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The pattern of the difference between two survival curves we often observe in randomized clinical trials for evaluating immunotherapy is not proportional hazards; the treatment effect typically appears several months after the initiation of the treatment (ie, delayed difference pattern). The commonly used logrank test and hazard ratio estimation approach will be suboptimal concerning testing and estimation for those trials. The long-term restricted mean survival time (LT-RMST) approach is a promising alternative for detecting the treatment effect that potentially appears later in the study. A challenge in employing the LT-RMST approach is that it must specify a lower end of the time window in addition to a truncation time point that the RMST requires. There are several investigations and suggestions regarding the choice of the truncation time point for the RMST. However, little has been investigated to address the choice of the lower end of the time window. In this paper, we propose a flexible LT-RMST-based test/estimation approach that does not require users to specify a lower end of the time window. Numerical studies demonstrated that the potential power loss by adopting this flexibility was minimal, compared to the standard LT-RMST approach using a prespecified lower end of the time window. The proposed method is flexible and can offer higher power than the RMST-based approach when the delayed treatment effect is expected. Also, it provides a robust estimate of the magnitude of the treatment effect and its confidence interval that corresponds to the test result., (© 2023 John Wiley & Sons Ltd.)

The hazard ratio (HR) has been the most popular measure to quantify the magnitude of treatment effect on time-to-event outcomes in clinical research. However, the traditional Cox's HR approach has several drawbacks. One major issue is that there is no clear interpretation when the proportional hazards (PH) assumption does not hold, because the estimated HR is affected by study-specific censoring time distribution in non-PH cases. Another major issue is that the lack of a group-specific absolute hazard value in each group obscures the clinical significance of the magnitude of the treatment effect. Given these, we propose average hazard with survival weight (AH-SW) as a summary metric of event time distribution and will use difference in AH-SW (DAH-SW) or ratio of AH-SW (RAH-SW) to quantify the treatment effect magnitude. The AH-SW is interpreted as a person-time incidence rate that does not depend on random censoring. It is defined as the ratio of cumulative incidence probability and restricted mean survival time (RMST), which can be estimated non-parametrically. Numerical studies demonstrate that DAH-SW and RAH-SW offer almost identical power to Cox's HR-based tests under PH scenarios and can be more powerful for delayed-difference patterns often seen in immunotherapy trials. Like median and RMST differences, the proposed approach is a good model-free alternative to the HR-based approach for evaluating the treatment effect magnitude. Such a model-free measure will increase the likelihood that results from clinical studies are correctly interpreted and generalized to future populations., (© 2023 John Wiley & Sons Ltd.)

Between-group comparison based on the restricted mean survival time (RMST) is getting attention as an alternative to the conventional logrank/hazard ratio approach for time-to-event outcomes in randomized controlled trials (RCTs). The validity of the commonly used nonparametric inference procedure for RMST has been well supported by large sample theories. However, we sometimes encounter cases with a small sample size in practice, where we cannot rely on the large sample properties. Generally, the permutation approach can be useful to handle these situations in RCTs. However, a numerical issue arises when implementing permutation tests for difference or ratio of RMST from two groups. In this article, we discuss the numerical issue and consider six permutation methods for comparing survival time distributions between two groups using RMST in RCTs setting. We conducted extensive numerical studies and assessed type I error rates of these methods. Our numerical studies demonstrated that the inflation of the type I error rate of the asymptotic methods is not negligible when sample size is small, and that all of the six permutation methods are workable solutions. Although some permutation methods became a little conservative, no remarkable inflation of the type I error rates were observed. We recommend using permutation tests instead of the asymptotic tests, especially when the sample size is less than 50 per arm., (© 2020 John Wiley & Sons, Ltd.)

Background: Fatigue is a common and distressing symptom for patients with gynecologic cancers. Few studies have empirically examined whether it spontaneously resolves. This study was aimed at identifying longitudinal patterns of fatigue and predictors of clinically significant fatigue 1 year after treatment completion., Methods: This was a prospective cohort study of women with newly diagnosed ovarian (n = 81) or endometrial cancer (n = 181) that did not progress or recur within 1 year of treatment completion. Symptoms of fatigue, depression, and anxiety were assessed after surgery and 6 and 12 months after treatment completion with the Fatigue Assessment Scale and the Hospital Anxiety and Depression Scale. Patients' fatigue scores over time were classified (scores of 22-50, clinically significant; scores of 10-21, not clinically significant). Logistic regression models were fit to examine associations between fatigue and patient characteristics., Results: Among 262 participants, 48% reported clinically significant fatigue after surgery. One year later, 39% reported fatigue. There were 6 patterns over time: always low (37%), always high (25%), high then resolves (18%), new onset (10%), fluctuating (6%), and incidental (5%). Patients with fatigue after surgery were more likely to report fatigue at 12 months in comparison with others (odds ratio [OR], 6.08; 95% confidence interval [CI], 2.82-13.11; P < .001). Patients with depressive symptoms also had higher odds of fatigue (OR, 3.36; 95% CI, 1.08-10.65; P = .039), although only one-third of fatigued patients reported depressive symptoms., Conclusion: Nearly half of women with gynecologic cancers had clinically significant fatigue after surgery, whereas 44% and 39% had fatigue 6 months and 1 year later; this suggests that spontaneous regression of symptoms is relatively rare. Women who reported fatigue, depressive symptoms, or 2 or more medical comorbidities had higher odds of reporting fatigue 1 year later. Future studies should test scalable interventions to improve fatigue in women with gynecologic cancers., (© 2020 American Cancer Society.)

Background: Patients with blood cancers experience high-intensity medical care near the end of life (EOL) and low rates of hospice use; attributes of goals of care (GOC) discussions may partly explain these outcomes., Methods: By using a retrospective cohort of patients with blood cancer who received care at Dana-Farber Cancer Institute and died in 2014, the authors assessed the potential relationship between timing, location, and the involvement of hematologic oncologists in the first GOC discussion with intensity of care near the EOL and timely hospice use., Results: Among 383 patients, 39.2% had leukemia/myelodysplastic syndromes, 37.1% had lymphoma, and 23.7% had myeloma. Overall, 65.3% of patients had a documented GOC discussion. Of the first discussions, 33.2% occurred >30 days before death, 34.8% occurred in the outpatient setting, and 46.4% included a hematologic oncologist. In multivariable analyses, having the first discussion >30 days before death (odds ratio [OR], 0.37; 95% CI, 0.17-0.81), in the outpatient setting (OR, 0.21; 95% CI, 0.09-0.50), and having a hematologic oncologist present (OR, 0.40; 95% CI, 0.21-0.77) were associated with lower odds of intensive care unit admission ≤30 days before death. The presence of a hematologic oncologist at the first discussion (OR, 3.07; 95% CI, 1.58-5.96) also was associated with earlier hospice use (>3 days before death)., Conclusions: In this large cohort of blood cancer decedents, most initial GOC discussions occurred close to death and in the inpatient setting. When discussions were timely, outpatient, or involved hematologic oncologists, patients were less likely to experience intensive health care use near death and were more likely to enroll in hospice., (© 2019 American Cancer Society.)

Background: Many parents of children with advanced cancer pursue curative goals when cure is no longer possible. To the authors' knowledge, no pediatric studies to date have prospectively evaluated prognosis communication or influences on decision making in poor-prognosis childhood cancer., Methods: The authors conducted a prospective cohort study at 9 pediatric cancer centers that enrolled 95 parents of children with recurrent or refractory, high-risk neuroblastoma (63% of those who were approached), a condition for which cure rarely is achieved. Parents were surveyed regarding the child's likelihood of cure; their primary goal of care; the child's symptoms, suffering, and quality of life; and regret concerning the last treatment decision. Medical records identified care and treatment decisions., Results: Only 26% of parents recognized that the chance of cure was <25%. When asked to choose a single most important goal of care, approximately 72% chose cure, 10% chose longer life, and 18% chose quality of life. Parents were more likely to prioritize quality of life when they recognized the child's poor prognosis (P = .002). Approximately 41% of parents expressed regret about the most recent treatment decision. Parents were more likely to experience regret if the child had received higher intensity medical care (odds ratio [OR], 3.14; 95% CI, 1.31-7.51), experienced suffering with limited benefit from the most recent treatment (OR, 4.78; 95% CI, 1.16-19.72), or experienced suffering from symptoms (OR, 2.91; 95% CI, 1.18-7.16)., Conclusions: Parents of children with poor-prognosis cancer frequently make decisions based on unrealistic expectations. New strategies for effective prognosis communication are needed., (© 2019 American Cancer Society.)

Purpose: The authors sought to understand bereaved family preferences for end-of-life (EOL) care, particularly among Black families and those in the South., Methods: Semi-structured interviews were conducted with parents of children who died of cancer ≥6 months before at Children's of Alabama. Themes were identified via content analysis. Quotes related to medical intensity, chemotherapy, and location of death (LOD) were scored on 5-point Likert scales, ranging from 1 (comfort care, chemotherapy, or home death) to 5 (medically intense care, avoidance of chemotherapy, or hospital death)., Results: Twenty-seven bereaved parents (12 Black) were interviewed. Children died at a mean of 13.1 years (SD = 6.1 years) and a median of 3 years before the interview (range = 1-12 years). Ten children (42%) had central nervous system tumors and the majority (63%) died in the hospital. Family decision-making involved maintaining hope, not causing harm, doing what was best for their child and themselves, and religious beliefs. There was no clear preference for home versus hospital death (3.0 [1.8-4.0]). Instead, parents considered their child's desires and/or medical needs, siblings, and prior experiences with death. To have a comfortable death, parents highlighted the need for comprehensive education about their child's EOL, a caring and comfortable environment, and 24/7 access to their care team. Families expressed a dual preference for comfort care (1.8 [1.3-2.8]) and chemotherapy (3.5 [2.7-4.1]) at EOL., Conclusions: Families did not see chemotherapy and comfort care as conflicting goals. They sought quality care emphasizing flexibility, quality time with their child, and open access to their care team, regardless of LOD., (© 2024 American Cancer Society.)

A person's phenotypic sex (i.e., endogenous expression of primary, secondary, and endocrinological sex characteristics) can impact crucial aspects of genetic assessment and resulting clinical care recommendations. In studies with genetics components, it is critical to collect phenotypic sex, information about current organ/tissue inventory and hormonal milieu, and gender identity. If researchers do not carefully construct data models, transgender, gender diverse, and sex diverse (TGSD) individuals may be given inappropriate care recommendations and/or be subjected to misgendering, inflicting medical and psychosocial harms. The recognized need for an inclusive care experience should not be limited to clinical practice but should extend to the research setting, where researchers must build an inclusive experience for TGSD participants. Here, we review three TGSD participants in the Family History and Cancer Risk Study (FOREST) to critically evaluate sex- and gender-related survey measures and associated data models in a study seeking to identify patients at risk for hereditary cancer syndromes. Furthermore, we leverage these participants' responses to sex- and gender identity-related questions in FOREST to inform needed changes to the FOREST data model and to make recommendations for TGSD-inclusive genetics research design, data models, and processes., (© 2023 The Authors. Journal of Genetic Counseling published by Wiley Periodicals LLC on behalf of National Society of Genetic Counselors.)

For a two-group comparative study, a stratified inference procedure is routinely used to estimate an overall group contrast to increase the precision of the simple two-sample estimator. Unfortunately, most commonly used methods including the Cochran-Mantel-Haenszel statistic for a binary outcome and the stratified Cox procedure for the event time endpoint do not serve this purpose well. In fact, these procedures may be worse than their two-sample counterparts even when the observed treatment allocations are imbalanced across strata. Various procedures beyond the conventional stratified methods have been proposed to increase the precision of estimation when the naive estimator is consistent. In this paper, we are interested in the case when the treatment allocation proportions vary markedly across strata. We study the stochastic properties of the two-sample naive estimator conditional on the ancillary statistics, the observed treatment allocation proportions and/or the stratum sizes, and present a biased-adjusted estimator. This adjusted estimator is asymptotically equivalent to the augmentation estimators proposed under the unconditional setting. Moreover, this consistent estimation procedure is also equivalent to a rather simple procedure, which estimates the mean response of each treatment group first via a stratum-size weighted average and then constructs the group contrast estimate. This simple procedure is flexible and readily applicable to any target patient population by choosing appropriate stratum weights. All the proposals are illustrated with the data from a cardiovascular clinical trial, whose treatment allocations are imbalanced., (© 2018 John Wiley & Sons, Ltd.)

Several methods in survival analysis are based on the proportional hazards assumption. However, this assumption is very restrictive and often not justifiable in practice. Therefore, effect estimands that do not rely on the proportional hazards assumption are highly desirable in practical applications. One popular example for this is the restricted mean survival time (RMST). It is defined as the area under the survival curve up to a prespecified time point and, thus, summarizes the survival curve into a meaningful estimand. For two-sample comparisons based on the RMST, previous research found the inflation of the type I error of the asymptotic test for small samples and, therefore, a two-sample permutation test has already been developed. The first goal of the present paper is to further extend the permutation test for general factorial designs and general contrast hypotheses by considering a Wald-type test statistic and its asymptotic behavior. Additionally, a groupwise bootstrap approach is considered. Moreover, when a global test detects a significant difference by comparing the RMSTs of more than two groups, it is of interest which specific RMST differences cause the result. However, global tests do not provide this information. Therefore, multiple tests for the RMST are developed in a second step to infer several null hypotheses simultaneously. Hereby, the asymptotically exact dependence structure between the local test statistics is incorporated to gain more power. Finally, the small sample performance of the proposed global and multiple testing procedures is analyzed in simulations and illustrated in a real data example., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

What can be considered an appropriate statistical method for the primary analysis of a randomized clinical trial (RCT) with a time-to-event endpoint when we anticipate non-proportional hazards owing to a delayed effect? This question has been the subject of much recent debate. The standard approach is a log-rank test and/or a Cox proportional hazards model. Alternative methods have been explored in the statistical literature, such as weighted log-rank tests and tests based on the Restricted Mean Survival Time (RMST). While weighted log-rank tests can achieve high power compared to the standard log-rank test, some choices of weights may lead to type-I error inflation under particular conditions. In addition, they are not linked to a mathematically unambiguous summary measure. Test statistics based on the RMST, on the other hand, allow one to investigate the average difference between two survival curves up to a pre-specified time point τ $$ \tau $$ -a mathematically unambiguous summary measure. However, by emphasizing differences prior to τ $$ \tau $$ , such test statistics may not fully capture the benefit of a new treatment in terms of long-term survival. In this article, we introduce a graphical approach for direct comparison of weighted log-rank tests and tests based on the RMST. This new perspective allows a more informed choice of the analysis method, going beyond power and type I error comparison., (© 2024 John Wiley & Sons Ltd.)

In randomized clinical trials where time-to-event is the primary outcome, almost routinely, the logrank test is prespecified as the primary test and the hazard ratio is used to quantify treatment effect. If the ratio of 2 hazard functions is not constant, the logrank test is not optimal and the interpretation of hazard ratio is not obvious. When such a nonproportional hazards case is expected at the design stage, the conventional practice is to prespecify another member of weighted logrank tests, eg, Peto-Prentice-Wilcoxon test. Alternatively, one may specify a robust test as the primary test, which can capture various patterns of difference between 2 event time distributions. However, most of those tests do not have companion procedures to quantify the treatment difference, and investigators have fallen back on reporting treatment effect estimates not associated with the primary test. Such incoherence in the "test/estimation" procedure may potentially mislead clinicians/patients who have to balance risk-benefit for treatment decision. To address this, we propose a flexible and coherent test/estimation procedure based on restricted mean survival time, where the truncation time τ is selected data dependently. The proposed procedure is composed of a prespecified test and an estimation of corresponding robust and interpretable quantitative treatment effect. The utility of the new procedure is demonstrated by numerical studies based on 2 randomized cancer clinical trials; the test is dramatically more powerful than the logrank, Wilcoxon tests, and the restricted mean survival time-based test with a fixed τ, for the patterns of difference seen in these cancer clinical trials., (Copyright © 2018 John Wiley & Sons, Ltd.)

Background: The optimal treatment for patients with brain metastases remains controversial as the use of stereotactic radiosurgery (SRS) alone, replacing whole-brain radiation therapy (WBRT), has increased. This study determined the patterns of care at multiple institutions before 2010 and examined whether or not survival was different between patients treated with SRS and patients treated with WBRT., Methods: This study examined the overall survival of patients treated with radiation therapy for brain metastases from non-small cell lung cancer (NSCLC; initially diagnosed in 2007-2009) or breast cancer (initially diagnosed in 1997-2009) at 5 centers. Propensity score analyses were performed to adjust for confounding factors such as the number of metastases, the extent of extracranial metastases, and the treatment center., Results: Overall, 27.8% of 400 NSCLC patients and 13.4% of 387 breast cancer patients underwent SRS alone for the treatment of brain metastases. Few patients with more than 3 brain metastases or lesions ≥ 4 cm in size underwent SRS. Patients with fewer than 4 brain metastases less than 4 cm in size (n = 189 for NSCLC and n = 117 for breast cancer) who were treated with SRS had longer survival (adjusted hazard ratio [HR] for NSCLC, 0.58; 95% confidence Interval [CI], 0.38-0.87; P = .01; adjusted HR for breast cancer, 0.54; 95% CI, 0.33-0.91; P = .02) than those treated with WBRT., Conclusions: Patients treated for fewer than 4 brain metastases from NSCLC or breast cancer with SRS alone had longer survival than those treated with WBRT in this multi-institutional, retrospective study, even after adjustments for the propensity to undergo SRS. Cancer 2016;122:2091-100. © 2016 American Cancer Society., (© 2016 American Cancer Society.)

With censored event time observations, the logrank test is the most popular tool for testing the equality of two underlying survival distributions. Although this test is asymptotically distribution free, it may not be powerful when the proportional hazards assumption is violated. Various other novel testing procedures have been proposed, which generally are derived by assuming a class of specific alternative hypotheses with respect to the hazard functions. The test considered by Pepe and Fleming (1989) is based on a linear combination of weighted differences of the two Kaplan-Meier curves over time and is a natural tool to assess the difference of two survival functions directly. In this article, we take a similar approach but choose weights that are proportional to the observed standardized difference of the estimated survival curves at each time point. The new proposal automatically makes weighting adjustments empirically. The new test statistic is aimed at a one-sided general alternative hypothesis and is distributed with a short right tail under the null hypothesis but with a heavy tail under the alternative. The results from extensive numerical studies demonstrate that the new procedure performs well under various general alternatives with a caution of a minor inflation of the type I error rate when the sample size is small or the number of observed events is small. The survival data from a recent cancer comparative study are utilized for illustrating the implementation of the process., (Copyright © 2015 John Wiley & Sons, Ltd.)