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Analysis of Categorical Data: Log-linear analysis
14. Analysing Categorical Data
Log-linear analysis
In clinical investigations we often have response and explanatory variables that are both
categorical. For example ill / not ill as response variable
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Poisson Regression Analysis
13. Poisson Regression Analysis
We have so far considered situations where the outcome variable is numeric and Normally distributed, or binary. In clinical work one often encounters situations where the outcome variable is
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Survival Analysis
12. Survival Analysis
In survival analysis we are interested in the time interval between entry into the study and an
event. The outcome of interest is time to an event. Survival analysis was originally developed
for studying time fr
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11. Analysis of Case-control Studies
Logistic Regression
This chapter builds upon and further develops the concepts and strategies described in Ch.6 of
Mother and Child Health: Research methods.
We have so far considered situations
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10. Analysis of Longitudinal Studies
Repeat-measures analysis
This chapter builds on the concepts and methods described in Chapters 7 and 8 of Mother and
Child Health: Research methods.
In repeat-measures designs each subject is obs
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9. Analysis of Intervention Studies - III Factorial Designs
In Chapters 7 and 8 the explanatory categorical variables were referred to as treatments or factors. These terms are often used interchangeably. So in a way we have already
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Two-way ANOVA
8. Analysis of Intervention Studies II
Two-way Analysis of Variance
In the data file about depression the subjects were divided by one factor viz. their mental state
(healthy; non-melancholic depressed; melancholic depressed). Many clinic
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7. Analysis of Intervention Studies I
One-way Analysis of Variance (ANOVA)
This chapter and two more that follow build on Chapter 8 of Mother and Child Health:
Research methods wherein the principles and the basic designs for interv
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Analysis of Cross-Sectional Studies
6. Analysis of Cross-Sectional Studies
Cross-sectional study designs and their variations have been described in the foundation
text - Mother and Child Health: Research Methods in Chapter 5. This chapter builds on th
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Regression Diagnostics
5. Regression Diagnostics
In the preceding chapters the broad principles of multiple linear regression analysis have been
described. The main features of the computer output have been presented, and what specific
features to look
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4. Multiple Regression in Practice
The preceding chapters have helped define the broad principles on which regression analysis
is based. What features one should look for in the computer output and their interpretation h
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3. Multiple Regression Analysis
The concepts and principles developed in dealing with simple linear regression (i.e. one
explanatory variable) may be extended to deal with several explanatory variables.
We begin with an exa
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2. Simple Linear Regression
Simple linear regression is a technique in parametric statistics that is commonly
used for analyzing mean response of a variable Y which changes according to the
magnitude of an intervention variable X.
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1. About Multivariate Methods
In most studies there are one or more outcome (or response) variables and several explanatory
variables together with a variety of variables, which add a characteristic particularity to the
situation.