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Repeated measures models for prescribing change.

Jabulani S Sithole1, Peter W Jones

  • 1Centre for Medical Statistics, Keele University, Stoke on Trent, ST5 5BG, U.K.

Statistics in Medicine
|February 12, 2002
PubMed
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This study used linear mixed models to analyze changes in primary care prescribing habits after an educational intervention. The models accounted for practice size and baseline prescribing, comparing results from different software.

Area of Science:

  • Medical research
  • Statistical modeling

Background:

  • Prescribing habits in primary care are influenced by various factors.
  • Educational interventions aim to modify physician behavior.
  • Evaluating intervention effectiveness requires robust statistical methods.

Purpose of the Study:

  • To detect changes in primary care prescribing habits following an educational intervention.
  • To compare the performance of different statistical software for analyzing repeated measures data.

Main Methods:

  • Linear mixed models were employed to analyze prescribing data.
  • Data included repeated measures before and after an intervention, with a control group.
  • Analyses were corrected for practice size, fundholding status, and baseline prescribing.

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Main Results:

  • The study identified changes in prescribing patterns attributable to the educational intervention.
  • Comparisons were made between multilevel modeling results from MLwiN and NLME v3.0 software.
  • The correlation structure within the data was analyzed.

Conclusions:

  • Linear mixed models are effective for evaluating educational interventions on prescribing habits.
  • Statistical software choice can influence results in multilevel modeling.
  • Adjusting for covariates is crucial for accurate inference.