Propensity score adjustment of a treatment effect with missing data in psychiatric health services research

Epidemiology Biostatistics and Public Health

Volumn 12, Issue 1, 2015, Pages 1-7

  Mayer, Benjamin ^a   Puschner, Bernd ^a  

^a UNIVERSITY OF ULM   (Germany)

Author keywords

Health services research; Imputation; Missing values; Propensity score

Indexed keywords

EID: 84925863566     PISSN: None     EISSN: 22820930     Source Type: Journal    
DOI: 10.2427/10214     Document Type: Article

References (20)

1. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983, 70(1): 41-55.
2. Guo S, Fraser MW. Propensity score analysis: statistical methods and applications. SAGE Publications, Inc., Thousand Oaks, 2010.
3. European Medicines Agency. Guideline on missing data in confirmatory clinical trials. European Medicines Agency, London, 2010.
4. Mayer B. Fehlende Werte in der Versorgungsforschung [Missing values in health services research]. Monitor Versorgungsforschung 2012, 3: 39-42.
5. Rubin D.B. Multiple imputation for nonresponse in surveys. John Wiley & Sons, New York, 1987.
6. Little RJA, Rubin DB. Statistical analysis with missing data. Second edition. John Wiley & Sons, New Jersey, 2002.
7. Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychological Methods 2002, 7(2): 147-77.
8. Vergouwe Y, Royston P, Moons KGM, Altman DG. Development and validation of a prediction model with missing predictor data: a practical approach. Journal of Clinical Epidemiology 2002, 63: 205-14.
9. Janssen KJM, Vergouwe Y, Donders ART, Harrell FE, Jr., Chen Q, Grobbee DE, Moons KGM. Dealing with missing predictor values when applying clinical prediction models. Clinical Chemistry 2009, 55: 994-1001.
10. Qu Y, Lipkovich I. Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach. Statistics in Medicine 2009, 28(9): 1402-14.
11. D’Agostino RB, Rubin DB. Estimating and using propensity scores with partially missing data. Journal of the American Statistical Association 2000, 95(451): 749-59.
12. Mattei A. Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing. Statistical Methods and Applications 2009, 18: 257-73.
13. Faris PD, Ghali WA, Brant R, Norris CM, Galbraith PD, Knudtson ML. Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses. Journal of Clinical Epidemiology 2002, 55(2): 184-91.
14. Franke GH. Die Symptom-Checkliste von Derogatis-Deutsche Version [Derogatis’ symptom checklist-German version]. Hogrefe Press, Goettingen, 1995.
15. Derogatis LR. SCL-90-R: Self report symptom inventory. In Collegium Internationale Psychiatriae Scalarum (Ed.), Internationale Skalen für Psychiatrie. Weinheim: Beltz. 1986.
16. Puschner B, Kordy H. Mit Transparenz und Ergebnisorientierung zur Optimierung der psychotherapeutischen Versorgung: Eine Studie zur Evaluation ambulanter Psychotherapie [With transparency and results-orientation to the optimum of psychotherapeutic care: a study to evaluate outpatient psychotherapy]. Psychotherapie, Psychosomatik, Medizinische Psychologie 2010, 60: 350-7.
17. Puschner B, Kraft S, Kaechele H, Kordy H. Course of improvement over 2 years in psychoanalytic and psychodynamic outpatient psychotherapy. Psychology and Psychotherapy: Theory, Research and Practice 2007, 80: 51-68.
18. Gallas C, Kaechele H, Kraft S, Kordy H, Puschner B. Inanspruchnahme, Verlauf und Ergebnis ambulanter Psychotherapie: Befunde der TRANS-OP-Studie und deren Implikationen für die Richtlinienpsychotherapie [Utilization, course and outcome of outpatient psychotherapy: Results of the TRANS-OP study and implication for guideline-oriented psychotherapy]. Psychotherapeut 2008, 53(6): 414-23.
19. Raudenbush SW, Bryk AS. Hierarchical linear models: applications and data analysis methods. Sage, Newbury Park, CA, 2001.
20. Jackson D, White IA, Leese M. How much can we learn about missing data?: an exploration of a clinical trial in psychiatry. Journal of the Royal Statistical Society 2010, 173(3): 593-612.