CI 0.58-1.00) for M1, 0.72 (0.55-0.96) for M2, and 0.69 (0.52-0.92) for M3. This corresponds to a lower risk of death of 24 % in M1, 28 % in M2, and 31 % in M3, for patients treated with RT plus PCV chemotherapy. AP resulted in an increased risk of death, with estimated HR of 1.06 (1.01-1.12) for M2 and 1.13 (1.03-1.23) for M3: Every 10-point increase of AP resulted in a 13 % increased risk of death in M3 as compared to 6 % in M2. Part of the survival benefit of treatment with RT plus PCV chemotherapy can be masked by the negative effect that this treatment has on patients' HRQoL. In our study, up to 7 % of the theoretical treatment efficacy was lost when AP was not adjusted through joint modeling.