Using propensity scores subclassification to estimate effects of longitudinal treatments: an example using a new diabetes medication.
BACKGROUND
When using observational data to compare the effectiveness of medications, it is essential to account parsimoniously for patients' longitudinal characteristics that lead to changes in treatments over time.


OBJECTIVE
We developed a method of estimating effects of longitudinal treatments that uses subclassification on a longitudinal propensity score to compare outcomes between a new drug (exenatide) and established drugs (insulin and oral medications) assuming knowledge of the variables influencing the treatment assignment. RESEARCH DESIGN/SUBJECTS: We assembled a retrospective cohort of patients with diabetes mellitus from among a population of employed persons and their dependents.


METHODS
The data, from i3Innovus, includes claims for utilization of medications and inpatient and outpatient services. We estimated a model for the longitudinal propensity score process of receiving a medication of interest. We used our methods to estimate the effect of the new versus established drugs on total health care charges and hospitalization.


RESULTS
We had data from 131,714 patients with diabetes filling prescriptions from June through December 2005. Within propensity score quintiles, the explanatory covariates were well-balanced. We estimated that the total health care charges per month that would have occurred if all patients had been continually on exenatide compared with if the same patients had been on insulin were minimally higher, with a mean monthly difference of $397 [95% confidence interval (CI), $218-$1054]. The odds of hospitalization were also comparable (relative odds, 1.02; 95% CI, 0.33-1.98).


CONCLUSIONS
We used subclassification of a longitudinal propensity score for reducing the multidimensionality of observational data, including treatments changing over time. In our example, evaluating a new diabetes drug, there were no demonstrable differences in outcomes relative to existing therapies.
Segal
Jodi B
JB
Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. jsegal@jhmi.edu
Griswold
Michael
M
Achy-Brou
Aristide
A
Herbert
Robert
R
Bass
Eric B
EB
Dy
Sydney M
SM
Millman
Anne E
AE
Wu
Albert W
AW
Frangakis
Constantine E
CE
D000293:Adolescent; D000328:Adult; D003920:Diabetes Mellitus / Q000188:drug therapy / Q000453:epidemiology; D011307:Drug Prescriptions / Q000706:statistics & numerical data; D004363:Drug Utilization / Q000706:statistics & numerical data*; D000077270:Exenatide; D005260:Female; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D019370:Observation / Q000379:methods*; D010349:Patient Compliance; D010455:Peptides / Q000627:therapeutic use; D017891:Pharmacoepidemiology / Q000379:methods / Q000706:statistics & numerical data*; D010818:Practice Patterns, Physicians'; D012189:Retrospective Studies; D016896:Treatment Outcome; D014688:Venoms / Q000627:therapeutic use
D007004:Hypoglycemic Agents; D010455:Peptides; D014688:Venoms; D000077270:Exenatide