Visualizing the target estimand in comparative effectiveness studies with multiple treatments.

Aim: Comparative effectiveness research using real-world data often involves pairwise propensity score matching to adjust for confounding bias. We show that corresponding treatment effect estimates may have limited external validity, and propose two visualization tools to clarify the target estimand. Materials & methods: We conduct a simulation study to demonstrate, with bivariate ellipses and joy plots, that differences in covariate distributions across treatment groups may affect the external validity of treatment effect estimates. We showcase how these visualization tools can facilitate the interpretation of target estimands in a case study comparing the effectiveness of teriflunomide (TERI), dimethyl fumarate (DMF) and natalizumab (NAT) on manual dexterity in patients with multiple sclerosis. Results: In the simulation study, estimates of the treatment effect greatly differed depending on the target population. For example, when comparing treatment B with C, the estimated treatment effect (and respective standard error) varied from -0.27 (0.03) to -0.37 (0.04) in the type of patients initially receiving treatment B and C, respectively. Visualization of the matched samples revealed that covariate distributions vary for each comparison and cannot be used to target one common treatment effect for the three treatment comparisons. In the case study, the bivariate distribution of age and disease duration varied across the population of patients receiving TERI, DMF or NAT. Although results suggest that DMF and NAT improve manual dexterity at 1 year compared with TERI, the effectiveness of DMF versus NAT differs depending on which target estimand is used. Conclusion: Visualization tools may help to clarify the target population in comparative effectiveness studies and resolve ambiguity about the interpretation of estimated treatment effects.

Psychosocial and demographic predictors of quality of life in a large sample of cancer patients.

In recent years, there has been increasing emphasis on understanding how the cancer experience affects individuals' quality of life (QOL). The goal of this study was to assess the QOL and to identify psychosocial predictors of QOL among a large sample of patients with a variety of cancers. Specifically, we assessed the unique contribution of demographic variables, medical variables, and social support on patients' QOL and psychological adjustment. Three hundred and fifty-one participants completed measures of social support, depression, anxiety, and QOL at their follow-up medical visits. Analyses indicated that patients who were older and had better social support reported less anxiety (p<0.001), and patients who were older, married or who had more social support reported less depressive symptoms (p<0.01). Men, individuals whose cancer had not recurred, and those not undergoing active treatment reported better QOL in the physical health domain (p<0.05). Patients who were older, married, with more formal education, less advanced disease, and better social support reported better QOL in the mental health domain (p<0.05). Demographic variables (age, gender, marital status, education) were generally associated with measures of adjustment and QOL whereas medical variables (time since diagnosis, recurrence status, treatment variables, stage of disease) were not. Patients with more support reported less anxiety and depression and better QOL in the mental health domain, independent of demographic and medical variables. Assessing patients' level of social support may help to identify patients at risk for distress.