Pharmaceutical Applications

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1 Creating an Input Dataset to Produce Incidence and Exposure Adjusted Special Interest AE's Pushpa Saranadasa, Merck & Co., Inc. Abstract Most adverse event (AE) summary tables display the number of patients experiencing an AE in each treatment group and include a standard percentage calculation. This standard way of calculating percentage is the number of patients experiencing an AE divided by the number of patients who were randomized to a particular treatment. But in studies with long-term follow-up, this percentage calculation may not be appropriate because there is a potential for differences in the follow-up duration between treatment groups. A more appropriate measure under this scenario is the exposure-adjusted incidence rate. The exposure-adjusted incidence rate is defined as the number of patients with a particular AE divided by the total exposure-time among patients in the respective treatment group at risk of an initial occurrence of the event. This paper will explain how to create an "input dataset" that can be used to obtain the standard percentage and exposure adjusted incidence rates with a "one proc away" approach. Introduction Safety is a major indicator for drug approval. When a Clinical Study Report (CSR) is submitted there are a number of tables with AE counts and inferential statistics. The duration of a clinical study can range from a few hours to many years. In studies with long-term follow-up, the standard percentage may not be appropriate because there is a potential for differences in the follow-up duration between treatment groups. Additionally, due to the chemical component of a drug given to the study patients, the clinician may desire to identify other defined AE events known as special interest AEs; i.e., those adverse events which are not usually a part of the standard AE dataset. For example, a special interest AE table may require information from domains not of direct concern to the study. These special interest AE events also can be categorized as Tier 1 AE's; some of these AE's are comprised of one or more associated incidents defined in the protocol. This paper focuses on the concept of creating an analysis ready dataset for both standard percentage and exposure adjusted special interest AEs. This is especially important when the information for the special interest events dataset comes from different domains. Objective The objective of this paper is to show how easy it is to create a dataset that can be used to output desired study results such as frequencies, proportions, AE rates and p-values using a one proc away method. 1

2 Procedure with an Example Throughout the paper special interest AEs are referred to as EVENTS and the AE incidences associated with those EVENTS are referred to as EVNTTERM. Figure 1 is the mock-up that illustrates the outcome of producing the dataset being discussed here. You can see how events and evntterms and the corresponding statistics are displayed. Figure 1 Number (%) of Patients with Special Interest AE 's by Treatment Group All Treated Patients Number (%) of AE Difference in Proportion of AE AE Rates Per Patient-years of Exposure TRT1 TRT2 TRT1 minus TRT2 TRT1 TRT2 XXX XXX Diff. (95% CI ) p-value With Diabetes xx(x.x) xx(x.x) x.x(x.x,x.x).xxx.xxx.xxx Anti-diabetic medications xx(x.x) xx(x.x) x.x(x.x,x.x) Elevations of Fasting xx(x.x) xx(x.x) x.x(x.x,x.x) Diabetes mellitus xx(x.x) xx(x.x) x.x(x.x,x.x) type 2 mellitus xx(x.x) xx(x.x) x.x(x.x,x.x) With worsening xx(x.x) xx(x.x) x.x(x.x,x.x).xxx.xxx.xxx Tolerances Fasting >100 xx(x.x) xx(x.x) x.x(x.x,x.x) Consecutive Elevations of xx(x.x) xx(x.x) x.x(x.x,x.x) Fasting Hyperglycemia xx(x.x) xx(x.x) x.x(x.x,x.x) Blood increased xx(x.x) xx(x.x) x.x(x.x,x.x) CI = Confidence intervals. Calculated using a method based on Wilson's score method. p-values are from Fisher's Exact test. AE rates per patient-years of exposure = (number of patients with AE/sum of days at risk for AE) x days/year. The following section explains how to create a dataset in such a way as to get all the information needed for reporting the desired statistics. 2

3 STEP 1 Identifying EVENTS: How many events are specified in the protocol? What event terms are associated with an event? The answers to these questions are determined by clinicians and are included in the study protocol. If there are 10 events to be included in the desired table then it is necessary to precisely understand what they are and how they are defined. For easy programming assign a numeric code to each event. For example, when there are only three events the numeric code would be 1= ' Diabetes' 2 = 'Worsening Tolerances' 3= 'ALT/AST elevations with muscle symptoms' Look at the hypothetical example in Figure 1 above, "Diabetes" is an event of interest and event terms are then listed as associated event terms for the main event. In this figure the event terms are "anti medication" taken from the CM (Concomitant Medications) domain, OR "elevations of fasting glucose" taken from the LB (Lab) domain OR "any AE s related to " (terms should be defined) taken from the AE domain. At this stage it is important to understand how the conditions of associated event terms are combined. The combinations could be either "OR", "AND", or both. Depending on the conditions for event terms the set or merge statements should be applied in order to reflect the conditions. Another hypothetical example: "Worsening of Tolerance" can be another event of interest. It can be defined as "not on anti-diabetic medications during pre treatment period" from CM domain AND "no medical history of " from MH (Medical History) domain AND "baseline Fasting glucose < 100" from LB domain AND "no onset terms" defined from AE domain AND "on treatment fasting >100" OR "two consecutive fasting glucose elevations" from LB domain OR "any terms related to indicate glucose tolerances" from AE domain. STEP 2 The next step is to capture the necessary information from each domain related to the incidents of interest. Make sure all the event dates are within the range defined in the protocol. The data should appear as in Figure 2 after assembling the data from each domain. 3

4 STEP 3 The following code captures the first incidence of each unique event term. Proc sort data=event; by subjid evntterm evntdt; - data event1; set event; by subjid evntterm evntdt; If first.evntterm; termfl =1; flag the unique associated event term per patient. Figure 2 EVENT dataset SUBJECT EVENT EVNTDT EVNTTERM EVNTCODE EVNTVALU 101 Diabetes 02/05/10 anti diabetic 1 1 medication 101 Diabetes 02/07/10 anti diabetic 1 1 medication 101 Diabetes 01/12/10 anti diabetic 1 1 medication 101 Diabetes 01/12/10 Increasing Diabetes 01/10/10 Increasing Diabetes 01/29/10 mellitus Diabetes 01/05/10 mellitus Diabetes 02/12/10 insulin resistant Diabetes 02/15/10 type mellitus 101 Diabetes 01/04/10 type mellitus 104 Diabetes 05/09/10 Increasing Diabetes 06/03/10 insulin resistant Diabetes 06/10/10 latent 1 1 autoimmune 104 Diabetes 07/02/10 latent autoimmune 1 1 4

5 The above sorting criteria should be applied for each event with associated event terms. Once we append all events you will have a dataset with all events and their associated event terms. The question then arises as to how we can get AE rates for each of the events? AE rates are calculated by summing all events (Yes, No) and dividing by sum of the times to the first event in the case of those subjects with an AE plus the total exposure of those reporting no event. It becomes evident that in order to do the AE rate calculations we need to find the patients who are included in the denominator but did not have any events. Briefly on needs to get the unique patients that will be in the denominator and create EVENTS dummies for them. If there are 10 EVENTS for each patient then the code that creates the set of dummies is: STEP 4 proc sort data=derived.adsl out=adme; by subjid; data dummy; set adme; by subjid; do evntcode = 1 to 10; output; end; Merge data set that was created for the event terms and merge with the just created dummy dataset by subjid and evntcode in order to assign event for each patient. STEP 5 data allevent; merge event(in=b)dummy(in=a) ; by subjid evntcode; if a and not b then evntvalu=0; <- assigning 0 when there are no any events. Apply this condition to capture the duration of the staying on the study for patients if evntvalu = 1 then toevent = (evntdt -stsdt)/365.25; if evntvalu= 0 then toevent = (stedt - stsdt)/365.25;. 5

6 STEP 6 Data tmevnt; set allevent; by an evntcode evntdt; if first.evntcode then evntfl = 1; <- this captures the first date of the event for each patient The final dataset with selected variables should appear as shown in Figure 3 Figure 3 SUBJID EVENT EVNTDT EVNTTERM EVNTCODE EVNTVALU EVNTFL TERMFL 101 Diabetes 01/04/10 Type Diabetes mellitus 101 Diabetes 01/05/10 Diabetes mellitus 101 Diabetes 01/10/10 Increasing Diabetes 01/12/10 Anti medication 101 Diabetes 02/12/10 Insulin resistant Diabetes Diabetes Diabetes 05/09/10 Increasing Diabetes 06/03/10 Insulin resistant Diabetes 06/10/10 Latent autoimmune 101 FG Tolerances 03/08/2010 FG > FG Tolerances 04/11/ Hyperglycemia 102 FG Tolerances 06/20/2010 Blood Increase 102 FG Tolerances 06/27/2010 Hyperglycemia FG Tolerances 01/22/2010 Consecutive elevation of FG 103 FG Tolerances 02/02/2010 Blood Increase 103 FG Tolerances 03/12/2010 Hyperglycemia FG Tolerances

7 Why is this process one proc away? With this simple code AE counts and rates for each EVENTS can be produced. proc sql; create table ae2 as select distinct evntcode, trtmnt, sum(evntvalu) as COUNTS counts for each event,put (round(sum(evntvalu)/sum(toevent),.001), 5.3) as AERATES from tmevnt where evntfl=1 group by trtmnt, evntcode order by evntcode; quit; After running the code you will have an output dataset as shown in Figure 4 Figure 4 TRTMNT EVNTCODE COUNTS AERATES TRT TRT TRT TRT TRT TRT TRT TRT TRT TRT Getting counts for each of the associated terms under each of the events is simple: proc freq data=tmevnt ; where evntterm ne ' ' and termfl=1; /*counts for each evntterm by evntcode and trtmnt*/ tables evntcode*evntterm*trtmnt/out=fterms; Make sure all the patients in the denominator are in the main dataset Finally, after making sure of the proper subsetting, the p-values for each event are computed as: proc freq data=tmevnt; where evntfl=1; tables evntcode*trtmnt*evntvalu/ exact fisher ; ods output FishersExact=fishall(where=(NAME1='XP2_FISH')); 7

8 Conclusion In this paper we considered the specific example of creating a one proc away dataset for the presentation of specific interest AE tables. There are certain ways in which a programmer can create datasets to produce desired results. The most important thing to understand is the specific result that we need to produce and how can it be done efficiently without any manipulations in the tables program. Two essential features are common to all one proc away datasets. First, there must be clear dataset specifications. Second, there must be clear mock-ups for the tables. If the one proc away datasets are analysis ready then the whole analytical process is efficient and can save a lot of time. References SAS Institute Inc. 2004, SQL Processing with SAS Course Notes Acknowledgments Thank you to Ram Radhakrishnan for reviewing this work and providing constructive feedback. AUTHOR CONTACT Your comments and questions are valued and welcome. Contact the author at: Pushpa Saranadasa Phone: (267) Pushpa_saranadasa@merck.com 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. 8

PROGRAMMER S SAFETY KIT: Important Points to Remember While Programming or Validating Safety Tables

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