Attributable Risk Function with Clustered Survival Data

Analysis of Survival Data with Clustered Events

Two methods to analyzing survival data with clustered events are presented. The first method is a proportional hazards model which adopts a marginal approach with a working independence assumption. This model can be fitted by SAS PROC PHREG with the robust sandwich estimate option. The second method is a likelihood-based random effects (frailty) model. In the second model, the baseline hazard c...

Estimation of Attributable Risk from Clustered Binary Data: The Case of Cross-Sectional and Cohort Studies

Effect sizes are estimated from several study designs when the subjects are individually sampled. When the samples are the aggregate cluster of individuals, the within cluster correlation must be accounted for to construct correct confidence intervals, and to conduct valid statistical inference. The purpose of this article is to propose and evaluate statistical procedures for the estimation of ...

Simulating Clustered Survival Data with Proportional Hazards Margins

A multiple imputation approach for clustered interval-censored survival data.

Multivariate interval-censored failure time data arise commonly in many studies of epidemiology and biomedicine. Analysis of these type of data is more challenging than the right-censored data. We propose a simple multiple imputation strategy to recover the order of occurrences based on the interval-censored event times using a conditional predictive distribution function derived from a paramet...

Attributable Risk

The attribrisk package has been created to make attributable risk (AR) inference easier to calculate. This vignette gives only a small amount of theory, users who wish more are directed to Khan [3]. The term attributable risk is used to refer to the amount of disease in a population that can be attributed to a particular exposure. The terms population attributable risk and etiologic fraction ha...