📄️ 5.1 Introduction: Assumptions and Datatypes
This chapter explores the significance of inferential statistics in health informatics, covering topics such as understanding population parameters from sample statistics, hypothesis testing, confidence intervals, p-values and significance, regression analysis, and a case study that demonstrates how these techniques are applied to analyze clinical trial data.
📄️ 5.2 Hypothesis Testing in Clinical Research
Hypothesis testing is a fundamental statistical technique used in clinical research to evaluate the validity of hypotheses about a population based on sample data. This section provides an in-depth exploration of hypothesis testing in the context of healthcare and clinical research. In health informatics, hypothesis testing can help answer questions like whether a new drug has a significant impact on patient outcomes or if there's a difference in treatment effectiveness for different patient groups.
📄️ 5.3 Statistical Test Selection
Selecting the right statistical test is a crucial step in the data analysis process. It ensures that the conclusions drawn from the analysis are accurate, reliable, and meaningful. However, with the availability of tools like Python and R, it's easy to fall into the trap of running tests without considering whether they are appropriate for the data and research question at hand. This highlights the importance of understanding the types of data, the research objectives, and the assumptions underlying each test.
📄️ 5.4 P-value interpretation and Clinical vs Statistical Significance
P-values and Significance
📄️ 5.5 Confidence Intervals in Health Studies
Confidence intervals are essential tools in health studies for estimating the range of values within which a population parameter is likely to fall. Confidence intervals are widely used in health informatics to quantify the precision of our estimates. This section delves into the concept of confidence intervals, their interpretation, and their significance in healthcare and medical research.
📄️ 5.6 T-tests and Chi-square Tests in Health Research
Both t-tests and chi-square tests are fundamental statistical tests widely employed in health research. While t-tests compare means, chi-square tests evaluate associations between categorical variables. Understanding the right context and assumptions for each is vital for their appropriate application.
📄️ 5.7 ANOVAs with Healthcare Data
The Analysis of Variance (ANOVA) is a statistical test used to determine whether there are any statistically significant differences between the means of three or more independent groups. In the context of healthcare, it might be used, for instance, to compare the mean blood pressure levels of patients across three different treatment regimens.
📄️ 5.8 Relative Risk and Odds Ratio and Related Terms
Intro
📄️ 5.9 Time to Event (TTE)
Introduction
📄️ 5.10 Preview to Regressions
Regressions offer a powerful way to understand how changes in one variable can influence the outcome of another. In the context of healthcare, regression analysis can be crucial for making informed decisions based on evidence from observational data.
📄️ End of Chapter Exercises
📄️ Resources for Further Exploration
- SciPy Stats for Health Research