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Statistics for Clinical Research – A practical Guide


1 : Statistics for Clinical Research – A practical Guide

Sensitivity, Specificity, Hazard Ratio, Life Tables, Clinical Statistic, SPSS, SPSS Result Interpretation and Reporting

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You've come to the correct place if you've ever glanced over the results section of a medical study because words like "confidence interval" or "p-value" confuse you. You might be a clinical practitioner who reads research publications to stay current on advancements in your area of expertise or a medical student who is unsure of how to do their own research. Both working professionals and those conducting their own research might gain from having more confidence in their comprehension of statistical analysis and the results.

Any clinical trial's design, conduct, analysis, and reporting all depend heavily on statistics for minimizing and managing biases, confounding variables, and random error measurement. Mastering statistical techniques is essential to comprehending the procedures and outcomes of randomised trials. We covered many important clinical statistical tests like Sensitivity, Specificity, Life Tables, Hypothesis, Probability, Hazard Ratio, Data Types, Distribution and its types and many other basic statistical test.

This course is a good place to start if you want to learn about clinical statistics including SPSS usage, result interpretation and interpretation. Without delving into complicated calculations, it provides a simple introduction to interpreting popular statistical ideas. The greatest method to delve into the world of clinical literature is to be able to interpret and comprehend these ideas. This course fills that need, so let's get started!

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What you'll learn

  • Fundamentals of Clinical Statistics
  • Hazard Ratio, Sensitivity, Specificity, Life Tables
  • Hypothesis Testing, Sampling, Population, Confidence interval
  • Central Limit Theorem, Probability, Distribution
  • ANOVA, Regression, Correlation, Hierarchical Regression
  • Distributions: Normal, Poission, Chi-square, t-distribution
  • Errors in hypothesis testing: Type-1 error, Type-2 error


  • Understanding of Basic Mathematics
  • Basic of Clinical Statistics
  • Basic of Research Methods
  • Statistical Package for Social Sciences (SPSS)

Who this course is for:

  • Medical Students, Nurses, Research Scholars, Students, Policy Makers, Teaching faculty, Academicians
  • Early Career Researchers, Medical Research Groups
  • PhD scholars and Graduate Students
  • Clinical Researchers
  • Doctors, Nurses and Medical Graduate

The use of statistics in clinical research helps identify relevant patterns in patient characteristics, health services, and outcomes to provide the framework for recommendations.

Some common statistical tests include t-tests, ANOVA and chi-square tests. Examples: race, smoking status, demographic group Examples: age, weight, heart rate, white blood cell count Table 1.

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The use of statistics in clinical trials allows the clinical researcher to form reasonable and accurate inferences from collected information, and sound decisions in the presence of uncertainty. Statistics are key in preventing errors and biases in medical research.

In the basic biomedical science area, most of the studies are randomized-control designed studies. Hypothesis testing is involved most of the time. 4.

Statistics allow psychologists to present data in ways that are easier to comprehend. Visual displays such as graphs, pie charts, frequency distributions, and scatterplots make it possible for researchers to get a better overview of the data and to look for patterns that they might otherwise miss.

Statistics are used to conduct research, evaluate outcomes, develop critical thinking, and make informed decisions. Statistics can be used to inquire almost any field of study to investigate why things happen, when they occur, and whether its reoccurrence is predictable.

Three criteria are decisive for the selection of the statistical test, which are as follows:
  1. the number of variables,
  2. types of data/level of measurement (continuous, binary, categorical) and.
  3. the type of study design (paired or unpaired).

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Statistical methods are essential for scientific research. In fact, statistical methods dominate the scientific research as they include planning, designing, collecting data, analyzing, drawing meaningful interpretation and reporting of research findings.

Essentially therefore, statistics is a scientific approach to analys- ing numerical data in order to enable us to maximise our interpretation, understanding and use. This means that statistics helps us turn data into information; that is, data that have been interpreted, understood and are useful to the recipient.

Statistics allows us to draw conclusions from a set of data and is often called the “Science of Data.” It can also help people in every industry answer their research or business questions, and can help predict outcomes, such as what show you might want to watch next on your favorite video app.

Box: Key questions to ask when assessing clinical data
  1. What are the study's limitations? ...
  2. Does this apply to my patient? ...
  3. Is my patient sufficiently similar to the patients in the studies examined? ...
  4. Does the treatment have a clinically relevant benefit that outweighs the harms? ...
  5. Is another treatment better?

Statistical Methods in Healthcare focuses on the application of statistical methodologies to evaluate promising alternatives and to optimize the performance and demonstrate the effectiveness of those that warrant pursuit is critical to success.

In clinical research, study results, which are statistically significant are often interpreted as being clinically important. While statistical significance indicates the reliability of the study results, clinical significance reflects its impact on clinical practice.

Muhammad Shakil Ahmad

Online Course CoupoNED based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.