Collapsing Longitudinal Data Across Related Events and Imputing Endpoints

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1 Collapsing Longitudinal Data Across Related Events and Imputing Endpoints James Joseph, INC Research, King of Prussia, PA INTRODUCTION When attempting to answer a research question, the best study design is determined by the information a solution requires and a product of the available data and analytical methods. Data collection methods for clinical research questions often involve performing assessments at scheduled time points. The resulting data is longitudinal and allows analysts to approximate the length of time that an event of interest was observed. The approximation can be performed using PROC MEANS: simply retrieve the minimum and maximum value of a variable for each classification of observations. The technique allows researchers to ask: How long have values from a particular lab test for this subject been out of range? Over what span of the trial did a subject adhere to protocol? Collapsing data and imputing its endpoints can also be useful when event durations are desired but data is spare or collected in an untimely fashion. An analyst exploring safety data may wonder whether an isolated record of a rash on a subject s neck is related to a more recent record of a rash on the same subject s torso. Scientific knowledge of the infection s distributional characteristics may provide enough evidence to claim that the two records indicate one infection (whose spreading behavior is reasonable) rather than two independent and isolated cases of infection. Collapsing this data is justified and can result in a more accurate depiction of a subject s condition over time. Classification variables may not be present in the data used in retrospective studies, thereby requiring additional programming steps before collapsing observations within each custom class. Perhaps you d like to discover the average number of one-syllable sounds a baby girl pronounces before she is able to pronounce a two-syllable word? When does she begin tackling three syllables? How do these estimates compare to those of baby boys? To answer these questions, a programmer locates the earliest and latest records of one-syllable words before a two-syllable word is observed for each subject. In this paper, we present a fictional case study that insists on classifying observations according to their relation and proximity to each other before collapsing the observations within each class and imputing their endpoints. The underlying is illustrated by utilizing DATA STEP programming. A more convenient solution using PROC MEANS is also offered. Computations are validated in BASE SAS 9.0 through 9.3. BACKGROUND Psychoactive drugs change the chemical makeup of the brain and influence thoughts, moods, sensations, and behaviors. They are used to treat a variety of indications pertaining to the mind, such as depression, anxiety, narcolepsy, and schizophrenia, to name a few. The subjective nature of these indications largely constrain treatment outcome data to be sourced from self-report measures like the Montgomery-Asberg Depression Rating Scale (MADRS) (used to measures the severity of depressive episodes) or the Suicide Intent Scale (SIS) (used to quantify suicidal intent). The reliability of self-report scales is challenged by both controllable and uncontrollable biasing factors. The interviewer or experimental setting can bestow unintended influence on the responder s feedback. The responder may exaggerate or downplay the degree of his or her response for any number of reasons that are known or unknown to responders and/or observers. The result can sometimes be data that features a high level of intra-individual variance. Sometimes this variance can be decreased by collapsing observations that are related to one another in some way that is scientifically justifiable.

2 PURPOSE A pharmaceutical company wishes to perform a retrospective study to derive estimates for the duration and incidence of psychotic episodes associated with amphetamine withdrawal amongst abusers of their popular drug, XYZ, an amphetamine salt preparation for the treatment of Attention Deficit Disorder. Two psychiatric symptoms common to amphetamine withdrawal that are of particular interest to the researchers are (a) depression, and (b) suicidal ideation. DEFINING THE PROBLEM An episode is a period of time characterized by impaired mental functioning. Researchers are interested in the nature of two types of episodes: depressive episodes and suicidal episodes. Each type of episode is characterized by a pattern of symptoms over time. Remission is period of time during which an episode was not observed in the patient. If the remission period is long enough, the patient is considered recovered from the episode and any subsequent episode is independent from the former. Scientific literature on the nature, time course and severity of amphetamine withdrawal symptoms prompted researchers to decide that it is highly unlikely that a subject fully recovers from depressive symptoms overnight. Therefore, if depressive symptoms are apparent on successive days, those records comprise a depressive episode. If severe suicidal thoughts appear within two days of either symptom (depression or suicidal ideation), the researchers claim that the set of proximate symptoms comprise a suicidal episode. Isolated symptoms are considered single-symptoms episodes of their respective type. These rules will be applied regardless of the existence of missing data. In order for the researchers to summarize the duration and incidence of depressive episodes and suicidal episodes due to withdrawal from XYZ amongst drug abusers, symptom records fitting the aforementioned patterns must be collapsed into a single event record that features a start date equivalent to that of the earliest symptom in the pattern and the end date equivalent to that of the last symptom in the pattern. Your expertise is requested in restructuring the dataset in Table 1 so that there is one record per episode. THE DATA Mental health data on patients whose medication history includes XYZ was acquired from a substance abuse treatment facility. The data originally contained the daily scores that patients earned on MADRS and SIS self-report measures. A MADRS score greater than 4 indicates the presence of depressive symptoms. SIS scores of 29 or above indicate high intent to commit suicide. For the purposes of this tutorial, only records showing any degree of depression (MADRS > 4) and high degrees of suicidal intent (SIS > 29) are considered. Those records are presented vertically in Table 1. A graphical representation is found in Figure 1. Notice that the data has been manufactured to represent a variety of symptom patterns. For example, according to the rules set forth to determine episode, subjects 1001 and 1002 have three symptoms and three episodes each, while subject 1006 has six symptoms and one episode. From the data representations below and the rules defined above, how many symptoms and episodes do you expect for each subject?

3 Variable Name: SUBJECT SYMFLAG SYMDECODE STDY Label: Subject ID Symptom Flag Symptom Decode Study day of Assessment A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent B High Suicidal Intent B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms A High Suicidal Intent A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent B High Suicidal Intent B High Suicidal Intent B High Suicidal Intent B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms 9 Table 1. Subject Symptom Records of Interest and Corresponding Study Day of Assessment

4 Figure 1. Timeline of Depressive (A) and Suicidal (B) Symptoms Amongst XYZ Abusers If depressive symptoms appear on successive days, those records comprise one depressive episode. If high suicidal intent is observed two or less days away from any other symptom or episode, that set of records comprise a suicidal episode.

5 COMPUTATION AND EXPLANATION Read in the data: data SOURCE; input subject 1-4 symflag $ 9 symdecode $ stdy 33; cards; 1001 A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent B High Suicidal Intent B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent B High Suicidal Intent B High Suicidal Intent B High Suicidal Intent B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms A Depressive Symptoms B High Suicidal Intent A Depressive Symptoms 9 ;

6 This procedure requires that the data be sorted by subject and ascending study data. proc sort data=source (keep=subject symflag stdy); by subject stdy; Conditional logic is used to classify distinct episodes. Create a variable that will change its value when event records are not sufficiently related. data IDENTIFY; set SOURCE; by subject; retain n; if first.subject then n = 0; Conditions to collapse: a) Depression records are 1 day apart, and b) Suicidal ideation within 2 days of any other symptom. Only perform these conditional operations within subjects, not between. if first.subject eq 0 and ((stdy - lag1(stdy)) <= 1 and symflag = "A") or ((stdy-lag1(stdy)) <=2 and symflag = "B") or ((stdy-lag1(stdy)) <=2 and lag1(symflag) = "B") then EPInum = n; If records are sufficiently related, then increase counter by one; else do; n+1; EPInum=n; end; drop n;

7 Label the episodes (classes) as either depressive or suicidal. If an episode contains a period of severe suicidal ideation, then it is a suicidal episode; otherwise, it is a depressive episode. data temp1; set IDENTIFY; length EPIname $50; if symflag = "B" then do; EPIname = "Suicidal"; output; end; drop stdy; Merge EPIname onto the data that has identified the individual episodes. EPInum uniquely identifies episodes within subjects; data NAMED; merge IDENTIFY temp1; by subject EPInum; if EPIname = " " then EPIname = "Depressive"; COLLAPSE RECORDS WITHIN AN EPISODE USING PROC MEANS EPInum can be used to identify unique episode within subjects, but it does not describe the type of episode that was experienced. data NAMED; set NAMED; EPISODE = compress(epiname, " ") " - " put(epinum, 2.) ; proc means data=named noprint; class subject EPISODE; var stdy; types subject*episode; output out=epiproc (drop=_type freq_) min=start max=end; title "Suicidal and Depressive Episodes PROC MEANS"; proc print data=epiproc;

8 COLLAPSE RECORDS WITHIN AN EPISODE MANUALLY Identify the endpoints of an episode and capture their study day. Delete non-endpoint records. data EPIMAN; set NAMED; by subject EPInum; if first.epinum then startday = stdy; if last.epinum then endday = stdy; if startday = endday =. then delete; The lag function cannot be used within a conditional statement, so in preparation for the final step, create variables that represent their values. STARTDAY_ will be used to pull STARTDAY into the latest record of an episode (which already contains ENDDAY). SUBJECT_ will be used to make sure we're operating within subjects; startday_ = lag1(startday); subject_ = lag1(subject); Finally, combine endpoint records into one episode record with start day and end day. The earlier record containing the start point is not of use anymore. if startday =. and last.epinum and subject_=subject then startday = startday_; if first.epinum and (first.epinum ^= last.epinum) then delete; drop symflag EPInum stdy startday_ subject_; title "Suicidal and Depressive Episodes - Manual"; proc print data=epiman;

9 COMPARE OUTPUTS Suicidal and Depressive Episodes PROC MEANS Obs subject EPISODE Start End Depressive Depressive Depressive Suicidal Suicidal Suicidal Depressive Depressive Suicidal Suicidal Suicidal Depressive Suicidal Suicidal Depressive Depressive Suicidal Suicidal Suicidal Suicidal and Depressive Episodes - Manual Obs subject EPIname startday endday Depressive Depressive Depressive Suicidal Suicidal Suicidal Depressive Suicidal Depressive Suicidal Suicidal Suicidal Depressive Suicidal Suicidal Depressive Depressive Suicidal Suicidal 6 9 Output 1. PROC MEANS endpoint imputation Manual endpoint imputation have produced the same episode records.

10 CONCLUSION We applied conditional logic to group symptom records by the patterns they exhibited over time and then explored two approaches that successfully created a dataset containing one record per episode. The manual approach was an exercise in DATA STEP programming logic while the PROC MEANS approach was presented to offer a more practical solution to collapsing related records and assigning it the appropriate endpoint dates. The fictional researchers can now use the resulting dataset to answer a number of questions: how many episodes of each type does a typical XYZ abuser experience before recovery? How long does the average episodes last? What subject characteristics influence these parameters? In this example, event records were collapsed over time. Applications may require collapsing records over other continuous scales, such as space. For example, imagine that data on the field distribution of two different species of wildflowers are available. It is later discovered that these wildflowers are co-dependent. To determine the distribution of their co-dependent colonies, researchers use the available data and classify individual wildflowers of both species into groups according to their proximity to each other. By classifying observations conditionally and collapsing related events, much more can be learned from data than originally intended! REFERENCES: The nature, time course and severity of amphetamine withdrawal (2004) Defining remission on the Montgomery-Asberg depression rating scale. Montgomery-Asberg Depression Rating Scale (MADRS) Assessment Summary Sheet Beck s Suicide Intent Scale (1975) clive.edublogs.org/files/2007/11/beck-suicide-intent-scale.doc SAS Learning Module Collapsing across observations in SAS

11 ACKNOWLEDGMENTS SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. CONTACT INFORMATION Questions and comments are valued and encouraged. Contact the author at: James Joseph INC Research 680 American Way King of Prussia, PA Work Phone: james.joseph@incresearch.com Web: * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *