Multilevel Analysis for Repeated Measures Data in Lambs1

Year 2018, Volume: 24 Issue: 2, 218 - 226, 01.06.2018

Suna Akkol , Ferda Karakuş Fırat Cengiz

https://doi.org/10.15832/ankutbd.446440

Cited By: 3

Abstract

The study was conducted to compare the individual growth curves models and to detect individual differences in the growth rate by a performing multilevel analysis. The data set used for this purpose consisted of live weight records of 52 crossbred lambs from birth to 182 days of age. There were 670 observations in level-1 units which were the repeated measurements over time, and there were 52 observations in level-2 units which were lambs. In the study, parameter estimation of timeindependent covariate factors, such as gender, birth type and birth weight, was performed by using five different models within the framework of multilevel modeling. LRT, AIC and BIC were used for the selection of the best model. The “Conditional Quadratic Growth Model-B” provided the best fit to the data set. The multilevel analysis indicated that linear and quadratic growth in lambs was significant. According to the results of the study, individual growth curves can be investigated using multilevel modeling in animal studies which is an important parameter of the individual growth rate.  

Keywords

Repeated data; Multilevel models; Intra-class correlation; Individual growth models

References

• Akaike H (1974). A new look at the statistical model identification. IEEE Transaction on Automatic Control 19(6): 716-724
• Bryk A S & Raudenbush S W (1986). A hierarchical model for studying school effects. Sociology of Education 59: 1-17
• Chen H & Cohen P (2006). Using individual growth model to analyze the change in quality of life from adolescence to adulthood. Health and Quality of Life Outcomes 4: 10
• DeLucia C & Pitts S C (2006). Applications of individual growth curve modelling for pediatric psychology research. Journal of Pediatric Psychology 31(10): 1002-1023
• Dudley W N, McGuire D B, Peterson D E & Wong B (2009). Application of multilevel growth-curve analysis in cancer treatment toxicities: The exemplar of oral mucositis and pain. Oncology Nursing Forum 36(1): 11-19
• Goldstein H (1999). Multilevel statistical models project. Available at http://www.ats.ucla.edu/stat/examples/ msm_goldstein/goldstein.pdf (Accessed on February 5, 2015)
• Green L E, Berriatua E & Morgan K L (1998). A multilevel model of data with repeated measures of the effect of lamb diarrhoea on weight. Preventive Veterinary Medicine 36: 85-94
• Gulliford M C, Ukoumunne O C & Chinn S (1999). Components of variance and intraclass correlations for the design of community-based surveys and intervention studies. American Journal of Epidemiology 149: 876-883
• Hedeker D (2004). An Introduction to Growth Modelling. In: D Kaplan (Ed), The Sage Handbook of Quantitative Methodology for the Social Sciences. Sage Publications, Thousand Oaks,
• CA Hedeker D & Gibbons R D (2006). Longitudinal data analysis. New York: Wiley. ISBN: 978-0-471-42027-9 Hox J J (2010). Multilevel analysis: Techniques and applications.
• Mahwah, NJ: Erlbaum Ip E H, Wasserman R & Barkin S (2011). Comparison of intraclass correlation coefficient estimates and standard errors between using cross-sectional and repeated measurement data: The safety check cluster randomized trial. Contemporary Clinical Trials 32(2): 225-232 doi:10.1016/j.cct.2010.11.001
• Kristjansson S D, Kircher J & Webb A K (2007). Multilevel models for repeated measures research designs in psychophysiology: An introduction to growth curve modelling. Psychophysiology 44: 728-736
• Lancelot R, Lesnoff M, Tillard E & McDermott J J (2000). Graphical approaches to support the analysis of linear-multilevel models of lamb pre-weaning growth in Kolda (Senegal). Preventive Veterinary Medicine 46: 225-247
• Leeden R V D (1998). Multilevel analysis of repeated measures data. Quality & Quantity 32: 25-29
• Leeuw J & Kreft I G G (1986). Random coefficient models for multilevel analysis. Journal of Educational Statistics 11: 55-85
• Littell C R, Pendergast J & Natarajan R (2000). Modelling covariance structure in the analysis of repeated measures data. Statistics in Medicine 19: 1793-1819
• MLwiN (2009). Center of Multilevel Modelling. University of Bristol. http://www.bristol.ac.uk/cmm/
• Peugh J L (2010). A practical guide to multilevel modelling. Journal of School Pyschology 48: 85-112 Raudenbush S W & Bryk A S (2002). Hierarchical linear models: Applications and data analysis methods, 2nd ed. Newbury Park, CA:
• Sage SAS (2014). SAS/STAT. Statistical analysis system for Windows. Released version 9.4. SAS Institute Incorporation, Cary, NC, USA Schwarz G (1978). Estimating the dimensions of a model. Annals of Statistics 6: 461-464
• Shek D T L & Ma C M S (2011). Longitudinal data analyses using linear mixed models in SPSS: Concepts, procedures and illustrations. The Scientific World Journal 11: 42-76
• Simsek B & Fırat M Z (2011). Application of multilevel analysis in animal sciences. Applied Mathematics and Computation 218: 1067-1071
• Singer J D (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics 24: 323-355
• Singer J D & Willett J B (2003). Applied longitudinal data analysis: Modelling change and event occurrence. New York: Oxford University Press
• Smeeth L & Ng E S W (2002). Intraclass correlations for cluster randomized trials in primary care: Data from the MRC trial of the assessment and management of older people in the community. Controlled Clinical Trials 23: 409-421