cshalizi + survival_analysis   4

Testing and Estimation of Social Network Dependence With Time to Event Data: Journal of the American Statistical Association: Vol 0, No 0
"Nowadays, events are spread rapidly along social networks. We are interested in whether people’s responses to an event are affected by their friends’ characteristics. For example, how soon will a person start playing a game given that his/her friends like it? Studying social network dependence is an emerging research area. In this work, we propose a novel latent spatial autocorrelation Cox model to study social network dependence with time-to-event data. The proposed model introduces a latent indicator to characterize whether a person’s survival time might be affected by his or her friends’ features. We first propose a score-type test for detecting the existence of social network dependence. If it exists, we further develop an EM-type algorithm to estimate the model parameters. The performance of the proposed test and estimators are illustrated by simulation studies and an application to a time-to-event dataset about playing a popular mobile game from one of the largest online social network platforms. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement."
to:NB  network_data_analysis  survival_analysis  statistics  social_influence  re:homophily_and_confounding 
12 weeks ago by cshalizi
[1212.6232] High-Dimensional Sparse Additive Hazards Regression
"High-dimensional sparse modeling with censored survival data is of great practical importance, as exemplified by modern applications in high-throughput genomic data analysis and credit risk analysis. In this article, we propose a class of regularization methods for simultaneous variable selection and estimation in the additive hazards model, by combining the nonconcave penalized likelihood approach and the pseudoscore method. In a high-dimensional setting where the dimensionality can grow fast, polynomially or nonpolynomially, with the sample size, we establish the weak oracle property and oracle property under mild, interpretable conditions, thus providing strong performance guarantees for the proposed methodology. Moreover, we show that the regularity conditions required by the $L_1$ method are substantially relaxed by a certain class of sparsity-inducing concave penalties. As a result, concave penalties such as the smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), and smooth integration of counting and absolute deviation (SICA) can significantly improve on the $L_1$ method and yield sparser models with better prediction performance. We present a coordinate descent algorithm for efficient implementation and rigorously investigate its convergence properties. The practical utility and effectiveness of the proposed methods are demonstrated by simulation studies and a real data example."
to:NB  statistics  survival_analysis  additive_models  high-dimensional_statistics  regression  sparsity 
december 2012 by cshalizi
Estimating the Effect of Premarital Cohabitation on Timing of Marital Disruption
"In this article, we extend the propensity score method by matching on multiple groups. Using data from first wave (1987–1988) and third wave (2001–2003) of National Survey of Families and Households (NSFH), we match married individuals with no premarital cohabitation, single premarital cohabitation with the spouse, and serial premarital cohabitations, and apply Cox proportional hazards models to explore how premarital cohabitation history affects marital disruption. Our results indicate that both selection and causation help explain the relationship. The selection effect played a large role in 1987–88 when cohabitation was uncommon but disappeared in 2001–03 when cohabitation became prevalent. Postmatching results demonstrate that the causal effect of cohabitation on marital disruption was strong among serial cohabitors and weak among one-time cohabitors with the spouse. The imputation-based sensitivity analysis shows that our conclusion is robust even with the presence of unobserved characteristics that have a moderate association with cohabitation and marital disruption."

- Case study/exam problem for uADA?
causal_inference  statistics  survival_analysis  living_in_sin  to_teach:undergrad-ADA  matching 
august 2012 by cshalizi

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