Mixture survival trees for cancer risk classification.

In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer.

Inferring latent heterogeneity using many feature variables supervised by survival outcome.

In cancer studies, it is important to understand disease heterogeneity among patients so that precision medicine can particularly target high-risk patients at the right time. Many feature variables such as demographic variables and biomarkers, combined with a patient's survival outcome, can be used to infer such latent heterogeneity. In this work, we propose a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution. The Bayesian information criterion is used for selecting the number of latent groups. Furthermore, we incorporate variable selection with the adaptive lasso into inference so that only a few feature variables will be selected to characterize the latent heterogeneity. We show that our adaptive lasso estimator has oracle properties when the number of parameters diverges with the sample size. The finite sample performance is evaluated by the simulation study, and the proposed method is illustrated by two datasets.

Empirical use of causal inference methods to evaluate survival differences in a real-world registry vs those found in randomized clinical trials.

With heighted interest in causal inference based on real-world evidence, this empirical study sought to understand differences between the results of observational analyses and long-term randomized clinical trials. We hypothesized that patients deemed "eligible" for clinical trials would follow a different survival trajectory from those deemed "ineligible" and that this factor could partially explain results. In a large observational registry dataset, we estimated separate survival trajectories for hypothetically trial-eligible vs ineligible patients under both coronary artery bypass surgery (CABG) and percutaneous coronary intervention (PCI). We also explored whether results would depend on the causal inference method (inverse probability of treatment weighting vs optimal full propensity matching) or the approach to combine propensity scores from multiple imputations (the "across" vs "within" approaches). We found that, in this registry population of PCI/CABG multivessel patients, 32.5% would have been eligible for contemporaneous RCTs, suggesting that RCTs enroll selected populations. Additionally, we found treatment selection bias with different distributions of propensity scores between PCI and CABG patients. The different methodological approaches did not result in different conclusions. Overall, trial-eligible patients appeared to demonstrate at least marginally better survival than ineligible patients. Treatment comparisons by eligibility depended on disease severity. Among trial-eligible three-vessel diseased and trial-ineligible two-vessel diseased patients, CABG appeared to have at least a slight advantage with no treatment difference otherwise. In conclusion, our analyses suggest that RCTs enroll highly selected populations, and our findings are generally consistent with RCTs but less pronounced than major registry findings.

Predictive accuracy of markers or risk scores for interval censored survival data.

Methods for the evaluation of the predictive accuracy of biomarkers with respect to survival outcomes subject to right censoring have been discussed extensively in the literature. In cancer and other diseases, survival outcomes are commonly subject to interval censoring by design or due to the follow up schema. In this article, we present an estimator for the area under the time-dependent receiver operating characteristic (ROC) curve for interval censored data based on a nonparametric sieve maximum likelihood approach. We establish the asymptotic properties of the proposed estimator and illustrate its finite-sample properties using a simulation study. The application of our method is illustrated using data from a cancer clinical study. An open-source R package to implement the proposed method is available on Comprehensive R Archive Network.

Initial Clinical Experience with Stationary Digital Breast Tomosynthesis.

RATIONALE AND OBJECTIVES: A linear array of carbon nanotube-enabled x-ray sources allows for stationary digital breast tomosynthesis (sDBT), during which projection views are collected without the need to move the x-ray tube. This work presents our initial clinical experience with a first-generation sDBT device. MATERIALS AND METHODS: Following informed consent, women with a "suspicious abnormality" (Breast Imaging Reporting and Data System 4), discovered by digital mammography and awaiting biopsy, were also imaged by the first generation sDBT. Four radiologists participated in this paired-image study, completing questionnaires while interpreting the mammograms and sDBT image stacks. Areas under the receiver operating characteristic curve were used to measure reader performance (likelihood of correctly identifying malignancy based on pathology as ground truth), while a multivariate analysis assessed preference, as readers compared one modality to the next when interpreting diagnostically important image features. RESULTS: Findings from 43 women were available for analysis, in whom 12 cases of malignancy were identified by pathology. The mean areas under the receiver operating characteristic curve was significantly higher (p < 0.05) for sDBT than mammography for all breast density categories and breast thicknesses. Additionally, readers preferred sDBT over mammography when evaluating mass margins and shape, architectural distortion, and asymmetry, but preferred mammography when characterizing microcalcifications. CONCLUSION: Readers preferred sDBT over mammography when interpreting soft-tissue breast features and were diagnostically more accurate using images generated by sDBT in a Breast Imaging Reporting and Data System 4 population. However, the findings also demonstrated the need to improve microcalcification conspicuity, which is guiding both technological and image-processing design changes in future sDBT devices.