Abstract
Statistical methods for analyzing recurrent events have attracted significant attention. The majority of existing works consider situations in which subjects are observed over time periods and events of interest that occurred during the course of follow-up are recorded. In some applications, a subject may leave the study for a period of time and then resume due to various reasons. During the absence, which is referred to as an intermittent gap in this study, it may be impossible to observe a recording of the event. A naive analysis disregards gaps and considers events to be a typical recurrent event dataset. However, this may result in biased estimations and misleading results. In this study, we build an additive rates model for recurrent event data considering intermittent gaps. We provide the asymptotic theories behind the proposed model, as well as the goodness of fit between observed and modeled values. Simulation studies reveal that the estimations perform well if intermittent gaps are taken into account. In addition, we utilized the longitudinal cohort of elderly patients who have type 2 diabetes and at least one record of a severe recurrent complication, hypoglycemia, from the National Health Insurance Research Database in Taiwan to demonstrate the proposed method.
| Original language | English |
|---|---|
| Pages (from-to) | 1343-1356 |
| Number of pages | 14 |
| Journal | Statistics in Medicine |
| Volume | 38 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2019 Apr 15 |
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All Science Journal Classification (ASJC) codes
- Epidemiology
- Statistics and Probability
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Semiparametric additive rates model for recurrent events data with intermittent gaps. / Su, Pei-Fang; Zhong, Junjiang; Ou, Huang-Tz.
In: Statistics in Medicine, Vol. 38, No. 8, 15.04.2019, p. 1343-1356.Research output: Contribution to journal › Article
TY - JOUR
T1 - Semiparametric additive rates model for recurrent events data with intermittent gaps
AU - Su, Pei-Fang
AU - Zhong, Junjiang
AU - Ou, Huang-Tz
PY - 2019/4/15
Y1 - 2019/4/15
N2 - Statistical methods for analyzing recurrent events have attracted significant attention. The majority of existing works consider situations in which subjects are observed over time periods and events of interest that occurred during the course of follow-up are recorded. In some applications, a subject may leave the study for a period of time and then resume due to various reasons. During the absence, which is referred to as an intermittent gap in this study, it may be impossible to observe a recording of the event. A naive analysis disregards gaps and considers events to be a typical recurrent event dataset. However, this may result in biased estimations and misleading results. In this study, we build an additive rates model for recurrent event data considering intermittent gaps. We provide the asymptotic theories behind the proposed model, as well as the goodness of fit between observed and modeled values. Simulation studies reveal that the estimations perform well if intermittent gaps are taken into account. In addition, we utilized the longitudinal cohort of elderly patients who have type 2 diabetes and at least one record of a severe recurrent complication, hypoglycemia, from the National Health Insurance Research Database in Taiwan to demonstrate the proposed method.
AB - Statistical methods for analyzing recurrent events have attracted significant attention. The majority of existing works consider situations in which subjects are observed over time periods and events of interest that occurred during the course of follow-up are recorded. In some applications, a subject may leave the study for a period of time and then resume due to various reasons. During the absence, which is referred to as an intermittent gap in this study, it may be impossible to observe a recording of the event. A naive analysis disregards gaps and considers events to be a typical recurrent event dataset. However, this may result in biased estimations and misleading results. In this study, we build an additive rates model for recurrent event data considering intermittent gaps. We provide the asymptotic theories behind the proposed model, as well as the goodness of fit between observed and modeled values. Simulation studies reveal that the estimations perform well if intermittent gaps are taken into account. In addition, we utilized the longitudinal cohort of elderly patients who have type 2 diabetes and at least one record of a severe recurrent complication, hypoglycemia, from the National Health Insurance Research Database in Taiwan to demonstrate the proposed method.
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UR - http://www.scopus.com/inward/citedby.url?scp=85056479090&partnerID=8YFLogxK
U2 - 10.1002/sim.8042
DO - 10.1002/sim.8042
M3 - Article
C2 - 30430610
AN - SCOPUS:85056479090
VL - 38
SP - 1343
EP - 1356
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 8
ER -