Clarifying contradictions: transportability in 17OHP-C trials and preterm birth outcomes using doubly debiased machine learning.

Journal: American journal of epidemiology
Published Date: Aug 5, 2026

Abstract

Following the Meis et al. trial that identified a benefit of 17-alpha-hydroxyprogesterone caproate (17OHP-C) in reducing the risk of recurrent preterm birth (PTB) (risk difference (RD) -18.6%; 95% CI, -28.2%, -9.2%), a confirmatory trial (PROLONG) identified no benefit of 17OHP-C (RD, 1.2%; 95% CI, -3.0%, 5.3%). The leading hypothesis is that the difference was due to the heterogeneity in PTB risk. We implemented state-of-the-art methods, using doubly debiased machine learning for transportability to investigate whether the conflicting trial results could be explained by measured differences between trial populations. The estimated RD when transporting the effect in Meis to the PROLONG trial population was -18.6% (95% CI, -55.9%, 8.8%) comparing 17OHP-C to placebo. The estimated RD when transporting PROLONG to Meis was 5.2% (95% CI, -17.3%, 18.1%) comparing 17OHP-C to placebo. Transporting from PROLONG to Meis did not recover the protective effect observed in Meis, which we hypothesize is due to a hidden violation of one or more causal assumptions for transportability, such as the presence of unmeasured effect measure modifiers. Transporting from Meis to PROLONG did not recover the null point estimate observed in PROLONG, though the confidence interval was wide. Future studies should explore effect heterogeneity by PTB.

Authors

  • Arti V Virkud
    Kidney Center School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Eric Tchetgen Tchetgen
    University of Pennsylvania, Perelman School of Medicine, Department of Biostatistics Epidemiology and Informatics.
  • Enrique F Schisterman
    University of Pennsylvania, Perelman School of Medicine, Department of Biostatistics Epidemiology and Informatics.
  • Beth Pineles
  • Lisa D Levine
    Maternal Fetal Medicine Research Program, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • Stephen R Cole
    Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill.
  • Stefanie N Hinkle
    University of Pennsylvania, Perelman School of Medicine, Department of Biostatistics Epidemiology and Informatics.
  • Sunni Mumford
    Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
  • Kristin D Gerson
    University of Pennsylvania, Perelman School of Medicine, Department of Pregnancy and Perinatal Research Center.
  • Brandie D Taylor
    Advocate Aurora Research Institute.
  • Sean Blackwell
    UTHealth Houston McGovern Medical School.
  • Alan Peaceman
    Northwestern Feinberg School of Medicine.
  • Samuel Parry
    University of Pennsylvania, Perelman School of Medicine, Department of Pregnancy and Perinatal Research Center.
  • Maria T Johnson
    PCORI Stakeholder Advisory Panel.
  • Dalynn Willis
    PCORI Stakeholder Advisory Panel.
  • Ellen C Caniglia
    University of Pennsylvania, Perelman School of Medicine, Department of Biostatistics Epidemiology and Informatics.

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