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Analysis of incomplete quality of life data in advanced stage cancer: A practical application of multiple imputation

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Abstract

This paper presents a practical approach to analyzing incomplete quality of life (QOL) data that contains non-ignorable dropouts in patients with advanced non-small-cell lung cancer (NSCLC). QOL scores for the physical domain at baseline and at the end of the first and second courses of chemotherapy were compared between two treatment groups in a phase III trial. One hundred and 103 eligible patients were randomized to receive cisplatin and irinotecan (CPT-P) or cisplatin and vindesine, respectively; of those two groups, 83 and 85, respectively, completed a QOL questionnaire at least at baseline. A multiple imputation incorporating auxiliary QOL variables was implemented as one of alternatives of sensitivity analyses; these were complete case, available case, and pattern mixture analyses. Although larger sensitivity to missing data was found for CPT-P treatment, none of the alternative analyses demonstrated a significant difference in estimated slopes over time between the groups. This study presents an analytical approach for dealing with the complex problem of missing QOL data. It must be noted, however, that the validity of the multiple imputation method we present is not certain unless we can specify sufficiently informative auxiliary variables to ensure the conversion of non-ignorable missingness to ignorable.

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Abbreviations

ABB:

approximate Bayesian bootstrap

CPT-P:

cisplatin + irinotecan

ECOG:

Eastern Cooperative Oncology Group

MAR:

missing at random

MCAR:

missing completely at random

MI:

multiple imputation

MNAR:

missing not at random

NSCLC:

non-small-cell lung cancer

PM:

pattern mixture

PS:

performance status

QOL:

quality of life

QOL-ACD:

Quality of Life Questionnaire for Cancer Patients Treated with Anticancer Drugs

VDS-P:

cisplatin + vindesine

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Authors and Affiliations

  1. Department of Epidemiology and Health Care Research, Kyoto University Graduate School of Medicine, Yoshidakonoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan

    Satoshi Morita

  2. The 2nd Department of Internal Medicine, Saitama Medical School, Saitama, Japan

    Kunihiko Kobayashi

  3. Division of Pulmonary Medicine, Tokai University School of Medicine, Kanagawa, Japan

    Kenji Eguchi

  4. Department of Respiratory Disease, Musashigaoka Hospital, Kumamoto, Japan

    Taketoshi Matsumoto

  5. Division of Respiratory Medicine, Tokyo Metropolitan Komagome Hospital, Tokyo, Japan

    Masahiko Shibuya

  6. Department of Internal Medicine, Mitoyo General Hospital, Kagawa, Japan

    Yasufumi Yamaji

  7. Department of Biostatistics/Epidemiology and Preventive Health Sciences, School of Health Sciences and Nursing, University of Tokyo, Tokyo, Japan

    Satoshi Morita & Yasuo Ohashi

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  1. Satoshi Morita
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  2. Kunihiko Kobayashi
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  3. Kenji Eguchi
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  4. Taketoshi Matsumoto
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  5. Masahiko Shibuya
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  6. Yasufumi Yamaji
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  7. Yasuo Ohashi
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Correspondence to Satoshi Morita.

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Morita, S., Kobayashi, K., Eguchi, K. et al. Analysis of incomplete quality of life data in advanced stage cancer: A practical application of multiple imputation. Qual Life Res 14, 1533–1544 (2005). https://doi.org/10.1007/s11136-004-7708-x

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