Socioeconomic Deprivation and Health Care Use in Patients Enrolled in SWOG Cancer Clinical Trials.

Importance: Reducing acute care use is an important strategy for improving value. Patients with cancer are at risk for unplanned emergency department (ED) visits and hospital stays (HS). Clinical trial patients have homogeneous treatment; despite this, structural barriers to care may independently impact acute care use. Objective: To examine whether ED visits and HS within 12 months of trial enrollment are more common among Medicare enrollees who live in areas of socioeconomic deprivation or have Medicaid insurance. Design, Setting, and Participants: This cohort study included patients with cancer who were 65 years or older and treated in SWOG Cancer Research Network trials from 1999 to 2018 using data linked to Medicare claims. Data were collected from 1999 to 2019 and analyzed from 2022 to 2024. Main Outcomes and Measures: Outcomes were ED visits, HS, and costs in the first year following enrollment. Neighborhood socioeconomic deprivation was measured using patients' zip code linked to the Area Deprivation Index (ADI), measured on a 0 to 100 scale for increasing deprivation and categorized into tertiles (T1 to T3). Type of insurance was classified as Medicare with or without commercial insurance vs dual Medicare and Medicaid. Demographic, clinical, and prognostic factors were captured from trial records. Multivariable regression was used, and the association of ADI and insurance with each outcome was considered separately. Results: In total, 3027 trial participants were analyzed. The median (range) age was 71 (65-98) years, 1280 (32.3%) were female, 221 (7.3%) were Black patients, 2717 (89.8%) were White patients, 90 (3.0%) had Medicare and Medicaid insurance, and 660 (22.3%) were in the areas of highest deprivation (ADI-T3). In all, 1094 patients (36.1%) had an ED visit and 983 patients (32.4%) had an HS. In multivariable generalized estimating equation, patients living in areas categorized as ADI-T3 were more likely to have an ED visit (OR, 1.34; 95% CI, 1.10-1.62; P = .004). A similar but nonsignificant pattern was observed for HS (OR, 1.36; 95% CI, 0.96-1.93; P = .08). Patients from areas with the highest deprivation had a 62% increase in risk of either an ED visit or HS (OR, 1.62; 95% CI, 1.25-2.09; P < .001). Patients with Medicare and Medicaid were 96% more likely to have an ED visit (OR, 1.96; 95% CI, 1.56-2.46; P < .001). Conclusions and Relevance: In this cohort of older patients enrolled in clinical trials, neighborhood deprivation and economic disadvantage were associated with an increase in ED visits and HS. Efforts are needed to ensure adequate resources to prevent unplanned use of acute care in socioeconomically vulnerable populations.

Biological correlates of biochemical recurrence free survival using multiple markers in a large tissue microarray cohort.

BACKGROUND: High-throughput analyses yielded a large number of predictive biomarkers in prostatic cancer (PCa) patients. Combinations of these biomarkers and with clinical features could improve on prediction. MATERIALS AND METHODS: Tissue microarrays (640 patients) with triplicate cores of non-neoplastic prostate, benign prostatic hyperplasia (BPH), and index tumor were immunostained with antibodies to numerous biomarkers, digitized, and quantified. We used tree-based classification algorithms to stratify patients into 3 risk strata on the basis of their clinical and pathologic data. Markers were tested for prognostic ability in each stratum (stratum 1 had <10% risk of recurrence; stratum 3 had >60% likelihood of recurrence over a period >12 years). Sub stratification of the clinico-pathologic strata was also pursued. RESULTS: We identified a number of significant predictors for PSA recurrence free survival, which were used to construct a predictive model that combines clinical and biomarker data. In the low-risk clinico-pathologic stratum, the markers were predominantly related to non-neoplastic tissues, in the moderate-risk stratum to stromal-epithelial interactions and angiogenesis, while those in the high-risk stratum were mostly oncogenes. Substratification of the intermediate risk group using stromal quantitation and proliferative index successfully, up or down, staged the risk strata for most patients. CONCLUSIONS: The fact that different biomarkers are most predictive of disease recurrence within different risk subgroups suggests an association between biological processes and prognostic ability. This is the first time that subgroup analysis of markers finds that prognostic ability is associated with biological processes and is proof of concept that distinct phenotypes are associated with risk of recurrence in different types of cancer.

Seamless phase I-II trial design for assessing toxicity and efficacy for targeted agents.

PURPOSE: The premise for phase I trials for cytostatic agents is different from that of cytotoxic agents. For cytostatic agents, toxicity and efficacy do not necessarily increase monotonically with increasing dose levels, but likely plateau after they reach maximal toxicity or efficacy. Here, we propose a phase I-II trial design to assess both toxicity and efficacy to find the best dose as well as a good dose. EXPERIMENTAL DESIGN: We propose a 2-step dose-finding trial for assessing both toxicity and efficacy for a targeted agent. The 1st step uses a traditional phase I trial design. This step only assesses toxicity and finds the maximal tolerated dose (MTD). For the 2nd step, we propose a modified phase II selection design for 2 or 3 dose levels at and below the MTD to determine efficacy and evaluate each dose level by both efficacy and toxicity. RESULTS AND CONCLUSION: Simulation studies are done on several combinations of toxicity and efficacy scenarios to assess the operating statistics of our proposed trial design. We then compare our results with a traditional phase I trial followed by a single-arm phase II trial using the same total sample size. The proposed design does better in most cases than a traditional design using the same overall sample size. This design allows assessing a few dose levels more closely for both efficacy and toxicity and provides greater certainty of having correctly determined the best dose level before launching into a large efficacy trial.

Randomized phase III clinical trial designs for targeted agents.

PURPOSE: Cancer therapies with mechanisms of action which are very different from the more conventional chemotherapies are now being developed. In this article, we investigate the performance of several phase III clinical trial designs, both for testing the overall efficacy of a targeted agent and for testing its efficacy in a subgroup of patients with a tumor marker present. We study different designs and different underlying scenarios assuming continuous markers, and assess the trade-off between the number of patients on the study and the effectiveness of treatment in the subgroup of marker-positive patients. EXPERIMENTAL DESIGN: We investigate binary outcomes and use simulation studies to determine sample size and power for the different designs and the various scenarios. We also simulate marker prevalence and marker misclassification and evaluate their effect on power and sample size. RESULTS: In general, a targeted design which randomizes patients with the appropriate marker status performs the best in all scenarios with an underlying true predictive marker. Randomizing all patients regardless of their marker values performs as well as or better in most cases than a clinical trial that randomizes the patient to a treatment strategy based on marker value versus standard of care. CONCLUSION: If there is the possibility that the new treatment helps marker-negative patients, or that the cutpoint determining marker status has not been well established and the marker prevalence is large enough, we recommend randomizing all patients regardless of marker values, but using a design such that both the overall and the targeted subgroup hypothesis can be tested.