PURPOSE: A key aspect of comparative effectiveness research is the assessment of competing treatment options and multiple outcomes rather than a single treatment option and a single benefit or harm. In this commentary, we describe a methodological framework that supports the simultaneous examination of a "matrix" of treatments and outcomes in non-randomized data. METHODS: We outline the methodological challenges to a matrix-type study (matrix design). We consider propensity score matching with multiple treatment groups, statistical analysis, and choice of association measure when evaluating multiple outcomes. We also discuss multiple testing, use of high-dimensional propensity scores for covariate balancing in light of multiple outcomes, and suitability of available software. CONCLUSION: The matrix design study methods facilitate examination of the comparative benefits and harms of competing treatment choices, and also provides the input required for calculating the numbers needed to treat and for a broader benefit/harm assessment that weighs endpoints of varying severity.
PURPOSE: A key aspect of comparative effectiveness research is the assessment of competing treatment options and multiple outcomes rather than a single treatment option and a single benefit or harm. In this commentary, we describe a methodological framework that supports the simultaneous examination of a "matrix" of treatments and outcomes in non-randomized data. METHODS: We outline the methodological challenges to a matrix-type study (matrix design). We consider propensity score matching with multiple treatment groups, statistical analysis, and choice of association measure when evaluating multiple outcomes. We also discuss multiple testing, use of high-dimensional propensity scores for covariate balancing in light of multiple outcomes, and suitability of available software. CONCLUSION: The matrix design study methods facilitate examination of the comparative benefits and harms of competing treatment choices, and also provides the input required for calculating the numbers needed to treat and for a broader benefit/harm assessment that weighs endpoints of varying severity.
Authors: Helena Canhão; Ana Maria Rodrigues; Ana Filipa Mourão; Fernando Martins; Maria José Santos; José Canas-Silva; Joaquim Polido-Pereira; José Alberto Pereira Silva; José António Costa; Domingos Araújo; Cândida Silva; Helena Santos; Cátia Duarte; José Antonio Pereira da Silva; Fernando M Pimentel-Santos; Jaime Cunha Branco; Elizabeth W Karlson; João Eurico Fonseca; Daniel H Solomon Journal: Rheumatology (Oxford) Date: 2012-07-28 Impact factor: 7.580
Authors: Anne Lübbeke; Alan J Silman; Daniel Prieto-Alhambra; Amanda I Adler; Christophe Barea; Andrew J Carr Journal: BMC Musculoskelet Disord Date: 2017-10-16 Impact factor: 2.362