We assessed whether relapse-free survival (RFS; time until recurrence/death) is a valid surrogate for overall survival (OS) among resected stage II–III melanoma patients through a meta-analysis of randomized controlled trials. Individual patient data (IPD) on RFS and OS were collected from 5826 patients enrolled in 11 randomized adjuvant trials comparing interferon (IFN) to observation. In addition, IPD from two studies comparing IFN and vaccination in 989 patients were included. A two-level modeling approach was used for assessing Spearman’s patient-level correlation (rho) of RFS and OS and the trial-level coefficient of determination (R²) of the treatment effects on RFS and on OS. The results were validated externally in 13 adjuvant studies without available IPD. We then tested the results on the European Organisation for Research and Treatment of Cancer (EORTC) 18071 double-blind trial comparing ipilimumab 10 mg/kg with placebo, which showed a statistically significant impact of the checkpoint inhibitor on RFS and OS. All statistical tests were two-sided. With a median follow-up of seven years, 12 of 13 trials showed a consistency between the IFN vs No IFN differences regarding RFS (hazard ratio [HR]_RFS = 0.88) and OS (HR_OS = 0.91), but the small trial, Eastern Cooperative Oncology Group 2696, was an outlier (HR_RFS = 0.72 vs HR_OS = 1.11). Therefore, even if rho was high, R² was low and could not reliably be estimated. Based on the 12 trials, rho remained high (0.89), and the hazard ratios for RFS and OS were strongly correlated (R² = 0.91). The surrogate threshold effect for RFS was estimated to be 0.77. For the EORTC 18071 trial, the hazard ratio for RFS was 0.75, predicting an effect of ipilimumab on OS. This was subsequently confirmed (HR_OS = 0.72, 95.1% confidence interval = 0.58 to 0.88, P = .001). In high-risk stage II–III melanoma, RFS appeared to be a valid surrogate end point for OS for adjuvant randomized studies assessing interferon or a checkpoint inhibitor. In future similar adjuvant studies, a hazard ratio for RFS of 0.77 or less would predict a treatment impact on OS.

When analyzing data with multiple regression model if there are collinearities, then one or several predictor variables are usually omitted from the model. However, there sometimes some reasons, for instance medical or economic reasons, the predictors are all important and should be included in the model. Ridge regression model is not uncommon in some researches to use to cope with collinearity. Through this modeling, weights for predictor variables are used for estimating parameters. The next estimation process could follow the concept of likelihood. Furthermore, for the estimation nowadays the Bayesian version could be an alternative. This estimation method does not match likelihood one in terms of popularity due to some difficulties; computation and so forth. Nevertheless, with the growing improvement of computational methodology recently, this caveat should not at the moment become a problem. This paper discusses about simulation process for evaluating the characteristic of Bayesian Ridge regression parameter estimates. There are several simulation settings based on variety of collinearity levels and sample sizes. The results show that Bayesian method gives better performance for relatively small sample sizes, and for other settings the method does perform relatively similar to the likelihood method.

The issue of food security is important since it affects the region’s economic cycle. The agricultural sector, which serves as a safety net for Indonesia’s food availability and must be optimized to prevent a crisis, is the crucial sector in terms of food security issues. Lamongan Regency is one of the regencies in East Java with the agricultural sector contributing to positive growth and support for sustainable agriculture. Sustainable agriculture demonstrates the existence of a long-term, integrated system linked to various aspects of life quality. This study aims to identify social capital and its relation to quality of life (QoL) in supporting sustainable agriculture. The method used in this research is descriptive analysis to determine the condition of sustainable agriculture and the characteristics of social capital in Sedayulawas Village. A Confirmatory Factor Analysis (CFA) followed by a Structural Equation Model (SEM) Analysis approach is used to determine the relationship between social capital and the QoL community in the village in supporting sustainable agriculture. Structural Equation Model (SEM) analysis shows that social capital, material, community, emotional, health, and safety variables influence the QoL. The community variables explain 81.7% of the variability in farmer QoL. This study advances knowledge about the role of social capital and QoL for communities, particularly farmers, in matters pertaining to sustainable agriculture and food security.

In this paper, we develop a simple diagnostic test for the random-effects distribution in mixed models. The test is based on the gradient function, a graphical tool proposed by Verbeke and Molenberghs to check the impact of assumptions about the random-effects distribution in mixed models on inferences. Inference is conducted through the bootstrap. The proposed test is easy to implement and applicable in a general class of mixed models. The operating characteristics of the test are evaluated in a simulation study, and the method is further illustrated using two real data analyses.

Pujon Kidul Village, Pujon District, Malang Regency, is an area with tourism potential that has been developed since 2017 with the concept of agricultural tourism. Throughout the development of tourism villages, Pujon Kidul Village has succeeded in accelerating economic growth and providing jobs for the community. However, during the COVID-19 pandemic, tourism villages have been severely affected, leading to the temporary closure of tourist attractions and community businesses. This research aimed to identify what indicators form social capital variables and the relationship between social capital variables and community adaptation patterns in dealing with pandemics in the study location. This was quantitative research with confirmatory factor analysis to determine the indicators of forming social capital and structural equation modeling analysis to determine the relationship between the variables. Based on the findings, it is known that trust in forming a social network is 0.468. Furthermore, the social network forms community actions of 0.046 and influences community resilience by 0.007. Therefore, good social capital will make it easier for the community to participate in collective action as a form of caring for each other during the pandemic. This action also influences the community to survive in a pandemic crisis, thus creating an adaptation pattern for the Pujon Kidul Tourism Village community in facing a pandemic.