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## Applied Longitudianal Data Analysis

by: Jordane Kemmer

22

0

12

# Applied Longitudianal Data Analysis ST 732

Marketplace > North Carolina State University > Statistics > ST 732 > Applied Longitudianal Data Analysis
Jordane Kemmer
NCS
GPA 3.79

Peter Bloomfield

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COURSE
PROF.
Peter Bloomfield
TYPE
Class Notes
PAGES
12
WORDS
KARMA
25 ?

## Popular in Statistics

This 12 page Class Notes was uploaded by Jordane Kemmer on Thursday October 15, 2015. The Class Notes belongs to ST 732 at North Carolina State University taught by Peter Bloomfield in Fall. Since its upload, it has received 22 views. For similar materials see /class/223930/st-732-north-carolina-state-university in Statistics at North Carolina State University.

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Date Created: 10/15/15
One Degree of Freedom Tests 0 Test for group X occasion interactions has number of groups 1 x number of occasions 1 degrees of freedom 0 This can dilute the significance of a departure from the null hypothesis 0 We can focus the test on departures of a particular form For example 0 if we expect all responses after the baseline to respond sim ilarly Mafter baseline Mbaseline Shift where shift depends on treatment group we estimate the dif ference of u1u4u63 u0 between treated and placebo groups 0051 MS4 MS63 Lisp 0031 124 MP63 120 c or we can focus on the area under the curve 2 gtltM1 Mo25 gtlt M4 Mo1 gtlt Mo M0 and again estimate the difference between the groups Test these hypotheses in PROC MIXED by adding the state ments contrast response vs baseline treatment week 1 1 1 3 1 1 1 3 chisq contrast AUC treatment week 2 25 1 55 2 25 1 5 5 chisq The additional output is Contrasts Num Den Label DF DF ChiSquare F Value Pr gt ChiSq Pr gt F response vs baseline 1 98 6732 6732 lt0001 lt0001 AUG 1 98 8052 8052 lt0001 lt0001 3 Exploiting the Baseline 0 Profile analysis tests for any differences in change over time 0 With baseline data and randomized groups we know that the baseline responses can differ between groups only by sampling variation c We can exploit this knowledge by converting the later responses into differences from base line or treating the baseline measurement as a covariate instead of a response Convert to differences data tlcuni Convert the data from multivariate form one record per subject with 4 responses to univariate form one record per response with a new variable week to identify the occasion differences from baseline set tlc drop yO y6 week 1 y y1 yO output week 4 y y4 yO output week 6 y y6 yO output run proc sort data tlcuni by treatment proc mixed data tlcuni order data class child treatment week model y treatment week treatment week solution chisq repeated week subject child type un run The key output is Mixed Model Analysis of TLC data Differences from baseline Type 3 Tests of Fixed Effects Num Den Effect DF DF ChiSquare F Value Pr gt ChiSq Pr gt F treatment 1 98 6732 6732 lt0001 lt0001 week 2 98 2220 1110 lt0001 lt0001 treatmentweek 2 98 3976 1988 lt0001 lt0001 Note that the main effect of treatment is now of substantive interest In fact it is the same as the simpler one degree of freedom test described above These treatment and treatmentgtlt week Chi Squares sum to the interaction term in the previous analysis Covariate approach data tlcuni Convert the data from multivariate form one record per subject with 4 responses to univariate form one record per response with a new variable week to identify the occasion keep yO as a covariate set tlc drop yl y6 week 1 y y1 output week 4 y y4 output week 6 y y6 output run proc sort data tlcuni by treatment proc mixed data tlcuni order data class child treatment week model y treatment week treatment week yO solution chisq repeated week subject child type un run The key output is Effect y0 Effect treatment week treatmentweek y0 Mixed Model Analysis of TLC data Use baseline data as a covariate The Mixed Procedure Solution for Fixed Effects Standard treatment week Estimate Error DF t Value 08045 009390 97 857 Type 3 Tests of Fixed Effects Num Den DE DE ChiSquare F Value Pr gt ChiSq 1 97 6858 6858 lt0001 2 97 2220 1110 lt0001 2 97 3976 1988 lt0001 1 97 7341 7341 lt0001 8 Prgt t lt0001 Pr gt F lt0001 lt0001 lt0001 lt0001 The week and treatmentxweek tests are the same as in the de viation from baseline analysis but the main effect of treatment is slightly more significant The yO coefficient of 08045 is more than 2 standard deviations less than 1 significant at the 5 level Constraining baseline means to be equal data tlcuni set tlc drop yO y6 week 1 y y1 output week 4 y y4 output week 6 y y6 output put baseline in its own group week O y y0 treatment 2 output run proc sort data tlcuni by treatment proc mixed data tlcuni order data class child treatment week model y week treatment week no main effect of treatment solution chisq repeated week subject child type un run 10 The key output is Mixed Model Analysis of TLC data baseline means equal The Mixed Procedure Solution for Fixed Effects Standard Effect treatment week Estimate Error Intercept 264060 04999 week 1 16445 07823 week 4 22313 08073 week 6 26420 08864 week 0 0 treatmentweek A 1 113410 10930 treatmentweek A 4 87653 11312 treatmentweek A 6 31199 12507 treatmentweek P 1 0 treatmentweek P 4 0 treatmentweek P 6 0 treatmentweek Z 0 0 11 OAA Type 3 Tests of Fixed Effects Num Den Effect DF DF ChiSquare F Value week 3 99 18461 6154 treatmentweek 3 99 11196 3732 This approach 0 exploits the information about baseline data o is as powerful as any other u uses data for a subject even when the baseline is missing Pr gt ChiSq lt0001 lt0001 12 Pr gt F lt0001 lt0001

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