Influence of internet usage on physical activity participation among Chinese residents: evidence from 2017 China General Social Survey.

Objectives: This study aimed to examine the impact of internet usage on physical activity participation among Chinese residents, utilizing data from the 2017 China General Social Survey (N = 12,264). The objectives were to investigate the relationship between internet usage and physical activity participation and to explore the moderating effects of gender, age, and education level. Methods: Multiple regression models and a binary Probit model were employed to analyze the data. The study focused on exploring the association between internet usage and physical activity participation, considering the moderating effects of gender, age, and education level. The sample consisted of 12,264 participants from the 2017 China General Social Survey. Results: The study found a positive association between increased internet usage and decreased engagement in physical activity, suggesting a negative influence of internet usage on physical activity. Significant age-related moderating effects were observed, indicating varying patterns of the internet-physical activity relationship across different age groups. Gender and education level were also found to significantly moderate this association, highlighting the impact of gender equality and educational attainment on individuals' utilization of the internet for physical activity purposes. Conclusion: This study underscores the evolving role of the internet in shaping physical activity behaviors in the Chinese context. It emphasizes the importance of considering age-related dynamics and societal factors such as gender equality and educational attainment in health promotion strategies.

Censored linear regression in the presence or absence of auxiliary survival information.

There has been a rising interest in better exploiting auxiliary summary information from large databases in the analysis of smaller-scale studies that collect more comprehensive patient-level information. The purpose of this paper is twofold: first, we propose a novel approach to synthesize information from both the aggregate summary statistics and the individual-level data in censored linear regression. We show that the auxiliary information amounts to a system of nonsmooth estimating equations and thus can be combined with the conventional weighted log-rank estimating equations by using the generalized method of moments (GMM) approach. The proposed methodology can be further extended to account for the potential inconsistency in information from different sources. Second, in the absence of auxiliary information, we propose to improve estimation efficiency by combining the overidentified weighted log-rank estimating equations with different weight functions via the GMM framework. To deal with the nonsmooth GMM-type objective functions, we develop an asymptotics-guided algorithm for parameter and variance estimation. We establish the asymptotic normality of the proposed GMM-type estimators. Simulation studies show that the proposed estimators can yield substantial efficiency gain over the conventional weighted log-rank estimators. The proposed methods are applied to a pancreatic cancer study for illustration.

Predicting survival time for metastatic castration resistant prostate cancer: An iterative imputation approach.

In this paper, we present our winning method for survival time prediction in the 2015 Prostate Cancer DREAM Challenge, a recent crowdsourced competition focused on risk and survival time predictions for patients with metastatic castration-resistant prostate cancer (mCRPC). We are interested in using a patient's covariates to predict his or her time until death after initiating standard therapy. We propose an iterative algorithm to multiply impute right-censored survival times and use ensemble learning methods to characterize the dependence of these imputed survival times on possibly many covariates. We show that by iterating over imputation and ensemble learning steps, we guide imputation with patient covariates and, subsequently, optimize the accuracy of survival time prediction. This method is generally applicable to time-to-event prediction problems in the presence of right-censoring. We demonstrate the proposed method's performance with training and validation results from the DREAM Challenge and compare its accuracy with existing methods.