title: Confounding and missing data in cost-effectiveness analysis: comparing different methods.
abstract: BACKGROUND
  Common approaches in cost-effectiveness analyses do not adjust for confounders. In nonrandomized studies this can result in biased results. Parametric models such as regression models are commonly applied to adjust for confounding, but there are several issues which need to be accounted for. The distribution of costs is often skewed and there can be a considerable proportion of observations of zero costs, which cannot be well handled using simple linear models. Associations between costs and effectiveness cannot usually be explained using observed background information alone, which also requires special attention in parametric modeling. Furthermore, in longitudinal panel data, missing observations are a growing problem also with nonparametric methods when cumulative outcome measures are used.
  
  
  METHODS
  We compare two methods, which can handle the aforementioned issues, in addition to the standard unadjusted bootstrap techniques for assessing cost-effectiveness in the Helsinki Psychotherapy Study based on five repeated measurements of the Global Severity Index (SCL-90-GSI) and direct costs during one year of follow-up in two groups defined by the Defence Style Questionnaire (DSQ) at baseline. The first method models cumulative costs and effectiveness using generalized linear models, multiple imputation and bootstrap techniques. The second method deals with repeated measurement data directly using a hierarchical two-part logistic and gamma regression model for costs, a hierarchical linear model for effectiveness, and Bayesian inference.
  
  
  RESULTS
  The adjustment for confounders mitigated the differences of the DSQ groups. Our method, based on Bayesian inference, revealed the unexplained association of costs and effectiveness. Furthermore, the method also demonstrated strong heteroscedasticity in positive costs.
  
  
  CONCLUSIONS
  Confounders should be accounted for in cost-effectiveness analyses, if the comparison groups are not randomized.
  
  
  BACKGROUND
  C1; C3; I1.
authors.affiliation: National Institute of Health and Welfare, Mannerheimintie 166, P,O,Box 30, FIN-00271 Helsinki, Finland. tommi.harkanen@thl.fi.
authors.forename: Tommi
authors.initials: T
authors.lastname: Härkänen
authors.forename: Timo
authors.initials: T
authors.lastname: Maljanen
authors.forename: Olavi
authors.initials: O
authors.lastname: Lindfors
authors.forename: Esa
authors.initials: E
authors.lastname: Virtala
authors.forename: Paul
authors.initials: P
authors.lastname: Knekt
references.citation: Soc Sci Med. 1993 Mar;36(6):725-33
references.pmid: 8480217
references.citation: Stat Med. 2005 Oct 30;24(20):3171-84
references.pmid: 15568210
references.citation: Health Econ. 1997 Jul-Aug;6(4):327-40
references.pmid: 9285227
references.citation: J Am Coll Cardiol. 2008 Dec 16;52(25):2119-26
references.pmid: 19095128
references.citation: Stat Med. 1999 Dec 15;18(23):3245-62
references.pmid: 10602149
references.citation: Health Econ. 2005 Dec;14(12):1217-29
references.pmid: 15945043
references.citation: Health Econ. 1997 May-Jun;6(3):243-52
references.pmid: 9226142
references.citation: Arch Gen Psychiatry. 1976 Sep;33(9):1111-5
references.pmid: 962494
references.citation: J Consult Clin Psychol. 1988 Dec;56(6):885-92
references.pmid: 3204198
references.citation: Arch Gen Psychiatry. 1989 May;46(5):455-60
references.pmid: 2785372
references.citation: Health Econ. 2008 Jan;17(1):67-81
references.pmid: 17533622
references.citation: J Chronic Dis. 1981;34(8):415-26
references.pmid: 7263831
references.citation: Health Econ. 2005 May;14(5):487-96
references.pmid: 15497198
references.citation: Psychopharmacol Bull. 1973 Jan;9(1):13-28
references.pmid: 4682398
references.citation: J Health Econ. 2008 Sep;27(5):1250-9
references.pmid: 18490067
references.citation: J Health Econ. 1998 Jun;17(3):247-81
references.pmid: 10180918
references.citation: Biometrics. 1999 Jun;55(2):652-9
references.pmid: 11318229
references.citation: J Ment Health Policy Econ. 2012 Mar;15(1):13-23
references.pmid: 22611089