Analysis of health-related quality of life data in clinical trial with non-ignorable missing based on pattern mixture model.

• Other Authors: Mo, Kwok Fai.
• Format: Others
• Language: English; Chinese
• Published: 2006
• Subjects: Clinical trials > Statistical methods; Quality of life > Research > Methodology; Clinical Trials as Topic; Quality of Life; Research Design
• Online Access: http://library.cuhk.edu.hk/record=b6074225; http://repository.lib.cuhk.edu.hk/en/item/cuhk-343854

Conclusion. The missing data is a common problem in clinical trial. The methodology development is urgently needed to detect the difference of two treatments drug in patient quality of life. The modified pattern mixture model incorporating generalized estimating equation method or multiple imputation method provides a solution to tackle the non-ignorable missing data problem. Different clinical trials with various treatment schedules, missing data patterns will be formed. Further studies are needed to study the optimal choice of patterns under the methods. === Introduction. Health-related Quality of Life (HRQoL) has now been included as a major endpoint in many cancer clinical trials in addition to the traditional endpoints such as tumor response and survival. It refers to how illness or its treatment affects patients' ability to function and whether it induces symptoms. Toxicity, progression and death are common outcome affecting patient's QOL in cancer trial. Since this type of missing data are not occurred at random and are called non-ignorable missing data, conventional methods of analyses are not appropriate. It is important to develop general methods to deal with this problem so that treatment effectiveness for improving patient's QOL or those with serious side effect that is detrimental to patient's QOL can be identified. === Methods. The generalized estimating equation based on modified pattern mixture model is constructed to deal with non-ignorable missing data problem. We conducted a simulation study to examine performance of the model for different types of data. Two scenarios were examined. The first case assumes that two groups have quadratic trend but with different rates of change. The second case assumes that one group has linear trend with time while the other group has quadratic trend with time. Moreover, the second methodology is the multiple imputation based on modified pattern mixture model. The main idea is to resample the data within each pattern to create the full data set and use the standard method to analyze the data. Comparison between two methods was carried out in this study. === Recently, joint models for the QOL outcomes and the indicators of drop-outs are used in longitudinal studies to correct for non-ignorable missing. Two broad classes of joint models, selection model and pattern mixture model, were used. Most of the methodology has been developed in the selection model while the pattern mixture model has attracted less attention due to the identifiability problem. Although pattern mixture model has its own limitation, a modified version of this model incorporating Generalized Estimating Equation can be used in practice. === Result. The power of generalized estimating equation alone is higher than pattern mixture model when the missing data is missing at random. Moreover, the bias of generalized estimating equation is less than that of pattern mixture model when the missing data is missing at random. However, the pattern mixture model performs well when the missing data is missing not at random. On the other hand, the modified pattern mixture model has higher power than the standard pattern mixture model if one group has quadratic trend and other group has linear trend. However, the power of modified pattern mixture model is similar or worst than the standard when the data is both quadratic trends with different rates of change. On the other hand, the results of multiple imputation based on modified pattern mixture model were similar but the power was less than the generalized estimating equation model. === Mo Kwok Fai. === "August 2006." === Adviser: Benny Zee. === Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6051. === Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. === Includes bibliographical references (p. 91-93). === Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. === Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. === Abstracts in English and Chinese. === School code: 1307.