PMID- 24117144
OWN - NLM
STAT- MEDLINE
DCOM- 20140805
LR  - 20151119
IS  - 1541-0420 (Electronic)
IS  - 0006-341X (Linking)
VI  - 69
IP  - 4
DP  - 2013 Dec
TI  - Multiple self-controlled case series for large-scale longitudinal observational 
      databases.
PG  - 893-902
LID - 10.1111/biom.12078 [doi]
AB  - Characterization of relationships between time-varying drug exposures and adverse 
      events (AEs) related to health outcomes represents the primary objective in 
      postmarketing drug safety surveillance. Such surveillance increasingly utilizes 
      large-scale longitudinal observational databases (LODs), containing time-stamped 
      patient-level medical information including periods of drug exposure and dates of 
      diagnoses for millions of patients. Statistical methods for LODs must confront 
      computational challenges related to the scale of the data, and must also address 
      confounding and other biases that can undermine efforts to estimate effect sizes. 
      Methods that compare on-drug with off-drug periods within patient offer specific 
      advantages over between patient analysis on both counts. To accomplish these 
      aims, we extend the self-controlled case series (SCCS) for LODs. SCCS implicitly 
      controls for fixed multiplicative baseline covariates since each individual acts 
      as their own control. In addition, only exposed cases are required for the 
      analysis, which is computationally advantageous. The standard SCCS approach is 
      usually used to assess single drugs and therefore estimates marginal associations 
      between individual drugs and particular AEs. Such analyses ignore confounding 
      drugs and interactions and have the potential to give misleading results. In 
      order to avoid these difficulties, we propose a regularized multiple SCCS 
      approach that incorporates potentially thousands or more of time-varying 
      confounders such as other drugs. The approach successfully handles the high 
      dimensionality and can provide a sparse solution via an L(1) regularizer. We 
      present details of the model and the associated optimization procedure, as well 
      as results of empirical investigations.
CI  - (c) 2013, The International Biometric Society.
FAU - Simpson, Shawn E
AU  - Simpson SE
AD  - Department of Statistics, Columbia University, New York, New York, U.S.A.
FAU - Madigan, David
AU  - Madigan D
FAU - Zorych, Ivan
AU  - Zorych I
FAU - Schuemie, Martijn J
AU  - Schuemie MJ
FAU - Ryan, Patrick B
AU  - Ryan PB
FAU - Suchard, Marc A
AU  - Suchard MA
LA  - eng
GR  - 5R01GM087600/GM/NIGMS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20131011
PL  - England
TA  - Biometrics
JT  - Biometrics
JID - 0370625
SB  - IM
MH  - *Case-Control Studies
MH  - *Data Interpretation, Statistical
MH  - *Databases, Factual
MH  - Drug-Related Side Effects and Adverse Reactions/*epidemiology
MH  - Humans
MH  - Incidence
MH  - *Longitudinal Studies
MH  - *Observational Studies as Topic
MH  - Population Surveillance/*methods
MH  - Risk Assessment
OTO - NOTNLM
OT  - Big Data
OT  - Conditional Poisson regression
OT  - Cyclic coordinate descent
OT  - Drug safety
OT  - Postmarketing surveillance
OT  - Regularized regression
OT  - Self-controlled case series
OT  - Statistical computing
EDAT- 2013/10/15 06:00
MHDA- 2014/08/06 06:00
CRDT- 2013/10/15 06:00
PHST- 2012/08/01 00:00 [received]
PHST- 2013/05/01 00:00 [revised]
PHST- 2013/05/01 00:00 [accepted]
PHST- 2013/10/15 06:00 [entrez]
PHST- 2013/10/15 06:00 [pubmed]
PHST- 2014/08/06 06:00 [medline]
AID - 10.1111/biom.12078 [doi]
PST - ppublish
SO  - Biometrics. 2013 Dec;69(4):893-902. doi: 10.1111/biom.12078. Epub 2013 Oct 11.