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Updated: Jun 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
[Secondary data for budget impact analyses]
J Sindern1, D Schröder-Bernhardi, L Slawik
1Health Economics & Pricing, Janssen-Cilag GmbH, Neuss. jsindern@its.jnj.com <jsindern@its.jnj.com>
Aim:
Budget impact analyses are performed to estimate the possible influence of a new health-care technology on the available budget. Scenarios should be if at all possible, evidence-based assumptions on the proportion of treated patients, the current and future mix as well as the direct costs of treatment. For the example of the atypical antipsychotic paliperidone, it is demonstrated that secondary data from routine health-care are well suited for this purpose.
Methods:
By means of the IMS database Disease Analyzer, which consists of the electronic records of a nation-wide representative panel of German physicians (family doctors/general practitioners and neurologists/pschiatrists), various subgroups of schizophrenia patients were identified and the composition and costs of antipsychotic therapy determined in a retrospective longitudinal or, respectively, cross-sectional analysis.
Results:
The demographic data were obtained via the patient records from the offices of 895 general practitioners/family doctors and 102 neurologists/psychiatrists. The number of ill and treated patients with schizophrenia (N=7 927) was determined from the diagnoses and prescriptions. By means of the prescription data (substance and form of administration) the treatment mix was defined. Total costs (2.7 million Euro in 2008) for the antipsychotic therapy were evaluated by links in the databank between individual prescription and the exact monthly pricelists of the pharmacy.
Conclusions:
With the example of a budget impact analysis for the atypical antipsychotic paliperidone ER it was shown that the specific secondary data of the IMS Disease Analyzers are suitable to set up an evidence-based, comprehensive model.
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