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PubMedDate not supplied

Growth curve model analysis for quality of life data.

There is increasing interest in measuring health related quality of life in cancer clinical trials. Most quality of life data are measured repeatedly over a fixed time schedule to capture changes and to reflect relative advantages of study treatments. A multivariate repeated measures model is usually used to analyse this type of data. However, one of the difficulties of this analysis is that quality of life may be affected by the occurrence of some critical events experienced by patients. We may separate a patient's lifetime during study into different 'health states'. The duration of these health states may vary among patients, and may relate to the efficacy of the study treatment. In some cases quality of life data may be missing due to one of the many different types of missing data mechanisms specific for a health state. It is reasonable to assume that the missing data mechanism for a treatment arm is homogeneous within a defined health state, and to control for the potential confounding effect to appropriately assess the impact of treatment on the quality of life. In this paper, we propose a growth curve model conditional on a time-dependent variable of defined health states in order to assess the overall treatment effect while taking into account occurrences of missing data and measurements from irregular visits. A specific contrast can be drawn within the overall model for testing a specific hypothesis without relying on the analysis of subgroups of patients based on a smaller number of repeated measurements. Quality of life data from a recently completed small-cell lung cancer randomized trial are used to illustrate this method.

Antineoplastic Combined Chemotherapy Protocols↗
PubMedDate not supplied

Comparison of several model-based methods for analysing incomplete quality of life data in cancer clinical trials.

This paper considers five methods of analysis of longitudinal assessment of health related quality of life (QOL) in two clinical trials of cancer therapy. The primary difference in the two trials is the proportion of participants who experience disease progression or death during the period of QOL assessments. The sensitivity of estimation of parameters and hypothesis tests to the potential bias as a consequence of the assumptions of missing completely at random (MCAR), missing at random (MAR) and non-ignorable mechanisms are examined. The methods include complete case analysis (MCAR), mixed-effects models (MAR), a joint mixed-effects and survival model and a pattern-mixture model. Complete case analysis overestimated QOL in both trials. In the adjuvant breast cancer trial, with 15 per cent disease progression, estimates were consistent across the remaining four methods. In the advanced non-small-cell lung cancer trial, with 35 per cent mortality, estimates were sensitive to the missing data assumptions and methods of analysis.

Breast Neoplasms↗