Why Dummy Tables are Smart! A Systematic Approach to Data Analysis for Your M.Sc. Thesis. Lisa Fredman, Ph.D. Department of Epidemiology, BUSPH CREST Seminar March 17, 2009 . Outline : 1. Research fundamentals (the basics) 2. Analytic plan in research a. Hypothesis guides plan
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A Systematic Approach to Data Analysis for Your M.Sc. Thesis
Lisa Fredman, Ph.D.
Department of Epidemiology, BUSPH
March 17, 2009
1. Research fundamentals (the basics)
2. Analytic plan in research
a. Hypothesis guides plan
b. Identify measures for E, D, and covariables
c. Descriptive statistics on E, D, and covariables
d. Analyses on E-D association
i. Crude analyses
ii. Evaluate potential confounders
iii. Multivariable analyses
3. Present results in tables and text
Aim: describe how dummy tables used in Steps 2a-d, 3
- systematic investigation of E-D association
- analysis follows sequential steps from
descriptive analyses -> univariate E-D association -> confounder assessment -> multivariate modeling
- document methods and variables
- document analytic steps, results at each step,
decisions that influence next steps
- clear communication throughout
- analytic steps
Definition: Dummy tables (aka mock tables) are shells of tables with variable names, SAS names, and statistical measures. Do not include data.
Brief notes on results, decisions, next steps
Stay focused on analyses to test YOUR hypothesis.
Provides template for systematic steps in your analysis.
Centralized record of analyses, results, decisions.
Analyze associations that look interesting but are tangential to their hypothesis.
DON’T BE TEMPTED TO DO THIS!
Revise analytic variables and not rename vars or record changes.
DON’T LET YOURSELF FALL INTO THIS TRAP!
Dummy tables help you avoid doing these dumb things.
Before starting analyses:
Start with 4-5 dummy tables:
While doing analyses, at each step:
Proceed to next stage
Generic dummy table aka “Shopping List”
Stop & Shop or Shaws? con’t:
Need subgroup analyses!
Fill in shopping list!
Make notes to improve recipe con’t:
LF: use fewer onions, more carrots
LF: definitely plan on 2 hrs!
Use less water
Main study hypothesis:
Summary of age-adjusted analyses: Respondents with low positive affect (PA) reported the fewest ADL limitations at baseline, and those with depressive symptoms reported the most. On average, respondents in each affect category reported more ADL limitations at each interview following the fracture. On the KatzADL variable, the high PA group reported the fewest ADL limitations 2-months through 18-months post-fracture. However, there were no statistically significant differences between respondents with high and low PA.
Summary: Age and 1 or more medical conditions (medsum42) met the criteria as potential confounders. I will also include race in the multivariable models since it may turn out to be a confounder in the models of the KatzADL outcome.
Summary: In the multivariable model, positive affect and followup time were associated with the KatzADL score over time. Mean KatzADL scores were significantly lower (ie, less impaired) in respondents with high positive affect compared to those with depressive symptoms at months 12 and 18; there were no differences between respondents with high and low positive affect.
ex: Positive Affect ADLs_datamemo3_050306
Dummy tables are an organizational tool to ensure that data analyses follow hypothesis and are systematically recorded.
Provide internal documentation.
Link analytic plan, interim results, final tables and manuscript.
That’s why dummy tables are smart!