Exporting Data for Analysis Michael A. Kohn, MD, MPP 16 August 2012.

Slides:



Advertisements
Similar presentations
Describing Quantitative Variables
Advertisements

Chapter 2 Exploring Data with Graphs and Numerical Summaries
UNIT 8: Statistical Measures
Percentiles and the Normal Curve
EPI 218 Web-Enabled Research Data Management Platforms Michael A. Kohn, MD,MPP 5 September 2013.
Planning and Budgeting for Data Management in a Clinical Research Study Michael A. Kohn, MD, MPP 4 February 2003.
Web-Based, Hosted Research Data Management Platforms 2/12/2008.
The Standard Deviation as a Ruler and the Normal Model.
IB Math Studies – Topic 6 Statistics.
Intermediate methods in observational epidemiology 2008 Quality Assurance and Quality Control.
LSP 121 Week 2 Intro to Statistics and SPSS/PASW.
Data Management for Research Michael A. Kohn, MD, MPP 7 January 2003.
Multiple Choice Review
EPI 218 Database Management for Clinical Research Tables, Relationships, Normalization, Data Types, and Data Dictionaries Michael A. Kohn, MD, MPP 1 August.
Think of a topic to study Review the previous literature and research Develop research questions and hypotheses Specify how to measure the variables in.
Review of Assignment 3, Loose Ends, Web-based Data Collection Michael A. Kohn, MD, MPP 3 February 2009.
Tutor: Prof. A. Taleb-Bendiab Contact: Telephone: +44 (0) CMPDLLM002 Research Methods Lecture 9: Quantitative.
Database Resources Final Project Database Demonstrations 2/9/2010.
SW388R6 Data Analysis and Computers I Slide 1 Central Tendency and Variability Sample Homework Problem Solving the Problem with SPSS Logic for Central.
Review of Assignment 3, Loose Ends, Security, Web-based Data Collection Michael A. Kohn, MD, MPP 2 February 2010.
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics Seventh Edition By Brase and Brase Prepared by: Lynn Smith.
Planning and Budgeting for Data Management in a Clinical Research Study Michael A. Kohn, MD, MPP 5 February 2008.
EPI 218 Web-Enabled Research Data Management Platforms Michael A. Kohn, MD,MPP Josh Senyak 22 August 2013.
UNIT 8:Statistical Measures Measures of Central Tendency: numbers that represent the middle of the data Mean ( x ): Arithmetic average Median: Middle of.
Review of Assignment 3, Loose Ends, Security, Web-based Data Collection Michael A. Kohn, MD, MPP 1 February 2011.
Slide 1 Statistics Workshop Tutorial 6 Measures of Relative Standing Exploratory Data Analysis.
Measures of Position & Exploratory Data Analysis
By: Amani Albraikan 1. 2  Synonym for variability  Often called “spread” or “scatter”  Indicator of consistency among a data set  Indicates how close.
EPI 218 Web-Enabled Research Data Management Platforms Michael A. Kohn, MD,MPP 30 August 2012.
Summary Statistics: Mean, Median, Standard Deviation, and More “Seek simplicity and then distrust it.” (Dr. Monticino)
Measures of Relative Standing Percentiles Percentiles z-scores z-scores T-scores T-scores.
Measures of Position. ● The standard deviation is a measure of dispersion that uses the same dimensions as the data (remember the empirical rule) ● The.
EPI 218 Web-Enabled Research Data Management Platforms Michael A. Kohn, MD,MPP 29 August 2013.
EPI 218 Database Management for Clinical Research Michael A. Kohn, MD, MPP January 10, 2010.
Data Management for Research Michael A. Kohn, MD, MPP January 4, 2005.
Describing and Displaying Quantitative data. Summarizing continuous data Displaying continuous data Within-subject variability Presentation.
EPI 218 Database Management for Clinical Research Michael A. Kohn, MD, MPP January 6, 2009.
Descriptive Statistics Chapter 2. § 2.5 Measures of Position.
EPI 218 Queries and On-Screen Forms Michael A. Kohn, MD, MPP 9 August 2012.
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 CHEBYSHEV'S THEOREM For any set of data and for any number k, greater than one, the.
Descriptive Statistics Chapter 2. § 2.5 Measures of Position.
Cumulative frequency Cumulative frequency graph
Royal College of Surgeons in Ireland Admissions Office Undergraduate Online Application Form Click through this PowerPoint presentation to familiarise.
PSY 325 AID Education Expert/psy325aid.com FOR MORE CLASSES VISIT
Descriptive Statistics Chapter 2. § 2.5 Measures of Position.
Assignments, Assessments and Grade Book
Describing Data: Two Variables
Figure 2-7 (p. 47) A bar graph showing the distribution of personality types in a sample of college students. Because personality type is a discrete variable.
Chapter 16: Exploratory data analysis: numerical summaries
Measures of Position & Exploratory Data Analysis
Unit 2 Section 2.5.
Jeopardy Final Jeopardy Chapter 1 Chapter 2 Chapter 3 Chapter 4
JUS 510 Competitive Success/snaptutorial.com
JUS 510 Education for Service-- snaptutorial.com.
JUS 510 Teaching Effectively-- snaptutorial.com
Percentiles and Box-and- Whisker Plots
Chapter 3 Section 4 Measures of Position.
Box plot of subarachnoid space measurements at each gestational week of age. Box plot of subarachnoid space measurements at each gestational week of age.
Number of Hours of Service
Box plot of head circumference measurements at each gestational week of age. Box plot of head circumference measurements at each gestational week of age.
pencil, red pen, highlighter, GP notebook, graphing calculator
Honors Statistics The Standard Deviation as a Ruler and the Normal Model Chapter 6 Part 3.
Day 52 – Box-and-Whisker.
Summary (Week 1) Categorical vs. Quantitative Variables
Box-and-Whisker Plots
Quantitative Data Who? Cans of cola. What? Weight (g) of contents.
Intermediate methods in observational epidemiology 2008
Box and Whisker Plots.
pencil, red pen, highlighter, GP notebook, graphing calculator
Limits of agreement between mean right and mean left tympanic membrane temperatures in 23 healthy, afebrile children (mean of all children). Limits of.
Presentation transcript:

Exporting Data for Analysis Michael A. Kohn, MD, MPP 16 August 2012

Lab 4 (8/23) uses REDCap You need a REDCap logon Web-based research data collection system developed at Vanderbilt Available free through UCSF Academic Research Systems You are both the Principal Investigator and User 1.

Final Project: Part A Send in or Demonstrate Your Study Database Due 9/20/2012 Send in a copy of your research study database*. We prefer a database that you are currently using or will use for a research study. However, a demonstration or pilot database is acceptable. *If you are unable to package your database in a file to , you can send us a link or work out another way to review your database.

If you are doing secondary analysis of data collected by someone else, obtain the data collection forms* used in the original data collection, set up a new database that you would use for a follow-up study. *Often easily obtained by doing a Google search or ing the author of the original study. Final Project: Part A Send in or Demonstrate Your Study Database Due 9/20/2012

General description of database Data collection and entry Error checking and data validation Analysis (e.g., export to Stata) Security/confidentiality Back up Final Project: Part B Submit Your Data Management Plan Due 9/20/2012

Final Project Due 9/20/2012 Start thinking about this now. Build your own study database as you work through the labs. Use extra time in lab to work on your study database. Set up appointments with course faculty early.

Normalization -- Lab Results (from last week) Occasionally, the subjects (in the Infant Jaundice Study) had blood tests. Robert had a CBC on 1/30/2010. Helen had a CBC on 1/30/2010, LFTs on 2/28/2010, and a CD-4 count on 3/31/2010.

Lab Results Amy had maximum daily T bili as follows: 1/13/ (DOB) 1/14/ /15/ /16/ /17/ Demonstration: Enter Amy’s T. Bili Results

Quiz: Field(s) Storing Amy’s T Bili Results Which Table? SubjectMeds LabResult Exam Subject None of the above

Quiz: Fields for Birth Weight and Gestational Age Which Table? SubjectMeds LabResult Exam Subject None of the above

Quiz: Field for Parental Education (Any College?) Which Table? SubjectMeds LabResult Exam Subject None of the above

Assignment 3 Extra Credit: Write a sentence or two for the “Methods” or “Results” section on inter-rater reliability. (Use Bland and Altman, BMJ 1996; 313:744) Lab 3: Exporting and Analyzing Data 8/16/2012 Determine if neonatal jaundice was associated with the 5-year IQ scores and create a table or paragraph appropriate for the “Results” section of a manuscript summarizing the association.

Newman T et al. N Engl J Med 2006;354:

Essential Elements Sample size (N 1 jaundiced, N 0 non-jaundiced) Indication of effect size (report both means, or the difference between them) Get direction of effect right. Indication of variability (Sample SDs, SEs of means*, CIs of means, or CI of difference between means.) *Not my favorite

Browner on Figures Figures should have a minimum of four data points. A figure that shows that the rate of colon cancer is higher in men than in women, or that diabetes is more common in Hispanics than in whites or blacks, [or that jaundiced babies had lower/higher IQs at age 5 years than non- jaundiced babies,] is not worth the ink required to print it. Use text instead. Browner, WS. Publishing and Presenting Clinical Research; 1999; Williams and Wilkins. Pg. 90

Takes the prize for ugliest figure.

Figure 1: In N 1 infants with neonatal jaundice, the average IQ scores were xxxxer compared to the N 0 non-jaundiced infants when evaluated at age 5 (p=xxxx).

Box Plot Median Line Box extends from 25 th to 75 th percentile Whiskers to upper and lower adjacent values Adjacent value = 75 th /25 th percentile ±1.5 x IQR (interquartile range) Values outside the adjacent values are graphed individually Would be nice if area of box were proportional to sample size (N). In some box plots the width of the box is proportional to log N, but not in Stata.

Extra Credit Report within-subject SD as a measure of reliability. Calculate repeatability Bland-Altman plot with mean difference and 95% limits of agreement

Methods: We assessed inter-rater reliability of the IQ test by having different examiners re-test some of the children. We calculated the within-subject standard deviation and repeatability. (Bland and Altman, BMJ 1996; 313:744) Results: Different examiners re-tested N retest children. The within-subject standard deviation was s w, so the “repeatability” was 2.77× s w, meaning that two examiners of the same subject would score within 2.77×s w points of each other 95 percent of the time. (Bland and Altman, BMJ 1996; 313:744) Methods/Results

N = N S&R (children examined by both Satcher and Richmond) Mean Difference = 0.49 (95% CI – 1.38) 95% Limits of Agreement: – 11.0

N = 142 (examined by both Satcher and Richmond) Mean difference = Limits of agreement (LLA - ULA)

Bland-Altman in Stata ssc install batplot batplot richmondscore satcherscore, notrend title(Agreement between Richmond and Satcher) ytitle(Difference (Richmond - Satcher)) xtitle(Average of Richmond and Satcher)

Lab 3