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Qualitative longitudinal analysis of symptoms in patients with primary and metastatic brain tumours

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  • Frank Rijmen
  • Edward H. Ip
  • Stephen Rapp
  • Edward G. Shaw

Abstract

Summary. Primary and metastatic brain tumour patients are treated with surgery, radiation therapy and chemotherapy. Such treatments often result in short‐ and long‐term symptoms that impact cognitive, emotional and physical function. Therefore, understanding the transition of symptom burden over time is important for guiding treatment and follow‐up of brain tumour patients with symptom‐specific interventions. We describe the use of a hidden Markov model with person‐specific random effects for the temporal pattern of symptom burden. Clinically relevant covariates are also incorporated in the analysis through the use of generalized linear models.

Suggested Citation

  • Frank Rijmen & Edward H. Ip & Stephen Rapp & Edward G. Shaw, 2008. "Qualitative longitudinal analysis of symptoms in patients with primary and metastatic brain tumours," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 739-753, June.
  • Handle: RePEc:bla:jorssa:v:171:y:2008:i:3:p:739-753
    DOI: 10.1111/j.1467-985X.2008.00529.x
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    References listed on IDEAS

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    1. Murray Aitkin, 1999. "A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models," Biometrics, The International Biometric Society, vol. 55(1), pages 117-128, March.
    2. Scott, Steven L. & James, Gareth M. & Sugar, Catherine A., 2005. "Hidden Markov Models for Longitudinal Comparisons," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 359-369, June.
    3. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
    4. Jeroen K. Vermunt & Rolf Langeheine & Ulf Bockenholt, 1999. "Discrete-Time Discrete-State Latent Markov Models with Time-Constant and Time-Varying Covariates," Journal of Educational and Behavioral Statistics, , vol. 24(2), pages 179-207, June.
    5. Frank Rijmen & Paul Boeck & Han Maas, 2005. "An IRT Model with a Parameter-Driven Process for Change," Psychometrika, Springer;The Psychometric Society, vol. 70(4), pages 651-669, December.
    6. Bobby L. Jones & Daniel S. Nagin & Kathryn Roeder, 2001. "A SAS Procedure Based on Mixture Models for Estimating Developmental Trajectories," Sociological Methods & Research, , vol. 29(3), pages 374-393, February.
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    Cited by:

    1. Delattre, M. & Lavielle, M., 2012. "Maximum likelihood estimation in discrete mixed hidden Markov models using the SAEM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2073-2085.
    2. Florence Chaubert-Pereira & Yann Guédon & Christian Lavergne & Catherine Trottier, 2010. "Markov and Semi-Markov Switching Linear Mixed Models Used to Identify Forest Tree Growth Components," Biometrics, The International Biometric Society, vol. 66(3), pages 753-762, September.
    3. Edward H. Ip & Alison Snow Jones & D. Alex Heckert & Qiang Zhang & Edward D. Gondolf, 2010. "Latent Markov Model for Analyzing Temporal Configuration for Violence Profiles and Trajectories in a Sample of Batterers," Sociological Methods & Research, , vol. 39(2), pages 222-255, November.

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