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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Inverse probability weighting methods for Cox regression with right-truncated data.

Bella Vakulenko-Lagun1,2, Micha Mandel3, Rebecca A Betensky4

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

Biometrics
|October 18, 2019
PubMed
Summary
This summary is machine-generated.

New inverse probability weighting (IPW) methods offer simpler, consistent estimation of covariate effects for right-truncated data. These approaches avoid complex baseline hazard calculations, improving Cox proportional hazards model analysis.

Keywords:
positivity assumptionproportional hazardsretrospective ascertainment reverse timeselection biasstabilized weights

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Right-truncated data, common in retrospective studies, bias Cox proportional hazards model estimations.
  • Existing methods for handling right-truncated data are computationally intensive, requiring simultaneous estimation of baseline hazards and covariate effects.
  • This bias arises because only subjects experiencing the event by the sampling time are included.

Purpose of the Study:

  • To develop and evaluate simpler, consistent methods for estimating covariate effects in Cox models with right-truncated data.
  • To address the numerical challenges associated with existing estimation techniques.
  • To investigate the impact of the positivity assumption on method validity.

Main Methods:

  • Two inverse probability weighting (IPW) estimating equation methods are proposed.
  • These methods allow consistent estimation of covariate effects without estimating the baseline hazard function, provided a positivity assumption holds.
  • Adjusted estimating equations are introduced to incorporate external observation probabilities for enhanced consistency.

Main Results:

  • The proposed IPW methods provide consistent estimation of covariate effects under the positivity assumption.
  • Problems of identifiability and consistency are discussed when positivity does not hold.
  • Simulations demonstrate the performance of the proposed methods, which are also applied to HIV latency data.

Conclusions:

  • Inverse probability weighting offers a computationally feasible alternative for analyzing right-truncated data.
  • The validity of IPW methods depends on the positivity assumption, though partial tests may offer some utility in its absence.
  • Adjusted IPW methods improve estimation consistency when observation probabilities are known.