Cost-effectiveness analysis using data from multinational trials: The use of bivariate hierarchical modeling

TY - JOUR

T1 - Cost-effectiveness analysis using data from multinational trials

T2 - The use of bivariate hierarchical modeling

AU - Manca, Andrea

AU - Lambert, Paul C.

AU - Sculpher, Mark

AU - Rice, Nigel

PY - 2007

Y1 - 2007

N2 - Health care cost-effectiveness analysis (CEA) often uses individual patient data (IPD) from multinational randomized controlled trials. Although designed to account for between-patient sampling variability in the clinical and economic data, standard analytical, approaches to CEA ignore the presence of between-location variability in the study results. This is a restrictive limitation given that countries often differ in factors that could affect the results of CEAs, such as the availability of health care resources, their unit costs, clinical practice, and patient case mix. The authors advocate the use of Bayesian bivariate hierarchical modeling to analyze multinational cost-effectiveness data. This analytical framework explicitly recognizes that patient-level costs and outcomes are nested within countries. Using real-life data, the authors illustrate how the proposed methods can be applied to obtain (a) more appropriate estimates of overall cost-effectiveness and associated measure of sampling uncertainty compared to standard CEA and (b) country-specific cost-effectiveness estimates that can be used to assess the between-location variability of the study results while controlling for differences in country-specific and patient-specific characteristics. It is demonstrated that results from standard CEA using IPD from multinational trials display a large degree of variability across the 17 countries included in the analysis, producing potentially misleading results. In contrast, "shrinkage estimates" obtained from the modeling approach proposed here facilitate the appropriate quantification of country-specific cost-effectiveness estimates while weighting the results based on the level of information available within each country. The authors suggest that the methods presented here represent a general framework for the analysis of economic data collected from different locations.

AB - Health care cost-effectiveness analysis (CEA) often uses individual patient data (IPD) from multinational randomized controlled trials. Although designed to account for between-patient sampling variability in the clinical and economic data, standard analytical, approaches to CEA ignore the presence of between-location variability in the study results. This is a restrictive limitation given that countries often differ in factors that could affect the results of CEAs, such as the availability of health care resources, their unit costs, clinical practice, and patient case mix. The authors advocate the use of Bayesian bivariate hierarchical modeling to analyze multinational cost-effectiveness data. This analytical framework explicitly recognizes that patient-level costs and outcomes are nested within countries. Using real-life data, the authors illustrate how the proposed methods can be applied to obtain (a) more appropriate estimates of overall cost-effectiveness and associated measure of sampling uncertainty compared to standard CEA and (b) country-specific cost-effectiveness estimates that can be used to assess the between-location variability of the study results while controlling for differences in country-specific and patient-specific characteristics. It is demonstrated that results from standard CEA using IPD from multinational trials display a large degree of variability across the 17 countries included in the analysis, producing potentially misleading results. In contrast, "shrinkage estimates" obtained from the modeling approach proposed here facilitate the appropriate quantification of country-specific cost-effectiveness estimates while weighting the results based on the level of information available within each country. The authors suggest that the methods presented here represent a general framework for the analysis of economic data collected from different locations.

KW - cost-effectiveness analysis

KW - statistics

KW - generalizability

KW - clinical trials

KW - ACUTE CORONARY SYNDROMES

KW - CONGESTIVE-HEART-FAILURE

KW - CLINICAL-TRIALS

KW - SUBGROUP ANALYSIS

KW - COVARIATE ADJUSTMENT

KW - ECONOMIC-ANALYSIS

KW - MULTILEVEL MODELS

KW - SURVIVAL ATLAS

KW - PURSUIT TRIAL

KW - HEALTH-CARE

U2 - 10.1177/0272989X07302132

DO - 10.1177/0272989X07302132

M3 - Article

SN - 0272-989X

VL - 27

SP - 471

EP - 490

JO - Medical Decision Making

JF - Medical Decision Making

IS - 4

ER -