Sensitivity analysis of methods for active surveillance of acute myocardial infarction using electronic databases.
BACKGROUND
The validity of conclusions from observational studies depends on decisions regarding design, analysis, data quality, and implementation. Through sensitivity analyses, we explored the impact of such decisions on balance control and risk estimates.


METHODS
Using as a template the Mini-Sentinel protocol for the active surveillance of acute myocardial infarction (MI) in association with use of antidiabetic agents, we defined cohorts of new users of metformin and second-generation sulfonylureas, baseline covariates and acute MI events using three combinations of washout and baseline periods. Using propensity-score matching, we assessed balance control and risk estimates using cumulative data for matching all patients compared with not rematching prior matches in quarterly analyses over the follow-up period.


RESULTS
A longer washout period increased the confidence in new-user status, but at the expense of sample size; a longer baseline period improved capture of covariates related to pre-existing chronic conditions. When all patients were matched each quarter, balance was improved and risk estimates were more robust, especially in the later quarters.


CONCLUSIONS
Durations of washout and baseline periods influence the likelihood of new-user status and sample size. Matching all patients tends to result in better covariate balance than matching only new patients. Decisions regarding the durations of washout and baseline periods depend on the specific research question and availability of longitudinal patient data within the database. This paper demonstrates the importance and utility of sensitivity analysis of methods for evaluating the robustness of results in observational studies.
From the aDepartment of Biostatistics, Indiana University School of Medicine and Richard M. Fairbanks School of Public Health, Indianapolis, IN; bDepartment of Epidemiology, Merck Sharp & Dohme, North Wales, PA; and cDepartment of Biomedical Informatics, Regenstrief Institute, Indianapolis, IN.
Xiaochun
X
Li
Cynthia J
CJ
Girman
Susan
S
Ofner
Changyu
C
Shen
Kimberly G
KG
Brodovicz
Linas
L
Simonaitis
Nancy
N
Santanello
D015331:Cohort Studies; D016208:Databases, Factual; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy*; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use*; D008687:Metformin / Q000627:therapeutic use*; D009203:Myocardial Infarction / Q000453:epidemiology*; D011358:Product Surveillance, Postmarketing / Q000379:methods* / Q000592:standards; D057216:Propensity Score; D013453:Sulfonylurea Compounds / Q000627:therapeutic use*
D007004:Hypoglycemic Agents; D013453:Sulfonylurea Compounds; D008687:Metformin