Generalized Estimating Equations (GEE) to handle missing data and time-dependent variables in longitudinal studies: an application to assess the evolution of Health Related Quality of Life in coronary patients.

OBJECTIVES

to analyse the evolution of Health Related Quality of Life (HRQL) in coronary patients (CP) and to identify predictive factors influencing this evolution in a situation with missing data and time-dependent variables.

DESIGN

prospective study with repeated measures.

SETTING AND PARTICIPANTS

a total of 175 CP were included. General Estimating Equations (GEE) models were used to assess the evolution of HRQL in these patients. These models, not commonly used in this context, are applied here as an alternative to traditional techniques that do not handle missing data and time-dependent covariates properly.

MAIN OUTCOME MEASURES

HRQL assessed by SF-36v1 Questionnaire at baseline, 3 and 6 months after discharge.

RESULTS

role physical, bodily pain, general health, vitality, and the physical component summary of SF-36 improved over the follow-up. Being woman, older, and having higher scores on GHQ-28 were associated to a decrease in HRQL throughout time. Previous history of coronary heart disease, comorbidities, revascularisation, rehospitalisation, and episode of angina had a negative impact on HRQL, especially between 3 and 6 months after discharge.

CONCLUSION

the analysis of the evolution of HRQL with a longitudinal approach using GEE models shows the predictive effect of the variables analysed during the follow-up, including the time itself and time-dependent covariates such as the evolution of mental health. In addition, it allows to particularise the predictive effect of covariates at each period within the follow-up.