A New Approach to Modeling the Cure Rate in the Presence of Interval Censored Data.

We consider interval censored data with a cured subgroup that arises from longitudinal followup studies with a heterogeneous population where a certain proportion of subjects is not susceptible to the event of interest. We propose a two component mixture cure model, where the first component describing the probability of cure is modeled by a support vector machine-based approach and the second component describing the survival distribution of the uncured group is modeled by a proportional hazard structure. Our proposed model provides flexibility in capturing complex effects of covariates on the probability of cure unlike the traditional models that rely on modeling the cure probability using a generalized linear model with a known link function. For the estimation of model parameters, we develop an expectation maximization-based estimation algorithm. We conduct simulation studies and show that our proposed model performs better in capturing complex effects of covariates on the cure probability when compared to the traditional logit link-based two component mixture cure model. This results in more accurate (smaller bias) and precise (smaller mean square error) estimates of the cure probabilities, which in-turn improves the predictive accuracy of the latent cured status. We further show that our model's ability to capture complex covariate effects also improves the estimation results corresponding to the survival distribution of the uncured. Finally, we apply the proposed model and estimation procedure to an interval censored data on smoking cessation.

Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Postsurgical tissue microarray sections from the Canary Foundation (n = 1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR, 2.72; CI, 1.46-5.06; P = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients.

Measures of explained variation under the mixture cure model for survival data.

Explained variation is well understood under linear regression models and has been extended to models for survival data. In this article, we consider the mixture cure models. We propose two approaches to define explained variation under the mixture cure models, one based on the Kullback-Leibler information gain and the other based on residual sum of squares. We show that the proposed measures have desired properties as measures of explained variation, similar to those under other regression models. A simulation study is conducted to demonstrate the properties of the proposed measures. They are also applied to real data analyses to illustrate the use of explained variation.

Mean residual life cure models for right-censored data with and without length-biased sampling.

We propose a semiparametric mean residual life mixture cure model for right-censored survival data with a cured fraction. The model employs the proportional mean residual life model to describe the effects of covariates on the mean residual time of uncured subjects and the logistic regression model to describe the effects of covariates on the cure rate. We develop estimating equations to estimate the proposed cure model for the right-censored data with and without length-biased sampling, the latter is often found in prevalent cohort studies. In particular, we propose two estimating equations to estimate the effects of covariates in the cure rate and a method to combine them to improve the estimation efficiency. The consistency and asymptotic normality of the proposed estimates are established. The finite sample performance of the estimates is confirmed with simulations. The proposed estimation methods are applied to a clinical trial study on melanoma and a prevalent cohort study on early-onset type 2 diabetes mellitus.

Improving marginal hazard ratio estimation using quadratic inference functions.

Clustered and multivariate failure time data are commonly encountered in biomedical studies and a marginal regression approach is often employed to identify the potential risk factors of a failure. We consider a semiparametric marginal Cox proportional hazards model for right-censored survival data with potential correlation. We propose to use a quadratic inference function method based on the generalized method of moments to obtain the optimal hazard ratio estimators. The inverse of the working correlation matrix is represented by the linear combination of basis matrices in the context of the estimating equation. We investigate the asymptotic properties of the regression estimators from the proposed method. The optimality of the hazard ratio estimators is discussed. Our simulation study shows that the estimator from the quadratic inference approach is more efficient than those from existing estimating equation methods whether the working correlation structure is correctly specified or not. Finally, we apply the model and the proposed estimation method to analyze a study of tooth loss and have uncovered new insights that were previously inaccessible using existing methods.

An additive hazards frailty model with semi-varying coefficients.

Proportional hazards frailty models have been extensively investigated and used to analyze clustered and recurrent failure times data. However, the proportional hazards assumption in the models may not always hold in practice. In this paper, we propose an additive hazards frailty model with semi-varying coefficients, which allows some covariate effects to be time-invariant while other covariate effects to be time-varying. The time-varying and time-invariant regression coefficients are estimated by a set of estimating equations, whereas the frailty parameter is estimated by the moment method. The large sample properties of the proposed estimators are established. The finite sample performance of the estimators is examined by simulation studies. The proposed model and estimation are illustrated with an analysis of data from a rehospitalization study of colorectal cancer patients.

Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction.

Left-truncated data are often encountered in epidemiological cohort studies, where individuals are recruited according to a certain cross-sectional sampling criterion. Length-biased data, a special case of left-truncated data, assume that the incidence of the initial event follows a homogeneous Poisson process. In this article, we consider an analysis of length-biased and interval-censored data with a nonsusceptible fraction. We first point out the importance of a well-defined target population, which depends on the prior knowledge for the support of the failure times of susceptible individuals. Given the target population, we proceed with a length-biased sampling and draw valid inferences from a length-biased sample. When there is no covariate, we show that it suffices to consider a discrete version of the survival function for the susceptible individuals with jump points at the left endpoints of the censoring intervals when maximizing the full likelihood function, and propose an EM algorithm to obtain the nonparametric maximum likelihood estimates of nonsusceptible rate and the survival function of the susceptible individuals. We also develop a novel graphical method for assessing the stationarity assumption. When covariates are present, we consider the Cox proportional hazards model for the survival time of the susceptible individuals and the logistic regression model for the probability of being susceptible. We construct the full likelihood function and obtain the nonparametric maximum likelihood estimates of the regression parameters by employing the EM algorithm. The large sample properties of the estimates are established. The performance of the method is assessed by simulations. The proposed model and method are applied to data from an early-onset diabetes mellitus study.

Joint analysis of longitudinal measurements and survival times with a cure fraction based on partly linear mixed and semiparametric cure models.

In a joint analysis of longitudinal quality of life (QoL) scores and relapse-free survival (RFS) times from a clinical trial on early breast cancer conducted by the Canadian Cancer Trials Group, we observed a complicated trajectory of QoL scores and existence of long-term survivors. Motivated by this observation, we proposed in this paper a flexible joint model for the longitudinal measurements and survival times. A partly linear mixed effect model is used to capture the complicated but smooth trajectory of longitudinal measurements and approximated by B-splines and a semiparametric mixture cure model with the B-spline baseline hazard to model survival times with a cure fraction. These two models are linked by shared random effects to explore the dependence between longitudinal measurements and survival times. A semiparametric inference procedure with an EM algorithm is proposed to estimate the parameters in the joint model. The performance of proposed procedures are evaluated by simulation studies and through the application to the analysis of data from the clinical trial which motivated this research.

Routine follow-up care after curative treatment of head and neck cancer: A survey of patients' needs and preferences for healthcare services.

OBJECTIVE: The experience of a cancer diagnosis and receiving treatment can have profound impacts on health and subsequently patients may require significant support. Often, these needs are not identified or addressed. Given that less is known about the follow-up requirements for head and neck cancer patients, this study aimed to describe their follow-up needs and preferences. METHODS: In Ontario, Canada from 2012-2014, 175 patients completed a questionnaire at an appointment one year after treatment. To identify associations between characteristics and follow-up needs, bivariate analyses and ordinal logistic regression models were employed. RESULTS: A diversity of follow-up requirements was found. The most commonly reported follow-up needs were having imaging tests performed (66%), receiving information on treatment side effects (84%) and prognosis details (95%). Many patients experienced an improvement in their health (79%) but notably, not all. Characteristics such as psychosocial and well-being measures (functional status, anxiety, fear of recurrence, quality of life), attitudes towards follow-up (reassurance, communication), demographics (age, sex, marital status), and stage of disease predicted needs and preferences for follow-up care (p < 0.05). CONCLUSION: While awaiting top-level evidence, this work demonstrates the variation in needs and supports the identification of patients with higher follow-up requirements by screening for well-being and enquiring about expectations in follow-up care.

Imaging use for low back pain by Ontario primary care clinicians: protocol for a mixed methods study - the Back ON study.

BACKGROUND: At any one time, one in every five Canadians has low back pain (LBP), and LBP is one of the most common health problems in primary care. Guidelines recommend that imaging not be routinely performed in patients presenting with LBP without signs or symptoms indicating a potential pathological cause. Yet imaging rates remain high for many patients who present without such indications. Inappropriate imaging can lead to inappropriate treatments, results in worse health outcomes and causes harm from unnecessary radiation. There is a need to understand the extent of, and factors contributing to, inappropriate imaging for LBP, and to develop effective strategies that target modifiable barriers and facilitators. The primary study objectives are to determine: 1) The rate of, and factors associated with, inappropriate lumbar spine imaging (x-ray, CT scan and MRI) for people with non-specific LBP presenting to primary care clinicians in Ontario; 2) The barriers and facilitators to reduce inappropriate imaging for LBP in primary care settings. METHODS: The project will comprise an inception cohort study and a concurrent qualitative study. For the cohort study, we will recruit 175 primary care clinicians (50 each from physiotherapy and chiropractic; 75 from family medicine), and 3750 patients with a new episode of LBP who present to these clinicians. Clinicians will collect data in the clinic, and each participant will be tracked for 12 months using Ontario health administrative and self-reported data to measure diagnostic imaging use and other health outcomes. We will assess characteristics of the clinicians, patients and encounters to identify variables associated with inappropriate imaging. In the qualitative study we will conduct in-depth interviews with primary care clinicians and patients. DISCUSSION: This will be the first Canadian study to accurately document the extent of the overuse of imaging for LBP, and the first worldwide to include data from the main healthcare professions offering primary care for people with LBP. This study will provide robust information about rates of inappropriate imaging for LBP, along with factors associated with, and an understanding of, potential reasons for inappropriate imaging.

Modeling clustered long-term survivors using marginal mixture cure model.

There is a great deal of recent interests in modeling right-censored clustered survival time data with a possible fraction of cured subjects who are nonsusceptible to the event of interest using marginal mixture cure models. In this paper, we consider a semiparametric marginal mixture cure model for such data and propose to extend an existing generalized estimating equation approach by a new unbiased estimating equation for the regression parameters in the latency part of the model. The large sample properties of the regression effect estimators in both incidence and the latency parts are established. The finite sample properties of the estimators are studied in simulation studies. The proposed method is illustrated with a bone marrow transplantation data and a tonsil cancer data.

Residual-based model diagnosis methods for mixture cure models.

Model diagnosis, an important issue in statistical modeling, has not yet been addressed adequately for cure models. We focus on mixture cure models in this work and propose some residual-based methods to examine the fit of the mixture cure model, particularly the fit of the latency part of the mixture cure model. The new methods extend the classical residual-based methods to the mixture cure model. Numerical work shows that the proposed methods are capable of detecting lack-of-fit of a mixture cure model, particularly in the latency part, such as outliers, improper covariate functional form, or nonproportionality in hazards if the proportional hazards assumption is employed in the latency part. The methods are illustrated with two real data sets that were previously analyzed with mixture cure models.

Jointly modeling longitudinal proportional data and survival times with an application to the quality of life data in a breast cancer trial.

Motivated by the joint analysis of longitudinal quality of life data and recurrence free survival times from a cancer clinical trial, we present in this paper two approaches to jointly model the longitudinal proportional measurements, which are confined in a finite interval, and survival data. Both approaches assume a proportional hazards model for the survival times. For the longitudinal component, the first approach applies the classical linear mixed model to logit transformed responses, while the second approach directly models the responses using a simplex distribution. A semiparametric method based on a penalized joint likelihood generated by the Laplace approximation is derived to fit the joint model defined by the second approach. The proposed procedures are evaluated in a simulation study and applied to the analysis of breast cancer data motivated this research.

Patterns of Referral for Adjuvant Chemotherapy for Stage II and III Colon Cancer: A Population-Based Study.

PURPOSE: Reasons for variable utilization of adjuvant chemotherapy (ACT) for colon cancer have not been well described. We report medical oncology (MO) referral patterns and subsequent use of ACT. METHODS: Treatment records were linked to the population-based Ontario Cancer Registry to describe MO referral and ACT use among 5289 patients with stage II-III colon cancer treated in 2002-2008. Modified Poisson regression was used to analyze factors associated with MO referral and ACT use. Multilevel modeling was used to explore the proportion of variation in practice attributable to providers. RESULTS: There was wide geographic variation in MO referral rates for stage II (range 37-80 %, p < 0.001) and stage III disease (range 77-98 %, p < 0.001). Use of ACT among referred patients varied across regions for stage II (range 12-49 %, p < 0.001) but not stage III (range 67-79 %, p = 0.353). For both stages, younger patients (p < 0.001) with less comorbidity (p < 0.010) were more likely to be referred to MO and treated with ACT. Applying the fitted regression model to nonreferred stage III patients suggests that 38 % had >50 % probability of having ACT if they had seen a MO. Among stage III patients, 15 % percent of the variance in MO referral rate and 6 % of the variance in ACT utilization rate is attributable to the surgeon and MO respectively. CONCLUSIONS: A substantial proportion of non-referred patients with stage III colon cancer may have been offered ACT if they had seen MO. A small proportion of variance in referral rate and ACT treatment is attributable to providers.

Utilisation of preoperative imaging for muscle-invasive bladder cancer: a population-based study.

OBJECTIVE: To test the hypotheses that: (i) use of preoperative imaging for muscle-invasive bladder cancer (MIBC) conforms to practice guidelines; (ii) preoperative imaging, through more accurate staging is associated with improved outcomes. PATIENT AND METHODS: In this population-based cohort study, records of treatment were linked to the Ontario Cancer Registry to identify all patients with MIBC treated with cystectomy from 1994 to 2008. Utilisation of chest, abdomen-pelvis and bone imaging were evaluated. Trends were evaluated over time. Logistic regression was used to analyse factors associated with utilisation. Cox model analyses were used to explore associations between imaging and survival. RESULTS: In all, 2 802 patients with MIBC underwent cystectomy during 1994-2008. Over the three 5-year study periods there was an increase in the proportion of patients having preoperative: chest X-ray (55%, 64%, 63%, P < 0.001), computed tomography (CT) of the chest (10%, 10%, 21%, P < 0.001), bone scan (30%, 34%, 36%; P = 0.04) and CT/magnetic resonance imaging/ultrasonography abdomen/pelvis (80%, 87%, 90%, P ≤ 0.001). Use of chest imaging was associated with age (odds ratio [OR] 1.24-1.59 compared with the youngest age group), N-stage (OR 0.79 for the NX group compared with the N+ group), surgeon volume (OR 0.47-0.53 compared with the highest volume quartile) and geographic region (OR 0.47-2.19 compared with the largest region). Use of bone scan was associated with N-stage (OR 0.57 for the NX group compared with the N+ group) and geographic region (OR 0.71-1.34 compared with the largest region). In adjusted analyses, we found that patients who did not have preoperative chest imaging had inferior overall survival (OS), hazard ratio (HR) 1.12 (95% confidence interval [CI] 1.01-1.25) but not cancer specific survival (CSS), HR 1.09 (95% CI 0.97-1.22); those who did not have preoperative bone scan had inferior OS (HR 1.11, 95% CI 1.01-1.22) and CSS (HR 1.09, 95% CI 1.01-1.25). Survival in the abdomen and pelvis imaging group was not evaluated due to lack of a suitable control group. CONCLUSION: In routine clinical practice there is considerable variation in use of preoperative chest, body, and bone imaging. Preoperative chest and bone imaging is associated with improved outcomes; this association probably reflects better patient selection for cystectomy.

Use of Palliative Chemotherapy for Advanced Bladder Cancer: Patterns of Care in Routine Clinical Practice.

BACKGROUND: Palliative chemotherapy for advanced bladder cancer is recommended in clinical practice guidelines. Patterns of care in routine clinical practice have not been well described. This article describes use rates of chemotherapy and referral rates to medical oncology in the last year of life among patients who have died of bladder cancer. METHODS: A population-based cohort of patients with bladder cancer was identified from the Ontario Cancer Registry; the study population included patients who died of bladder cancer between 1995 and 2009. Electronic records of treatment and physician billing records were used to identify treatment patterns and referral to medical oncology. Log-binomial and modified Poisson regression were used to examine factors associated with chemotherapy use and medical oncology consultation. RESULTS: A total of 8,005 patients died of bladder cancer, 25% (n=1,964) of whom received chemotherapy in the last year of life. Use was independently associated with patient age, comorbidities, socioeconomic status, sex, time period, and treatment region. A total of 68% (n=5,426) of patients were seen by a medical oncologist. Referral to medical oncology was associated with age, comorbidities, year of death. Geographic variation was seen with chemotherapy use-from 18% to 30%-that persisted on adjusted analysis. CONCLUSIONS: The efficacy of palliative chemotherapy demonstrated in clinical trials and recommended in guidelines has not translated into widespread use in practice. Understanding the extent to which patient preferences and health system factors influence use is needed. Access to acceptable palliative systemic treatments remains an unmet need for most patients dying of bladder cancer.

Use and Effectiveness of Adjuvant Chemotherapy for Stage III Colon Cancer: A Population-Based Study.

BACKGROUND: International guidelines recommend adjuvant chemotherapy (ACT) for patients with stage III colon cancer. Whether efficacy observed in clinical trials translates to effectiveness in routine practice is less well understood. Here we describe use and outcomes of ACT in routine practice. METHODS: All cases of colon cancer treated with surgery in Ontario 2002-2008 were identified using the population-based Ontario Cancer Registry. Linked electronic records of treatment identified surgery and ACT use. Pathology reports were obtained for a random 25% sample of all cases; patients with stage III disease were included in the study population. Modified Poisson regression was used to evaluate factors associated with ACT. Cox proportional hazards model and propensity score analysis were used to explore the association between ACT and cancer-specific survival (CSS) and overall survival (OS). RESULTS: The study population included 2,801 patients with stage III colon cancer; 66% (n=1,861) received ACT. ACT use rates varied substantially across age groups; 90% among patients aged 20 to 49 years versus 68% among those aged 70 to 79 years (P<.001). ACT use was inversely associated with comorbidity (P<.001) and socioeconomic status (P=.049). In adjusted analyses advanced age is associated with inferior CSS and OS. Use of ACT was associated with decreased risk of death from cancer (hazard ratio [HR], 0.63; 95% CI, 0.54-0.73) and decreased risk of death from any cause (HR, 0.63; 95% CI, 0.55-0.71). This result was consistent in the propensity score analysis. CONCLUSIONS: One-third of patients with stage III colon cancer in the general population do not receive ACT. Use of ACT in routine practice is associated with a substantial improvement in CSS and OS.

Adherence to complementary feeding recommendations for infants and implications for public health.

OBJECTIVE: The current study investigates (i) the extent to which breast-feeding and non-breast-feeding mothers follow the Canadian Nutrition for Healthy Term Infants (NHTI) recommendations; (ii) the first complementary foods given and the differences by breast-feeding status; (iii) whether any breast-feeding is associated with earlier introduction to complementary foods relative to non-breast-feeding, after controlling for potentially confounding factors; and (iv) the need for improvements in timing and resources of interventions by examining breast-feeding rates over time and information sources used by mothers. DESIGN: Longitudinal data from the Kingston, Frontenac, and Lennox & Addington (KFL&A) Infant Feeding Survey were used. Mothers completed a survey at the end of their hospital stay and were interviewed by telephone at 2, 4, 6 and 12 months thereafter. SETTING: The study took place in the KFL&A region of Ontario, Canada. SUBJECTS: The sample consisted of 325 mothers who gave birth to a live infant of at least 36 weeks' gestation and a birth weight of at least 1500 g at Kingston General Hospital between January and July of 2008. RESULTS: Four in five mothers introduced complementary foods prior to 6 months. Mothers not breast-feeding at 6 months introduced water, juice, infant cereals, fruit and vegetables, and foods not recommended by Canada's Food Guide sooner than breast-feeding mothers. Breast-feeding mothers were more likely to introduce milks appropriately, but had low adherence to giving their infants vitamin D supplements. CONCLUSIONS: To support adherence to NHTI recommendations, interventions should be conducted during early infancy and deliver consistent, evidence-based recommendations.

Pathological factors associated with survival benefit from adjuvant chemotherapy (ACT): a population-based study of bladder cancer.

OBJECTIVE: To evaluate whether pathological factors are associated with differential effect of adjuvant chemotherapy (ACT). PATIENTS AND METHODS: In this population-based retrospective cohort study, we linked electronic records of treatment and surgical pathology to the Ontario Cancer Registry. The study population included all patients with muscle-invasive bladder cancer undergoing cystectomy in Ontario 1994-2008. Factors associated with overall (OS) and cancer-specific survival (CSS) were evaluated using Cox proportional hazards. We tested for interaction between the following variables and ACT effect-size: N-stage, margin status, T-stage, and lymphovascular invasion (LVI). RESULTS: The study population included 2802 patients; 19% were treated with ACT. Interaction terms with ACT for OS/CSS are: N-stage (both P < 0.001); margin status (P = 0.054/P = 0.048); T-stage (P = 0.509/P = 0.286); and LVI (P = 0.361/P = 0.405). Magnitude of effect for ACT was greater for patients with node-positive disease [OS: hazard ratio (HR) 0.56, 95% confidence interval (CI) 0.47-0.67; CSS: HR 0.60, 95% CI 0.49-0.72] than for patients with node-negative disease (OS: HR 0.80, 95% CI 0.61-1.03; CSS: HR 0.79, 95% CI 0.59-1.07). ACT was also associated with greater effect among patients with involved margins (OS: HR 0.45, 95% CI 0.33-0.62; CSS: HR 0.40, 95% CI 0.28-0.57) compared with patients with negative margins (OS: HR 0.75, 95% CI 0.65-0.87; CSS: HR 0.79, 95% CI 0.67-0.93). CONCLUSIONS: In this population-based cohort study we observe evidence of interaction between ACT effect and nodal stage and surgical margin status. Our results suggest that patients at highest risk of disease recurrence may derive greatest benefit from ACT.

Integrating health-related quality of life findings from randomized clinical trials into practice: an international study of oncologists' perspectives.

PURPOSE: Although health-related quality of life (HRQL) is increasingly assessed in randomized controlled trials (RCTs), HRQL findings are not always incorporated into clinical decision making. We examined the current perspectives of oncologists on the use of HRQL findings from RCTs, and variation of these perspectives between countries and specialties. METHODS: A cross-sectional e-survey of oncologist members of the NCIC Clinical Trials Group, the United Kingdom National Cancer Research Institute Clinical Studies Groups, and the Australia/New Zealand cancer clinical trials network was conducted. Respondents reported their perceptions of the usefulness of RCT HRQL outcomes for informing practice, their use of HRQL findings in practice, barriers/facilitators to this use, and preferences for HRQL data presentation in RCT publications. Chi-square tests compared responses between countries and specialties. RESULTS: A total of 396 oncologists participated (estimated response rate: 30%). The most frequently reported specialties were medical (46%) and radiation (37%) oncology. HRQL findings from RCTs were reported as useful (73%), but were infrequently used to guide clinical decisions with patients. Perceived barriers were lack of time (67%) and understanding (57%), and concerns about generalizability of results (68%). Identified facilitators included joint publication of HRQL/clinical outcomes (96%) and summary of clinical implications of HRQL findings in RCT publications (76%). Use of HRQL findings, perceived barriers/facilitators, and presentation preferences did not differ by country or specialty. CONCLUSIONS: Oncologists support HRQL findings from RCTs, but perceive important barriers to their use in clinical decision making, regardless of country or specialty. Combined, clear reporting of HRQL/clinical data may facilitate their clinical application.