####### Week 7 itn Swedish diabetes ## Pub Table 2, baseline characteristics > famdep_2 = matrix(c(.219*4815,.122*4815, .153*28785,.197*28785), nr = 2, dimnames = + list("famsize" = c("none", "four+"),"deprivation" = c("Low", "High"))) > famdep_2 deprivation famsize Low High none 1054.485 4404.105 four+ 587.430 5670.645 > prop.table(round(famdep_2),1) deprivation famsize Low High none 0.19311103 0.8068890 four+ 0.09379994 0.9062001 > chisq.test(round(famdep_2)) Pearson's Chi-squared test with Yates' continuity correction data: round(famdep_2) X-squared = 237.9, df = 1, p-value < 2.2e-16 ##### certainly not close to random, could be consequence systematic assignment ##diabetes outcome ("unadjusted") > didep = matrix(c(4815 - 281,28785 - 2278,281,2278), nr = 2, dimnames = + list("deprivation" = c("Low", "High"), "Diabetes" = c("N", "Y"))) > didep Diabetes deprivation N Y Low 4534 281 High 26507 2278 > margin.table(didep) [1] 33600 > prop.table(didep, 1) Diabetes deprivation N Y Low 0.9416407 0.05835929 High 0.9208616 0.07913844 > prop.table(didep, 1)[2,2]/ prop.table(didep, 1)[1,2] [1] 1.356055 ## relative risk ~35%, about 2 percentage point increase > .15*(.05836) # 15% increase over advantaged in paper [1] 0.008754 ############################################################# > install.packages("vcd") ##for odds ratio > library(vcd) > or = oddsratio(didep log = F) # in odds metric "unadjusted" > summary(or) z test of coefficients: Estimate Std. Error z value Pr(>|z|) Low:High/N:Y 1.386655 0.090462 15.329 < 2.2e-16 *** > confint(or) 2.5 % 97.5 % Low:High/N:Y 1.220219 1.575792 ####################################################################################################### # cautionary lesson--did this wrong first two times, cond'l tables are a bitch but 2.2e-16 is as low as it goes > famdep = matrix(c(.219*10347,.122*11179, .153*10347,.197*11179), nr = 2, dimnames = + list("famsize" = c("none", "four+"),"deprivation" = c("Low", "High"))) > famdep # could round to get perfect counts deprivation famsize Low High none 2265.993 1583.091 four+ 1363.838 2202.263 > chisq.test(round(famdep)) Pearson's Chi-squared test with Yates' continuity correction data: round(famdep) X-squared = 314.2, df = 1, p-value < 2.2e-16 > chisq.test(famdep) Pearson's Chi-squared test with Yates' continuity correction data: famdep X-squared = 314.33, df = 1, p-value < 2.2e-16