Outline 1) Descriptive Statistics 2) Define “association”. 3) Practice reading Table 1 for evidence of confounding, effect modification. 4) Practice reading.

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Presentation transcript:

Outline 1) Descriptive Statistics 2) Define “association”. 3) Practice reading Table 1 for evidence of confounding, effect modification. 4) Practice reading scatterplots for evidence of confounding effect modification. 5) Review comparing adjusted and unadjusted analysis for confounding effect modification. 6) Read STATA output: saturated model predicted group summary is actual group summary correlation evidence of association from overall hypothesis test

Descriptive Statistics Goals: 1)Identify measurement of data entry errors 2)Characterize the methods and materials 3)Assess the validity of the scientific and statistical assumptions 4)Get a straight forward estimate of the association you are interested in 5)Explore data to generate hypothesis for future studies Reporting descriptive analysis : Give the reader the ability to judge the scientific evidence and the importance of your work. Methods: Describe what you did so that the reader could reproduce your work. Results: Report what you actually realized in this repetition of the research. Present a preliminary estimate of the association. Indicate whether this sample supported the scientific and statistical assumptions used to do the statistical analysis.

Association : The distribution of two variables are not independent The conditional distribution of an outcome variable changes depending on the value of the predictor. Rather than look at the entire distribution we use a summary measure for the distribution of the outcome and compare the value of that summary measure at different values of the predictor

Prior History of Cardiovascular Disease (CVD) No Prior CVDPrior CVDAll Subjects C reactive protein (mg/L) 3.38 (5.90; ; n=3,802 / 3,851)4.40 (6.88; ; n=1,131 / 1,149)3.61 (6.15; ; n=4,933 / 5,000) Fibrinogen (mg/dL) CRP: 0 mg/L 277 (48.5; ; n=348 / 350)290 (57.9; ; n=78 / 78)280 (50.5; ; n=426 / 428) CRP: 1 mg/L 298 (48.5; ; n=1,238 / 1,246)304 (52.5; ; n=292 / 295)299 (49.3; ; n=1,530 / 1,541) CRP: 2 mg/L 314 (51.2; ; n=835 / 841)317 (52.5; ; n=246 / 247)314 (51.5; ; n=1,081 / 1,088) CRP: 3-4 mg/L 335 (56.2; ; n=711 / 716)337 (64.2; ; n=222 / 224)336 (58.1; ; n=933 / 940) CRP: 5-8 mg/L 353 (62.6; ; n=330 / 333)365 (70.0; ; n=126 / 128)356 (64.9; ; n=456 / 461) CRP: 9-16 mg/L 377 (70.9; ; n=222 / 223)391 (81.5; ; n=110 / 111)382 (74.7; ; n=332 / 334) CRP: mg/L 419 (109.2; ; n=59 / 59)442 (83.0; ; n=36 / 36)428 (100.3; ; n=95 / 95) CRP: > 33 mg/L 498 (115.4; ; n=34 / 34)522 (102.3; ; n=12 / 12)504 (111.5; ; n=46 / 46) CRP: Missing 308 (41.7; ; n=14 / 49)332 (50.2; ; n=2 / 18)311 (41.7; ; n=16 / 67) All Subjects 320 (64.8; ; n=3,791 / 3,851)334 (74.1; ; n=1,124 / 1,149)323 (67.3; ; n=4,915 / 5,000) Descriptive Statistics- Straightforward estimate of association. CRP Fib Assess the validity of your assumptions (ie confounding, effect modification) Straightforward estimate of association. CRP Fib Assess the validity of your assumptions (ie confounding, effect modification) CVD Straightforward estimate of association. Assess the validity of your assumptions ( ie confounding, effect modification ) CVD Level 1: CRP FIB CVD Level 2: CRP FIB distribution of outcome is different depending on the value of the predictor.

But wait, it is not that simple. Effect modification depends on the type of summary measure you use. RD: no effect modificationRD: effect modification RR: yes effect modificationRR: no effect modification

Distribution of POI depends on the value of confounder With either the additive or multiplicative contrast there is effect modification. Distribution of POI DOESN”T depend on the value of third variable The Third Variable (again, but in pictures)

In a saturated regression model, different combinations of the beta parameters can be used to find the summary measure for each group.

How should I represent my predictor of interest in the regression model?