On the Addams family of discrete frailty distributions for modeling multivariate case I interval-censored data.

Random effect models for time-to-event data, also known as frailty models, provide a conceptually appealing way of quantifying association between survival times and of representing heterogeneities resulting from factors which may be difficult or impossible to measure. In the literature, the random effect is usually assumed to have a continuous distribution. However, in some areas of application, discrete frailty distributions may be more appropriate.

Methods for non-proportional hazards in clinical trials: A systematic review.

For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under non-proportional hazard.

Risk for psychotherapy drop-out in survival analysis: The influence of general change mechanisms and symptom severity.

Dropping out of psychotherapeutic treatment (i.e., the patient ending treatment unilaterally) poses a problem for patients, therapists, and the health care sector.