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The Importance of Clinical Variables in Comparative Analyses Using Propensity-Score Matching

The Case of ESA Costs for the Treatment of Chemotherapy-Induced Anaemia

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  • 14 Citations

Abstract

Background

The erythropoiesis-stimulating agents (ESAs) epoetin alfa (EA) and darbepoetin alfa (DA) have comparable efficacy in treating chemotherapy-induced anaemia (CIA). Therapy choice depends on many factors, including cost. Previous estimates of ESA cost differences have been derived from claims data. These data lack clinical variables, such as baseline haemoglobin (Hb) level, which are likely to influence choice of ESA, dosing and costs. We estimated cost differences between DA and EA in patients with cancer receiving chemotherapy, using a propensity-score matched analysis of baseline patient characteristics with and without Hb values to assess the effect of this clinical variable on ESA cost estimates.

Methods

Data were extracted from electronic medical records in two US databases between January 2004 and December 2006. The study sample included 6743 patients receiving chemotherapy, with one or more visits during the study period, who received an ESA during a chemotherapy episode. Episodes of chemotherapy care were constructed using a 90-day gap in administration to identify the start and end. Patients receiving both DA and EA during their initial chemotherapy episode or with missing data were excluded, representing 42% of patients with CIA receiving an ESA. Drug costs were calculated from the cumulative dose multiplied by 106% of the average sales price (ASP) for DA or EA. Two propensity-score matches were conducted: first using variables available in administrative billing claims systems, then adding the baseline Hb test result. Regression-adjusted cost differences were estimated with and without baseline Hb, using generalized linear models.

Results

Using baseline Hb levels resulted in a better match of the baseline characteristics for the EA and DA treatment groups than the original sample or the matched sample without Hb variables. Mean ESA costs (year 2007 values) for the original sample were $US4171 for EA and $US3811 for DA (mean difference $US360; p<0.001, standard error [SE] $US99). With propensity-score matching without Hb variables, mean estimated costs were $US3836 for EA and $US3599 for DA (mean difference $US237; p = 0.053, SE $US123). With propensity-score match including Hb variables, mean costs were $US3965 for EA and $US3536 for DA (mean difference $US429; p = 0.001, SE $US125). Cost differences in sensitivity analyses ranged between $US102 (p = 0.201) and $US261 (p = 0.003).

Conclusions

Addition of baseline Hb level as a variable in propensity score and ESA cost models affects ESA treatment cost estimates in patients with cancer receiving chemotherapy. Cost comparisons based on observational data should use analytical methods that account for differences in clinical variables between treatment groups.

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Correspondence to Dr Jerrold Hill.

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Polsky, D., Eremina, D., Hess, G. et al. The Importance of Clinical Variables in Comparative Analyses Using Propensity-Score Matching. Pharmacoeconomics 27, 755–765 (2009). https://doi.org/10.2165/11313860-000000000-00000

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Keywords

  • Propensity Score
  • Cost Difference
  • Darbepoetin Alfa
  • Matched Sample
  • Average Sales Price