BACKGROUND: Adaptation to changing health presents a challenge to measuring change in quality of life (QoL). Previous studies have shown that different methods for measuring change yield different results, but it is unclear which method provides a more valid measurement of change. We investigated which of 3 commonly used methods for measuring change in QoL yielded the strongest associations with clinical measures of change in health status. METHODS: A total of 268 human immunodeficiency virus (HIV)-1-infected patients starting highly active antiretroviral therapy completed QoL measures at baseline and after 36 weeks. We calculated Pearson correlation coefficients between change in QoL according to a prospective baseline and prospective follow-up measurement, a retrospective baseline and prospective follow-up measurement, or retrospective transition questions and change in CD4-cell count, plasma viral-load, body mass index, and hemoglobin concentration. RESULTS: Patients evaluated their QoL significantly worse on the retrospective than on the prospective baseline measurement (P<0.01). These results may indicate that patients' reference value by which they evaluate their QoL changed over time, which would invalidate prospective serial QoL measurements. The retrospective baseline measurement method yielded strongest associations with clinical measures of change (P=0.02 to P<0.01). Distinguishing between the change inferred from the conventional prospective and the retrospective baseline measurement method was meaningful because the prospective method did not lead to changes that would be considered clinically significant, whereas the retrospective baseline measurement method did. CONCLUSIONS: A method for measuring change in QoL incorporating a retrospective baseline-measurement showed strongest associations with change in clinical indicators of health status, suggesting a more valid measurement of change in QoL than a conventional prospective method.
BACKGROUND: Adaptation to changing health presents a challenge to measuring change in quality of life (QoL). Previous studies have shown that different methods for measuring change yield different results, but it is unclear which method provides a more valid measurement of change. We investigated which of 3 commonly used methods for measuring change in QoL yielded the strongest associations with clinical measures of change in health status. METHODS: A total of 268 human immunodeficiency virus (HIV)-1-infectedpatients starting highly active antiretroviral therapy completed QoL measures at baseline and after 36 weeks. We calculated Pearson correlation coefficients between change in QoL according to a prospective baseline and prospective follow-up measurement, a retrospective baseline and prospective follow-up measurement, or retrospective transition questions and change in CD4-cell count, plasma viral-load, body mass index, and hemoglobin concentration. RESULTS:Patients evaluated their QoL significantly worse on the retrospective than on the prospective baseline measurement (P<0.01). These results may indicate that patients' reference value by which they evaluate their QoL changed over time, which would invalidate prospective serial QoL measurements. The retrospective baseline measurement method yielded strongest associations with clinical measures of change (P=0.02 to P<0.01). Distinguishing between the change inferred from the conventional prospective and the retrospective baseline measurement method was meaningful because the prospective method did not lead to changes that would be considered clinically significant, whereas the retrospective baseline measurement method did. CONCLUSIONS: A method for measuring change in QoL incorporating a retrospective baseline-measurement showed strongest associations with change in clinical indicators of health status, suggesting a more valid measurement of change in QoL than a conventional prospective method.
Authors: Daniel R Reissmann; Antje Erler; Christian Hirsch; Ira Sierwald; Carolina Machuca; Oliver Schierz Journal: Qual Life Res Date: 2017-10-23 Impact factor: 4.147
Authors: Samuel M Brown; Emily Wilson; Angela P Presson; Chong Zhang; Victor D Dinglas; Tom Greene; Ramona O Hopkins; Dale M Needham Journal: Thorax Date: 2016-07-20 Impact factor: 9.139
Authors: Eva M Bitzer; Marco Petrucci; Christoph Lorenz; Rugzan Hussein; Hans Dörning; Alf Trojan; Stefan Nickel Journal: Health Qual Life Outcomes Date: 2011-04-11 Impact factor: 3.186