One-to-Many Propensity Score Matching in Cohort Studies

Division of Pharmacoepidemiology and Pharmacoeconomics
Pharmacoepidemiology and Drug Safety (Impact Factor: 2.94). 05/2012; 21 Suppl 2(S2):69-80. DOI: 10.1002/pds.3263
Source: PubMed


Among the large number of cohort studies that employ propensity score matching, most match patients 1:1. Increasing the matching ratio is thought to improve precision but may come with a trade-off with respect to bias.
To evaluate several methods of propensity score matching in cohort studies through simulation and empirical analyses.
We simulated cohorts of 20,000 patients with exposure prevalence of 10%-50%. We simulated five dichotomous and five continuous confounders. We estimated propensity scores and matched using digit-based greedy ("greedy"), pairwise nearest neighbor within a caliper ("nearest neighbor"), and a nearest neighbor approach that sought to balance the scores of the comparison patient above and below that of the treated patient ("balanced nearest neighbor"). We matched at both fixed and variable matching ratios and also evaluated sequential and parallel schemes for the order of formation of 1:n match groups. We then applied this same approach to two cohorts of patients drawn from administrative claims data.
Increasing the match ratio beyond 1:1 generally resulted in somewhat higher bias. It also resulted in lower variance with variable ratio matching but higher variance with fixed. The parallel approach generally resulted in higher mean squared error but lower bias than the sequential approach. Variable ratio, parallel, balanced nearest neighbor matching generally yielded the lowest bias and mean squared error.
1:n matching can be used to increase precision in cohort studies. We recommend a variable ratio, parallel, balanced 1:n, nearest neighbor approach that increases precision over 1:1 matching at a small cost in bias.

61 Reads
  • Source
    • "These ratios and matchings were chosen because our preliminary analysis revealed a prescription imbalance, with significantly more eligible patients in the insulin glargine group when compared with the insulin detemir group, and a much lower number of patients in the switcher group as compared to the continuers group. Additionally, one-to-many matching has previously been validated as a method to increase precision in cohort studies when compared with one-to-one matching, and has also been supported in a recent review assessing the quality of statistical methodologies in matched case–control studies [25, 26]. Matching was implemented without replacement and any patient without at least one match was excluded from the analysis. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Type-2 diabetes mellitus (T2DM) is a progressive disease, and many patients eventually require insulin therapy. This study examined real-world outcomes of switching basal insulin analogs among patients with T2DM. Using two large United States administrative claims databases (IMPACT(®) and Humana(®)), this longitudinal retrospective study examined two cohorts of adult patients with T2DM. Previously on insulin glargine, Cohort 1 either continued insulin glargine (GLA-C) or switched to insulin detemir (DET-S), while Cohort 2 was previously on insulin detemir, and either continued insulin detemir (DET-C) or switched to insulin glargine (GLA-S). One-year follow-up treatment persistence and adherence, glycated hemoglobin (HbA1c), hypoglycemia events, healthcare utilization and costs were assessed. Selection bias was minimized by propensity score matching between treatment groups within each cohort. A total of 5,921 patients (mean age 60 years, female 50.0%, HbA1c 8.6%) were included in the analysis (Cohort 1: IMPACT(®): n = 536 DET-S matched to n = 2,668 GLA-C; Humana(®): n = 256 DET-S matched to n = 1,262 GLA-C; Cohort 2: n = 419 GLA-S matched to n = 780 DET-C), with similar baseline characteristics between treatment groups in each cohort. During 1-year follow-up, in Cohort 1, DET-S patients, when compared with GLA-C patients, had lower treatment persistence/adherence with 33-40% restarting insulin glargine, higher rapid-acting insulin use, worse HbA1c outcomes, significantly higher diabetes drug costs, and similar hypoglycemia rates, health care utilization and total costs. However, in Cohort 2 overall opposite outcomes were observed and only 19.8% GLA-S patients restarted insulin detemir. This study showed contrasting clinical and economic outcomes when patients with T2DM switched basal insulin analogs, with worse outcomes observed for patients switching from insulin glargine to insulin detemir and improved outcomes when switching from insulin detemir to insulin glargine. Further investigation into the therapeutic interchangeability of insulin glargine and insulin detemir in the real-world setting is needed.
    Advances in Therapy 05/2014; 31(5). DOI:10.1007/s12325-014-0120-1 · 2.27 Impact Factor
  • Source

    Pharmacoepidemiology and Drug Safety 05/2012; 21 Suppl 2(S2):1-5. DOI:10.1002/pds.3270 · 2.94 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Under Medicare Part D, patient characteristics influence plan choice, which in turn influences Part D coverage gap entry. We compared predefined propensity score (PS) and high-dimensional propensity score (hdPS) approaches to address such "confounding by health system use" in assessing whether coverage gap entry is associated with cardiovascular events or death. We followed 243,079 Medicare patients aged 65+ years with linked prescription, medical, and plan-specific data in 2005-2007. Patients reached the coverage gap and were followed until an event or year's end. Exposed patients were responsible for drug costs in the gap; unexposed patients (patients with non-Part D drug insurance and Part D patients receiving a low-income subsidy) received financial assistance. Exposed patients were 1:1 PS-matched or hdPS-matched to unexposed patients. The PS model included 52 predefined covariates; the hdPS model added 400 empirically identified covariates. Hazard ratios for death and any of five cardiovascular outcomes were compared. In sensitivity analyses, we explored residual confounding using only low-income subsidy patients in the unexposed group. In unadjusted analyses, exposed patients had no greater hazard of death (HR = 1.00; 95%CI, 0.84-1.20) or other outcomes. PS-matched (HR = 1.29; 0.99-1.66) and hdPS-matched (HR = 1.11; 0.86-1.42) analyses showed elevated but non-significant hazards of death. In sensitivity analyses, the PS analysis showed a protective effect (HR = 0.78; 0.61-0.98), whereas the hdPS analysis (HR = 1.06; 0.82-1.37) confirmed the main hdPS findings. Although the PS-matched analysis suggested elevated but non-significant hazards of death among patients with no financial assistance during the gap, the hdPS analysis produced lower estimates that were stable across sensitivity analyses.
    Pharmacoepidemiology and Drug Safety 05/2012; 21 Suppl 2(S2):90-8. DOI:10.1002/pds.3250 · 2.94 Impact Factor
Show more