Catálogo de publicaciones - libros

In this chapter, the reader will learn how to define the patient cohort to best address a research question.

In this chapter we highlight the importance of reproducibility and collaboration in clinical research. We also outline common categories of hospital data and provide practical examples for working with relational databases.

In this chapter, the reader will gain knowledge and practical skills about preparing raw clinical data for secondary statistical analysis.

In this chapter, the reader will learn about common sources for missing data, how missing data can be classified depending on the origin of missingness, what options are available for handling missing data and how to choose the most appropriate technique for a specific dataset.

In this chapter, the reader will learn about methods for identifying outliers in a dataset, and how different methods can be compared.

In this chapter, the reader will learn about the most common tools available for exploring a dataset, which is essential in order to gain a good understanding of the features and potential issues of a dataset, as well as helping in hypothesis generation.

This chapter presents an overview of data analysis for health data. We give a brief introduction to some of the most common methods for data analysis of health care data, focusing on choosing appropriate methodology for different types of study objectives, and on presentation and the interpretation of data analysis generated from health data. We will provide an overview of three very powerful analysis methods: linear regression, logistic regression and Cox proportional hazards models, which provide the foundation for most data analysis conducted in clinical studies.

In this chapter, the author will learn about the principles of model validation and how to conduct sensitivity analyses.

Since the publication of the original landmark trial detailing the mortality benefits of low tidal volume ventilation among patients with the acute respiratory distress syndrome (non-cardiogenic pulmonary edema) (Amato et al. in The New England Journal of Medicine 342(18):1301–1308), epidemiological studies have demonstrated that tidal volumes used for mechanically ventilated patients in medical intensive care units have become lower over time (Esteban et al. in American Journal of Respiratory and Critical Care Medicine 177(2):170–177; Esteban et al. in American Journal of Respiratory and Critical Care Medicine 188(2):220). Because patients with heart failure (cardiogenic pulmonary edema) have been systematically excluded from studies investigating low tidal volume mechanical ventilation, the benefit of a low tidal volume strategy among cardiac patients is unclear. We sought to determine whether evidence supporting use of low tidal volumes in patients with non-cardiogenic edema has been generalized into the care of patients with cardiogenic pulmonary edema.

Sources of variation in treatments received that are exogenous to patients can be used to estimate causal effects from observational data. We present an example of this methodology that estimates the effect of critically ill patients being cared for in “non-target ICUs” due to capacity constraints—a process known as