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Testing ignorable missingness in estimating equation approaches for longitudinal data

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  • Annie Qu

Abstract

We address the matter of determining whether or not missing data in longitudinal studies are ignorable with regard to quasilikelihood or estimating equations approaches. This involves testing for whether or not the zero-mean property of estimating equations holds true. Chen & Little (1999) proposed testing for significant differences among parameter estimators calculated from sample subsets with different patterns of missing data, whereas we propose a more unified generalised score-type test. This avoids exhaustive estimation of parameters for each missing-data pattern, testing instead with a single quadratic score test statistic whether or not there is a common parameter under which the means of all the pattern-specific estimating equations are zero. Comparisons are made for the two approaches with both simulations and real data examples. Copyright Biometrika Trust 2002, Oxford University Press.

Suggested Citation

  • Annie Qu, 2002. "Testing ignorable missingness in estimating equation approaches for longitudinal data," Biometrika, Biometrika Trust, vol. 89(4), pages 841-850, December.
    • Handle: RePEc:oup:biomet:v:89:y:2002:i:4:p:841-850
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    Cited by:

    1. Jun Li & Yao Yu, 2015. "A Nonparametric Test of Missing Completely at Random for Incomplete Multivariate Data," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 707-726, September.
    2. Lu Tang & Peter X.‐K. Song, 2021. "Poststratification fusion learning in longitudinal data analysis," Biometrics, The International Biometric Society, vol. 77(3), pages 914-928, September.
    3. Gary Madden & María Rosalía Vicente & Paul Rappoport & Andy Banerjee, 2017. "A contribution on the nature and treatment of missing data in large market surveys," Applied Economics, Taylor & Francis Journals, vol. 49(22), pages 2179-2187, May.
    4. Breunig, Christoph, 2017. "Testing missing at random using instrumental variables," SFB 649 Discussion Papers 2017-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    5. Ke-Hai Yuan & Mortaza Jamshidian & Yutaka Kano, 2018. "Missing Data Mechanisms and Homogeneity of Means and Variances–Covariances," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 425-442, June.
    6. Breunig, Christoph, 2017. "Testing Missing At Random Using Instrumental Variables," Rationality and Competition Discussion Paper Series 59, CRC TRR 190 Rationality and Competition.
    7. repec:hum:wpaper:sfb649dp2015-016 is not listed on IDEAS
    8. repec:hum:wpaper:sfb649dp2017-007 is not listed on IDEAS
    9. Breunig, Christoph, 2015. "Testing missing at random using instrumental variables," SFB 649 Discussion Papers 2015-016, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

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