The purpose of this study was to investigate the patient characteristics and PROs associated with drop-out and death in a non-randomized intervention study in severely cancer patients. Family status, subjective social support, low values of global health status/QoL and role functioning of the EORTC QLQ-C30 at baseline and low value of physical functioning at the visit before drop-out were associated with time-to-dropout whereas study site, diagnosis, low values of global health status/QoL, physical functioning, and role functioning, high values of symptoms (e.g. fatigue, nausea and vomit, and appetite lose) of the EORTC QLQ-C30 at both baseline and the visit before death were associated with time-to-death.
Around 50% of subjects completed all study visits in OSCAR. In other words, there was a relatively high drop-out rate in our study compared to prior oncological studies (6, 7, 9, 21, 22). The major reasons for non-completion were early death, accounting for approximately 60% (102/169) of discontinuation. A high rate of early death is expected in this population of severely affected cancer patients and it was vulnerably related to the patient’s recruitment which having severely cancer diagnosis. However, this expectation has to be considered when planning a further study e.g. closely continue monitoring after the patient misses an assessment, shorter time windows between follow up visits, post-recruitment and shorter studies (6). Although we have rate of deceased patients, the overall attrition (drop-out) rate in the OSCAR of 18.5% (withdrawal, 15.2%; loss-to-follow up, 2.8%; other reasons, 0.5%) was considerably modest compared to rates reported in other oncological studies, which ranges between 18% and 31% (6, 9, 23).
Our results show that PROs are in fact characteristics associated with both drop-out and death. Cancer patients with poor quality of life and high symptom burden at baseline and at the visit before drop-out or death played an important role in the likelihood of early drop-out and death in our study. These findings are similar to those of other studies (6, 7, 21, 22). In addition, regarding specific reasons to drop-out, we found that participants who dropped out due to illness and other reasons had a lower disease burden and better functionalities than participants who died. In specific, we found that fatigue, nausea and vomiting, and appetite loss were associated with early death for both time points at baseline and at the visit before drop-out. These symptoms have been identified as early signs of upcoming death in cancer patients, especially fatigue symptoms (24).
The probability of early death differed between study sites and it could relate to the difference of the distribution of diagnosis between the study sites. We found that study site 3 had enrolled around 70% of cancer patients with malignant neoplasm of bronchus and lung, metastatic colorectal cancer or colon carcinoma, and malignant neoplasm of pancreas, whilst study site 1 enrolled around 15% of those cancer types and about 60% of cancer patients with acute leukemia and aggressive lymphoma (data not shown). It is relevant to our finding that patients who were diagnosed with malignant neoplasm of pancreas had a higher risk of early death. This result has implications to research design, patient selection, and further data interpretation.
Older age was not markedly related to drop-out or death in our study, although there was a weak association between older age and early death. This result is in line with previous cancer studies (6, 7, 21), some studies reported contrary findings (9, 10). Males seemed more likely to early drop-out compared to females in our study; however, this association was reversed in some other studies (22, 23). Additionally yet other studies found no association between sex and the probability of drop-out (7, 9). Participants with lower educational status presented early drop-out, but this association was only weak in our study. Similarly, Spiers et al. and Roick et al. found that low education was associated with loss to follow-up (9, 10). Another finding in this study was that being divorced or widowed was related to drop out. Similar findings were observed among cancer patients in a cluster-randomized controlled trial (9), other studies could not confirm this association (6, 7). Moreover, lack of social support was associated with early drop-out among cancer patient. These findings are in line with ours, where family status is playing a role as social support and it has a positive effect on patients’ health, quality of life, and coping behavior (25). Therefore, lack of social support from family or friends might be one explanation for the lack of motivation to continue a study.
Surprisingly, there was no marked differential drop-out between intervention and control groups, despite our expectation of lower drop-out rates in the control group. In contrast we found slightly higher rates of drop-out and death in the intervention group compared to the control group. Yielding unbiased results depend on the appropriate handling of missing data within the analytical approach (26). Our findings, that baseline characteristics and QoL were associated to drop-out, could be useful to determine the potential missing data mechanism, which is a prerequisite to considering the choice of handling missing PROs data e.g. imputation methods. In addition, poor baseline QoL scores should prompt researchers to assess more auxiliary data such as Eastern Cooperative Oncology Group (ECOG) performance status and reasons for incomplete PROMs questionnaires are collected to assist in determining the type of missingness and using those auxiliary data as covariate in the model or using in multiple imputation methods (27). In presence of unavoidable missing PROs from death, alternative models such as pattern mixture models or joint models might be used. Independent of the particular statistical method used to handle missing PRO data, sensitivity analyses should be conducted and reported (27).
Limitations of this study are: no information on the severity of a patient’s disease, as e.g. the stage of cancer, which could affect the time to death, and multivariable analyses were not possible and the statistical power was restricted, due to multi-collinearity within the PROs and a low number of observations in some variables e.g. education, family status, and HLS-EU-Q6. As there was no correction for multiple testing and without a multivariable analysis, p-values should be interpreted with caution and even small p-values cannot be interpreted as demonstrations of substantial effects.