Application of Seemingly Unrelated Regression in Medical Data with Intermittently Observed Time-Dependent Covariates

Background. In many studies with longitudinal data, time-dependent covariates can only be measured intermittently (not at all observation times), and this presents difficulties for standard statistical analyses. This situation is common in medical studies, and methods that deal with this challenge would be useful. Methods. In this study, we performed the seemingly unrelated regression (SUR) based models, with respect to each observation time in longitudinal data with intermittently observed time-dependent covariates and further compared these models with mixed-effect regression models (MRMs) under three classic imputation procedures. Simulation studies were performed to compare the sample size properties of the estimated coefficients for different modeling choices. Results. In general, the proposed models in the presence of intermittently observed time-dependent covariates showed a good performance. However, when we considered only the observed values of the covariate without any imputations, the resulted biases were greater. The performances of the proposed SUR-based models in comparison with MRM using classic imputation methods were nearly similar with approximately equal amounts of bias and MSE. Conclusion. The simulation study suggests that the SUR-based models work as efficiently as MRM in the case of intermittently observed time-dependent covariates. Thus, it can be used as an alternative to MRM.

[1]  Haiqun Lin,et al.  Psychosocial stress predicts future symptom severities in children and adolescents with Tourette syndrome and/or obsessive-compulsive disorder. , 2007, Journal of child psychology and psychiatry, and allied disciplines.

[2]  P. Bech,et al.  Imipramine: Clinical effects and pharmacokinetic variability , 1977, Psychopharmacology.

[3]  Jeffrey M. Woodbridge.  Econometric Analysis of Cross Section and Panel Data , 2002 .

[4]  C Waternaux,et al.  Investigating drug plasma levels and clinical response using random regression models. , 1989, Psychopharmacology bulletin.

[5]  David B. Allison,et al.  Validity and Power of Missing Data Imputation for Extreme Sampling and Terminal Measures Designs in Mediation Analysis , 2011, Front. Gene..

[6]  H. Stern,et al.  The use of multiple imputation for the analysis of missing data. , 2001, Psychological methods.

[7]  K. Bassett,et al.  Human albumin for intradialytic hypotension in haemodialysis patients. , 2010, The Cochrane database of systematic reviews.

[8]  Nathaniel Schenker,et al.  Analysis of Censored Survival Data with Intermittently Observed Time-Dependent Binary Covariates , 1998 .

[9]  Charles E McCulloch,et al.  Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal Studies , 2004, Biometrics.

[10]  R. Woolson,et al.  Generalized multivariate models for longitudinal data , 1992 .

[11]  A. Mack.  Streptococcal Upper Respiratory Tract Infections and Psychosocial Stress Predict Future Tic and Obsessive-Compulsive Symptom Severity in Children and Adolescents with Tourette Syndrome and Obsessive-Compulsive Disorder , 2011 .

[12]  Mingxiu Hu,et al.  Analysis of Missing Mechanism in IVUS Imaging Clinical Trials with Missing Covariates , 2011, Journal of biopharmaceutical statistics.

[13]  J. Schafer,et al.  Missing data: our view of the state of the art. , 2002, Psychological methods.

[14]  Ana Ivelisse Avilés,et al.  Linear Mixed Models for Longitudinal Data , 2001, Technometrics.

[15]  H. I. Patel.  Analysis of repeated measures designs with changing covariates in clinical trials , 1986 .

[16]  David E. Booth,et al.  Applied Multivariate Analysis , 2003, Technometrics.

[17]  Donald Hedeker,et al.  Longitudinal Data Analysis , 2006 .

[18]  Ahmed M. Gad,et al.  Sensitivity analysis of longitudinal data with intermittent missing values , 2007 .

[19]  W. N. Venables,et al.  An extension of the growth curve model , 1988 .

[20]  Ken P Kleinman,et al.  Much Ado About Nothing , 2007, The American statistician.

[21]  Magdy Elsharkawy,et al.  Intradialytic changes of serum magnesium and their relation to hypotensive episodes in hemodialysis patients on different dialysates , 2006, Hemodialysis international. International Symposium on Home Hemodialysis.

[22]  Xihong Lin,et al.  Missing covariates in longitudinal data with informative dropouts: bias analysis and inference. , 2005, Biometrics.

[23]  Ian R White,et al.  Comparison of imputation and modelling methods in the analysis of a physical activity trial with missing outcomes. , 2004, International journal of epidemiology.

[24]  James P Turley,et al.  Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU , 2011, Theoretical Biology and Medical Modelling.

[25]  B. Tom,et al.  Intermittent observation of time‐dependent explanatory variables: a multistate modelling approach , 2011, Statistics in medicine.

[26]  Yang Zhao,et al.  Likelihood Methods for Regression Models with Expensive Variables Missing by Design , 2009, Biometrical journal. Biometrische Zeitschrift.

[27]  Gina D'Angelo,et al.  Covariates missing by design: comparison of the efficient score to other weighted methods. , 2007, Statistics in medicine.

[28]  R. Jennrich,et al.  Unbalanced repeated-measures models with structured covariance matrices. , 1986, Biometrics.

[29]  Jason Roy,et al.  Analysis of Multivariate Longitudinal Outcomes With Nonignorable Dropouts and Missing Covariates , 2002 .