Repeated Events Survival Models: The Conditional Frailty Model (ICPSR 1339)

Published: Jan 15, 2007

Principal Investigator(s):
Janet M. Box-Steffensmeier, Ohio State University; Suzanna DeBoef, Pennsylvania State University

https://doi.org/10.3886/ICPSR01339.v1

Version V1

Repeated events processes are ubiquitous across a great range of important health, medical, and public policy applications, but models for these processes have serious limitations. Alternative estimators often produce different inferences concerning treatment effects due to bias and inefficiency. We recommend a robust strategy for the estimation of effects in medical treatments, social conditions, individual behaviors, and public policy programs in repeated events survival models under three common conditions: heterogeneity across individuals, dependence across the number of events, and both heterogeneity and event dependence. We develop a new model for repeated events processes that accurately accounts for the various conditions of heterogeneity and event dependence by using a frailty term, stratification, and gap time formulation of the risk set. We examine the performance of these models and others that are commonly used in applied work using Monte Carlo simulations, and apply the findings to data on chronic granulomatous disease and cystic.

Box-Steffensmeier, Janet M., and DeBoef, Suzanna. Repeated Events Survival Models: The Conditional Frailty Model. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2007-01-15. https://doi.org/10.3886/ICPSR01339.v1

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National Science Foundation (SES-0083418)

(1) There are ten data files and a readme file. These will be released as a zipped package. (2) These data are part of ICPSR's Publication-Related Archive and are distributed exactly as they arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigators if further information is desired.

2007-01-15

2007-01-15

Notes

  • These data are flagged as replication datasets and are distributed exactly as they arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.

  • The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.

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