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Stratified Sampling Method01:16

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Propensity score matching and stratification using multiparty data without pooling.

Jixian Wang1, Roland Marion-Gallois1

  • 1Bristol Myers Squibb, Boudry, Switzerland.

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|June 23, 2022
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Summary
This summary is machine-generated.

New methods enable confounding bias adjustment in indirect treatment comparisons without pooling individual patient data. This research addresses challenges in acute myeloid leukemia (AML) studies where data sharing is restricted.

Keywords:
causal inferencedata confidentialitypropensity score matchingsecure multiparty computation

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Area of Science:

  • Biostatistics
  • Health Economics
  • Clinical Trials

Background:

  • Indirect treatment comparisons (ITCs) commonly use propensity score (PS) matching and stratification to reduce confounding bias.
  • Implementing these methods typically requires pooling individual patient data (IPD), which is often not feasible due to privacy and confidentiality concerns.
  • This limitation hinders robust comparative effectiveness research, particularly in settings like acute myeloid leukemia (AML) with restricted data sharing.

Purpose of the Study:

  • To develop and evaluate novel statistical approaches for reducing confounding bias in ITCs when IPD cannot be pooled.
  • To enable valid comparisons between single-armed trials and external control registries without compromising data confidentiality.
  • To adapt methods for survival analysis, specifically using restricted mean survival time (RMST).

Main Methods:

  • Proposed methods combine linear discriminant analysis for matching/stratification with secure multiparty computation (SMC) for variance estimation.
  • SMC is utilized for calculations involving multiple control sources and within-pair sample variance estimation, requiring only offline aggregated data sharing.
  • An RMST approach is introduced for survival data analysis, accommodating mixtures of continuous and binary covariates.

Main Results:

  • A simulation study demonstrated the robustness and efficiency of the proposed methods across various scenarios, including complex covariate mixtures.
  • The developed approaches successfully adjusted for confounding bias without requiring IPD pooling.
  • The methods proved effective even with multiple external control sources.

Conclusions:

  • The proposed non-IPD pooling methods offer a viable solution for confounding adjustment in ITCs under data-sharing restrictions.
  • These techniques enhance the reliability of comparative effectiveness research in oncology and other fields where IPD pooling is challenging.
  • The study provides practical tools for real-world data analysis, illustrated with an AML registry example.