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For BDS dataset, variables such as PARAMTYP and DTYPE may be required to be created when coming to complicated study. This post will introduce the difference among these two variables and how to develop them.

Introduction on PARAMTYP and DTYPE

Following table is from ADaM IG. You can see that PARAMTYP should be applied when you need to create a new parameter. Suppose that height and weight are collected in VS domain, but BMI (Body Mass Index) is not collected, what should you do if you need to develop a table which contains summary of BMI? In this case, we need to create a new record having PARAMCD equals to BMI (values of BMI should be derived based on HEIGHT and WEIGHT) for each subject, each visit. And for these new records, we have to populate PARAMTYP as DERIVED.

Variable Name Variable Label Codelist Controlled Term/ CDISC Notes
PARAMTYP Parameter Type (PARAMTYP) Indicator of whether the parameter is derived as a function of one or more other parameters.
DTYPE Derivation Type (DTYPE) Analysis value derivation method. DTYPE is used to denote, and is required to be populated, when the value of AVAL or AVALC has been imputed or derived differently than the other values within the parameter. DTYPE is required to be populated even if AVAL and AVALC are null on the derived record.

As for DTYPE, here is a common senario. For example, HEMOGLOBIN is supposed to be collected at all visits from SCREENING, WEEK 12, WEEK 24, WEEK 48 to WEEK 72, but for one subject, HEMOGLOBIN is missing at visits WEEK 24 and WEEK 72. In this case, the missing value can be imputed as non-missing values from last visit. For these imputed values, we have to create a new record, give it a flag variable – DTYPE and populate DTYPE as LOCF (Last Observation Carried Forward).
Here is a summary of situations when DTYPE should be populated.

1 A new row is added within a parameter with the analysis value populated based on other rows within the parameter,
2 A new row is added within a parameter with the analysis value populated based on a constant value or data from other subjects,
3 An analysis value (AVAL or AVALC) on an existing record is being replaced with a value based on a pre-specified algorithm.

And here are the common DTYPE values.

LOCF Last Observation Carried Forward.
WOCF Worst Observation Carried Forward.
AVERAGE AVERAGE of values.

Case 1: How to derive PARAMTYP

Suppose that we have WEIGHT and HEIGHT collected in CRF and need to derive BMI based on WEIGHT and HEIGHT. Here is the dummy data which contains in an excel file. The excel file named ADVS is stored in D:\ driver.

USUBJID TRTA TRTAN VSTESTCD VSTEST VISIT VISITNUM VSSTRESN VSSTRESU
001-001 Placebo 1 HEIGHT HEIGHT SCREENING 1 196 cm
001-001 Placebo 1 WEIGHT WEIGHT SCREENING 1 116 kg
001-001 Placebo 1 WEIGHT WEIGHT WEEK 14 2 114 kg
001-001 Placebo 1 WEIGHT WEIGHT WEEK 28 3 112 kg
001-001 Placebo 1 WEIGHT WEIGHT WEEK 56 4 110 kg
001-002 Placebo 1 HEIGHT HEIGHT SCREENING 1 172 cm
001-002 Placebo 1 WEIGHT WEIGHT SCREENING 1 50 kg
001-002 Placebo 1 WEIGHT WEIGHT WEEK 14 2 52 kg
001-002 Placebo 1 WEIGHT WEIGHT WEEK 28 3 49 kg
001-002 Placebo 1 WEIGHT WEIGHT WEEK 56 4 48 kg

Click here to hide/show code


proc import datafile=”D:\ADVS.xlsx”
out=ADVS
dbms=xlsx
replace;
sheet=”Sheet1″;
startrow=2;
getnames=Yes;
run;

Derive BMI based on (Screening) HEIGHT and WEIGHT

Here is again to demonstrate power of RETAIN statement and By statement. We can populate n as 1 for HEIGHT record and 2 for other records in order to make HEIGHT observation as the first record for each subject.

Click here to hide/show code


data advs;
set advs;
if vstestcd = “HEIGHT” then n = 1;
else n = 2;
run;
proc sort data=advs; by usubjid n; run;
data advs1(drop=height n);
length paramtyp $20.;
retain height;
set advs;
by usubjid n;
if first.usubjid and n = 1 then height = vsstresn;
if vstestcd = “WEIGHT”;
paramtyp = “DERIVED”;
if height ne . and vsstresn ne . then vsstresn = vsstresn/((height/100)**2);
vstestcd = “BMI”;
vstest = “Body Mass Index”;
vsstresu = “kg/m^2”;
run;

data advs;
set advs(drop=n) advs1;
run;

proc sort data=advs; by usubjid visitnum visit paramtyp vstestcd; run;

After submitting above code, you will get results like below. 116/(1.96^2) equals to 30.195 which is exactly what we have derived.

Case 2: How to derive DTYPE

In one of my previous posts, I have already told you how to derive DTYPE when DTYPE equals to AVERAGE. Here I’d like to introduce another situation – LOCF.  Suppose that we have LB data which is stored in ADLB.xlsx.

USUBJID TRTA PARAM PARAMCD PARCAT1 AVISIT AVISITN AVAL
001-001 Placebo Hemoglobin (g/dL) HGB HEMATOLOGY Screening 101 11.4
001-001 Placebo Hemoglobin (g/dL) HGB HEMATOLOGY Week 1 107 11.2
001-001 Placebo Hemoglobin (g/dL) HGB HEMATOLOGY Week 3 121 12.7
001-001 Placebo Hemoglobin (g/dL) HGB HEMATOLOGY Week 5 135 12.4
001-001 Placebo Hemoglobin (g/dL) HGB HEMATOLOGY Week 7 149 13.1
001-002 Placebo Hemoglobin (g/dL) HGB HEMATOLOGY Screening 101 12.3
001-002 Placebo Hemoglobin (g/dL) HGB HEMATOLOGY Week 1 107 12.1
001-002 Placebo Hemoglobin (g/dL) HGB HEMATOLOGY Week 5 135
001-002 Placebo Hemoglobin (g/dL) HGB HEMATOLOGY Week 8 156 12.5

Read excel into SAS to create dummy data

Click here to hide/show code


proc import datafile=”D:\ADLB.xlsx”
out=ADLB
dbms=xlsx
replace;
sheet=”Sheet1″;
startrow=2;
getnames=Yes;
run;

Create Dummy data to produce missing records

LB data are supposed to be collected at Screening, Week 1, Week 2, Week 3… and Week 8. But we do not have Week 2, Week 4, Week 6, Week 8 values for subject 001-001. What we need to do is to create these 4 records and populate AVAL as the last non-missing values. For subject 001-002, we have to create 5 new records for Week 2, Week 3, Week 4, Week 6 and Week 7 respectively.

Click here to hide/show code


proc sort data=adlb out=dummy nodupkey; by usubjid parcat1 param; run;
data dummy(drop=i);
set dummy;
do i = 1 to 9;
if i = 1 then avisitn = 101;
else avisitn = 100 + 7*(i-1);
if i = 1 then avisit = “Screening”;
else avisit = “Week “||strip(put(i-1,best.));
aval = .;
output;
end;
run;

proc sort data=adlb; by usubjid parcat1 param avisitn; run;
proc sort data=dummy; by usubjid parcat1 param avisitn; run;
data adlb;
update dummy adlb(drop=trta paramcd);
by usubjid parcat1 param avisitn;
run;

So far, you will get something like below. All missing records have been created and there are 9 records in total for each subject.

Derive LOCF

Click here to hide/show code


proc sort data=adlb; by usubjid parcat1 param avisitn; run;
data adlb;
retain aval_;
set adlb;
by usubjid parcat1 param avisitn;
if aval ne . then aval_ =aval;
if aval = . then do;
aval = aval_;
dtype = “LOCF”;
end;
run;

After running above code, you can see that AVAL for missing records has been populated. If you pay attention to the Week 5 for subject 001-002, you will see that AVAL for this record was also populated as 12.1 which is the last non-missing value from visit Week 1.

Note:

PARAMTYP will be retired from the ADaMIG in the next version because it was confused with the concept of DTYPE and therefore was being misused. The variable metadata should be adequate to indicate when a parameter is wholly derived.

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