03GLS_data - PubH8452 Longitudinal Data Analysis Fall 2011...

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Unformatted text preview: PubH8452 Longitudinal Data Analysis - Fall 2011 GLM Case Study General Linear Models - Case Study Treatment of Lead-Exposed Children (TLC) Trial Exposure to lead, often due to deteriorating lead-based paint in older homes, can damage cognitive function, especially in children. The CDC has decided that children with blood lead level over 10 μ g / dL are at risk. Chelating agents can be used to treat lead poisoning, which were usually introduced by injection and required hospitalization. A new agent, succimer, can be given orally. In 1990, the Treatment of Lead- Exposed Children (TLC) Trial Group conducted a placebo-controlled, randomized trial of succimer in children with blood lead levels of 20-44 μ g / dL. The children in the study were aged 12-33 months at enrollment. They received up to three 26-day courses of succimer or placebo and were followed for 3 years. The data we will look at were a random sample of 100 children, with blood levels measured at baseline, weeks 1, 4 and 6. Question of Interest : whether succimer reduces blood lead levels over time relative to placebo. 1 PubH8452 Longitudinal Data Analysis - Fall 2011 GLM Case Study Data Table 1: Blood lead levels ( μ g / dL) at baseline, week 1, 4 and 6 for 10 children in the TLC trial ID Group Baseline Week 1 Week 4 Week 6 1 P 30.8 26.9 25.8 23.8 2 A 26.5 14.8 19.5 21.0 3 A 25.8 23.0 19.1 23.2 4 P 24.7 24.5 22.0 22.5 5 A 20.4 2.8 3.2 9.4 6 A 20.4 5.4 4.5 11.9 7 P 28.6 20.8 19.2 18.4 8 P 33.7 31.6 28.5 25.1 9 P 19.7 14.9 15.3 14.7 10 P 31.1 31.2 29.2 30.1 2 PubH8452 Longitudinal Data Analysis - Fall 2011 GLM Case Study Summary Statistics Read in the data and compute some summary statistics > tlc <- read.table ("data/tlc.txt", + col.names = c("ID", "Group", "week.0", + "week.1", "week.4", "week.6")) > tlc[1:4,] ID Group week.0 week.1 week.4 week.6 1 1 P 30.8 26.9 25.8 23.8 2 2 A 26.5 14.8 19.5 21.0 3 3 A 25.8 23.0 19.1 23.2 4 4 P 24.7 24.5 22.0 22.5 > > > do.call ("rbind", tapply (tlc$week.0, tlc$Group, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. A 19.7 22.13 26.20 26.54 29.55 41.1 P 19.7 21.88 25.25 26.27 29.73 38.1 3 PubH8452 Longitudinal Data Analysis - Fall 2011 GLM Case Study > by (tlc[,-(1:2)], tlc$Group, function(x) cbind(mean = mean(x), sd = sd(x), + min=apply(x,2,min),max=apply(x,2,max))) tlc$Group: A mean sd min max week.0 26.54 5.021 19.7 41.1 week.1 13.52 7.672 2.8 39.0 week.4 15.51 7.852 3.0 40.4 week.6 20.76 9.246 4.1 63.9------------------------------------------------------------------------------------------------------- tlc$Group: P mean sd min max week.0 26.27 5.024 19.7 38.1 week.1 24.66 5.461 14.9 40.8 week.4 24.07 5.753 15.3 38.6 week.6 23.65 5.640 13.5 43.3 4 PubH8452 Longitudinal Data Analysis - Fall 2011 GLM Case Study Explore the Data First we need convert it to long format: > tlcL <- reshape (tlc, direction = "long", idvar = "ID", varying = 3:6) > names (tlcL)[3:4] <- c("Week", "Lead") > tlcL[95:105,] ID Group Week Lead 95.0 95 A 0 31.2 96.0 96 A 0 31.4 97.097....
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This note was uploaded on 11/21/2011 for the course PUBH 8452 taught by Professor Xianghualuo during the Fall '11 term at Minnesota.

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03GLS_data - PubH8452 Longitudinal Data Analysis Fall 2011...

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