Le-Rademacher, Jennifer G

Background Lymphedema is an adverse effect of breast cancer treatment that causes swelling and pain in the arm and hand. We tested 2 lymphedema prevention interventions and their impact on health‐related quality of life (HRQOL) in a group‐randomized trial at 38 cooperative group sites within the United States. Methods Patients were recruited before breast surgery. Sites were randomly assigned to education‐only (EO) lymphedema prevention or education plus exercise and physical therapy (LEAP). Lymphedema was defined as a ≥10% difference in arm volume at any time from baseline to 18 months postsurgery. HRQOL was assessed using the Functional Assessment of Cancer Therapy–Breast plus 4 lymphedema items (FACT‐B+4). Longitudinal mixed model regression analysis, adjusting for key demographic and clinical variables, examined participants' HRQOL by intervention group and lymphedema status. Results A total of 547 patients (56% LEAP) were enrolled and completed HRQOL assessments. The results revealed no differences between the interventions in preventing lymphedema ( P = .37) or HRQOL (FACT‐B+4 total score; P = .8777). At 18 months, the presence of lymphedema was associated with HRQOL at borderline significance ( P = .0825). However, African American patients reported greater lymphedema symptoms ( P = .0002) and better emotional functioning ( P = .0335) than patients of other races or ethnicities. Lower HRQOL during the intervention was associated with younger age ( P ≤ .0001), Eastern Cooperative Oncology Group performance status >0 ( P = .0002), ≥1 positive lymph nodes ( P = .0009), having no education beyond high school ( P < .0001), having undergone chemotherapy ( P = .0242), and having had only axillary node dissection or sentinel node biopsy versus both ( P = .0007). Conclusion The tested interventions did not differ in preventing lymphedema or in HRQOL outcomes. African American women reported greater HRQOL impacts due to lymphedema symptoms than women of other races or ethnicities.

Background Lymphedema affects many women who are treated for breast cancer. We examined the effectiveness of an education‐only (EO) versus education plus sleeve compression/exercise intervention (lymphedema education and prevention [LEAP]) on lymphedema incidence and range of motion (ROM) in a group‐randomized trial across 38 cooperative group sites. Methods The treating institution was randomly assigned to either EO or LEAP by a study statistician. All patients at a treating institution participated in the same intervention (EO or LEAP) to minimize contamination bias. Participants completed surveys, arm volume measurements, and self‐reported ROM assessments before surgery and at 12 and 18 months after surgery. Lymphedema was defined as a ≥10% difference in limb volume at any time post‐surgery up to 18 months after surgery or diagnosis by a health provider. Cochran‐Mantel‐Haenszel tests were used to compare lymphedema‐free rates between groups, stratified by lymph node surgery type. Self‐reported ROM differences were compared between groups. Results A total of 554 participants (56% LEAP) were included in the analyses. At 18 months, lymphedema‐free rates were 58% (EO) versus 55% (LEAP) ( P = .37). ROM for both arms was greater in LEAP versus EO at 12 months; by 18 months, most women reported full ROM, regardless of group. In LEAP, only one‐third wore a sleeve ≥75% of the time; 50% performed lymphedema exercises at least weekly. Conclusion Lymphedema incidence did not differ by intervention group at 18 months. Poor adherence in the LEAP group may have contributed. However, physical therapy may speed recovery of ROM. Further research is needed to effectively reduce the incidence and severity of lymphedema in patients who have breast cancer.

Background/aims: The difference in mean survival time, which quantifies the treatment effect in terms most meaningful to patients and retains its interpretability regardless of the shape of the survival distribution or the proportionality of the treatment effect, is an alternative endpoint that could be used more often as the primary endpoint to design clinical trials. The underuse of this endpoint is due to investigators’ lack of familiarity with the test comparing the mean survival times and the lack of tools to facilitate trial design with this endpoint. The aim of this article is to provide investigators with insights and software to design trials with restricted mean survival time as the primary endpoint. Methods: A closed-form formula for the asymptotic power of the test of restricted mean survival time difference is presented. The effects of design parameters on power were evaluated for the mean survival time test and log-rank test. An R package which calculates the power or the sample size for user-specified parameter values and provides power plots for each design parameter is provided. The R package also calculates the probability that the restricted mean survival time is estimable for user-defined trial designs. Results: Under proportional hazards and late differences in survival, the power of the mean survival time test can approach that of the log-rank test if the restriction time is late. Under early differences, the power of the restricted mean survival time test is higher than that of the log-rank test. Duration of accrual and follow-up have little influence on the power of the restricted mean survival time test. The choice of restriction time, on the other hand, has a large impact on power. Because the power depends on the interplay among the design factors, plotting the relationship between each design parameter and power allows the users to select the designs most appropriate for their trial. When modification is necessary to ensure the difference in restricted mean survival time is estimable, the three available modifications all perform adequately in the scenarios studied. Conclusion: The restricted mean survival time is a survival endpoint that is meaningful to investigators and to patients and at the same time requires less restrictive assumptions. The biggest challenge with this endpoint is selection of the restriction time. We recommend selecting a restriction time that is clinically relevant to the disease and the clinical setting of the trial of interest. The practical considerations and the R package provided in this work are readily available tools that researchers can use to design trials with restricted mean survival time as the primary endpoint.

AbstractLongitudinal data serve an important role in understanding the cancer anorexia weight loss syndrome and in testing interventions to palliative and treat patients who develop this syndrome. The element of time and the interrelatedness of data points define longitudinal data and add to the richness of this type of data. However, longitudinal data can also give rise to non‐random, missing data that can lead to flawed conclusions. This paper discusses these issues and suggests practical considerations for design and analysis of longitudinal cancer anorexia weight loss studies.

Abstract Background Less than 3% of older patients with cancer are enrolled in clinical trials. To reverse this underrepresentation, we compared older patients enrolled with older-patient-specific trials, defined as those designed for older patients with cancer, with those enrolled in age-unspecified trials. Materials and Methods We focused on individual patient data from those ≥65 years (younger patients excluded) and included all Alliance phase III adjuvant breast cancer trials from 1985–2012. Results Among 2,277 patients, 1,014 had been enrolled to older-patient-specific and 1,263 to age-unspecified trials. The median age (range) in the older-patient-specific trials was 72 (65–89) years compared with 68 (65–84) years in the cohort of older patients in age-unspecified trials; p &lt; .0001. A greater percentage of patients 75 years or older had enrolled in older-patient-specific trials compared with the cohort of age-unspecified trials: 26% versus 6% (p &lt; .0001). Median overall survival (OS) was 12.8 years (95% confidence interval [CI], 11.9–13.7) and 13.5 years (95% CI, 12.9–14.1) for older-patient-specific and age-unspecified trials, respectively. OS was comparable (hazard ratio [HR], 1.08; 95% CI, 0.92–1.28; p = .34; referent: age-unspecified trials), after adjusting for age, estrogen receptor status, tumor size, and lymph node status. Similar findings were reached for recurrence-free survival. A lower rate of grade 3–5 adverse events (hematologic and nonhematologic) was reported in older-patient-specific trials (43% vs. 58%; p &lt; .0001). Sensitivity analysis with chemotherapy only trials and subset analysis, adjusted for performance score, yielded similar OS results. Conclusion Older-patient-specific trials appear to address this underrepresentation of older patients with ostensibly comparable outcomes. Clinical trial identification numbers. NCT00003088 (CALGB 9741); NCT00024102 (CALGB 49907); NCT00068601 (CALGB 40401); NCT00005970 (NCCTG N9831)

The commentary remarks on a recently published article on a study that examined predictors of poor outcomes in cancer patients using the SOFA score, answering to the question of whether the results of the study can completely inform decisions about terminal care management.

Background/aims The goal of this article is to illustrate the utility of multi-state models in cancer clinical trials. Our specific aims are to describe multi-state models and how they differ from standard survival methods, to illustrate how multi-state models can facilitate deeper understanding of the treatment effect on multiple paths along the disease process that patients could experience in cancer clinical trials, to explain the differences between multi-state models and time-dependent Cox models, and to briefly describe available software to conduct such analyses. Methods Data from 717 newly diagnosed acute myeloid leukemia patients who enrolled in the CALGB 10603 trial were used as an illustrative example. The current probability-in-state was estimated using the Aalen–Johansen estimator. The restricted mean time in state was calculated as the area under the probability-in-state curves. Cox-type regression was used to evaluate the effect of midostaurin on the various clinical paths. Simulation was conducted using a newly constructed shiny application. All analyses were performed using the R software. Results Multi-state model analyses of CALGB 10603 suggested that the overall improvement in survival with midostaurin seen in the primary analysis possibly resulted from a higher complete remission rate in combination with a lower risk of relapse and of death after complete remission in patients treated with midostaurin. Simulation results, in a three-state illness-death without recovery model, demonstrate that multi-state models and time-dependent Cox models evaluate treatment effects from different frameworks. Conclusion Multi-state models allow detailed evaluation of treatment effects in complex clinical trial settings where patients can experience multiple paths between study enrollment and the final outcome. Multi-state models can be used as a complementary tool to standard survival analyses to provide deeper insights to the effects of treatment in trial settings with complex disease process.