Laying out the data Designing effective tables FETP India.

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Presentation transcript:

Laying out the data Designing effective tables FETP India

Competency to be gained from this lecture Lay out data effectively in tables

Key areas Essential rules when arranging a table Common tables in epidemiology

Communicating patterns and messages contained in your data Show the patterns inherent in the data Focus attention on these patterns Serve as a basis for narrative or discussion Lead observer to insight, discussion, conclusions

Avoid visual puzzles in tables Poorly organized data Series of complicated numbers Important data obscured Unnecessary frames, lines, coloring Decoration Basic table rules

Column headings Data Footnotes Title Row headings Typical table layout with components

Making sure that a table is understandable without referral to other material Title  Person  Time  Place  Any measurement found in all columns Row and column headings  Content of the row or column  Any modifier applied to all cells of a row or column  Unit of measurement  Abbreviations, if necessary Basic table rules

Using footnotes in a table Clarify points of potential ambiguity Explain all:  Abbreviations  Symbols  Codes Note exclusions Mention data source if applicable Basic table rules

*ASC Ehrenberg, J R Statis Soc A, 140(3): , 1977 Suggestions for data arrangement in tables * 1.Round data to 2 meaningful figures 2.Summarize rows and columns 3.Compare numbers in columns 4.Arrange key data by magnitude 5.Help the reader with easy table layout 6.Align numbers by decimal Basic table rules

Table with excessive number of meaningful figures FactorCasesRate Rate Ratiopapa None Ref b A B C a. p-value b. Reference exposure category Up to five meaningful figures Rate ratios difficult to compare 1. Round data to 2 meaningful figures Basic table rules

Rounding data in a table to 2 meaningful figures Factor Cases (1000s)Rate Rate ratiop None Ref* C >0.100 A <0.050 B <0.001 a. p-value b. Reference exposure category 2 meaningful figures Rate ratios easier to compare 1. Round data to 2 meaningful figures Basic table rules

YearMF Both Sexes Mean Summary of the columns Summary of the rows 2. Summarize rows and columns Summarizing rows and columns with totals, averages or other statistics Basic table rules

Compare numbers in columns Difficult to compare numbers in rows st improvement: Right-justify numbers vertically nd improvement: Sort numbers 3. Compare numbers in columns Basic table rules

Organize data by magnitude Exposure Cases (1000s)Rate Rate ratioPaPa A > B < C > None Ref b 4. Arrange key data by magnitude a. p-value b. Reference exposure category Basic table rules

Organize data by magnitude Exposure Cases (1000s)Rate Rate ratiopapa B < C < A > None Ref b a. p-value b. Reference exposure category 4. Arrange key data by magnitude Basic table rules

Year Both sexesMaleFemale Spaced out table layout: Comparisons difficult for the reader 5. Help the reader with easy table layout Basic table rules

Year Both sexesMaleFemale Help the reader with easy table layout Drawing columns and rows close together facilitates comparisons Basic table rules

Intervening statistics: Separated numbers are harder to compare Rate per 1000 YearMaleFemaleAll Help the reader with easy table layout Basic table rules

Rate per 1000 (SE) YearMaleFemaleAll (2.3) 78 (2.2) 80 (1.9) (2.5) 66 (2.7) 63 (1.8) (2.1) 54 (2.0) 56 (1.7) (2.0) 45 (2.0) 51 (1.7) Moving and minimizing intervening numbers facilitates readability 5. Help the reader with easy table layout Basic table rules

Rate per 1000 a YearMFAll a. Standard errors for all rates less than 5% of rate. Remove intervening numbers entirely if consequence minimal 5. Help the reader with easy table layout Basic table rules

Align columns by decimal Difficult to compare numbers in rows Keeping the zeros or not is a question of personal style 6. Align numbers by decimal Basic table rules

Arranging common types of tables in epidemiology Text table Line listing Two variable table Complex table Cohort study Case-control study Common tables

Text table Between July 1 and August 31, 72 cases occurred in three classes of school A. Class Cases No cases occurred in the six other classes. a. Text table Common tables

StateAge 1 SexDays 2 Dose New York02M031 California03M 1 Pennsylvania06M031 Pennsylvania02M041 Colorado04F 1 California07M042 Kansas02F051 Colorado03M051 New York03F051 North Carolina04F051 Missouri11M051 Pennsylvania03F071 California04F142 Pennsylvania02M291 California05M Age in months * MMWR, 48 (27): Days from dose to symptom onset Reported cases of intussusception among recipients of rotavirus vaccine, by state, United States, * b. Line listing Common tables

New cases of primary and secondary syphilis by age group and sex, United States, 1989 Age groupCases (100’s) (years)MaleFemaleTotal   Total c. Two variable table Common tables

Complex table Children CharacterExp % (n=205) Not exp% (n=8729)p Gestational age (weeks) at birth < NS Birthweight (kg) NS NS d. Complex table Common tables

Exposed %YesNo ExposureRes. a %AR b RR c (95% CI d ) Type 1 Sub Type 1-A( - ) Sub Type 1-B( - ) Sub Type 1-C( - ) Type 2( - ) Type 3( - ) Type 4: a. Res. = Responded c. RR = Risk Ratio b. AR = Attack Rate – cases per ___ d. 95% CI = 95% confidence interval of the RR e. Cohort study Risk of ______ by exposure, among #### residents of Place, time Common tables

Exposed Exposure%AR a RR b 95% CI c Type or Level 3 Type or Level 2 Type or Level 1 None or Level 01.0Referent b. RR = Risk Ratio c. 95% CI = 95% confidence interval of the RR a. AR = Attack Rate – cases per ___ Risk of ______ by exposure, among #### residents of Place, time e. Cohort study (reference group) Common tables

Exposed % (n) a ExposureCasesControlsOR b 95% CI c Type 1 (n) ( – ) Sub Type 1-A (n) ( – ) Sub Type 1-B (n) ( – ) Sub Type 1-C (n) ( – ) Type 2 (n) ( – ) Type 3 (n) ( – ) c. 95% CI = 95% confidence interval of the OR a. n = subjects respondingb. OR = Odds Ratio Exposures (%) among ### cases and ### controls, Place, Time f. Case control study Common tables

Take home messages Design your table around the message that is contained in your data Refer to templates that apply to the table you are trying to design