Statistics for clinical research An introductory course.

Slides:



Advertisements
Similar presentations
Departments of Medicine and Biostatistics
Advertisements

Testing means, part III The two-sample t-test. Sample Null hypothesis The population mean is equal to  o One-sample t-test Test statistic Null distribution.
Chapter Seventeen HYPOTHESIS TESTING
PSY 307 – Statistics for the Behavioral Sciences
Statistics: Data Analysis and Presentation Fr Clinic II.
Data Freshman Clinic II. Overview n Populations and Samples n Presentation n Tables and Figures n Central Tendency n Variability n Confidence Intervals.
Statistics. Overview 1. Confidence interval for the mean 2. Comparing means of 2 sampled populations (or treatments): t-test 3. Determining the strength.
BHS Methods in Behavioral Sciences I
Statistics: Data Presentation & Analysis Fr Clinic I.
Practical Meta-Analysis -- D. B. Wilson
Statistics By Z S Chaudry. Why do I need to know about statistics ? Tested in AKT To understand Journal articles and research papers.
Basic Statistical Concepts Donald E. Mercante, Ph.D. Biostatistics School of Public Health L S U - H S C.
PSY 307 – Statistics for the Behavioral Sciences Chapter 19 – Chi-Square Test for Qualitative Data Chapter 21 – Deciding Which Test to Use.
1 (Student’s) T Distribution. 2 Z vs. T Many applications involve making conclusions about an unknown mean . Because a second unknown, , is present,
5-3 Inference on the Means of Two Populations, Variances Unknown
Hypothesis Testing :The Difference between two population mean :
Review for Exam 2 Some important themes from Chapters 6-9 Chap. 6. Significance Tests Chap. 7: Comparing Two Groups Chap. 8: Contingency Tables (Categorical.
The Chi-Square Test Used when both outcome and exposure variables are binary (dichotomous) or even multichotomous Allows the researcher to calculate a.
Analysis of Variance. ANOVA Probably the most popular analysis in psychology Why? Ease of implementation Allows for analysis of several groups at once.
AM Recitation 2/10/11.
Estimation and Hypothesis Testing Faculty of Information Technology King Mongkut’s University of Technology North Bangkok 1.
ANALYSIS OF VARIANCE. Analysis of variance ◦ A One-way Analysis Of Variance Is A Way To Test The Equality Of Three Or More Means At One Time By Using.
Inferential Statistics: SPSS
Two Sample Tests Ho Ho Ha Ha TEST FOR EQUAL VARIANCES
 Mean: true average  Median: middle number once ranked  Mode: most repetitive  Range : difference between largest and smallest.
STAT 5372: Experimental Statistics Wayne Woodward Office: Office: 143 Heroy Phone: Phone: (214) URL: URL: faculty.smu.edu/waynew.
Econ 3790: Business and Economics Statistics Instructor: Yogesh Uppal
Education 793 Class Notes T-tests 29 October 2003.
T-distribution & comparison of means Z as test statistic Use a Z-statistic only if you know the population standard deviation (σ). Z-statistic converts.
More About Significance Tests
Copyright © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 17 Inferential Statistics.
Copyright © 2008 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 22 Using Inferential Statistics to Test Hypotheses.
Confidence Intervals for Means. point estimate – using a single value (or point) to approximate a population parameter. –the sample mean is the best point.
Chapter 9 Hypothesis Testing and Estimation for Two Population Parameters.
Chapter 15 Data Analysis: Testing for Significant Differences.
Chapter 11 HYPOTHESIS TESTING USING THE ONE-WAY ANALYSIS OF VARIANCE.
Measures of Dispersion CUMULATIVE FREQUENCIES INTER-QUARTILE RANGE RANGE MEAN DEVIATION VARIANCE and STANDARD DEVIATION STATISTICS: DESCRIBING VARIABILITY.
Chapter 13 – Difference Between Two Parameters Math 22 Introductory Statistics.
Biostat 200 Lecture 7 1. Hypothesis tests so far T-test of one mean: Null hypothesis µ=µ 0 Test of one proportion: Null hypothesis p=p 0 Paired t-test:
Maximum Likelihood Estimator of Proportion Let {s 1,s 2,…,s n } be a set of independent outcomes from a Bernoulli experiment with unknown probability.
1 SMU EMIS 7364 NTU TO-570-N Inferences About Process Quality Updated: 2/3/04 Statistical Quality Control Dr. Jerrell T. Stracener, SAE Fellow.
Lecture 8 Simple Linear Regression (cont.). Section Objectives: Statistical model for linear regression Data for simple linear regression Estimation.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
April 4 Logistic Regression –Lee Chapter 9 –Cody and Smith 9:F.
Contingency tables Brian Healy, PhD. Types of analysis-independent samples OutcomeExplanatoryAnalysis ContinuousDichotomous t-test, Wilcoxon test ContinuousCategorical.
The binomial applied: absolute and relative risks, chi-square.
Statistics - methodology for collecting, analyzing, interpreting and drawing conclusions from collected data Anastasia Kadina GM presentation 6/15/2015.
MBP1010 – Lecture 8: March 1, Odds Ratio/Relative Risk Logistic Regression Survival Analysis Reading: papers on OR and survival analysis (Resources)
© Copyright McGraw-Hill 2000
STATISTICAL ANALYSIS FOR THE MATHEMATICALLY-CHALLENGED Associate Professor Phua Kai Lit School of Medicine & Health Sciences Monash University (Sunway.
I271B The t distribution and the independent sample t-test.
The exam is of 2 hours & Marks :40 The exam is of two parts ( Part I & Part II) Part I is of 20 questions. Answer any 15 questions Each question is of.
Introduction to Inference: Confidence Intervals and Hypothesis Testing Presentation 4 First Part.
Statistics for Business and Economics 8 th Edition Chapter 7 Estimation: Single Population Copyright © 2013 Pearson Education, Inc. Publishing as Prentice.
1 G Lect 7a G Lecture 7a Comparing proportions from independent samples Analysis of matched samples Small samples and 2  2 Tables Strength.
Statistical Inference Statistical inference is concerned with the use of sample data to make inferences about unknown population parameters. For example,
Confidence Intervals for a Population Proportion Excel.
Section 6.4 Inferences for Variances. Chi-square probability densities.
ENGR 610 Applied Statistics Fall Week 7 Marshall University CITE Jack Smith.
Chapter 9: Introduction to the t statistic. The t Statistic The t statistic allows researchers to use sample data to test hypotheses about an unknown.
Hypothesis Tests u Structure of hypothesis tests 1. choose the appropriate test »based on: data characteristics, study objectives »parametric or nonparametric.
Chapter 7 Inference Concerning Populations (Numeric Responses)
Objectives (BPS chapter 12) General rules of probability 1. Independence : Two events A and B are independent if the probability that one event occurs.
Hypothesis Testing and Statistical Significance
GOSSET, William Sealy How shall I deal with these small batches of brew?
Educational Research Inferential Statistics Chapter th Chapter 12- 8th Gay and Airasian.
Confidence Intervals. Point Estimate u A specific numerical value estimate of a parameter. u The best point estimate for the population mean is the sample.
Chapter 14 Single-Population Estimation. Population Statistics Population Statistics:  , usually unknown Using Sample Statistics to estimate population.
NURS 306, Nursing Research Lisa Broughton, MSN, RN, CCRN RESEARCH STATISTICS.
The 2 nd to last topic this year!!.  ANOVA Testing is similar to a “two sample t- test except” that it compares more than two samples to one another.
Presentation transcript:

Statistics for clinical research An introductory course

Session 2 Comparing two groups

Previous session Normal distribution Normal distribution Standard Deviation (of measurements) Standard Deviation (of measurements) Standard Error (of the mean) Standard Error (of the mean) Confidence Interval of measurements Confidence Interval of measurements Confidence Interval of the mean Confidence Interval of the mean

Main overview Dealing with both Means and Proportions Dealing with both Means and Proportions Two groups will be compared Two groups will be compared Effect Size along with its Confidence Interval (C.I.) will be calculated from data Effect Size along with its Confidence Interval (C.I.) will be calculated from data Remember the C.I. tells us about the uncertainty of the effect size Remember the C.I. tells us about the uncertainty of the effect size The different calculations for effect sizes The different calculations for effect sizes

Means Means calculated from measured data Means calculated from measured data Standard Deviation (of Measurements) Standard Deviation (of Measurements) Standard Error (of the Mean) Standard Error (of the Mean) Effect Size = Difference in Means Effect Size = Difference in Means

Proportions Proportion Proportion Binary outcome (e.g. yes/no) Binary outcome (e.g. yes/no) Number between 0 and 1 Number between 0 and 1 2x2 table 2x2 table Effect sizes Effect sizes Risk Difference (RD); Relative Risk (RR); Risk Difference (RD); Relative Risk (RR); Odds Ratio (OR) Group 1 Group 2 Positive p1p1p1p1 p2p2p2p2 Negative n1n1n1n1 n2n2n2n2

Comparing two groups Two proportions Risk Difference Risk Difference Number Needed to Treat Number Needed to Treat Relative Risk Relative Risk Odds Ratio Odds Ratio Fisher’s Exact Probability Fisher’s Exact Probability Two means The t-distribution The t-distribution Difference between means Difference between means

Risk Difference Risk is a proportion (number between 0 and 1) Risk is a proportion (number between 0 and 1) Each group incorporate its own risk Each group incorporate its own risk Group 1: 15 people are given money… Group 1: 15 people are given money… Happy = 12 Not happy = 3 Total = 15 Risk of happiness = 12/15 = 0.8 Group 2: 10 people are not given money… Group 2: 10 people are not given money… Happy = 5 Not happy = 5 Total = 10 Risk of happiness = 5/10 = 0.5

Risk Difference Risk Difference (RD) is the risk of one group subtracted from the risk of the other group Risk Difference (RD) is the risk of one group subtracted from the risk of the other group RD = 0.8 – 0.5 = 0.3 RD = 0.8 – 0.5 = 0.3 Excel file “TwoGroups.xls” Excel file “TwoGroups.xls”

Comparing two groups Two proportions Risk Difference Risk Difference Number Needed to Treat Number Needed to Treat Relative Risk Relative Risk Odds Ratio Odds Ratio Fisher’s Exact Probability Fisher’s Exact Probability Two means The t-distribution The t-distribution Difference between means Difference between means

Number Needed to Treat NNT = 1 / Risk Difference NNT = 1 / Risk Difference If RD = 0.21 (21%), then need to treat 100 to prevent 21 adverse events If RD = 0.21 (21%), then need to treat 100 to prevent 21 adverse events NNT = 1 / 0.21 = 5 (rounded up) NNT = 1 / 0.21 = 5 (rounded up) 5 need to be treated to prevent 1 additional adverse event 5 need to be treated to prevent 1 additional adverse event Excel file “TwoGroups.xls” Excel file “TwoGroups.xls”

Comparing two groups Two proportions Risk Difference Risk Difference Number Needed to Treat Number Needed to Treat Relative Risk Relative Risk Odds Ratio Odds Ratio Fisher’s Exact Probability Fisher’s Exact Probability Two means The t-distribution The t-distribution Difference between means Difference between means

Relative Risk (RR) Risk is a proportion Risk is a proportion Each of the two groups has its own risk Each of the two groups has its own risk Relative Risk (RR) is the ratio of two risks Relative Risk (RR) is the ratio of two risks RR is mostly used for cohort studies RR is mostly used for cohort studies Ratios do not have a Normal distribution Ratios do not have a Normal distribution log(RR) has a Normal distribution log(RR) has a Normal distribution Confidence interval calculations require a Normal distribution Confidence interval calculations require a Normal distribution Excel file “TwoGroups.xls” Excel file “TwoGroups.xls”

Relative Risk (RR) If Confidence Interval… If Confidence Interval… Contains 1: No difference in outcome between two groups Contains 1: No difference in outcome between two groups <1: Less risk in group 1 <1: Less risk in group 1 >1: Greater risk in group 1 >1: Greater risk in group 1

Comparing two groups Two proportions Risk Difference Risk Difference Number Needed to Treat Number Needed to Treat Relative Risk Relative Risk Odds Ratio Odds Ratio Fisher’s Exact Probability Fisher’s Exact Probability Two means The t-distribution The t-distribution Difference between means Difference between means

Odds Ratio (OR) Odds – the number who have an event divided by the number who do not Odds – the number who have an event divided by the number who do not Odds of an event occurring is obtained for both groups Odds of an event occurring is obtained for both groups OR mostly used for case-control studies OR mostly used for case-control studies Ratios are not Normally distributed Ratios are not Normally distributed log(OR) has a Normal distribution log(OR) has a Normal distribution Confidence Interval calculations require a Normal distribution Confidence Interval calculations require a Normal distribution Extra: Logistic regression is typically used to adjust odds ratios to control for potential confounding by other variables Extra: Logistic regression is typically used to adjust odds ratios to control for potential confounding by other variables Excel file “TwoGroups.xls” Excel file “TwoGroups.xls”

Odds Ratio (OR) If Confidence Interval… If Confidence Interval… Contains 1: No difference in outcome between two groups Contains 1: No difference in outcome between two groups <1: Odds in group 1 significantly less <1: Odds in group 1 significantly less >1: Odds in group 1 significantly greater >1: Odds in group 1 significantly greater

Comparing two groups Two proportions Risk Difference Risk Difference Number Needed to Treat Number Needed to Treat Relative Risk Relative Risk Odds Ratio Odds Ratio Fisher’s Exact Probability Fisher’s Exact Probability Two means The t-distribution The t-distribution Difference between means Difference between means

Fisher’s Exact Test Determines if significant associations exist between group and outcome Determines if significant associations exist between group and outcome Used when sample sizes are small Used when sample sizes are small i.e. cell count < 5 in a 2x2 table i.e. cell count < 5 in a 2x2 table Alternative to the Chi-Square test Alternative to the Chi-Square test Test only provides a p-value (no C.I.) Test only provides a p-value (no C.I.) Probability of observing a result more extreme than that observed Probability of observing a result more extreme than that observed

Comparing two groups Two proportions Risk Difference Risk Difference Number Needed to Treat Number Needed to Treat Relative Risk Relative Risk Odds Ratio Odds Ratio Fisher’s Exact Probability Fisher’s Exact Probability Two means The t-distribution The t-distribution Difference between means Difference between means

The t-distribution Population SD is unknown and is estimated from the data Population SD is unknown and is estimated from the data Blue curve = Normal distribution Blue curve = Normal distribution Green = t-distribution with 1 degree of freedom (df) Green = t-distribution with 1 degree of freedom (df) Red = t-distribution, 2 df Red = t-distribution, 2 df Underlying theory of the t-test Underlying theory of the t-test

Comparing two groups Two proportions Risk Difference Risk Difference Number Needed to Treat Number Needed to Treat Relative Risk Relative Risk Odds Ratio Odds Ratio Fisher’s Exact Probability Fisher’s Exact Probability Two means The t-distribution The t-distribution Difference between means Difference between means

Difference between means Two sample t-test is used to test the difference between two means Two sample t-test is used to test the difference between two means Measurements must be considered Normally distributed Measurements must be considered Normally distributed Quite powerful. A decision can be made with a small sample size…much smaller than when compared to proportions Quite powerful. A decision can be made with a small sample size…much smaller than when compared to proportions Excel file “TwoGroups.xls” Excel file “TwoGroups.xls”

Forest Plot Plot effect sizes with confidence intervals Plot effect sizes with confidence intervals Useful in comparing multiple effect sizes Useful in comparing multiple effect sizes Go to applet on website: Go to applet on website:

Additional topics Normality tests (e.g. Shapiro-Wilk) Normality tests (e.g. Shapiro-Wilk) Test for equality of variances (e.g. Bartlett’s test) Test for equality of variances (e.g. Bartlett’s test) t-test for unequal variances t-test for unequal variances Paired t-test for dependent samples Paired t-test for dependent samples Comparing more than two groups (e.g. one-way ANOVA) Comparing more than two groups (e.g. one-way ANOVA) Nonparametric tests Nonparametric tests