PMID- 23006045
OWN - NLM
STAT- MEDLINE
DCOM- 20131018
LR  - 20141120
IS  - 1541-0420 (Electronic)
IS  - 0006-341X (Linking)
VI  - 69
IP  - 1
DP  - 2013 Mar
TI  - A positive event dependence model for self-controlled case series with 
      applications in postmarketing surveillance.
PG  - 128-36
LID - 10.1111/j.1541-0420.2012.01795.x [doi]
AB  - A primary objective in the application of postmarketing drug safety surveillance 
      is to ascertain the relationship between time-varying drug exposures and 
      recurrent adverse events (AEs) related to health outcomes. The self-controlled 
      case series (SCCS) method is one approach to analysis in this context. It is 
      based on a conditional Poisson regression model, which assumes that events at 
      different time points are conditionally independent given the covariate process. 
      This requirement is problematic when the occurrence of an event can alter the 
      future event risk. In a clinical setting, for example, patients who have a first 
      myocardial infarction (MI) may be at higher subsequent risk for a second. In this 
      work, we propose the positive dependence self-controlled case series (PD-SCCS) 
      method: a generalization of SCCS that allows the occurrence of an event to 
      increase the future event risk, yet maintains the advantages of the original 
      model by controlling for fixed baseline covariates and relying solely on data 
      from cases. As in the SCCS model, individual-level baseline parameters drop out 
      of the PD-SCCS likelihood. Data sources used for postmarketing surveillance can 
      contain tens of millions of people, so in this situation it is particularly 
      advantageous that PD-SCCS avoids doing a costly estimation of individual 
      parameters. We develop expressions for large sample inference and optimization 
      for PD-SCCS and compare the results of our generalized model with the more 
      restrictive SCCS approach.
CI  - Copyright (c) 2013, The International Biometric Society.
FAU - Simpson, Shawn E
AU  - Simpson SE
AD  - Department of Statistics, Columbia University, New York, New York, USA. 
      shawn@stat.columbia.edu
LA  - eng
PT  - Comparative Study
PT  - Journal Article
DEP - 20120924
PL  - England
TA  - Biometrics
JT  - Biometrics
JID - 0370625
RN  - 0 (Lactones)
RN  - 0 (Sulfones)
RN  - 0QTW8Z7MCR (rofecoxib)
RN  - KG60484QX9 (Omeprazole)
RN  - N3PA6559FT (Esomeprazole)
SB  - IM
MH  - Computer Simulation
MH  - Esomeprazole/adverse effects
MH  - Humans
MH  - Lactones/adverse effects
MH  - *Models, Statistical
MH  - Myocardial Infarction/chemically induced
MH  - Omeprazole/adverse effects
MH  - Product Surveillance, Postmarketing/*methods
MH  - *Research Design
MH  - Sulfones/adverse effects
EDAT- 2012/09/26 06:00
MHDA- 2013/10/19 06:00
CRDT- 2012/09/26 06:00
PHST- 2012/09/26 06:00 [entrez]
PHST- 2012/09/26 06:00 [pubmed]
PHST- 2013/10/19 06:00 [medline]
AID - 10.1111/j.1541-0420.2012.01795.x [doi]
PST - ppublish
SO  - Biometrics. 2013 Mar;69(1):128-36. doi: 10.1111/j.1541-0420.2012.01795.x. Epub 
      2012 Sep 24.