BIOSTATICS, Lecture notes of Biostatistics

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ACKNOWLEDGMENTS

TEAM MEMBERS

YAZEED ALKHAYAL

SHAHAD ALDUMKH

REVIEWER

ASEEL BADUKHON

KSU COLLEGE OF MEDICINE

BIOSTATICS

Special thanks to SARAH ALENEZY & 436 TEAM

PLAGIARISM PAGE 02

TABLE OF CONTENTS

Original, refrased. Our notes. Doctors notes. Golden notes.

LECTURE OBJECTIVES

By the end of this lecture, I am able to:

QUIZ^ Definition of statistics and biostatistics To understand different Levels of measurements To understand different Types of data To use these concepts appropriately

BIOSTATISTICS

THIS LECTURE

IS

IMPORTANT!!!! EXTREMELY

● Biostatistics is the science that helps in managing medical uncertainties and variability of data ● Statistics arising out of biological sciences, particularly from the fields of medicine and public health. ● The methods used in dealing with statistics in the fields of medicine, biology, and public health for planning, conducting, and analyzing data which arise in investigations of these branches. ● Any health related subject where we’re using statistics is called biostatistics or medical statistics or clinical statistics. ● In case of complicated surgeries or accident victims, doctors/surgeons can’t guarantee if patients are gonna survive this is an example of medical uncertainties. Basic concepts: Data : Set of values of one or more variables recorded on one or more observational units (singular: Datum) Sources of data:

1. Routinely kept records
2. Surveys
3. Experiments
4. External source Categories of data:
5. Primary data: observation, questionnaire, record form, interviews, survey. (you’re collecting data by your own)
6. Secondary data: census, medical record, registry. (already collected by someone else)

Biostatistics

Datasets and Data Tables

● Dataset: Data for a set of variables collection a group of persons ● Data Table: A dataset organized into a table, with one column for each variable and one row for each person.

Typical Data Table:

Definitions for variables: Age: Age in years BMI: Body mass index (weight/height^2 ) in kg/m^2 FFNUM: The average number of times eating “fast food” in a week. TEMP: High temperature for the day GENDER: 1-Female 2-Male EXERCISE LEVEL: 1-Low 2-Medium 3-High QUESTION: What is your satisfaction rating for this Biostatistics session? 1-Very Satisfied 2-Somewhat Satisfied 3- Neutral 4- Somewhat dissatisfied 5- Dissatisfied

OBS AGE BMI FFNUM TEMP (0F) GENDER EXERCISE LEVEL QUESTION 1 26 23.2 0 61.0 0 1 1 2 30 30.2 9 65.5 1 3 2 3 32 28.9 17 59.6 1 3 4 4 37 22.4 1 68.4 1 2 3 5 33 25.5 7 64.5 0 3 5 6 29 22.3 1 70.2 0 2 2 7 32 23.0 0 67.3 0 1 1 8 33 26.3 1 72.8 0 3 1 9 32 22.2 3 71.5 0 1 4 10 33 29.1 5 63.2 1 1 4 11 26 20.8 2 69.1 0 1 3 12 34 20.9 4 73.6 0 2 3 13 31 36.3 1 66.3 0 2 5 14 31 36.4 0 66.9 1 1 5 15 27 28.6 2 70.2 1 2 2 16 36 27.5 2 68.5 1 3 3 17 35 25.6 143 67.8 1 3 4 18 31 21.2 11 70.7 1 1 2 19 36 22.7 8 69.8 0 2 1 20 33 28.1 3 67.8 0 2 1

What is difference between uncertainties and variability of data? Variability: It is a quantitative description of the range or spread of a set of values and can expressed through statistical metrics such as variance, standard deviation. Also variability can’t be reduced, but it can be better characterized. Uncertainties: incomplete understanding of the context of the risk assessment decision. It can be qualitative or quantitative. It can be reduced or eliminated with more or better data.

● When collecting or gathering data we collect data from individuals cases on particular variables. ● A variable is a unit of data collection whose value can vary. (from someone to another) ● Variables can be defined into types according to the level of mathematical scaling that can be carried out on the data. ● There are four types of data or levels of measurements: ○ 1. Nominal 2. Ordinal 3. Interval 4. Ratio

Types of variables & data

Scales of Measurement

Nominal scale variables

Ordinal scale variables

Interval scale variables

Ratio scale variables

A type of categorical data in which objects fall into unordered categories Studies measuring nominal data must ensure that each category is mutually exclusive and the system of measurement needs to be exhaustive Variables that have only two responses i.e. Yes or No, are known as Dichotomies.

Ordinal data is data that comprises of categories that can be rank ordered.

Similarly with nominal data, the distance between each category cannot be calculated but the categories can be ranked above or below each other.

Fahrenheit temperature scale -zero is arbitrary- 40 degrees is not twice as hot as 20 degrees. IQ tests. No such thing as zero IQ. 120 IQ is not twice as intelligent as 60 IQ. Can we assume that attitudinal data represents real, quantifiable measured categories? (i,e, Very happy is twice as happy as plain “Happy”, or “Very Unhappy” means no happiness at all). “Statisticians are not in agreement with this.

The distance between any two adjacent units of measurement (intervals) is the same and there is a meaningful zero point. Income; someone earning SAR20,000 earns twice as much as someone who earns SAR10,000. Height Age Weight.

(names) Its mean I can’t mix between the data. For example: females and males data, smoker and non smoker data.

Mathematically not strong, zero value isn’t fixed (we can’t compare)

Mathematically strong, zero value is fixed (we can compare)

Remember the highlighted words!

Examples of ordinal: grades, cancer stages.

Examples of categorical (nominal & ordinal) data: Eye color: (Nominal) Blue, brown, black, green, etc. Smoking status: (Nominal) Smoker, non-smoker Attitudes towards the death penalty: (Ordinal) Strongly disagree, disagree, neutral, agree, strongly agree.

Nominal data (Binary) & Ordinal data: What is your gender? ☐ Male ☐ Female Did you enjoy the teaching session? ☐ Yes ☐ No What is the level of satisfaction with the new curriculum at a medical school received? ☐ Very satisfied ☐ Somewhat satisfied ☐ Neutral ☐ Somewhat dissatisfied ☐ Very dissatisfied

Quantitative Data

● The objects being studied are ‘measured’ based on some quantitative trait. ● The resulting data are a set of numbers. ● Interval & Ratio scales will be used to measure quantitative data. Examples: Pulse rate Exam marks Height Time to complete a biostatistics exam Age Number of cigarettes smoked

Quantitative Data

Discrete Continuous

Discrete Data: (Whole numbers) Only certain values are possible (there are gaps between the possible values). Implies counting.

Continuous Data: ( Decimal points) Theoretically, with a fine enough measuring device. Implies measuring.

What is difference between Discrete and Continuous Quantitative data? Discrete: can take on only integer (target) values (counted data). For example: the number of students in a hall (you can't have half a student). Continuous: can take on any value (measured data) For example: heights, weight..etc (you can have half data)

● Discrete data - Gaps between possible values:

Quantitative Data (cont.)

Number of children

● Continuous data - Theoretically, no gaps between possible values

Hb

● Examples of Discrete Data: ○ Number of children in a family ○ Number of students passing a stats exam ○ Number of crimes reported to the police ○ Number of bicycles sold in a day Generally, discrete data are counts. We wouldn’t expect to find 2.2 children in a family, or 88.5 students passing an exam, or 127.2 crimes being reported to the police, or half a bicycle sold in a day.

● Examples of Continuous Data: ( All clinical data examples) ○ Age (in years) ○ Height (in cm) ○ Weight (in kg) ○ Sys.BP, Hb, etc. Generally, continuous data comes from measurements.

Variables

Categorical Quantitative

Nominal Ordinal Discrete Continuous

Relationships between variables:

CONTINUOUS DATA QUALITATIVE DATA

Weight (kg): underweight, normal, overweight

Height (cm): short, medium, tall