Purpose: To investigate equivalency of results from multivariable regression (MR) and propensity score matching (PSM) models, observational research methods used to mitigate bias stemming from non-randomization (and consequently unbalanced groups at baseline), using, as an example, a large study of chronic obstructive pulmonary disease (COPD) initial maintenance therapy.
Methods: Patients were 32,338 health plan members, age ≥40 years, with COPD initially treated with fluticasone propionate/salmeterol combination (FSC), tiotropium (TIO), or ipratropium (IPR) alone or in combination with albuterol. Using MR and PSM methods, the proportion of patients with COPD-related health care utilization, mean costs, odds ratios (ORs), and incidence rate ratios (IRRs) for utilization events were calculated for the 12 months following therapy initiation.
Results: Of 12,595 FSC, 9126 TIO, and 10,617 IPR patients meeting MR inclusion criteria, 89.1% (8135) of TIO and 80.2% (8514) of IPR patients were matched to FSC patients for the PSM analysis. Methods produced substantially similar findings for mean cost comparisons, ORs, and IRRs for most utilization events. In contrast to MR, for TIO compared to FSC, PSM did not produce statistically significant ORs for hospitalization or outpatient visit with antibiotic or significant IRRs for hospitalization or outpatient visit with oral corticosteroid. As in the MR analysis, compared to FSC, ORs and IRRs for all other utilization events, as well as mean costs, were less favorable for IPR and TIO.
Conclusion: In this example of an observational study of maintenance therapy for COPD, more than 80% of the original treatment groups used in the MR analysis were matched to comparison treatment groups for the PSM analysis. While some sample size was lost in the PSM analysis, results from both methods were similar in direction and statistical significance, suggesting that MR and PSM were equivalent methods for mitigating bias.
Keywords: chronic obstructive; multivariate analysis; outcomes research; propensity score; pulmonary disease; statistical bias; statistical models.