Notes

#!/usr/bin/env python
# coding: utf-8

# In[1]:


A=['5 Models for Special-Purpose Domains ',
'Subject Elements (SE) and Subject Visits (SV) ',
'Subject Visits (SV) ',
'SV – Description/Overview for the Subject Visits Domain Model ',
'SV – Specification for the SUBJECT VISITS Domain Model ']


# In[2]:


B=['5 Models for Special-Purpose Domains ',
'Subject Elements (SE) and Subject Visits (SV) ',
'The Trial Elements, Trial Arms, and Trial Visits datasets in the Trial Design model describe the planned design of the study (see Section 7.2 - Experimental ',
'Design: Trial Elements (TE), Section 7.2 - Experimental Design: Trial Arms (TA) and Section 7.3 - Schedule for Assessments: Trial Visits (TV)), but it is also ',
'necessary to collect the corresponding actual data. Subject assignment to an Arm is reported in the ARM variable in Demographics. Actual Elements and Visits ',
'data for each subject are described in two additional datasets:',
'uf0b7 The Subject Elements dataset [Section 5 - SE Domain]',
'uf0b7 The Subject Visits dataset [Section 5 - SV Domain]',
'Subject Visits (SV) ',
'SV – Description/Overview for the Subject Visits Domain Model ',
'The Subject Visits domain consolidates information about the timing of subject visits that is otherwise spread over domains that include the visit variables (VISITNUM and ',
'possibly VISIT and/or VISITDY). Unless the beginning and end of each visit is collected, populating the Subject Visits dataset will involve derivations. In a simple case, ',
'where, for each subject visit, exactly one date appears in every such domain, the Subject Visits dataset can be created easily, by populating both SVSTDTC and SVENDTC ',
'with the single date for a visit. When there are multiple dates and/or date/times for a visit for a particular subject, the derivation of values for SVSTDTC and SVENDTC may ',
'be more complex. The method for deriving these values should be consistent with the visit definitions in the Trial Visits dataset [see Section 7.3 - Schedule for ',
'Assessments: Trial Visits (TV)]. For some studies, a visit may be defined to correspond with a clinic visit that occurs within one day, while for other studies, a visit may ',
'reflect data collection over a multi-day period. ',
'The Subject Visits dataset provides reviewers with a summary of a subject’s Visits. Comparison of an individual subject’s SV dataset with the TV dataset [Section 7.3 - ',
'Schedule for Assessments: Trial Visits (TV)], which describes the planned Visits for the trial, quickly identifies missed Visits and “extra” Visits. Comparison of the values ',
'of STVSDY and SVENDY to VISIT and/or VISITDY can often highlight departures from the planned timing of Visits. ',
'SV – Specification for the SUBJECT VISITS Domain Model ',
'sv.xpt, Subject Visits — Version 3.2,. One record per subject per actual visit. ',
'Controlled ',
'Variable ',
'Variable Label Type Terms, Codelist Role CDISC Notes Core ',
'Name ',
'or Format ',
'STUDYID Study Identifier Char Identifier Unique identifier for a study. Req ',
'DOMAIN Domain Char SV Identifier Two-character abbreviation for the domain. Req ',
'Abbreviation ']


# In[3]:


indx=[]
for i in A:
indx.append(B.index(i))
indx.append(len(B))


# In[4]:


mydict={}
for i in range(len(A)):
if i<=len(A):
temp=B[indx[i]+1:indx[i+1]]
if len(temp)==0:
mydict[A[i]]={}
else:
mydict[A[i]]="".join([i for i in temp])
mydict