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 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

Research fundamentals: - 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 - hypothesis - methods - analytic steps - results

Dummy tables Definition: Dummy tables (aka mock tables) are shells of tables with variable names, SAS names, and statistical measures. Do not include data. Create dummy tables when develop analysis plan. Fill in dummy tables as perform analyses. Use dummy tables to guide analyses record SAS programs used for analyses names of measures used document interim results draft methods and results

Example of generic dummy table: Title: Distribution of key variables (SAS program used to generate results, date) Variable Distribution Exposure: Variable (VARNAME) (mean, std, range) Outcome: Variable (VARNAME) (%) Covariables Covar1 (VARNAME) (%) Covar2 (VARNAME) (%) Covar3 (VARNAME) (%) … . . Brief notes on results, decisions, next steps

Why are dummy tables smart? Stay focused on analyses to test YOUR hypothesis. Provides template for systematic steps in your analysis. Internal documentation. Centralized record of analyses, results, decisions. Communication aid.

Dumb things that smart researchers often do: 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.

Guide to dummy tables for analyses for epidemiologic study: Before starting analyses: Write down hypothesis Make dummy table for each stage of analysis Make note to write summary of table, decisions, next steps.

Guide to dummy tables for analyses for epidemiologic study, con’t: Start with 4-5 dummy tables: Descriptive analyses: variable distributions Crude analyses Bivariate analyses Confounder analysis Multivariable analyses

Guide to dummy tables for analyses for epidemiologic study, con’t: While doing analyses, at each step: Fill in dummy table and/or checklist at each stage Make decisions based on analyses at this stage (operationalizing variables, selecting confounders, excluding variables from multivariate model) that will influence next stage Write each decision and rationale for it Proceed to next stage

EX: Making Corned Beef with Cabbage dinner

Generic dummy table aka “Shopping List” EX: Making Corned Beef with Cabbage dinner Generic dummy table aka “Shopping List” Shopping list for Corned Beef dinner Ingredients Amount Cost Cabbage 1 head Carrots 3 large Corned brisket or beef 4 lbs Toadstools 6 small … Stop & Shop, or Shaws? (Title) (Variables)

Stop & Shop or Shaws? Need subgroup analyses!

Shopping list for Corned Beef dinner EX: Making Corned Beef with Cabbage dinner Fill in shopping list! Shopping list for Corned Beef dinner Ingredients Amount Cost Cabbage 1 head Carrots 3 large Corned brisket or beef -- Hummell 4 lbs $1.49/lb Toadstools 6 small … Stop & Shop, or Shaws? Either (Title) (Variables)

Make notes to improve recipe LF: use fewer onions, more carrots LF: definitely plan on 2 hrs! Use less water

Another example: is positive affect associated with better recovery in physical functioning following hip fracture? Main study hypothesis: Elderly hip fracture patients with high positive affect will show recovery in more ADLs, and in more mobility-related ADLs over 2-years following fracture than patients with low positive affect or depression.

Dummy tables for Positive Affect study: Title: Table 1 (manuscript): baseline characteristics of hip fracture sample, by positive affect category (OCESD) (SAS pgm used for results, date) Total sample High PA (n=xxx) Low PA (n=xxx) Depressed (n=xxx) p-value Sociodemographic variables Age groups: % (AGE) Sex: % female (RACE) Medical conditions Past stroke: % (V508) Past hip fx: % (V515) Functional status at baseline ADL limitations (0-7): mean, std (KATZ0)

More dummy tables: Age-adjusted mean KATZ ADL score at each interview point, by baseline Positive Affect Category Positive Affect category Baseline (KATZ0) 2-month (KATZ02) 6-months (KATZ06) 12-months (KATZ12) 18- months (KATZ18) 24-months (KATZ24)  (OCESD) Mean (se) High pos. affect Low pos. affect Depressive symptoms

Filled-in dummy table and summary: Age-adjusted mean KATZ ADL score at each interview point, by baseline Positive Affect Category (pgm=hipKatz2_age adjusted means, 5/3/06) Positive Affect category Baseline (KATZ0) 2-month (KATZ02) 6-months (KATZ06) 12-months (KATZ12) 18- months (KATZ18) 24-months (KATZ24)  (OCESD) Mean (se) High pos. affect 0.72 (0.12) 3.76 (0.13) 2.49 (0.16) 2.03 (0.17) 1.98 (0.19) 2.02 (0.18) Low pos. affect 0.49 (0.20) 3.82 (0.21) 2.59 (0.27) 2.28 (0.28) 2.14 (0.30) 1.91 (0.28) Depressive symptoms 1.29 (0.10) 4.20 (0.11) 3.05 (0.13) 2.83 (0.14) 2.86 (0.16) 2.63 (0.15) 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.

Dummy table for confounder assessment: Confounder assessment for Positive Affect_ADLs analyses Beta coefficient Beta coefficients for models with individual potential confounders Outcome OCESD level Age % change Race %change medsum42 KATZ ADL measure: model with cesd* time interaction term  OCESD-level 1 OCESD-level 2

Filled-in dummy table for confounder assessment: Confounder assessment for Positive Affect_ADLs analyses Beta coefficient Beta coefficients for models with individual potential confounders Outcome OCESD level Age % change Race %change medsum42 KATZ ADL measure: model with cesd* time interaction term  OCESD-level 1 -0.3805 -0.354 107.5 -0.3969 95.9 -0.3612 105.3 OCESD-level 2 -0.2796 -0.4252 65.8 -0.3021 92.6 -0.2544 109.9 from hipKatzmix1_mixed models baseline, 5/3/06 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.

Dummy tables for multivariable analyses: Predicted mean KATZ ADL score at each interview point, by baseline Positive Affect Category, PROC MIXED results (pgm=hipKatzmix2_mixed models, prelim multivariable models, 5/4/06) Positive Affect category 2-months (KATZ02) 6-months (KATZ06) 12-months (KATZ12) 18-months (KATZ18) 24-months (KATZ24) (n=352) (n=321) (n=306) (n=245) (n=232)  (OCESD) Mean (se) High positive affect Low positive affect Depressive symptoms Differences and 95% CI’s: High vs. low positive affect High positive affect vs. depr.

Filled-in dummy tables and summary for multivariable analyses: Predicted mean KATZ ADL score at each interview point, by baseline Positive Affect Category, PROC MIXED results (pgm=hipKatzmix2_mixed models, prelim multivariable models, 5/4/06) Positive Affect category 2-months 6-months 12-months 18-months 24-months (n=352) (n=321) (n=306) (n=245) (n=232)   Mean (se) High positive affect 3.87 (0.14) 2.62 (0.14) 2.18 (0.14) 2.19 (0.15) 2.35 (0.16) Low positive affect 3.96 (0.23) 2.75 (0.24) 2.51 (0.23) 2.35 (0.24) 2.27 (0.25) Depressive symptoms 3.97 (0.12) 2.94 (0.12) 2.75 (0.12) 2.88 (0.13) 2.70 (0.13) Differences and 95% CI’s: High vs. low positive affect -0.09 (-0.61,0.43) -0.14 (-.68,0.41) -0.34 (-.88,0.20) -0.15 (-0.72,0.42) 0.07 (-0.50,.65) High positive affect vs. depr. -0.10 (-0.46,0.25) -0.32 (-.68,0.05) -0.57 (-0.94,-.20) -0.68 (-1.08, .29) -0.35 (-0.76,.06) 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.

Additional records to supplement dummy tables: Data memos to co-investigators/self Footers and WORD file names with filename and date created/revised ex: Positive Affect ADLs_datamemo3_050306

Conclusion: 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!