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Adjustment for Propensity Score in Nonrandomized Clinical Studies: Comparison of Sivelestat Versus Conventional Therapy for Acute Lung Injury in Acute Respiratory Distress Syndrome

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Abstract

Background

To confirm the effectiveness of sivelestat, a clinical trial was conducted comparing sivelestat with conventional treatment in an open, nonrandomized, multicenter study of patients with systemic inflammatory response syndrome (SIRS)–associated acute lung injury. The primary endpoint was ventilator-free days (VFD).

Methods

This study adopted a “cluster entry” method to control for patient selection bias arising from the unblinded and nonrandomized clinical trial. Thus, all patients in the same hospital during the same entry period entered the same treatment arm, and entry periods did not overlap. In the primary analysis of VFD, adjusted mean VFD values were compared between groups using the inverse probability of treatment weighted (IPTW) method, based on propensity score, for control of confounding factors.

Results

There were 374 patients in the sivelestat group and 168 in the conventional therapy group. The primary analysis confirmed that sivelestat was effective (between-group difference of adjusted mean was 3.5 [2-sided 95% confidence interval, 1.3-5.8]; P =.0022).

Conclusions

In general, a study where all patients in the same cluster enter the same treatment arm has within-cluster correlations, which need to be considered in the study analysis. However, in analysis using the IPTW method, it is usual to use a robust variance estimator, the sandwich variance estimator, which is consistent regardless of whether the specification of within-cluster correlation structure is correct. Thus, in the analysis using the IPTW method, it was found that it was not necessary to adopt any other adjustment method for within-cluster correlation.

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References

  1. Bernard GR, Artigas A, Brigham KL, et al. The American-European Consensus Conference on ARDS. Definitions, mechanisms, relevant outcomes, and clinical trial coordination. Am J Respir Crit Care Med. 1994;149:818–824.

    Article  CAS  Google Scholar 

  2. Members of the American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference Committee American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: definitions for 15/20 sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Crit Care Med. 1992; 20:864–874.

  3. Kodama T, Yukioka H, Kato T, Kato N, Hato F, Kitagawa S. Neutrophil elastase as a predicting factor for development of acute lung injury. Intern Med. 2007;46:699–704.

    Article  Google Scholar 

  4. Kawabata K, Suzuki M, Sugitani M, Imaki K, Toda M, Miyamoto T. ONO-5046, a novel inhibitor of human neutrophil elastase. Biochem Biophys Res Commun.1991;177:814–820.

    Article  CAS  Google Scholar 

  5. Aikawa N, Ishizaka A, Hirasawa H, et al. Reevaluation of the efficacy and safety of the neutrophil elastase inhibitor, Sivelestat for the treatment of acute lung injury associated with systemic inflammatory response syndrome; a phase IV study. Pulm Pharmacol Ther. 2011;24:549–554.

    Article  CAS  Google Scholar 

  6. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55.

    Article  Google Scholar 

  7. D’Agostino RB, Jr. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med. 1998;17:2265–2281.

    Article  Google Scholar 

  8. Kurth T, Walker AM, Glynn RJ, et al. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol. 2006;163:262–270.

    Article  Google Scholar 

  9. Austin PC, Grootendorst P, Normand SL, Anderson GM. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study. Stat Med. 2007;26:754–768.

    Article  Google Scholar 

  10. Rubin D. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Stat Med. 2007;26:20–36.

    Article  Google Scholar 

  11. Schoenfeld DA, Bernard GR. ARDS Network: Statistical evaluation of ventilator-free days as an efficacy measure in clinical trials of treatments for acute respiratory distress syndrome. Crit Care Med. 2002;30:1772–1777.

    Article  Google Scholar 

  12. The Acute Respiratory Distress Syndrome Network. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. N Engl J Med. 2000;342:1301–1308.

    Article  Google Scholar 

  13. Robins JM Marginal structural models. Proceedings of the American Statistical Association—Section on Bayesian Statistical Science. 1997; 1–10.

  14. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550–560.

    Article  CAS  Google Scholar 

  15. Diggle PJ, Liang KY, Zeger SL. Analysis of Longitudinal Data. Oxford: Oxford University Press; 1994.

    Google Scholar 

  16. Hoshino T, Shigemasu K. Estimation of causal effect and adjustment of survey data using propensity scores [in Japanese]. Jpn J Behaviormetr. 2004;31:43–61.

    Article  Google Scholar 

  17. Hoshino T, Maeda T. Applying propensity-score adjustment to social surveys with non-random sampling and a selection criteria for covariate [in Japanese]. Proc Inst Stat Math. 2006;54:191–206.

    Google Scholar 

  18. Heckman JJ, Ichimura H, Smith J, Todd P. Sources of selection bias in evaluating programs: an interpretation of conventional measures and evidence on the effectiveness of matching as a program evaluation method. Proc Natl Acad Sci U S A. 1996;93:13416–13420.

    Article  CAS  Google Scholar 

  19. Austin PC. Goodness-of-fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score. Pharmacoepidemiol Drug Saf. 2008;17:1202–1217.

    Article  Google Scholar 

  20. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28:3083–3107.

    Article  Google Scholar 

  21. Lunceford JK, Davidian M. Marginal structural models and causal inference in epidemiology. Stat Med. 2004;23:2937–2960.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Data Science Development Headquarters, Ono Pharmaceutical Co Ltd, 8-2 Kyutaromachi 1-Chome, Chuo-Ku, Osaka, 8564, Japan

    Satoru Fukimbara PhD & Kouji Niibe

  2. Department of Clinical Trial Data Management, Graduate School of Medicine, University of Tokyo, Tokyo, Japan

    Satoru Fukimbara PhD & Takuhiro Yamaguchi PhD

  3. Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan

    Michio Yamamoto PhD

  4. Division of Biostatistics, Tohoku University Graduate School of Medicine, Sendai, Japan

    Takuhiro Yamaguchi PhD

Authors
  1. Satoru Fukimbara PhD
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  2. Kouji Niibe
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  3. Michio Yamamoto PhD
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  4. Takuhiro Yamaguchi PhD
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Correspondence to Satoru Fukimbara PhD.

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Fukimbara, S., Niibe, K., Yamamoto, M. et al. Adjustment for Propensity Score in Nonrandomized Clinical Studies: Comparison of Sivelestat Versus Conventional Therapy for Acute Lung Injury in Acute Respiratory Distress Syndrome. Ther Innov Regul Sci 51, 89–99 (2017). https://doi.org/10.1177/2168479016659103

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