PUH 5302, Applied Biostatistics 1
Course Learning Outcomes for Unit I Upon completion of this unit, students should be able to:
1. Explain the basic concepts of biostatistical analysis. 1.1 Examine the definition of biostatistics and its application to the public health environment. 1.2 Discuss the importance of biostatistics in public health.
7. Evaluate the role of biostatistical analysis in public health research. 7.1 Define major terms associated with biostatistics. 7.2 Identify various types of study designs, including their strengths and weaknesses.
Course/Unit Learning Outcomes
1.1 Unit Lesson Chapter 1 Unit I Assessment
1.2 Unit Lesson Chapter 1 Unit I Assessment
7.1 Unit Lesson Chapter 2 Unit I Assessment
7.2 Unit Lesson Chapter 2 Unit I Assessment
Reading Assignment Chapter 1: Introduction Chapter 2: Study Designs
Unit Lesson Welcome to Unit I. In this course, you will learn how to apply statistical principles and methods to help identify and solve problems in the public health environment. You will learn the difference between a population and a sample and the issues involved in designing a statistical study, collecting and analyzing data, and generating appropriate inferences. We will use examples from medical and public health literature to serve as discussion points. As a partial fulfilment for the completion of this course, you will be given the opportunity to demonstrate what you have learned by writing a hypothetical research paper for publication. Detailed instructions for this assignment are given in later units of this course. Introduction to Biostatistics in Public Health The collection and use of statistical data has increased over the last decade with the use of computers and statistical software packages. The healthcare industry, including public health, relies heavily on collection of data and often deploys the use of statistical analysis to report on the number of casualties resulting from disasters, auto accidents, new cases of diseases, and so on. To collect, calculate, interpret, and report healthcare data appropriately, the user needs an understanding of the basic concepts relating to biostatistics. To understand biostatistics, we must first try to understand what is the meant by the term statistics.
UNIT I STUDY GUIDE
Overview of Biostatistics
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According to Koch (2015), statistics has two meanings.
1. Statistics is a quantitative fact, for example: number of deaths, number of births, the age of a patient. 2. Statistics is also a discipline or field of study.
However, Koch also gave a more comprehensive definition of statistics. Koch (2015) defines statistics as a way to collect, organize, analyze, present, and report numerical data. Therefore, biostatistics is the application of statistical principles to report public health issues. Sullivan (2018) considers biostatistics to be a method that allows the user to draw conclusions and make decisions based on data. Biostatistics helps us make scientific and intelligent decisions based on collected data from an appropriate sample that reflects the general population. Researchers collect data from a sample that reflects a given population and process and analyze the data using statistical software, for example, Statistical Packages for the Social Sciences (SPSS Statistics). With the help of this tool, the researcher is able to come up with results from which he or she draws inferences that may be generalized to the entire population. Types of statistics: There are two main aspects to statistics: theoretical statistics and applied statistics. Our concern in this course is applied statistics, which involves the application of statistical theories, rules, and laws to solve public health problems. Applied statistics is divided into two disciplines.
1. Descriptive statistics describes and analyzes a specific group without making inferences to the larger group in which the specific group is contained. They generally describe the entire population. Data collected and obtained can be presented in the form of tables or charts.
2. Inferential statistics allows researchers to make claims about a group or population based on data or information. Sample results are used to make decisions or predictions about a population (Koch, 2015).
In biostatistics, experts deploy both descriptive and inferential statistics to collect, analyze, interpret, and report public health findings. This brings us to the importance of biostatistics in solving public health issues. Importance of biostatistics: Biostatistics is useful in many public health and medical studies. Public health officials use biostatistical principles to collect and analyze findings of research studies, disease and casualty figures, and natural disaster occurrences within populations. With the help of biostatistics, researchers are able to make evidence-based decisions or provide reliable information for the benefit of everyone. Sometimes, public health officials may not study an entire population but instead may use samples within a population to investigate a phenomenon of interest. Principles of biostatistics are used to enable results of research findings to be generalized to the entire population. Consequently, public health students, instructors, and the general population are in the position to make generalized statements or describe events on a local, regional, or global scale with some degree of reliability. Study Designs and Common Terms in Biostatistics Public health researchers attempt to collect information by selecting the appropriate study design for the research question. That is, they select a study design that may provide the appropriate answer to the research question. Study design: Study design is the methodology or procedure used to collect data related to the research question. The data, when analyzed, should provide answers to the research question. There are various types of study designs used in public health and other epidemiological studies. The method of data collection that is used to address the research question(s), which helps in obtaining reliable results regarding the phenomenon under study, is called the research design. It is important for investigators to use the appropriate research design that addresses the research question being studied. The wrong research design may affect the results of the study. Below are few examples of research designs categorized under observational and randomized study designs. Observational study designs: Observational studies are an alternative to experimental studies. They involve designs wherein the participants are observed over a period of time without any statistical randomization. They are sometimes referred to as natural experiments where participants are grouped into classes such as exposed to or not exposed to a condition. In research, subjects or participants could be items or individuals of interest specific to the study. In some cases, these research studies are named after their
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nature or the way they are conducted. For example, a cohort study involves two or more groups of subjects followed over a period of time to see if an event under investigation may occur. Randomized study designs: A randomized study design is one that assigns participants into experimental groups or a control group in a random manner. In this type of study, the main difference between the two groups is the outcome variable under study. Randomized studies have some advantages:
eliminating any population bias by showing that no variance exists between groups under study,
being useful in some cases to cure diseases where a known cure has not been discovered,
allowing for statistical analysis using regular statistical tools, and
marking of participants for identification purposes.
Some of the disadvantages of randomized studies include the following issues.
Research often takes a long time to conclude or complete.
They are expensive to undertake.
There is an element of bias in selection of participants.
They do not answer questions relating to causation. In other words, they cannot explain why some treatments work for some participants and not for others.
There are problems of internal and external validity.
Observational Study Designs Randomized Study Designs
Case report/case series
Nested case-control study
Randomized control trial
Active randomized trial
Placebo controlled trial
Examples of study design
Strengths and Weaknesses of Study Designs Researchers have used different study designs in their quest to seek answers to the research questions associated with issues they are investigating. We will identify and outline some of the strengths and weaknesses of some of these studies. Case series/case report: Case reports are specific and detail oriented relating to a particular case, while a case series is a thorough review of common aspects of cases. These types of reports are useful in the medical field because of the ease and speed at which they can be conducted; however, they do have drawbacks in that they do not answer specific questions and are uncontrolled (Sullivan, 2018). Cross-sectional survey: This type of survey is useful when the research question is concentrated on the prevalence of the disease or an opinion. Their deployment in research is useful for estimating a specific risk factor and for making comparison between groups. Inferences from such studies are often limited to the time period the study was conducted, and as such, cannot be generalized to future time frames (Sullivan, 2018). Cohort study: This type of study uses a group of participants who meet certain criteria for participation. Participants are normally followed over a period of time as a group, while associations are drawn between the risk factor and the disease condition (Sullivan, 2018). Cohort studies permit calculation of incidence of disease exposure, and hence the calculation of absolute risk, relative risk, risk difference, and attribution proportion. Cohort studies facilitate studies of real exposures and allow examination of multiple effects of a single exposure. Disadvantages of cohort studies include the problems of following a large number of participants for a long time. They are expensive and are not applicable for rare diseases.
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Case control: A case control study compares patients who have a disease with those without the disease and retrospectively compares frequency of the exposure to a risk factor that is present in each group. The researcher then determines the relationship between the two groups (Sullivan, 2018). Unfortunately, no attempt is made to alter the disease. There are some advantages associated with case control studies. They are relevant in studying rare conditions, involve less time in conducting the study, and provide answers that cannot be answered by using other study designs. The disadvantages include problems with data quality because there is an evidence of recall bias. In addition, they are not good for evaluating diagnostic tests, and it can be difficult to find a suitable control group for the study. Terms Used in Biostatistics Below, you will find a list of common terms used in statistics. For additional terms, please check the glossary of your textbook.
Bias occurs when the results of the study are being affected by a systematic error or the research
influences the study in order to effectuate a certain outcome. Bias in a study affects the validity of the study.
A cohort is a group of participants who are studied over time. Typically, they share similar
characteristics like being 30-35 year old Caucasian females.
Incidence is how many new cases of a disease are seen during a period of time.
Placebo is an inactive substance introduced in the study having the resemblance of the active ingredient or the experimental treatment. Sometimes, placebos produce the desired effect in some experiments because the subject may perceive the placebo as the active substance.
Randomization is when participants receive different treatments in a study. The process is based on
A retrospective is a study in which information is collected on a previous date (Sullivan, 2018). Summary The use of statistical methods in public health and clinical research has been vital to the academic and professional world. Public health professionals have applied principles of statistics to research and draw inferences from those findings to make generalizations regarding a particular phenomenon under study. Experts have used various designs to study diseases and conditions in order to get desired outcomes. The studies are broadly categorized into observational and randomized study designs based on the phenomenon under study and the intent or research questions under investigation by the researcher. Some of these designs include cross-sectional, cohort, crossover trial, and case control studies, to list a few.
References Koch, G. (2015). Basic allied health statistics and analysis (4th ed.). Stamford, CT: Cengage Learning. Sullivan, L. M. (2018). Essentials of biostatistics in public health (3rd ed.). Burlington, MA: Jones & Bartlett
Suggested Reading Take a few minutes to read the article below as a good introduction to the use of statistics in the public health field. It discusses how the Centers for Disease Control and Prevention (CDC) use and analyze data. Sieber, W. K., Green, T., & Williamson, G. D. (2006). Statistics and public health at CDC. Morbidity and
Mortality Weekly Report, 55(Supp. 02), 22–24. Retrieved from https://www.cdc.gov/mmwr/preview/mmwrhtml/su5502a9.htm
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In this unit, you were introduced to different types of research design. The video below will further explore this topic. You are encouraged to view it. Lynch, E. (2013, November 2). Types of study design [Video file]. Retrieved from
https://www.youtube.com/watch?v=PzEyl-VrhKU Click here to view a transcript of this video.
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