Systematic Inductive Method for Imputing Partial Missing Dates in Clinical Trials

Transcription

1 Paper PO05 Systematic Inductive Method for Imputing Partial Missing Dates in Clinical Trials Ping Liu ABSTRACT In any phase of clinical trials, dates are very critical throughout the entire trial. However, since incomplete dates are inevitable during the clinical data collection process, and missing dates will cause numerous problems to the study, there is a need to investigate the method of handling the missing dates. We do have different imputation methods for partial missing dates in different companies or different therapeutic areas, but we don t have a systematic method for varied missing situations. Developing a systematic inductive method for imputing the missing dates in varied situations is the topic of this paper. The paper will categorize missing dates into three status (whole date missing, month and day missing, only day missing), and utilize a systematic inductive method to impute the partial missing dates. A simple SAS macro will be included. This method serves as a hint for handling missing dates in different situations and different time points. INTRODUCTION Working in different companies or different therapeutic areas, we often encounter the situation in which quite an amount of time is being spent to discuss rules and develop imputation algorithms when incomplete dates occur in different studies. Sometimes we don t even recognize that we have encountered and discussed the same situations before because these missing dates situations are handled on an ad-hoc basis rather than being summarized and processed in a systematic way. Thus, it doesn t matter whether the missing dates are the start date or end date of drug compliance, medication history, concomitant medication, AE; whether the missing dates are the study completion date, discontinued date or death date; whether the missing dates are the sample dates in LAB, ECG or Vital Signs. We can summarize the missing dates into three categories: (1) the whole date is missing, (2) both of day and month are missing and (3) only day is missing. Furthermore, the imputation algorithms are developed based on a target date. This paper will discuss all of the three missing categories, but mostly focus on categories (2) and (3). Section 1 will describe how to categorize the clinical trial missing dates and how to handle the whole missing dates. Section 2 will categorize the partial missing dates and define the target date and variables names to utilize a systematic inductive method for imputing partial missing dates. Section 3 will provide a SAS macro implementing the method described in Section 2.

2 1. WHOLE DATE MISSING In clinical trials, dates can be grouped according to their related procedures, date related to the medical intervention, date related to an unplanned event and date related to a planned clinical evaluation. No matter what type of date it is, complete date information should be collected whenever possible. However, due to numerous reasons, missing dates are very often seen in clinical trials at different stages. Missing dates may occur because of errors by patients, investigators or data monitors, or during the data transfer. No matter what the cause, missing dates can be categorized into (1) due to human error and (2) due to unknown reasons. Some human errors can be fixed by queries during the data cleaning process, but some human errors can not be fixed because of numerous reasons (e.g., tight timeline, clinical procedure orders, etc.). Those unfixed errors may cause problems in the analysis of the study. How to handle missing dates due to unfixed errors is the topic of this paper. Generally, the imputation algorithms depend on the type of events and the time when those missing dates occurred. Different cases may adopt different imputation algorithms, thus adopting appropriate imputations algorithm becomes critical. In some cases, the traditional last observation carried forward (LOCF) method can be adopted while in some other cases the imputation algorithm needs to be based on the specific situation. The imputation algorithms also need to be dependent on the way the data is collected. The following summarizes the type of date, according to the relationship to the clinical procedure, and some current practices of imputation when the whole date is missing. 1.1 Date of Intervention Date related to medical intervention such as investigational treatment, concurrent medical treatment or surgical procedures. In most cases, these dates are rarely missing, however, if the start date of the intervention is missing, an ad-hoc date is used to impute the missing date. If the end date of the intervention is missing, the date of the last visit to the clinical site is used to impute the missing date. If the interim date of intervention is missing, the corresponding interim visit date is used to impute the missing date. 1.2 Date of Events Date of occurrence or incidence which is independent of planned clinical evaluations. The examples of these dates are AE, Medical History or Concomitant Medications. It is common to see missing dates for these events, especially the month and day missing. The way to handle the missing whole date depends on the specific situation, where the LOCF is often used or the study cut point date is used to impute the missing end date. The interim study date (e.g. study visit date, lab date) sometimes is used to impute the missing interim date. 1.3 Date of Findings Date on which the results from planned evaluations are observed. The examples of these dates are physical exams, test results (ECG, LAB, Vital Signs, other tests), and questionnaires. It is rare that the planned evaluation date is missing. If it is missing, then it is common practice to use the closest visit date to impute the missing date.

3 2. PARTIAL DATE MISSING 2.1 Method and Definition In Section 1 we discussed the situations where the whole date (year, month and day) is missing. In this section, we will discuss the situations where only partial date (month or day) is missing. It is often seen in clinical trials that the date of some past event is often recalled by year or best by year and month. There are numerous methods available for handling the partial missing date. However, these methods are adopted on an ad-hoc basis and hence there is lack of consistency among these methods. A systematic way of handling the partial missing date is developed. The basic concept of this method is to impute the partial missing date based on a target date rather than using an ad-hoc date. The target date is defined as the reference date for the study analysis. It will be driving the points of the study analysis. We can define the treatment randomization date as the target date to impute the treatment start date. We can define the treatment start date as the target date to impute the AE partial missing dates. We can define the visit date as the target date to impute a partial missing date for planned evaluations dates etc. After target date is defined, we can sum up the imputation dates in three ways: 1. The imputing event date could only happen before the target date 2. The imputing event date could only happen after the target date 3. The imputing event date could happen either before or after the target date. We simplify all partial missing date imputations by the target date. How to distinguish it will be discuss in the following sections. 2.2 Define the Dates Variables Before we discuss the imputation methods, let us setup the date variables as follows: Variable name Datevar Yvar Mvar Dvar Trtstd Trty Trtm Trtd Variable descript event date event year event month event day target date target year target month target day 2.3 The Event Dates Occurred Before the Target Date For example of datasets: Randomization Datasets Medication History Concomitant History (sometimes) prior to start of study date Therapeutic Disease Medication prior to start of study date Any Medication Information prior to start of study date First study date is defined as the target date for those kinds of imputing partial missing date. For this type of partial missing date imputation proposal is as follows:

4 YVAR^=. YVAR^=TRTY MVAR=. 01/JULY/YVAR YVAR=TRTY MVAR=. MVAR^=TRTM MVAR=TRTM TRTSTD-1 15/MVAR/YVAR min(15/mvar/yvar,trtstd-1) 2.4 The Event Date Occurred After the Target Date For example of datasets: Adverse Events after treatment study date Study completion information after treatment study date For this type of dataset, partial missing date imputation proposal is as follows: YVAR^=. YVAR^=TRTY MVAR=. 01/JULY/YVAR YVAR=TRTY MVAR=. MVAR^=TRTM MVAR=TRTM TRTSTD+1 15/MVAR/YVAR max(15/mvar/yvar,trtstd+1) 2.5 The Event Date Occurred Crossing the Target Date For example of datasets: Vital signs A_VSN ECG Laboratory A_LRS Bone Markers Quality of Life YVAR^=. YVAR^=TRTY MVAR=. 01/JULY/YVAR YVAR=TRTY MVAR=. 01/JULY/YVAR 2.6 Summarized above three types of date sets imputation as follows: YVAR^=. YVAR^=TRTY MVAR=. 01/JULY/YVAR YVAR=TRTY MVAR=. 1. TRTSTD-1 if TRTSTD<01/JULY/YVAR 01/JULY/YVAR otherwise 2. TRTSTD+1 if TRTSTD>01/JULY/YVAR

5 01/JULY/YVAR otherwise 3. 01/JULY/YVAR MVAR^=. MVAR^=TRTM 15/MVAR/YVAR MVAR=TRTM 1.min(15/MVAR/YVAR,TRTSTD-1) 2.max(15/MVAR/YVAR,TRTSTD+1) 3. 15/MVAR/YVAR Notes: 1--The event dates in the dataset are happen before the target date. 2-- The event dates in the dataset are happen after the target date. 3-- The event dates in the dataset are happen crossing the target date In the summarized partial missing date imputation above, we can change imputation numbers of 01/JULY, 15, TRTSTD-1 and TRTSTD+1 to apply in terms of different purpose of the study analysis. 3. MACRO FOR IMPUTING PARTIAL MISSING DATES Based on the methods discussed in Section 2, a SAS macro is provided to implement these methods. The macro is simple, can be understood easily, and is also flexible. You may need to make a simple modification based on your target date to satisfy the macro s requirement. The program creating a dummy dataset is provided in Section 3.2. You will get tips for creating a dummy dataset and for how to manipulate dates. Two examples will show you how to handle and modify different date formats to apply the macro. 3.1 Macro for Partial Missing Dates ******************************************************** * Macro for impute partial missing dates * this macro can only be used inside data steps * all the parameter variables are assumed numeric except trtdt * macro variables definition: * datvar: imputed date * dvar: need to be imputed day * mvar: need to be imputed month * yvar: need to be imputed year * imflag: flag to be used for distinguishing type of imputing date * trtsdt: target variable * trty: year of the target date * trtm: month of the target date * trtd: day of the target date ***************************************************; %macro missdat(datvar=,dvar=, mvar=, yvar=,imflag=,trtsdt=, trty=,trtm=,trtd=); if &datvar eq. and &yvar ne. then do; if &mvar eq. then do; if &imflag=1 and &yvar=&trty and &trtsdt<input(( 01jul &yvar)d,date9.) then do; &mvar=&trtm; &dvar=&trtd.-1; end; else if &imflag=2 and &yvar=&trty and &trtsdt>input(( 01jul &yvar)d,date9.) then do; &mvar=&trtm; &dvar=&trtd.+1; end; else do; &mvar = 7; &dvar = 15; end;

6 end; else if &dvar eq. then do; if &imflag=1 and &trtd<&dvar and &yvar=&trty and &mvar=&trtm then &dvar=min(15,&trtd.-1); else if &imflag=2 and &trtd>&dvar and &yvar=&trty and &mvar=&trtm then &dvar=max(15,&trtd.+1); else &dvar = 15; end; datvar = mdy(&mvar,&dvar,&yvar); end; %mend missdat; 3.2 Dummy Datasets ************************************* * create a dummy datasets **************************************; data sample(keep=pat visit fdosdt mon day yr sdtc); retain pat visit fdosdt sampdtn sampdtc mon day yr sdtc; format fdosdt sampdtn date9.; do pat=1 to 5; fdosdt='15apr2003'd + pat*30; do visit=1 to 3; sampdtn='11apr2003'd+visit*2 +pat*30 ; sampdtc=put(sampdtn,date9.); if visit=2 and pat in(2,4) then do; yr=year(sampdtn); mon=.; day=.; end; else if visit=2 and pat in(3,5) then do; yr=year(sampdtn); mon=month(sampdtn); day=.; end; else if visit=3 and pat in(2,5) then do; yr=year(sampdtn); mon=.; day=.; end; else if visit=3 and pat in(1,2) then do; yr=year(sampdtn); mon=month(sampdtn); day=.;end; else do;yr=year(sampdtn);mon=month(sampdtn);day=day(sampdtn);end; if mon in(0,.) then do; sdtc=right(' ' put(yr,z4.)); end; else if day in(0,.) then do; sdtc=right(' ' substr(sampdtc,3,3) put(yr,z4.)); end; else sdtc=sampdtc; output; end; end; run; proc print; run; DATASET SAMPLE Obs pat visit fdosdt mon day yr sdtc MAY MAY MAY MAY MAY MAY JUN JUN JUN JUN

7 JUL JUL JUL JUL JUL JUL AUG AUG AUG AUG AUG SEP SEP SEP SEP SEP Examples ******************************************************************** Example 1. Each date already have separated year and month and day variables. *************************************************************************************; data sample1; set sample; trty=year(fdosdt); trtm=month(fdosdt); trtd=day(fdosdt); %missdat(datvar=impdt,dvar=day, mvar=mon, yvar=yr, imflag=1,trtsdt=fdosdt, trty=trty,trtm=trtm,trtd=trtd); run; ******************************************************************** Example 2. Sample date and target date do not have numerical year, month and day variables. *************************************************************************************; data sample2; set sample; format impdt date9.; **get year month and day variables from sample date; dday=input(substr(sdtc,1,2),best.); **month=letter for conversion; mon1 = '01' substr(sdtc,3,3) substr(sdtc,6); mon = month(input(mon1,?? date9.)); yr =input(substr(sdtc,6 ),best.); **get year month and day variables from target date; dayt=day(fdosdt); mont= month(fdosdt); yrt =year(fdosdt); %missdat(datvar=impdt2,dvar=dday, mvar=mon, yvar=yr,imflag=1,trtsdt=fdosdt,trty=yrt,trtm=mont,trtd=dayt); drop dday mon1 mon yr vis1d dayt mont yrt; run;

8 CONCLUSION Based on pharmaceutical industry experience, this presentation summarizes the simple ways to impute the whole missing date and develops a systematic method for imputing partial missing date. This presentation serves as a hint for the basic imputation references for missing dates as well as tips for using SAS date and string functions from the macro and samples, and tips for how to create a dummy dataset. ACKNOWLEDGEMENTS The author would like to thank Ms. Margaret McAlary, my supervisor, for her encouragement and comments during the development of this presentation. CONTACT INFORMATION Ping Liu Biostatistics & Statistical Reporting Novartis Pharmaceuticals Corporation One Health Plaza East Hanover, NJ ping.liu@novartis.com