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Combining planned and discovered comparisons in observational studies.

Paul R Rosenbaum1

  • 1Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.

Biostatistics (Oxford, England)
|September 28, 2018
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Summary

This study introduces a new statistical method for analyzing multiple treatment outcomes in observational studies. The approach balances statistical power with robustness against bias, offering flexibility in outcome combination strategies.

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Causal inferencePrincipal componentsReduced-form analysisScheffe projectionsSensitivity analysisSpur to receive treatment

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Area of Science:

  • Statistics
  • Observational Studies
  • Treatment Effect Analysis

Background:

  • Observational studies often involve multiple outcomes of varying quality and relevance to assess treatment effects.
  • A single planned combination of outcomes can enhance statistical power and reduce bias, but may not capture all relevant information.

Purpose of the Study:

  • To propose a novel statistical method for analyzing multiple treatment outcomes in observational studies.
  • To develop a flexible approach that considers both planned outcome combinations and exhaustive analysis of all possibilities.
  • To provide a method that offers a mild correction for multiple testing while allowing for full correction.

Main Methods:

  • The proposed method utilizes the joint distribution of two statistics derived from the treatment effect vector T.
  • It involves an a priori chosen comparison vector kappa and considers the maximum of scaled linear combinations of T.
  • The method is implemented in the R package sensitivitymult and applied to cognitive decline measures.

Main Results:

  • The method allows for a planned combination of outcomes with mild multiple testing correction, enhancing power.
  • It also enables an exhaustive search of all outcome combinations with full multiple testing correction.
  • The approach demonstrates a favorable trade-off between power and multiple testing correction compared to traditional methods.

Conclusions:

  • The proposed statistical method provides a robust and flexible framework for analyzing multiple outcomes in observational studies.
  • It offers improved statistical power and sensitivity to unmeasured bias compared to single-outcome analyses.
  • The R package sensitivitymult facilitates the implementation of this advanced statistical technique.