Kuzularda Tekrarlamalı Veriler için Çok Düzeyli Analiz

Bu çalışma, çok düzeyli analizler kullanılarak bireysel büyüme eğrisi modellerini karşılaştırmak ve büyüme oranındaki bireysel farklılıkları belirlemek amacıyla yapıldı. Bu amaç için kullanılan veri seti, 52 baş melez kuzunun doğumdan 182 günlük yaşa kadar olan canlı ağırlık kayıtlarını içermektedir. Zaman içinde tekrarlanan ölçümlerin olduğu seviye-1’de toplamda 670 gözlem ve kuzuların olduğu seviye 2’de 52 gözlem bulunmaktadır. Bu çalışmada, çok seviyeli modelleme yapısı içinde beş farklı model kullanılarak cinsiyet, doğum tipi ve doğum ağırlığı gibi zamana bağlı olmayan kovaryet etkilere ilişkin parametre tahmini yapıldı. En iyi model seçimi için LRT, AIC ve BIC kullanıldı. Veri setini en iyi açıklayan “Conditional Quadratic Growth Model-B” olarak belirlendi. Çok düzeyli analiz, kuzularda doğrusal ve ikinci dereceden büyümenin önemli olduğunu gösterdi. Çalışmanın sonuçlarına göre, bireysel büyüme oranının önemli olduğu hayvancılık çalışmalarında bireysel büyüme eğrileri, çok düzeyli modelleme kullanılarak araştırılabilir.

Multilevel Analysis for Repeated Measures Data in Lambs1

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.

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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
Singer J D & Willett J B (2003). Applied longitudinal data analysis: Modelling change and event occurrence. New York: Oxford University Press
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
Simsek B & Fırat M Z (2011). Application of multilevel analysis in animal sciences. Applied Mathematics and Computation 218: 1067-1071
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
SAS (2014). SAS/STAT. Statistical analysis system for Windows. Released version 9.4. SAS Institute Incorporation, Cary, NC, USA
Raudenbush S W & Bryk A S (2002). Hierarchical linear models: Applications and data analysis methods, 2nd ed. Newbury Park, CA: Sage
Peugh J L (2010). A practical guide to multilevel modelling. Journal of School Pyschology 48: 85-112
MLwiN (2009). Center of Multilevel Modelling. University of Bristol. http://www.bristol.ac.uk/cmm/
Littell C R, Pendergast J & Natarajan R (2000). Modelling covariance structure in the analysis of repeated measures data. Statistics in Medicine 19: 1793-1819
Leeuw J & Kreft I G G (1986). Random coefficient models for multilevel analysis. Journal of Educational Statistics 11: 55-85
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
Leeden R V D (1998). Multilevel analysis of repeated measures data. Quality & Quantity 32: 25-29
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
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
Hox J J (2010). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum
Hedeker D & Gibbons R D (2006). Longitudinal data analysis. New York: Wiley. ISBN: 978-0-471-42027-9
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
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
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
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)
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
DeLucia C & Pitts S C (2006). Applications of individual growth curve modelling for pediatric psychology research. Journal of Pediatric Psychology 31(10): 1002-1023
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
Bryk A S & Raudenbush S W (1986). A hierarchical model for studying school effects. Sociology of Education 59: 1-17
Akaike H (1974). A new look at the statistical model identification. IEEE Transaction on Automatic Control 19(6): 716-724