A discrete-time multilevel mixture model for event history data with long-term survivors, with an application to an analysis of contraceptive sterilization in Bangladesh

Nodal Heterogeneity can Induce Ghost Triadic Effects in Relational Event Models

Temporal network data is often encoded as time-stamped interaction events between senders and receivers, such as co-authoring scientific articles or communication via email. A number of relational event frameworks have been proposed to address specific issues raised by complex temporal dependencies. These models attempt to quantify how individual behaviour, endogenous and exogenous factors, as well as interactions with other individuals modify the network dynamics over time. It is often of interest to determine whether changes in the network can be attributed to endogenous mechanisms reflecting natural relational tendencies, such as reciprocity or triadic effects. The propensity to form or receive ties can also, at least partially, be related to actor attributes. Nodal heterogeneity in the network is often modelled by including actor-specific or dyadic covariates. However, comprehensively capturing all personality traits is difficult in practice, if not impossible. A failure to account for heterogeneity may confound the substantive effect of key variables of interest. This work shows that failing to account for node level sender and receiver effects can induce ghost triadic effects. We propose a random-effect extension of the relational event model to deal with these problems. We show that it is often effective over more traditional approaches, such as in-degree and out-degree statistics. These results that the violation of the hierarchy principle due to insufficient information about nodal heterogeneity can be resolved by including random effects in the relational event model as a standard.

Individual Mobility across Clusters: The Impact of Ignoring Cross-Classified Data Structures in Discrete-Time Survival Analysis

A multilevel-discrete time survival model may be appropriate for purely hierarchical data, but when data are non-purely hierarchical due to individual mobility across clusters, a cross-classified discrete time survival model may be necessary. The purpose of this research was to investigate the performance of a cross-classified discrete-time survival model and assess the impact of ignoring a cross-classified data structure on the model parameters of a conventional discrete-time survival model and a multilevel discrete-time survival model. A Monte Carlo simulation was used to examine the performance of three discrete-time survival models when individuals are mobile across clusters. Simulation factors included the value of the between-clusters variance, number of clusters, within-cluster sample size, Weibull scale parameter, and mobility rate. The results suggest that substantial relative parameter bias, unacceptable coverage of the 95% confidence intervals, and severely biased standard errors are possible for all model parameters when a discrete-time survival model is used that ignores the cross-classified data structure. The findings presented in this study are useful for methodologists and practitioners in educational research, public health, and other social sciences where discrete-time survival analysis is a common methodological technique for analyzing event-history data.

Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application

Discrete survival data are routinely encountered in many fields of study including behavior science, economics, epidemiology, medicine, and social science. In this paper, we develop a class of proportional exponentiated link transformed hazards (ELTH) models. We carry out a detailed examination of the role of links in fitting discrete survival data and estimating regression coefficients. Several interesting results are established regarding the choice of links and baseline hazards. We also characterize the conditions for improper survival functions and the conditions for existence of the maximum likelihood estimates under the proposed ELTH models. An extensive simulation study is conducted to examine the empirical performance of the parameter estimates under the Cox proportional hazards model by treating discrete survival times as continuous survival times, and the model comparison criteria, AIC and BIC, in determining links and baseline hazards. A SEER breast cancer dataset is analyzed in details to further demonstrate the proposed methodology.

Using simulation to interpret a discrete time survival model in a complex biological system: fertility and lameness in dairy cows

The ever-growing volume of data routinely collected and stored in everyday life presents researchers with a number of opportunities to gain insight and make predictions. This study aimed to demonstrate the usefulness in a specific clinical context of a simulation-based technique called probabilistic sensitivity analysis (PSA) in interpreting the results of a discrete time survival model based on a large dataset of routinely collected dairy herd management data. Data from 12,515 dairy cows (from 39 herds) were used to construct a multilevel discrete time survival model in which the outcome was the probability of a cow becoming pregnant during a given two day period of risk, and presence or absence of a recorded lameness event during various time frames relative to the risk period amongst the potential explanatory variables. A separate simulation model was then constructed to evaluate the wider clinical implications of the model results (i.e. the potential for a herd's incidence rate of lameness to influence its overall reproductive performance) using PSA. Although the discrete time survival analysis revealed some relatively large associations between lameness events and risk of pregnancy (for example, occurrence of a lameness case within 14 days of a risk period was associated with a 25% reduction in the risk of the cow becoming pregnant during that risk period), PSA revealed that, when viewed in the context of a realistic clinical situation, a herd's lameness incidence rate is highly unlikely to influence its overall reproductive performance to a meaningful extent in the vast majority of situations. Construction of a simulation model within a PSA framework proved to be a very useful additional step to aid contextualisation of the results from a discrete time survival model, especially where the research is designed to guide on-farm management decisions at population (i.e. herd) rather than individual level.

A whole-blood RNA transcript-based prognostic model in men with castration-resistant prostate cancer: a prospective study

BACKGROUND

Survival for patients with castration-resistant prostate cancer is highly variable. We assessed the effectiveness of a whole-blood RNA transcript-based model as a prognostic biomarker in castration-resistant prostate cancer.

METHODS

Peripheral blood was prospectively collected from 62 men with castration-resistant prostate cancer on various treatment regimens who were enrolled in a training set at the Dana-Farber Cancer Institute (Boston, MA, USA) from August, 2006, to June, 2008, and from 140 patients with castration-resistant prostate cancer in a validation set from Memorial Sloan-Kettering Cancer Center (New York, NY, USA) from August, 2006, to February, 2009. A panel of 168 inflammation-related and prostate cancer-related genes was assessed with optimised quantitative PCR to assess biomarkers predictive of survival.

FINDINGS

A six-gene model (consisting of ABL2, SEMA4D, ITGAL, and C1QA, TIMP1, CDKN1A) separated patients with castration-resistant prostate cancer into two risk groups: a low-risk group with a median survival of more than 34·9 months (median survival was not reached) and a high-risk group with a median survival of 7·8 months (95% CI 1·8-13·9; p<0·0001). The prognostic utility of the six-gene model was validated in an independent cohort. This model was associated with a significantly higher area under the curve compared with a clinicopathological model (0·90 [95% CI 0·78-0·96] vs 0·65 [0·52-0·78]; p=0·0067).

INTERPRETATION

Transcriptional profiling of whole blood yields crucial prognostic information about men with castration-resistant prostate cancer. The six-gene model suggests possible dysregulation of the immune system, a finding that warrants further study.

FUNDING

Source MDX.

Modeling fecundity in the presence of a sterile fraction using a semi-parametric transformation model for grouped survival data

The analysis of fecundity data is challenging and requires consideration of both highly timed and interrelated biologic processes in the context of essential behaviors such as sexual intercourse during the fertile window. Understanding human fecundity is further complicated by presence of a sterile population, i.e. couples unable to achieve pregnancy. Modeling techniques conducted to date have largely relied upon discrete time-to-pregnancy survival or day-specific probability models to estimate the determinants of time-to-pregnancy or acute effects, respectively. We developed a class of semi-parametric grouped transformation cure models that capture day-level variates purported to affect the cycle-level hazards of conception and, possibly, sterility. Our model's performance is assessed using simulation and longitudinal data from one of the few prospective cohort studies with preconception enrollment of women followed for 12 menstrual cycles at risk for pregnancy.

Discrete-time survival models with long-term survivors

Discrete-time survival data typically possess three features: discreteness, ties, and concomitant information, which require appropriate discrete-time models to analyze. In this paper, we first review some existing discrete-time survival models and then extend them to discrete-time cure survival models, which account for the presence of long-term survivors (cured individuals). The maximum likelihood estimation as well as approximate partial likelihood approaches are used to estimate the model parameters. Simulation results are shown to support the suitability of such models for discrete-time survival data with long-term survivors. An example of applications on a set of bladder tumor recurrence data is also presented.

Widowhood and mortality among the elderly: the modifying role of neighborhood concentration of widowed individuals

The effect of death of a spouse on the mortality of the survivor (the "widowhood effect") is well-established. We investigated how the effect of widowhood on mortality depends on the neighborhood concentration of widowed individuals in the United States. We developed a large, nationally representative, and longitudinal dataset from Medicare claims and other data sources characterizing 200,000 elderly couples, with nine years of follow-up (1993-2002), and estimated multilevel grouped discrete-time hazard models. In neighborhoods with a low concentration of widowed individuals, widowhood increased the odds of death for men by 22% and for women by 17%, compared to 17% for men, and 15% for women in neighborhoods with a high concentration of widowed individuals. Our findings suggest that neighborhood structural contexts - that provide opportunities for interacting with others and favoring new social engagements - could be potential modifiers of the widowhood effects and as such requires more systematic consideration in future research of widowhood effects on well-being and mortality.