Although statistical models serve as the foundation of data analysis in clinical studies, their interpretation requires sufficient understanding of the underlying statistical framework. Statistical modeling is inherently a difficult task because of the general lack of information of the nature of observable data. In this article, we aim to provide some guidance when using regression models to aid clinical researchers to better interpret results from their statistical models and to encourage investigators to collaborate with a statistician to ensure that their studies are designed and analyzed appropriately
Several studies indicate that the statistical education model and level in medical training fails to...
In many settings, researchers may not have direct access to data on 1 or more variables needed for a...
With the increasing number and variety of clinical trials and observational data analyses, producers...
Although statistical models serve as the foundation of data analysis in clinical studies, their inte...
In the last decades, statistical methodology has developed rapidly, in particular in the field of re...
In the last decades, statistical methodology has developed rapidly, in particular in the field of re...
Statistical analysis, a necessity for observational studies. Statistical models can help us to under...
Regression modeling is one of the most important statistical techniques used in analytical epidemiol...
When analysing and presenting results of randomised clinical trials, trialists rarely report if or h...
Regression is a statistical approach for modelling the relationship between a response variable y an...
Latent variable models are commonly used in medical statistics, although often not referred to under...
textabstractThis book describes various ways of approaching and interpreting the data produced by cl...
Latent variable models are commonly used in medical statistics, although often not referred to under...
Randomized controlled trials have become the cornerstone of current practice evidence-based medicine...
There are many books that are excellent sources of knowledge about individual statistical tools (sur...
Several studies indicate that the statistical education model and level in medical training fails to...
In many settings, researchers may not have direct access to data on 1 or more variables needed for a...
With the increasing number and variety of clinical trials and observational data analyses, producers...
Although statistical models serve as the foundation of data analysis in clinical studies, their inte...
In the last decades, statistical methodology has developed rapidly, in particular in the field of re...
In the last decades, statistical methodology has developed rapidly, in particular in the field of re...
Statistical analysis, a necessity for observational studies. Statistical models can help us to under...
Regression modeling is one of the most important statistical techniques used in analytical epidemiol...
When analysing and presenting results of randomised clinical trials, trialists rarely report if or h...
Regression is a statistical approach for modelling the relationship between a response variable y an...
Latent variable models are commonly used in medical statistics, although often not referred to under...
textabstractThis book describes various ways of approaching and interpreting the data produced by cl...
Latent variable models are commonly used in medical statistics, although often not referred to under...
Randomized controlled trials have become the cornerstone of current practice evidence-based medicine...
There are many books that are excellent sources of knowledge about individual statistical tools (sur...
Several studies indicate that the statistical education model and level in medical training fails to...
In many settings, researchers may not have direct access to data on 1 or more variables needed for a...
With the increasing number and variety of clinical trials and observational data analyses, producers...