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Applied Longitudianal Data Analysis

by: Jordane Kemmer

Applied Longitudianal Data Analysis ST 732

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

Marie Davidian

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Marie Davidian
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This 7 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 Marie Davidian in Fall. Since its upload, it has received 14 views. For similar materials see /class/223938/st-732-north-carolina-state-university in Statistics at North Carolina State University.


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Date Created: 10/15/15
CHAPTER 7 ST 732 M DAVIDIAN 7 Drawbacks and limitations of classical methods 71 Introduction It is worth noting that both the univariate and multivariate classical methods we have discussed so far may be extended to more complicated situations For example 0 The group designations may in fact be the result of a factorial arrangement eg in an experiment to compare the change over time of body weight of rats the groups may be de ned by the 2X3 factorial arrangement of genders and drugs Interest may focus on how the rate of change in body weight over time differs across genders averaged over drugs and doses averaged across genders Interest may also focus on whether the way this change differs across drugs is different for the two genders the drug by gender interaction These are between unit comparisons o The time factor may in fact be the result of a factorial arrangement eg in an agricultural study plots may be randomized to different rates of fertilizer Then at each of 4 different time points core samples are taken from each plot at 3 different depths and a measurement of nutrient content is recorded for each Here then each plot is seen under 4 X 3 12 different conditions We do not discuss these extensions see for example Vonesh and Chinchilli 1997 section 33 The fact that these fancy extensions are possible still does not alter the fact that the classical models and methods have some serious limitations some of which we have remarked upon in our development so far Now that we are familiar with these so called classical methods and the statistical models underlying them we are in a position to be more speci c about these limitations 72 Assumptions and restrictions of classical methods Here we provide a laundry list of the assumptions made by classical methods and the restrictions that they impose The rest of the course will be devoted to statistical models and associated analysis methods that seek to address some or all of these restrictions PAGE 201 CHAPTER 7 ST 732 M DAVIDIAN 1 BALANCE A prominent feature both of the univariate and multivariate classical models and meth ods is the requirement that all units be observed at the same 71 time points That is not only must each data vector YZ39 be of the same length n for all units but each element Yij j 1 n must have been observed at the same set of times t1 tn say 0 In some situations this may not be much of a restriction For example in agricultural or industrial experimentation where it is possible to have a good deal of control over experimental conditions an experiment may be carefully planned and executed It may thus be perfectly reasonable to expect that observations expected to be taken at certain times would be available 0 However even in the best of situations it is often the case that things may go awry For example suppose that the K are are responses on plots planted with different varieties of soybean over the growing season At a given time 3 plants from a plot are sampled their leaves are harvested ag gregated and ground up and the resulting leaf sample is assayed for concentration of a particular chemical substance It is an unfortunate fact of life that samples may be misplaced or mistakenly discarded or that error may be made in conducting the assay leading to erroneous measurements In such circumstances measurements may thus be unavailable at certain time points for certain plots thus destroying the balance necessary for classical models and methods to be applied 0 When the units are humans this becomes even more of a problem even if a study is carefully designed For example suppose that a study is conducted to compare several cholesterol lowering drugs Subjects are randomly assigned to take regular doses of one of the drugs and are required to return at 3 month intervals for 2 years so that a measure of serum cholesterol may be taken from blood samples drawn at each visit Thus if time for each subject is measured from the subject s entry into the study the subject should have observations on serum cholesterol at n 8 times 3 6 9 12 21 and 24 months However reality may cause this ideal setup to be compromised 7 Subjects may move away during the course of the study so that only measurements up to their last visit before moving are available 7 A subject may be out of town and miss his 9 month visit but come to the clinic at 105 months instead PAGE 202 CHAPTER 7 ST 732 M DAVIDIAN 7 Blood samples may be mislabelled or dropped in the lab so that observations on serum cholesterol for some times for some subjects may be impossible to obtain 7 Errors by technicians in performing the analytic laboratory techniques required to measure the cholesterol level may render other measurements erroneous or unavailable The bottom line is that real life often conspires to make balance an unachievable ideal for many longitudinal studies Although some researchers have discussed ways to adjust the classical approaches to handle some types of imbalance just as with the adjusted F tests in univariate analysis these X ups skirt the real issue which is that a model that requires balance may simply be too restrictive to represent real life 2 FORM OF OOVARIANCE MATRIX Both the classical univariate and multivariate procedures we have discussed assume that the covariance matrix of each data vector YZ39 2 1 m is the same for all 2 regardless of group membership or anything else we discuss this assumption below Provided we believe this assumption is reasonable and take 2 to be this common n X n covariance matrix we are still faced with the issue of what we assume about the structure of 2 o The univariate methods make the assumption of compound symmetry which implies a very speci c pattern of correlation among observations taken on the same unit at different times one that may be quite unrealistic for longitudinal data This model says that the correlation among all observations on a given unit is the same regardless of how near or far apart the observations are taken in time Thus the univariate methods are based on an assumption about the covariance structure that may be too restrictive if Withinunit sources of correlation are not negligible The multivariate methods make no assumption about the structure of 2 Thus these methods do not attempt to take into account at all the way in which observations arise in the longitudinal setting There are two acknowledged sources of variation 7 Random biological variation among units 7 Within unit variation due to the way in which measurements are taken on a unit error in measuring device correlation due to time separation etc PAGE 203 CHAPTER 7 ST 732 M DAVIDIAN The model underlying the multivariate methods does not explicitly recognize these two distinct sources Rather the methods allow for the possibility that the covariance structure could be virtually anything thus including as possibilities structures that are unlikely to represent data subject to the two distinct sources above Thus the multivariate methods are based on an assumption about the covariance structure that is likely too vague 3 COMMON CO VARIANCE MATRIX Both the univariate and multivariate approaches assume that the covariance matrix of a data vector is the same for all units regardless of group or anything else This is akin to making the usual assumption in linear regression or scalar analysis of variance that variance is the same for all scalar observations This is often adopted without much thought however it is quite reasonable to expect that this assumption may be incorrect For example suppose the units are human subjects and the groups are determined by assignment to either a particular hypertension medication or placebo A common observation with such data is that subjects with high systolic blood pressure tend to exhibit much more variability in their within individual measured pressures than do subjects with low systolic blood pressure That is in terms of the conceptual model in Chapter 4 the within subject ucutations for subjects with high blood pressure tend to be of greater magnitude than those for subjects with low blood pressure More formally var61 j is smaller for subjects with low blood pressure than for those with high blood pressure This would lead to overall variance of E that is smaller for lower values of 3 Suppose the drug is quite effective in lowering systolic blood pressure We would thus expect observa tions on subjects in the drug group particularly toward the end of the study to be lower than those for the placebo group In symbols if YZ39 is a data vector for a subject in the drug group 1 we might expect Yil Y f varYm 720 Ym while for a subject in the placebo group 0 we might expect PAGE 204 CHAPTER 7 ST 732 M DAVIDIAN Under these conditions assuming that YZ39 from both groups have the same covariance matrix 2 would be inappropriate because we would doubt that the 7171 element is the same for data vectors from both groups A better model would say that there are two different covariance matrices ie varY 20 is subject 2 is in the placebo group and varY 21 is subject 2 is in the drug group It is possible to modify the classical models and methods to handle this situation One common approach is to work on a transformed scale on which one believes variances may be similar eg one may model the logarithmically transformed data A problem with this approach is that the results may be di cult to interpret as inferences about what happens on the original scale of measurement are of interest Alternatively methods such as Hotelling s T2 may be modi ed to allow a different covariance matrix for each group However this may make statistical power even lower 7 now we must estimate a separate covariance matrix for each group Later in the course we will see methods that address the issue of lack of common covariance matrix in more realistic ways 4 INCORPORATION OF INFORMATION A characteristic shared both by the univariate and mul tivariate classical methods we have discussed is that because balance is assumed time itself does not appear explicitly in the model for the mean of a data vector Rather time enters the model only through the speci cation of separate parameters y and T4027 As will become clear when we study more exible models this can pose an obstacle to answering some key questions of interest see 5 be low too This problem may be partially addressed by inspecting for example orthogonal polynomial contrasts in time but a more direct representation of time in the model is much more useful In addition we may wish to incorporate other covariate information For example in the cholesterol study in 1 above we may believe that a subject s age at the start of the study may play a role in how heshe responds to cholesterol lowering medication Or we may believe that this response over time may be affected by a subject s systolic blood pressure which may also be changing over time Just as ordinary analysis of variance is modi ed to incorporate covariates by analysis of covariance one may wish to do something similar in the case of repeated measurements Things are more complicated however 0 In the rst example the covariate age at start of study is something that is time independent or xed over the time points at which the unit is observed being measured only once at the start of the study Both univariate and multivariate analyses may be modi ed to take account of timeindependent covariates these are discussed in sections 26 and 34 of Vonesh and Chinchilli 1997 PAGE 205 CHAPTER 7 ST 732 M DAVIDIAN We do not discuss them here because as discussed above they still require balance moreover the way in which the covariates may be included in the model is limited Models we will discuss later in the course allow more exibility to address common questions about the effect of covariates o In the second example the covariate systolic blood pressure may be measured at each of the same time points as the response and thus is timedependent or changing with time Incorporation of such covariate information poses di icult conceptual challenges The models we have discussed represent the mean response at each time point as a function of information such as group membership ie possibly different means for each group If we consider models that incorporate changing information important questions arise For example does the mean cholesterol at a particular time only depend on systolic blood pressure at that time Or does it depend on systolic blood pressure at several previous times as well We will return to this issue later for now note that although it is possible to introduce time dependent covariates into modeling of repeated measurements a key issue is this conceptual one It is possible to modify the univariate analysis to incorporate timedependent covariates however modi cation of the MANOVA analyses is not possible Still another issue arises in the inclusion of group information Recall the guinea pig diet example Here dose groups were labelled zero low and high In the model the parameters T2 and T4027 incorporate different groups Suppose however that the actual numerical dose values were available say 0 100 and 500 ugg As we discuss in 5 below it might be useful if the actual dose levels rather than just classi cations were incorporated in the model We will discuss other models and methods where inclusion of such covariate information is more direct and interpretable 5 QUESTIONS OF INTEREST AND INTERPRETATION The analysis based on classical methods focuses on hypothesis testing ie general questions of interest are stated in terms of the model and the quality of the evidence in the data to refute the null hypothesis is assessed A pronouncement is then made we do or don t reject the null hypothesis However in many situations this does not really address the objectives of the investigator For example consider the cholesterol study described in 1 above The investigators may wish to do more than just claim that the way in which cholesterol changes on average over time on the different drugs is different They may actually wish to use the results of their study to make recommendations on how to treat future patients Thus they may wish to make more specialized inferences PAGE 206 CHAPTER 7 ST 732 M DAVIDIAN o How different is the rate of cholesterol lowering among the drugs Eg if they knew that Drug 1 lowered cholesterol at the rate of 5 mm Hg per month and Drug 2 lowered cholesterol at rate 15 mm Hg per month this information might help them to decide which drug mild Drug 1 or aggressive Drug 2 might be more appropriate for a certain patient Thus the investigators might be interested in actually estimating the rate of change in the mean response over time for each group 0 What would the cholesterol trajectory look like for a new male patient 45 years of age after 8 months on one of the drugs That is before treatment the investigators might wish to be able to predict what the cholesterol pro le might look like over 8 months for a patient with speci c characteristics and what his cholesterol level might be at the end of that time based on his measurement at time zero Note that 8 months is not even one of the time points every 3 months included in the original study Clearly in order to address such questions a more exible model that incorporates time and rate of change in a more explicit way is needed A further illustration is provided by the guinea pig diet example as discussed in 4 above Suppose the investigators would like to be able to understand how the rate of change in body weight of the pigs over time is associated with the actual numerical dose Does rate of change increase as we change the dose By how much per unit change of dose If the actual dose amount could be incorporated explicitly in the model these questions could be addressed It should be clear from this brief discussion that the classical models and methods have serious limitations with respect to these important issues A serious drawback alone is that of the need for balance Another is failure of the models to represent explicitly important features like rate of change with time We begin our discussion in the next chapter with models and methods that seek to address these problems PAGE 207


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