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C A S E S T U D Y
B I O S T A T I S T I C S
CASE STUDY - BIOSTATISTICS1
Statistical analysis of clinical trial data needs to
be performed with utmost attention to each
aspect of the trial to avoid losing information,
performing inappropriate analysis and making
wrong interpretation of the results. The analysis
comprises of primary and secondary analysis,
the former is performed to obtain top line
tables whereas the latter to obtain safety and
methods undergo dynamic alterations based on
the primary and secondary analysis so that
timely corrections can be made. To make right
choices during initial statistical planning or
later when modifications are made to the initial
plan, we require statisticians with a keen eye
for detail and a good number of years offor
detail and a good number of years of
experience in studies similar to the trial in
question.
A top 10 Pharma company based in Germany
was conducting a phase III trial on COPD
studies. They performed the clinical operations
and Data Management (DM) in-house but the
statistical analysis was outsourced to
MakroCare. Sponsor sent us the SAS data
extracts, along with Protocol, Statistical Analysis
Plan (SAP), Clinical Study Report (CSR), Case
Report Form (CRF) and we performed primarya
nalysis using descriptive statistics and
frequency tables to check data. The analysis
revealed that some of the data points were
missing. By tracing backwards, it was found
that the spirometry and diary data
from some of the patients were not recorded,
due to mechanical/machine fault. Since the trial
was complete and the DM database was
locked, sponsor couldn’t collect this data. To
come with an alternative, our statisticians did a
thorough analysis of the Protocol, Statistical
Analysis Plan (SAP) and Derived Data Set
(DDS) Plan prepared by Sponsor. We decided
to use multiple imputation method, which was
the most appropriate method for this analysis.
In this method, missing values are replaced
with “Plausible” substitutes based on the
distributions of the given data and determined
by performing the right amount of
randomness.This ensured that the uncertainty
factor is drastically reduced.
After filling the missing data with “Plausible”
substitutes, statistical analysis was performed.
The results showed an interesting outcome. The
use of “Plausible” substitutes in place of missing
data points gave better results since they
contributed to the information in support of
drug efficacy. Adding this missing
Optimization of statistical analysis, key to best results!
At times, some clinical data seems insignificant and this might cause loss of highly crucial
information and wrong inference about the study. Our statistics team makes sure that
such data is not overlooked and is further exploited to yield optimum results.
CASE STUDY - BIOSTATISTICS2
information increased the percentage of
population showing positive response to
treatment (from severe COPD to moderate
COPD and from moderate COPD to mild
COPD) by 4.4%. This was a significant increase
since the efficacy levels were as such low; only
22% population responded positively to the
treatment.
This shows the ability of our team to judge the
worth of missing clinical data and to suitably
alter the statistical methods to utilize this data.
We have always strived to understand our
client’s needs and to make a significant
difference to the outcome of their trials.
About MakroCare
MakroCare, a global clinical services firm,
provides clinical research support to
pharmaceutical, biotechnology, and
medical device industries. The company
offers site selection, patient recruitment,
clinical monitoring, quality assurance,
medical writing, MS/Pharmacovigilance,
clinical data management, biostatistics and
regulatory assistance.
MakroCare has offices in USA (New Jersey,
Illinois, Pennsylvania, California), India
(Hyderabad, New Delhi, Mumbai, Bengaluru),
Europe (Germany - Frankfurt) and Japan (Tokyo).
Visit us at: www.makrocare.com
Copyright ,2009 by MakroCare. All Rights Reserved.

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SAS Programming India | MakroCare

  • 1. C A S E S T U D Y B I O S T A T I S T I C S CASE STUDY - BIOSTATISTICS1 Statistical analysis of clinical trial data needs to be performed with utmost attention to each aspect of the trial to avoid losing information, performing inappropriate analysis and making wrong interpretation of the results. The analysis comprises of primary and secondary analysis, the former is performed to obtain top line tables whereas the latter to obtain safety and methods undergo dynamic alterations based on the primary and secondary analysis so that timely corrections can be made. To make right choices during initial statistical planning or later when modifications are made to the initial plan, we require statisticians with a keen eye for detail and a good number of years offor detail and a good number of years of experience in studies similar to the trial in question. A top 10 Pharma company based in Germany was conducting a phase III trial on COPD studies. They performed the clinical operations and Data Management (DM) in-house but the statistical analysis was outsourced to MakroCare. Sponsor sent us the SAS data extracts, along with Protocol, Statistical Analysis Plan (SAP), Clinical Study Report (CSR), Case Report Form (CRF) and we performed primarya nalysis using descriptive statistics and frequency tables to check data. The analysis revealed that some of the data points were missing. By tracing backwards, it was found that the spirometry and diary data from some of the patients were not recorded, due to mechanical/machine fault. Since the trial was complete and the DM database was locked, sponsor couldn’t collect this data. To come with an alternative, our statisticians did a thorough analysis of the Protocol, Statistical Analysis Plan (SAP) and Derived Data Set (DDS) Plan prepared by Sponsor. We decided to use multiple imputation method, which was the most appropriate method for this analysis. In this method, missing values are replaced with “Plausible” substitutes based on the distributions of the given data and determined by performing the right amount of randomness.This ensured that the uncertainty factor is drastically reduced. After filling the missing data with “Plausible” substitutes, statistical analysis was performed. The results showed an interesting outcome. The use of “Plausible” substitutes in place of missing data points gave better results since they contributed to the information in support of drug efficacy. Adding this missing Optimization of statistical analysis, key to best results! At times, some clinical data seems insignificant and this might cause loss of highly crucial information and wrong inference about the study. Our statistics team makes sure that such data is not overlooked and is further exploited to yield optimum results.
  • 2. CASE STUDY - BIOSTATISTICS2 information increased the percentage of population showing positive response to treatment (from severe COPD to moderate COPD and from moderate COPD to mild COPD) by 4.4%. This was a significant increase since the efficacy levels were as such low; only 22% population responded positively to the treatment. This shows the ability of our team to judge the worth of missing clinical data and to suitably alter the statistical methods to utilize this data. We have always strived to understand our client’s needs and to make a significant difference to the outcome of their trials. About MakroCare MakroCare, a global clinical services firm, provides clinical research support to pharmaceutical, biotechnology, and medical device industries. The company offers site selection, patient recruitment, clinical monitoring, quality assurance, medical writing, MS/Pharmacovigilance, clinical data management, biostatistics and regulatory assistance. MakroCare has offices in USA (New Jersey, Illinois, Pennsylvania, California), India (Hyderabad, New Delhi, Mumbai, Bengaluru), Europe (Germany - Frankfurt) and Japan (Tokyo). Visit us at: www.makrocare.com Copyright ,2009 by MakroCare. All Rights Reserved.