Graphpad Prism 1 Introduction

Download & Install You can obtain GraphPad/Prism by going to “Software Download Services” in MOLE. Look for “Graphpad Prism for Windows or Mac”

Uniqueness of Prism Prism caters for analysis and graphs for scientific publication, especially for laboratory and biomedical research. Data are usually entered and manipulated using spreadsheet such as Microsoft Excel. Data needed for analysis are copied into specific tables within Prism. Specific analysis requires specific tables. So you must know exactly what analysis is required.

The 6 tables within Prism.

XY Data Tables

Column tables

Grouped tables

Contingency tables

Survival tables

Parts of whole tables

Choosing the appropriate statistical tests Use these tables to choose the appropriate statistical tests.

Parametric Statistical Tests Independent Dependent Test Qualitative Dichotomus Quantitative Normally distributed data Student's t Test (more than 3 categories) ANOVA Repeated measurement of the Paired t Test same individual & item (e.g. Hb level before & after treatment). Normally distributed data Quantitative - continous Pearson Correlation & Linear Regresssion

Non-parametric Statistical Tests Independent Dependent Test Qualitative Dichotomus Quantitative Data not normally distributed Wilcoxon Rank Sum Test or U Mann- Whitney Test Kruskal-Wallis One Polinomial Way ANOVA Test Repeated measurement of the same individual & item Wilcoxon Rank Sign Test Quantitative - continous/ordina l continous Spearman/Kendall Rank Correlation

Statistical Tests for Qualitative Data Variable 1 Variable 2 Criteria Type of Test Qualitative Sample size > 20 dan no expected value < 5 Chi Square Test (X2) Sample size > 30 Proportionate Test Dichotomus Sample size > 40 but with at least one expected value < 5 X2 Test with Yates Correction Sample size < 20 or (< 40 but with at least one expected value < 5) Fisher Test

Data – Factors Related to SGA

A study to identify factors that can cause small for gestational age (SGA) was conducted. Among the factors studied were the mothers’ body mass index (BMI). It is believed that mothers with lower BMI were of higher risk to get SGA babies. 4. Conduct the appropriate statistical test to test whether there is any association between BMI and OUTCOME. 5. Conduct the appropriate statistical test to find any association between OBESCLAS (Underweight/Normal/Overweight) and BIRTHWGT. 6. Assuming that both variables mBMI & BIRTHWGT are normally distributed, conduct an appropriate statistical test to prove the association between the two variables. – Demonstrate the association using the appropriate chart. Determine the coefficient of determination. 7. Conduct Simple Linear Regression using BIRTHWGT as the dependent variable. Try to come out with a formula that will predict the baby’s birthweight based on the mother’s BMI. 1. Create a new variable mBMI (Mothers’ Body Mass Index) from the mothers’ HEIGHT (in metre) & WEIGHT (first trimester weight in kg). mBMI = weight in kg/(height in metre)2. Calculate the following for mBMI; Mean Standard deviation 2. Create a new variable OBESCLAS (Classification of Obesity) from mBMI. Use the following cutoff point; <20 = Underweight – 20 – 24.99 = Normal 25 or larger = Overweight Create a frequency table for OBESCLAS. 3. Conduct the appropriate statistical test to test whether there is any association between OBESCLAS (Underweight/ Normal/Overweight) and OUTCOME. – y = a + bx

Exercise 1 & 2 1. Create a new variable mBMI (Mothers’ Body Mass Index) from the mothers’ HEIGHT (in metre) & WEIGHT (first trimester weight in kg). mBMI = weight in kg/(height in metre)2. Calculate the following for mBMI; Mean Standard deviation 2. Create a new variable OBESCLAS (Classification of Obesity) from mBMI. Use the following cutoff point; <20 = Underweight – 20 – 24.99 = Normal 25 or larger = Overweight Create a frequency table for OBESCLAS.

Compute BMI Drag down to fill up the cells

Recode BMI into OBESCLAS Type =IF(F2<20,"Underweigh t",IF(F2>25,"Overweight ","Normal")) in cell G2 and press Enter. Then drag down cell G2 until G101 to fill up the rest of the cells. Try to “understand” the code below: If (“condition 1 satisfied” , then put Value 1 , otherwise put Value2). (Comma is very important here!) Instead of Value 2 you can introduce another IF! Can you see how it works?

Recode BMI into OBESCLAS

Recode BMI into 1,2 or 3 We should also recode BMI into numeric OBESCLAS2 for import into Prism. Prism doesn’t accept string data. • =IF(F2<20,“1",IF(F2>25,“3 ",“2")) in cell H2 and press Enter. Then drag down cell H2 until H101 to fill up the rest of the cells.

Pivot Table: Frequency (Count) of data by category of BMI Select column “OBESCLAS” Click “Insert” -> “Pivot Table” Click OK and it will open a new sheet in Excel. Drag “OBESCLAS” in “Row” once. Drag “OBESCLAS” in Sigma value. You will see a table of count of data. To switch to percentage, you can right click on one cell of the table and “show value as percentage Total”. You can create a Pie chart by clicking on the table and going in Pie Chart. You can also right click on one of the slices of the Pie Chart and click on “Add Data Labels”. You can also modify the title of the graph by clicking on “Total” and changing the text.

Import Excel Data Into Prism Select all the data from • Excel. Copy. Open Prism, select “Columns”, “Enter replicate values..” & click “Create”

Paste Into Prism Click the cell between “Group A” and row Y and paste. drtamil@gmail.com

Checking Normality Click on the “Analyze” button. Select “Column Statistics”. Select the variables with continuous data. Then click “OK”.

Click on the following; Test if the values from a Gaussian distribution.

Only Height is normally distributed But for the purpose of today’s exercise, we are going to ASS-U-ME that all these continuous variables are normally distributed.

Question 1 – BMI Column Statistics also generates the Mean & S.D.;

Frequency Distribution Go back to the data by clicking on the data table on left side of screen. Then click on the “Analyze” button again. Select “Frequency Distribution” Tick on OBESCLAS2. Then click on “OK”.

Frequency Distribution Then click on OK again. You will get the following frequency distribution table.

Question 2 – Obese Classification UW – 17% N – 40% OW – 43%

Exercise 3 3. Conduct the appropriate statistical test to test whether there is any association between OBESCLAS SGA Normal TOTAL UnderW OverW TOTAL 50 50 100 (Underweight/Normal/ Overweight) and OUTCOME. Therefore most suitable analysis is Pearson Chi- square. Variable 1 Variable 2 Criteria Type of Test Qualitative Sample size > 20 dan no expected value < 5 Chi Square Test (X2) Dichotomus Sample size > 30 Proportionate Test Sample size > 40 but with at least one expected value < 5 X2 Test with Yates Correction Sample size < 20 or (< 40 but with at least one expected value < 5) Fisher Test

Pivot Table in Excel Click on “Insert”, “Pivot Table” in Excel. Select all your earlier Excel data.

Pivot Table On the right side of the screen, pull FREQ into values, OBESCLAS into row labels and OUTCOME into column labels. Now select the created contingency table (excluding the “Grand Total”), and copy it using Ctrl-C.

Paste Pivot Table Into Prism Click “New”, “New Data Table”. Select “Contingency”, “Start with an empty table”. Then paste the pivot table into Prism.

The Pasted Pivot Table

Chi-Square Analysis Click on “Analyze”, “Contingency table analysis”, then “Chi- square”, then OK again twice.

Chi-Square Results from Prism Contingency 60 SGA Normal Frequency 40 20 Normal Overweight Underweight Mothers' Weight Classification Prism only states that there is a significant association (p < 0.0001) between mother’s weight classification and small for gestational age. But it doesn’t show which group has the higher rate of SGA.

Combine Results From Excel & Prism There is a significant difference (p<0.0001) of SGA rates between underweight, normal and overweight mothers. Underweight mothers has a higher rate (94%) of SGA, compared to normal mothers (58%) and overweight mothers (26%).

Underweight vs Normal? There is a significant difference (p<0.01) of SGA rates between underweight and normal mothers. Underweight mothers has a significantly higher rate (94%) of SGA, compared to normal mothers (58%).

Question 3

Question 3

Exercise 4 4. Conduct the appropriate statistical test to test whether there is any association between BMI and OUTCOME. Basically we are comparing the mean BMI of SGA mothers against BMI of Normal mothers. Therefore the appropriate test is Student’s t-test. Independent Dependent Test Qualitative Dichotomus Quantitative Data not normally distributed Wilcoxon Rank Sum Test or U Mann- Whitney Test Kruskal-Wallis One Polinomial Way ANOVA Test Repeated measurement of the same individual & item Wilcoxon Rank Sign Test Quantitative - continous/ordina l continous Spearman/Kendall Rank Correlation

Copy BMI Column Into Prism Click “New”, “New Data Table”. Select “Column”, “Enter replicate values into stacked columns”. Then paste the BMI of SGA mothers into column A & BMI of Normal mothers into column B.

The Pasted BMI Data

Student’s T-Test Click on “Analyze”, “Column analysis”, then “t-tests”, then OK again. Tick “Unpaired”, “Yes, parametric”, then “equal SDs”, then OK again.

T-Test Results from Prism Prism states that there is a significant mean difference of BMI (p < 0.0001) between SGA mother’s (22.52) and normal mothers (26.46). Therefore mean BMI of SGA mothers is significantly lower than the normal mothers. And it also proves that there is equal variances of the two means.

BMI

Question 4

Question 4

Exercise 5 5. Conduct the appropriate statistical test to find any association between OBESCLAS (Underweight/Normal/Overweight) and BIRTHWGT. Basically we are comparing the mean BIRTHWEIGHT of underweight mothers, normal weight mothers and overweight mothers. Therefore the appropriate test is Analysis of Variance (ANOVA).

Sort Excel Data By BMI To Facilitate Copy & Paste

Copy Birth Weight Column Into Prism Click “New”, “New Data Table”. Select “Column”, “Enter replicate values into stacked columns”. Then paste the babies’ birth weight of underweight mothers into column A, babies’ birth weight of normal weight mothers into column B & babies birth weight of overweight mothers in column C.

The Pasted Birth Weight Data

ANOVA Click on “Analyze”, “Column analysis”, then “One-way ANOVA”, then OK again. Tick “No matching”, “Yes, ANOVA”, then click “Multiple Comparison” tab. Click OK

ANOVA – post hoc Click OK

ANOVA Results from Prism Prism states that there is a significant mean difference of mean birth weight (p < 0.0001) between underweight mothers’ (2.187), normal mothers ‘(2.768) & overweight mothers’(3.245). Unfortunately it also proves that there is unequal variances of the three means. So it fails the homogeneity of variances assumption.

ANOVA Results – post hoc Post-hoc tests indicate there is significant difference of birth weight between ALL the three groups. Underweight mothers’ have the lowest mean birth weight of 2.187kg.

Compare Babies Birth Weight by Mother's Weight ANOVA 5 4 3 2 1 Underweight Normal Overweight Compare Babies Birth Weight by Mother's Weight Birth weight

Question 5

Question 5

Exercise 6 6. Assuming that both variables mBMI & BIRTHWGT are normally distributed, conduct an appropriate statistical test to prove the association between the two variables. – Demonstrate the association using the appropriate chart. Determine the coefficient of determination.

Pearson Correlation Qualitative Dichotomus Quantitative Normally distributed data Student's t Test Polinomial ANOVA Repeated measurement of the same individual & item (e.g. Hb level before & after treatment). Normally distributed data Paired t Test Quantitative - continous Pearson Correlation & Linear Regresssion mBMI and birth weight are both normally distributed continuous data. Since the aim is to measure the strength and direction of the association between these two continuous variable, therefore Pearson Correlation is the most appropriate test.

Copy BMI & Birth Weight Into Prism Click “New”, “New Data Table”. Select “XY”, “Enter and plot a single Y value for each point”. Then paste the BMI into column X & BIRTHWGT into column A.

The Pasted BMI & Birth weight Data BMI is coded as X since it is the risk factor. Birth weight is coded as Y since it is the outcome of interest. Risk factor first, then Outcome. X comes first before Y. Capisce? (Understand?)

Pearson’s Correlation Click on “Analyze”, “XY analysis”, then “Correlation”, then OK again. Tick “Compute r between two selected data sets”, “Yes, Pearson correlation coefficients”, then “Two-tailed”, then OK again.

Correlation Results from Prism Prism states that there is a significant, positive & fair (r=0.4812) correlation between mothers’ BMI and babies’ birth weight. Therefore as BMI increases, the birth weight also increases. 23.15% (r2=0.2315) variability of the birth weight is determined by the variability of the mothers’ BMI.

Scatter Diagram - BMI vs Birth weight 5 4 3 2 1 Birth weight 10 20 30 40 50 BMI

Question 6

Question 6

Exercise 7 7. Conduct Simple Linear Regression using BIRTHWGT as the dependent variable. Try to come out with a formula that will predict the baby’s birth weight based on the mother’s BMI. – y = a + bx

Simple Linear Regression Qualitative Dichotomus Quantitative Normally distributed data Student's t Test Polinomial ANOVA Repeated measurement of the same individual & item (e.g. Hb level before & after treatment). Normally distributed data Paired t Test Quantitative - continous Pearson Correlation & Linear Regresssion mBMI and birth weight are both normally distributed continuous data. Since the aim is to come out with a regression formula between these two continuous variable, therefore Simple Linear Regression is the most appropriate test.

Reuse BMI & Birth weight Data BMI is coded as X since it is the risk factor. Birth weight is coded as Y since it is the outcome of interest. Since the SLR uses the same variables, we will reuse the XY table from Exercise 6.

Simple Linear Regression Click on “SLR” icon, it is just above the “Analyze” icon. Just change the range so that the line will start at the y axis (X=0). We can set the line to end at the maximum value (it is X=41 in this exercise). Click OK

SLR Results from Prism Prism states that there is a significant regression coefficient (b=0.07323). The constant (a) is 1.081 23.15% (r2=0.2315) variability of the birth weight is determined by the variability of the mothers’ BMI. BW = 1.081 + 0.073BMI For every increase of BMI of 1 unit, BW increases 0.07kg.

Scatter Diagram - BMI vs Birth weight 5 4 3 2 1 Birth weight 10 20 30 40 50 BMI

Question 7

Question 7 Slight difference of the constant value. Prism calculated 1.081 instead of 1.079. Maybe it was due to decimal difference of the BMI upon import.

Question 7 d r t a m i l @ g m a i l c o m . Birth weight

The End