Group sequential method for observational data by using generalized estimating equations: application to Vaccine Safety Datalink

type="main" xml:id="rssc12076-abs-0001"> Post-market medical product surveillance is important for detecting rare adverse events that are not identified during preapproval. The goal of surveillance is to assess over time for elevated rates of adverse events for new medical products. These studies utilize administrative databases from multiple large health plans. We propose a group sequential method using a permutation approach with generalized estimating equations to account for confounding. A simulation study is conducted to evaluate the performance of the group sequential generalized estimating equation method compared with two other approaches. The methods are then applied to a vaccine safety application from the Vaccine Safety Datalink.

[1]  A. Tsiatis,et al.  Approximately optimal one-parameter boundaries for group sequential trials. , 1987, Biometrics.

[2]  S. Pocock.  Group sequential methods in the design and analysis of clinical trials , 1977 .

[3]  Lingling Li,et al.  A conditional sequential sampling procedure for drug safety surveillance , 2009, Statistics in medicine.

[4]  S. Emerson,et al.  A Unifying Family of Group Sequential Test Designs , 1999, Biometrics.

[5]  R. Davis,et al.  The Vaccine Safety Datalink: immunization research in health maintenance organizations in the USA. , 2000, Bulletin of the World Health Organization.

[6]  Andrea J Cook,et al.  Statistical approaches to group sequential monitoring of postmarket safety surveillance data: current state of the art for use in the Mini‐Sentinel pilot , 2012, Pharmacoepidemiology and drug safety.

[7]  P. O'Brien,et al.  A multiple testing procedure for clinical trials. , 1979, Biometrics.

[8]  Andrea J Cook,et al.  Adapting group sequential methods to observational postlicensure vaccine safety surveillance: results of a pentavalent combination DTaP-IPV-Hib vaccine safety study. , 2013, American journal of epidemiology.

[9]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[10]  M. Kulldorff,et al.  A Maximized Sequential Probability Ratio Test for Drug and Vaccine Safety Surveillance , 2011 .

[11]  K. K. Lan,et al.  Discrete sequential boundaries for clinical trials , 1983 .

[12]  Jennifer C. Nelson,et al.  Statistical performance of group sequential methods for observational post-licensure medical product safety surveillance: A simulation study , 2012 .

[13]  W Katherine Yih,et al.  Active Surveillance for Adverse Events: The Experience of the Vaccine Safety Datalink Project , 2011, Pediatrics.

[14]  Michael Rosenblum,et al.  Marginal Structural Models , 2011 .

[15]  Steven J. Jacobsen,et al.  The Vaccine Safety Datalink: A Model for Monitoring Immunization Safety , 2011, Pediatrics.

[16]  Andrea J Cook,et al.  Challenges in the design and analysis of sequentially monitored postmarket safety surveillance evaluations using electronic observational health care data , 2012, Pharmacoepidemiology and drug safety.

[17]  F. Destefano,et al.  The Vaccine Safety Datalink project , 2001, Pharmacoepidemiology and drug safety.

[18]  D. Gillen,et al.  Frequentist evaluation of group sequential clinical trial designs , 2007, Statistics in medicine.