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A multiplicative structural nested mean model for zero-inflated outcomes.

Miao Yu1, Wenbin Lu1, Shu Yang1

  • 1Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, North Carolina 27695, U.S.A.

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Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing zero-inflated data, common in areas like mobile gaming. The method accurately estimates treatment effects even with complex confounding factors.

Keywords:
Bidirectional asymptoticsMultiplicative structural nested mean modelTimewise randomizationZero-inflated outcome

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Zero-inflated nonnegative outcomes are prevalent across various fields, including healthcare and business analytics.
  • Analyzing such data requires specialized models that can handle the excess zeros and the nonnegative nature of the outcomes.
  • Time-varying confounders and sequential treatments add complexity to standard statistical modeling.

Purpose of the Study:

  • To propose a novel class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes.
  • To develop a robust statistical framework for estimating treatment effects in the presence of time-varying confounders.
  • To provide a method that is accurate and computationally efficient for analyzing complex longitudinal data.

Main Methods:

  • Developed a doubly robust estimating equation for the proposed structural nested mean models.
  • Estimated nuisance functions (propensity scores and conditional outcome means) using parametric or nonparametric approaches.
  • Modeled conditional means in two parts: probability of positive outcomes and mean outcome given positivity, to enhance accuracy for zero-inflated data.

Main Results:

  • The proposed estimator is shown to be consistent and asymptotically normal.
  • The variance of treatment effect estimators can be consistently estimated using the standard sandwich formula.
  • The method demonstrated strong empirical performance in simulation studies and a real-world application.

Conclusions:

  • The proposed structural nested mean models offer a flexible and robust approach for analyzing zero-inflated nonnegative outcomes.
  • The doubly robust estimation strategy effectively handles complex dependencies and time-varying confounders.
  • The method provides reliable estimates of treatment effects, applicable to diverse fields with similar data structures.