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Area of Science:

  • Healthcare research methodology
  • Clinical practice improvement
  • Statistical analysis in medicine

Background:

  • Healthcare professionals often need to assess practice changes.
  • Before-after study designs are common but prone to bias.
  • Cluster randomized trials, while optimal, are frequently impractical.

Purpose of the Study:

  • To discuss biases in before-after study designs.
  • To present methods for robustly evaluating systematic practice changes.
  • To focus on segmented regression of interrupted time series analysis.

Main Methods:

  • Discusses biases in before-after designs (confounding, regression to the mean, Hawthorne effect).
  • Highlights segmented regression of interrupted time series (ITS) analysis.
  • Presents alternative designs: difference-in-difference, stepped wedge, and cluster randomization.
  • Emphasizes the need for sufficient time points and confounding variables in ITS.

Main Results:

  • Segmented regression of ITS compares pre- and post-intervention trends without a concurrent control.
  • Difference-in-difference methods incorporate a concurrent control for stronger inference.
  • Methods allow for robust inference on intervention effects, acknowledging limitations.

Conclusions:

  • Appropriate designs and analyses can mitigate biases in evaluating practice changes.
  • Segmented regression of ITS is a valuable tool when concurrent controls are not feasible.
  • The discussed methods, when applied correctly, enable reliable assessment of intervention impact.