Modeling repeated ordinal responses using a family of power transformations: application to neonatal hypothermia data.
BACKGROUND
For analyzing a repeated ordinal response, it is common to use a multivariate cumulative logit model. This model may fit poorly, especially when a nonsymmetric response is available. In these cases, alternative strategies should be utilized.


METHODS
In this paper, we present a family of power transformations for the cumulative probabilities to model asymmetric departures from the random-intercept cumulative logit model. To illustrate this method, we analyze the data from an epidemiologic study to identify risk factors of hypothermia among newly born infants in some referral university hospitals in Tehran, Iran.


RESULTS
For hypothermia data, using this family of transformations and comparing the goodness-of-fit statistics showed that a model with the cumulative complementary log-log link gives us a better fit compared to a model with the cumulative logit link.


CONCLUSIONS
In some areas, using the ordinary cumulative logit link function does not lead to the best fit. So, other link functions should be evaluated to discover the best transformation for the cumulative probabilities.
Zayeri
Farid
F
Department of Biostatistics, School of Medical Sciences, Tarbiat Modarres University, Tehran, Iran. fzayeri@yahoo.com
Kazemnejad
Anoshirvan
A
Khanafshar
Navid
N
Nayeri
Fatemeh
F
D003696:Delivery Rooms / Q000706:statistics & numerical data; D015340:Epidemiologic Research Design; D006785:Hospitals, University / Q000706:statistics & numerical data; D006801:Humans; D007035:Hypothermia / Q000175:diagnosis / Q000453:epidemiology*; D015994:Incidence; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal / Q000706:statistics & numerical data; D007492:Iran / Q000453:epidemiology; D016015:Logistic Models; D015233:Models, Statistical; D009873:Operating Rooms / Q000706:statistics & numerical data; D011336:Probability; D012307:Risk Factors; D012720:Severity of Illness Index