Skip to main content

Advertisement

Log in

Characterization of Missing Data in Clinical Registry Studies

  • Clinical Trials
  • Published:
Therapeutic Innovation & Regulatory Science Aims and scope Submit manuscript

Abstract

Patterns of missing data are seldom well-characterized in observational research. This study examined the magnitude of, and factors associated with, missing data across multiple observational studies. Missingness was evaluated for demographic, clinical, and patient-reported outcome (PRO) data from a procedure registry (TOPS), a rare disease (cystic fibrosis) registry (Port-CF), and a comparative effectiveness registry (glaucoma, RiGOR). Generalized linear mixed effects models were fit to assess whether patient characteristics or follow-up methods predicted missingness. Data from 156,707 surgical procedures, 32,118 cystic fibrosis patients, and 2373 glaucoma patients were analyzed. Data were rarely missing for demographics, treatments, and outcomes. Missingness for clinical variables varied by registry and measure and depended on whether a variable was required. Within RiGOR, PRO forms were missing more often when collected by e-mail compared with office-based paper data collection. In Port-CF, missingness varied based on insurance status and sex. Strategic consideration of operational approaches affecting missing data should be performed prior to data collection and assessed periodically during study conduct.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Turner JR. The 50th anniversary of the Kefauver-Harris Amendments: efficacy assessment and the randomized clinical trial. J Clin Hypertens (Greenwich). 2012;14(11):810–815.

    Article  Google Scholar 

  2. Dziura JD, Post LA, Zhao Q, Fu Z, Peduzzi P. Strategies for dealing with missing data in clinical trials: from design to analysis. Yale J Biol Med. 2013;86:343–358.

    PubMed  PubMed Central  Google Scholar 

  3. Dreyer NA, Tunis SR, Berger M, Ollendorf D, Mattox P, Gliklich R. Why observational studies should be among the tools used in comparative effectiveness research. Health Aff (Millwood). 2010;29:1818–1825.

    Article  Google Scholar 

  4. Kim HM, Goodman M, Kim BI, Ward KC. Frequency and determinants of missing data in clinical and prognostic variables recently added to SEER. J Registry Manag. 2011;38:120–131.

    PubMed  Google Scholar 

  5. Ommen ES, LaPointe RD, Medapalli RK, Schroppel B, Murphy B. When good intensions are not enough: obtaining follow-up data in living kidney donors. Am J Transplant. 2011;11:2575–2581.

    Article  CAS  Google Scholar 

  6. Gillespie BW, Merion RM, Ortiz-Rios E, et al.; on behalf of the A2ALL Study Group. Database comparison of the adult-to-adult living donor liver transplantation cohort study (A2ALL) and the SRTR US Transplant Registry. Am J Transplant. 2010;10:1621–1633.

    Article  CAS  Google Scholar 

  7. Hosmer DW, Lemeshow S. Goodness of fit tests for the multiple logistic regression model. Communication in Statistics—Theory and Methods. 1980;9:1043–1069.

    Article  Google Scholar 

  8. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis. 1987;40:373–383.

    Article  CAS  Google Scholar 

  9. Rosenstein BJ, Cutting GR; for the Cystic Fibrosis Foundation Consensus Panel. The diagnosis of cystic fibrosis: a consensus statement. J Pediatr. 1998;132:589–595.

    Article  CAS  Google Scholar 

  10. Farrell PM, Rosenstein BJ, White TB, et al. Guidelines for diagnosis of cystic fibrosis in newborns through older adults: Cystic Fibrosis Foundation Consensus Report. J Pediatr. 2008;153:S4–S14.

    Article  Google Scholar 

  11. Rosenfeld M, Davis R, FitzSimmons S, Pepe M, Ramsey B. Gender gap in cystic fibrosis mortality. Am J Epidemiol. 1997;124:794–803.

    Article  Google Scholar 

  12. Parad RB, Gerard CJ, Zurakowski D, Nichols DP, Pier GB. Pulmonary outcome in cystic fibrosis is influenced primarily by mucoid Pseudomonas aeruginosa infection and immune status and only modestly by genotype. Infect Immun. 1999;67:4744–4750.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Corey M, Edwards L, Levison H, Knowles M. Longitudinal analysis of pulmonary decline in patients with cystic fibrosis. J Pediatr. 1997;131:809–814.

    Article  CAS  Google Scholar 

  14. Stern M, Wiedemann B, Wenzlaff P; on behalf of the German Cystic Fibrosis Quality Assessment Group. From registry to quality management: the German Cystic Fibrosis Quality Assessment project 1995–2006. Eur Respir J. 2008;31:29–35.

    Article  CAS  Google Scholar 

  15. Coons SJ, Gwaltney CJ, Hays RD, et al.; on behalf of the ISPOR ePRO Task Force. Recommendations on evidence needed to support measurement equivalence between electronic and paper-based patient-reported outcome (PRO) measures: ISPOR ePRO Good Research Practices Task Force report. Value Health. 2009;12:419–429.

    Article  Google Scholar 

  16. Bezjak A, Lee CW, Ding K, et al. Quality-of-life outcomes for adjuvant chemotherapy in early-stage non-small cell lung cancer: results from a randomized trial, JBR.10. J Clin Oncol. 2008;26:5052–5059.

    Article  Google Scholar 

  17. Klassen AC, Curriero F, Kulldorff M, Alberg AJ, Platz EA, Neloms ST. Missing stage and grade in Maryland prostate cancer surveillance data, 1992–1997. Am J Prev Med. 2006;30:S77–S87.

    Article  Google Scholar 

  18. Schechter MS, Margolis PA. Relationship between socioeconomic status and disease severity in cystic fibrosis. J Pediatr. 1998;132:260–264.

    Article  CAS  Google Scholar 

  19. Parsons HM, Henderson WG, Ziegenfuss JY, Davern M, Al-Refaie WB. Missing data and interpretation of cancer surgery outcomes at the American College of Surgeons National Surgical Quality Improvement Program. J Am Coll Surg. 2011;213:379–391.

    Article  Google Scholar 

  20. Ommen ES, LaPointe RD, Medapalli RK, Schroppel B, Murphy B. When good intentions are not enough: obtaining follow-up data in living kidney donors. Am J Transplant. 2011;11:2575–2581.

    Article  CAS  Google Scholar 

  21. Norris CM, Ghali WA, Knudtson ML, Naylor CD, Saunders LD. Dealing with missing data in observational health care outcome analyses. J Clin Epidemiol. 2000;53:377–383.

    Article  CAS  Google Scholar 

  22. Groenwold RH, Donders AR, Roes KC, Harrell FE Jr. Moons KG. Dealing with missing outcome data in randomized trials and observational studies. Am J Epidemiology. 2012;175:210–217.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aaron B. Mendelsohn MPH, PhD.

Additional information

Presented at the 28th International Conference on Pharmacoepidemiology and Therapeutic Risk Management, August 2012, Barcelona, Spain [abstract published in Pharmacoepidemiol Drug Saf. 2012;21(issue suppl s3):No. 268].

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mendelsohn, A.B., Dreyer, N.A., Mattox, P.W. et al. Characterization of Missing Data in Clinical Registry Studies. Ther Innov Regul Sci 49, 146–154 (2015). https://doi.org/10.1177/2168479014532259

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1177/2168479014532259

Keywords

Navigation