Restricted Survival Time Research Articles

MP47-18 SUSTAINABLE MULTIDISCIPLINARY TEAM REFERRAL FOR NON-METASTATIC RENAL CELL CARCINOMA: A SURVIVAL-BASED RECOMMENDATION

You have accessJournal of UrologyCME1 May 2022MP47-18 SUSTAINABLE MULTIDISCIPLINARY TEAM REFERRAL FOR NON-METASTATIC RENAL CELL CARCINOMA: A SURVIVAL-BASED RECOMMENDATION Giuseppe Basile, Alberto Martini, Giuseppe Fallara, Antony Pellegrino, Alessandro Larcher, Daniele Raggi, Riccardo Campi, Sumanta Pal, Philippe Spiess, Francesco Montorsi, Toni K. Choueiri, Andrea Necchi, and Umberto Capitanio Giuseppe BasileGiuseppe Basile More articles by this author , Alberto MartiniAlberto Martini More articles by this author , Giuseppe FallaraGiuseppe Fallara More articles by this author , Antony PellegrinoAntony Pellegrino More articles by this author , Alessandro LarcherAlessandro Larcher More articles by this author , Daniele RaggiDaniele Raggi More articles by this author , Riccardo CampiRiccardo Campi More articles by this author , Sumanta PalSumanta Pal More articles by this author , Philippe SpiessPhilippe Spiess More articles by this author , Francesco MontorsiFrancesco Montorsi More articles by this author , Toni K. ChoueiriToni K. Choueiri More articles by this author , Andrea NecchiAndrea Necchi More articles by this author , and Umberto CapitanioUmberto Capitanio More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002618.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To date, multidisciplinary team (MDT) evaluation, enrollment in pre-surgical trials and deferred active treatments are optional strategies for patients with Renal Cell Carcinoma (RCC), which are under investigation to maximize cancer control and implement health care policies. To provide recommendations regarding which patients with RCC may benefit the most from an early MDT evaluation. METHODS: We relied on a prospective dataset including patients diagnosed with RCC in 1998-2019 and treated by means of surgery alone at a tertiary referral center. The risk of other cause mortality (OCM) was evaluated against the risk of recurrence over time by means of the Weibull regression. Patients were stratified based on clinical stage (cT1a; cT1b; cT2; cT3-4), age (<60; 60-70; >70) and comorbidities [Charlson comorbidity index (CCI) 0 vs ≥1]. For each combination of cT stage, age and CCI, the potential need for a MDT referral was defined when the risk of recurrence exceeded the risk of OCM within the lower limit of the 95%CI of the mean time to recurrence. RESULTS: Overall, 1,162 (51%) patients had no comorbidities. Median follow-up was 7 years. Patients who would benefit most from a MDT evaluation are those diagnosed with A) cT3-4 disease (any age or comorbidity) or B) cT2 cancers if healthy and younger than 70 years or younger than 60 years with at least 1 comorbidity or C) cT1b if younger than 60 years and without comorbidities. Figure 1 depicts the risk of other cause mortality versus risk of recurrence following surgery for non-metastatic renal cell carcinoma according to age and cT stage in patients with no comorbidities or in presence of comorbidities. The continuous lines depict the risk of recurrence that decreases with time. Vertical lines depict the mean time to recurrence and associated 95% confidence interval calculated with mean restricted survival time. Dashed lines depict other cause mortality risk, which increases with time. CONCLUSIONS: Our findings can help selecting the optimal candidates for multidisciplinary evaluations and to consider RCC patients for clinical trials, deferred treatment, and treatment policy improvement, especially in the case of major healthcare disruptions, such as pandemics. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e817 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Giuseppe Basile More articles by this author Alberto Martini More articles by this author Giuseppe Fallara More articles by this author Antony Pellegrino More articles by this author Alessandro Larcher More articles by this author Daniele Raggi More articles by this author Riccardo Campi More articles by this author Sumanta Pal More articles by this author Philippe Spiess More articles by this author Francesco Montorsi More articles by this author Toni K. Choueiri More articles by this author Andrea Necchi More articles by this author Umberto Capitanio More articles by this author Expand All Advertisement PDF DownloadLoading ...

Methods in cell biology: Cell-derived matrices.

Three-dimensional (3D) in vitro skin and skin cancer models have become an invaluable tool in skin research. They go back to 1979, when Bell and colleagues reported on the establishment of a fibroblast-dependent collagen tissue (Bell, Ivarsson, & Merrill, 1979). On top of such tissue a stratified and differentiated epidermis could be established (Bell, Merrill, & Solomon, 1979). Hydrogel-based dermal equivalents have been generated ever since and upon co-culture with normal human skin keratinocytes, these constructs were then termed skin equivalents. Due to a number of deficiencies, the most important one being their restricted survival time, new developments helped to circumvent premature fibroblast activation and tissue destruction. By avoiding collagen for the dermal equivalent (DE), we proposed, a scaffold-based DE, allowing fibroblasts to reorganize the primary fibrin solution into an "authentic" dermal matrix (Boehnke et al., 2007; Stark et al., 2004, 2006). With this, our goal of a long-term skin equivalent-successful cultivation for several months-was achieved. Nevertheless, also this model presented limitations. One being its opaqueness made it difficult to image the intact tissue. Another draw-back was that tumor cells upon invasion used the scaffold as a guardrail leaving behind an unspecific invasion pattern. All this could be avoided by an approach, the fibroblast-derived matrix-based model, based on the work by Ahlfors and Billiar (2007) We here provide a protocol for this type of model, thereby providing the basis for future work in the field of skin research.

Modeling restricted mean survival time under general censoring mechanisms.

Restricted mean survival time (RMST) is often of great clinical interest in practice. Several existing methods involve explicitly projecting out patient-specific survival curves using parameters estimated through Cox regression. However, it would often be preferable to directly model the restricted mean for convenience and to yield more directly interpretable covariate effects. We propose generalized estimating equation methods to model RMST as a function of baseline covariates. The proposed methods avoid potentially problematic distributional assumptions pertaining to restricted survival time. Unlike existing methods, we allow censoring to depend on both baseline and time-dependent factors. Large sample properties of the proposed estimators are derived and simulation studies are conducted to assess their finite sample performance. We apply the proposed methods to model RMST in the absence of liver transplantation among end-stage liver disease patients. This analysis requires accommodation for dependent censoring since pre-transplant mortality is dependently censored by the receipt of a liver transplant.

Multiple imputation of censored survival data in the presence of missing covariates using restricted mean survival time

Missing covariates data with censored outcomes put a challenge in the analysis of clinical data especially in small sample settings. Multiple imputation (MI) techniques are popularly used to impute missing covariates and the data are then analyzed through methods that can handle censoring. However, techniques based on MI are available to impute censored data also but they are not much in practice. In the present study, we applied a method based on multiple imputation by chained equations to impute missing values of covariates and also to impute censored outcomes using restricted survival time in small sample settings. The complete data were then analyzed using linear regression models. Simulation studies and a real example of CHD data show that the present method produced better estimates and lower standard errors when applied on the data having missing covariate values and censored outcomes than the analysis of the data having censored outcome but excluding cases with missing covariates or the analysis when cases with missing covariate values and censored outcomes were excluded from the data (complete case analysis).

Cost-effectiveness of lung transplantation and its evolution: the Portuguese case

We analyzed the costs and outcomes of lung transplant in Portugal and its evolution since it was first performed in 2000. Data were collected for all patients waiting for transplant from 31 December 2000 until 31 December 2010 (n = 61). Patients waiting for transplant were used as the comparison group. We calculated the mean restricted survival time using the Kaplan-Meier estimation method. The time horizon was closed upon the death of the last surviving patient. Costs and consequences were discounted at 5% per annum. We calculated the incremental cost-effectiveness ratio of transplant as compared to no transplant. Deterministic and probabilistic sensitivity analyses were performed. We tested volume-outcome effects using a parametric survival regression. The cost of lung transplantation was 77,223€ per QALY and 121,276€ per life-year. Assuming a 50,000€/QALY willingness-to-pay, lung transplant would be cost-effective with a 37% probability. Survival improved substantially from 5.15 years over the 2001-2010 period to 6.94 years for the 2008-2010 period. When restricting our analysis to the 2008-2010 period, the cost-effectiveness ratio decreased to €79,016 per life-year gained and €69,241 per QALY. Survival was significantly associated with the number of procedures performed. Although above commonly accepted cost-effectiveness thresholds, the economic value of lung transplant holds promise in Portugal due to the large improvement in survival over the 2001-2010 period. The life-threatening character of the motivating conditions, the absence of alternative treatment, and equity concerns should be also accounted for when assessing the "value for money" of lung transplant.

Cox Regression Methods for Two‐Stage Randomization Designs

Two-stage randomization designs (TSRD) are becoming increasingly common in oncology and AIDS clinical trials as they make more efficient use of study participants to examine therapeutic regimens. In these designs patients are initially randomized to an induction treatment, followed by randomization to a maintenance treatment conditional on their induction response and consent to further study treatment. Broader acceptance of TSRDs in drug development may hinge on the ability to make appropriate intent-to-treat type inference within this design framework as to whether an experimental induction regimen is better than a standard induction regimen when maintenance treatment is fixed. Recently Lunceford, Davidian, and Tsiatis (2002, Biometrics 58, 48-57) introduced an inverse probability weighting based analytical framework for estimating survival distributions and mean restricted survival times, as well as for comparing treatment policies at landmarks in the TSRD setting. In practice Cox regression is widely used and in this article we extend the analytical framework of Lunceford et al. (2002) to derive a consistent estimator for the log hazard in the Cox model and a robust score test to compare treatment policies. Large sample properties of these methods are derived, illustrated via a simulation study, and applied to a TSRD clinical trial.

Regression Models for the Mean of the Quality‐of‐Life‐Adjusted Restricted Survival Time Using Pseudo‐Observations

In this research we develop generalized linear regression models for the mean of a quality-of-life-adjusted restricted survival time. Parameter and standard error estimates could be obtained from generalized estimating equations applied to pseudo-observations. Simulation studies with moderate sample sizes are conducted and an example from the International Breast Cancer Study Group Ludwig Trial V is used to illustrate the newly developed methodology.

Estimation of the Causal Effects on Survival of Two‐Stage Nonrandomized Treatment Sequences for Recurrent Diseases

In the treatment of cancer, patients commonly receive a variety of sequential treatments. The initial treatments administered following diagnosis can vary, as well as subsequent salvage regimens given after disease recurrence. This article considers the situation where neither initial treatments nor salvage treatments are randomized. Assuming there are no unmeasured confounders, we estimate the joint causal effects on survival of initial and salvage treatments, that is, the effects of two-stage treatment sequences. For each individual treatment sequence, we estimate the survival distribution function and the mean restricted survival time. Different treatment sequences are then compared using these estimates and their corresponding covariances. Simulation studies were conducted to evaluate the performance of the methods, including their sensitivity to the violation of the assumption of no unmeasured confounders. The methods are illustrated by a retrospective study of patients with soft tissue sarcoma, which motivated this research.

Optimal estimator for the survival distribution and related quantities for treatment policies in two-stage randomization designs in clinical trials.

Two-stage designs, where patients are initially randomized to an induction therapy and then depending upon their response and consent, are randomized to a maintenance therapy, are common in cancer and other clinical trials. The goal is to compare different combinations of primary and maintenance therapies to find the combination that is most beneficial. In practice, the analysis is usually conducted in two separate stages which does not directly address the major objective of finding the best combination. Recently Lunceford, Davidian, and Tsiatis (2002, Biometrics58, 48-57) introduced ad hoc estimators for the survival distribution and mean restricted survival time under different treatment policies. These estimators are consistent but not efficient, and do not include information from auxiliary covariates. In this article we derive estimators that are easy to compute and are more efficient than previous estimators. We also show how to improve efficiency further by taking into account additional information from auxiliary variables. Large sample properties of these estimators are derived and comparisons with other estimators are made using simulation. We apply our estimators to a leukemia clinical trial data set that motivated this study.

Estimation of Survival Distributions of Treatment Policies in Two‐Stage Randomization Designs in Clinical Trials

Some clinical trials follow a design where patients are randomized to a primary therapy at entry followed by another randomization to maintenance therapy contingent upon disease remission. Ideally, analysis would allow different treatment policies, i.e., combinations of primary and maintenance therapy if specified up-front, to be compared. Standard practice is to conduct separate analyses for the primary and follow-up treatments, which does not address this issue directly. We propose consistent estimators for the survival distribution and mean restricted survival time for each treatment policy in such two-stage studies and derive large-sample properties. The methods are demonstrated on a leukemia clinical trial data set and through simulation.

Localization of CEA, HCG, lysozyme, alpha-1-antitrypsin, and alpha-1-antichymotrypsin in gastric cancer and prognosis.

Carcinoembryonic antigen (CEA), beta-human chorionic gonadotropin (HCG), alpha-1-antichymotrypsin (ACT), alpha-1-antitrypsin (AAT) and Lysozyme (LYS) were traced by immunoperoxidase staining in gastric carcinomas. The immunohistological results were evaluated in relation to histological types (WHO and Laurén), stage of disease, grade and survival time. CEA was demonstrated in 96% of the tumours, HCG in 34%, ACT in 78%, AAT in 42%, and LYS in 71%. Comparing the staining patterns of the antigens and the intensity of staining some differences were notable. Except for signet-ring cell carcinomas, all of which were intensively positive, CEA expression decreased significantly with loss of differentiation. This observation was not seen with the other marker substances. None of the tested markers was characteristic for one particular histological type, nor could they be correlated with the tumour stage or grade. The marker positivity of CEA, ACT and LYS was not related to survival time. For HCG only, a correlation between tissue expression and a restricted survival time was established. Patients with AAT positive carcinomas had a significantly better survival probability.