Key Statistical Considerations. Dr Delva Shamley

Transcription

1 Key Statistical Considerations Dr Delva Shamley

2 WHAT STATISTICAL INFORMATION DO YOU NEED FOR A RESEARCH PROJECT? 1. Sample Size 2. Details of the type of data you will get from your outcome measures 3. JusMficaMon for tests you use to answer specific quesmons

3 LEARNING OUTCOMES By the end of this session you will: 1. Understand when to use parametric and non- parametric sta:s:cs 2. Know why you need to determine a sample size 3. Know why se?ng up a good database is cri:cal 4. How to choose a sta:s:cal test to answer your analysis ques:ons

4 PARAMETRIC VS NON- PARAMETRIC STATISTICAL TESTS Normal Distribu:on is the underlying assump:on of the most powerful sta:s:cal tests!

5 HOW DO YOU DETERMINE THE SAMPLE SIZE? Before you see a stamsmcian Decide which is the primary outcome you wish to measure, And then find ar:cles that: 1. Have similar popula:on and/or se?ng 2. Show a change in same outcome as you will be measuring 3. If using validated measure find clinical significant difference/change

6 WHAT TYPE OF DATA ARE YOU COLLECTING?

7 UNDERSTANDING THE DIFFERENT DATA TYPES Researchers collect data for three reasons 0 50 agemths To describe the characteris:cs of the popula:on sampled (summarize the data) To test hypothesis about this popula:on (test the data) To communicate the findings (publica:on/thesis) def tb not tb poss tb

8 WHAT DATA DO WE COLLECT/MEASURE? Demographics, educa:onal and socio- economic Baseline characteris:cs (Eg. before treatment) Treatment and outcomes (Eg. disease status) Measurements over :me (Eg. Weights at follow- up visits) 5. Laboratory samples (Eg. Haemoglobin, drug concentra:on) NB: Data MUST Support the Research QuesMon

9 DATA TYPES A sample consists of observa:ons/measurements Any aspect of an individual that is measured/recorded = variable Eg. Age, gender, weight, height these are all variables Two broad categories 1. CATEGORICAL 2. NUMERICAL

10 EXAMPLES OF DATA TYPES CATEGORICAL Examples: gender M/F, educa:on level, hair color, smoker (Y/N) Binary: Alloca:on of observa:ons into one of two categories e.g. Yes/No Nominal: Alloca:on of observa:ons into more than two categories (no order) e.g. TB categories (definite TB/possible TB/not TB) Ordinal: Alloca:on of observa:ons into more than two levels e.g. Grade of symptoms (mild/moderate/severe)

11 EXAMPLES OF DATA TYPES NUMERICAL CONTINUOUS (numbers) measurements that can assume any value within a specified range Eg. age, weight, height, pain, haemoglobin DISCRETE A set of data is said to be discrete if the values are counts Eg: Number of pa:ents in a clinic, number of births in a week

12 HOW TO SET-UP COLLECTING YOUR DATA Use your protocol to list the variables that you will need for your analysis Define the data types Do your variables have sub- categories? Is disease of interest a combina:on of symptoms/ variables If you are looking for an associa:on between the disease and a variable do you need to define these? malnutri:on how is this diagnosed?

13 LIST THE VARIABLES DEFINE DATA TYPES variable name data type Length/allowed values coding pa:ent number text 4 date of birth date dd- mmm- yyyy date of examina:on date dd- mmm- yyyy gender text M,F 1,2 intraoccular pressure L num 2(3) intraoccular pressure R num 2(3) cornea thickness L num 2(3) corneal thickness R num 2(3) method of examina:on text 8 1,2,3,4 IOR num 3 symptoms Text mild,moderate,severe 1,2,3

14 HOW DO YOU CHOOSE THE CORRECT STATISTICAL TEST?

15 ALTERNATE AND NULL HYPOTHESIS H1 = Alternate hypothesis Ho= Null hypothesis The p- value disproves the null hypothesis and therefore accepts the alternate hypothesis

16 TASK 1. What is the research ques:on? 2. What is the primary outcome? 3. What type of data is the outcome measuring? 4. What ques:ons were asked in order to generate the data in this table? 5. Which tests were used to generate the different p values.