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Veterinary Epidemiologic Research: Modelling Survival Data – Parametric and Frailty Models

July 5, 2013
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Veterinary Epidemiologic Research: Modelling Survival Data – Parametric and Frailty Models

Last post on modelling survival data from Veterinary Epidemiologic Research: parametric analyses. The Cox proportional hazards model described in the last post make no assumption about the shape of the baseline hazard, which is an advantage if you have no idea about what that shape might be. With a parametric survival model, the survival time

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Veterinary Epidemiologic Research: Modelling Survival Data – Semi-Parametric Analyses

June 4, 2013
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Veterinary Epidemiologic Research: Modelling Survival Data – Semi-Parametric Analyses

Next on modelling survival data from Veterinary Epidemiologic Research: semi-parametric analyses. With non-parametric analyses, we could only evaluate the effect one or a small number of variables. To evaluate multiple explanatory variables, we analyze data with a proportional hazards model, the Cox regression. The functional form of the baseline hazard is not specified, which make

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Veterinary Epidemiologic Research: Modelling Survival Data – Non-Parametric Analyses

May 23, 2013
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Veterinary Epidemiologic Research: Modelling Survival Data – Non-Parametric Analyses

Next topic from Veterinary Epidemiologic Research: chapter 19, modelling survival data. We start with non-parametric analyses where we make no assumptions about either the distribution of survival times or the functional form of the relationship between a predictor and survival. There are 3 non-parametric methods to describe time-to-event data: actuarial life tables, Kaplan-Meier method, and

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Veterinary Epidemiologic Research: Count and Rate Data – Poisson Regression and Risk Ratios

May 10, 2013
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Veterinary Epidemiologic Research: Count and Rate Data – Poisson Regression and Risk Ratios

As noted on paragraph 18.4.1 of the book Veterinary Epidemiologic Research, logistic regression is widely used for binary data, with the estimates reported as odds ratios (OR). If it’s appropriate for case-control studies, risk ratios (RR) are preferred for cohort studies as RR provides estimates of probabilities directly. Moreover, it is often forgotten the assumption

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Veterinary Epidemiologic Research: Count and Rate Data – Zero Counts

May 6, 2013
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Veterinary Epidemiologic Research: Count and Rate Data – Zero Counts

Continuing on the examples from the book Veterinary Epidemiologic Research, we look today at modelling count when the count of zeros may be higher or lower than expected from a Poisson or negative binomial distribution. When there’s an excess of zero counts, you can fit either a zero-inflated model or a hurdle model. If zero

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Veterinary Epidemiologic Research: Count and Rate Data – Poisson & Negative Binomial Regressions

April 22, 2013
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Veterinary Epidemiologic Research: Count and Rate Data – Poisson & Negative Binomial Regressions

Still going through the book Veterinary Epidemiologic Research and today it’s chapter 18, modelling count and rate data. I’ll have a look at Poisson and negative binomial regressions in R. We use count regression when the outcome we are measuring is a count of number of times an event occurs in an individual or group

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Veterinary Epidemiologic Research: GLM (part 4) – Exact and Conditional Logistic Regressions

March 22, 2013
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Veterinary Epidemiologic Research: GLM (part 4) – Exact and Conditional Logistic Regressions

Next topic on logistic regression: the exact and the conditional logistic regressions. Exact logistic regression When the dataset is very small or severely unbalanced, maximum likelihood estimates of coefficients may be biased. An alternative is to use exact logistic regression, available in R with the elrm package. Its syntax is based on an events/trials formulation.

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Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression Models (part 3)

March 19, 2013
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Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression Models (part 3)

Third part on logistic regression (first here, second here). Two steps in assessing the fit of the model: first is to determine if the model fits using summary measures of goodness of fit or by assessing the predictive ability of the model; second is to deterime if there’s any observations that do not fit the

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Veterinary Epidemiologic Research: GLM – Logistic Regression (part 2)

March 17, 2013
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Veterinary Epidemiologic Research: GLM – Logistic Regression (part 2)

Second part on logistic regression (first one here). We used in the previous post a likelihood ratio test to compare a full and null model. The same can be done to compare a full and nested model to test the contribution of any subset of parameters: Interpretation of coefficients Note: Dohoo do not report the

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Veterinary Epidemiologic Research: GLM – Logistic Regression

March 14, 2013
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Veterinary Epidemiologic Research: GLM – Logistic Regression

We continue to explore the book Veterinary Epidemiologic Research and today we’ll have a look at generalized linear models (GLM), specifically the logistic regression (chapter 16). In veterinary epidemiology, often the outcome is dichotomous (yes/no), representing the presence or absence of disease or mortality. We code 1 for the presence of the outcome and 0

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