SUBSTANTIATION AND IDENTIFICATION OF MODELS FOR ASSESSING THE EFFECT OF PROGRAMS OF PREVENTION OF THE DEVELOPMENT OF SPASM OF ACCOMMODATION
DOI:
https://doi.org/10.24144/2077-6594.3.2019.191646Abstract
The purpose is to substantiate and identify models for assessing the effect of programs for the prevention of accommodation spasm on the basis of modern analytical methods and designs.
Materials and methods. Our research programs have been developed, with a total sample size of 1,115 students. Used panel nested block design. The primary case study investigated primary cases of maladaptation in schoolchildren, along with health, organizational and population conditioning factors during 2014–2018, through participation in standard and extended prevention programs. Analytical methods included a seven-parameter Cox model, a fratil model, a fratil model with an insensitive fraction.
The results indicated the absence of a layer of schoolchildren insensitive to the development of accommodation spasm (AS). Other identification of the form included an individual distribution of sensitivities (the Fralty model), which is uniquely supported by the data. Therefore, the hypothesis of the presence of individual sensitivity to the development of AS and subsequent myopia is supported. Participation in a prevention program (PP) was found to significantly reduce the risk of AS regardless of the presence of acquired sensitivity (risk factors) and congenital (freilty).
The conclusion is robust by three models. Therefore, PP is appropriate for assigning each student unconditionally for individual characteristics. The average annual reduction in AS risk due to participation in PP under different models was 3.75%, 2.50%, and 4.08%. Moreover, the effect is reproduced regardless of the moment of participation in the PP.
References
Bennett S (1983). Log-logistic regression models for survival data. Applied Statistics, 32, 165–71.
Br¨uderl J, Diekmann A (1995). The log-logistic rate model: Two generalizations with an application to demographic data. Sociological Methods & Research, 24, 158–86.
Chen M-H, Ibrahim J, Sinha D (2001). A new Bayesian model for survival data with a surviving fraction. Journal of the American Statistical Association, 94, 909–19.
Chen M-H, Ibrahim J, Sinha D (2002) Bayesian inference for multivariate survival data with a cure fraction. Journal of Multivariate Analysis, 80, 101–26.
Abbring J, van den Berg G (2003). The identifiability of the mixed proportional hazards competing risks model. Journal Royal Statistical Society: Series B, 65, 701–10.
Lancaster T (1990). The econometric analysis of transition data. Cambridge:Cambridge University Press.
Abbring J, van den Berg G (2007) The unobserved heterogeneity distribution in duration analysis. Biometrika, 94, 87–99.
Rozenbaum, P.R., and Rubin D.B. (1983). The Central Role of the Propensity Score in Observational Studies for Casual Effects. Biometrika, 70, 41-45.
Wooldridge, J.M. (2004), “Estimating average partial effects under conditional moment independence assumptions”, the Institute for fiscal studies, Department of economics, ucl cemmap working paper cwp 03/04, 38p.
