Download Data Analysis in Clinical Trials

Data Analysis in Clinical Trials
Dr. Abha Aggarwal
Deputy Director
National Institute of Medical Statistics
Ansari Nagar, New Delhi-110029
Introduction
In clinical research we want the results of a trial to be
accurate and reliable in order to provide a valid and
unbiased assessment of true efficacy and safety of the
study medication.
The accuracy and reliability are usually referred to
the the closeness of the results. The degree of closeness
of results to the true value of patient population is
termed as reliability.
Statistical Considerations at Planning Stage
Protocol Designing
Sample Size
Interim Analysis and Data Monitoring
Statistical and Clinical Inference
Efficacy and safety assessment
Statistical Data Analysis
DATA ANALYSIS CONSIDERATION
Prespecification of the Analysis
Analysis set
Full Analysis Set
Per Protocol Set
Role of Different Analyses Set
Missing Values and Outliers
Data Transformation
Estimation, Confidence Interval and Hypothesis
Testing
Adjustment of Significance and confidence Levels
Subgroup Interactions and Covariates
Integrity of Data and Computer Software Validity
Type of Data
Three types of data commonly occur in clinical trials.
1. Dichotomous outcome- 0/1 values, success/failure,
yes/no, dead/alive.
2. Continuous outcomes- measurements with a whole
range of possibilities: wt., BP counts etc.
3. Normally Distributed Data A characteristic property of the
Normal distribution is that 68% of all of its observations fall within
a range of ±1 standard deviation from the mean, and a range of ±2
standard deviations includes 95% of the scores
Data Analysis in Clinical Trials
Descriptive Statistics
Inferential Statistics
Descriptive methods include preparation of statistical tables,
drawing diagrams & graphs and computation of statistical
parameters such as percentages, average, variation and
correlation & regression coefficients.
Inference methods include computing confidence intervals for
the estimates of parameters and various tests of statistical
significance, depending upon the type of variable and the
number of groups involved in the trial
Estimation
One of the main purpose of a clinical trial is to estimate
the magnitude of improvement with one treatment over the
other-in terms of percentages / mean values.
There are two types of estimation
-Point estimate (mean, percentage)
and Interval Estimate - Confidence limits
Confidence Limits for Proportion
(p1 - p2 ):±1.96 Sqrt (A) where A = [ { p (100 - p) x {( 1/n1)
+ (1/n2) }}] Where p is the overall percentage of
improvement in the combined groups.
A= Sqrt [ (70x30)x(2/100)] A= Sqrt (42) ie; A=6.48 .
ie; 95% confidence Limits for the population treatment
difference are 20±: 12.7 ie; 7.3 % and 32.7 %
P-value
Interpretation
P< 0.01
very strong evidence against H0
0.01<= P < 0.05
moderate evidence against H0
0.05<= P < 0.10
suggestive evidence against H0
0.10<= P
little or no real evidence against H0
Hypotheses testing and p value
Hypothesis is an assumption or statement that is made
about the population regarding the efficacy safety and
other outcomes of a drug. In clinical trial any scientific
question is translated into hypotheses.
Example; Drug A is more effective than drug B
P value- is the probability that the observed results is
extreme or not under the null hypotheses. We
generalized the results of clinical trial.
STATISTICAL SIGNIFICANCE TESTS
The second most important aim in a clinical trial is to see
whether the observed differences in the response variable
between the various treatment groups are statistically
significant or not.
ie; whether the difference observed is really due to the
additional effect of the new drug compared to that of the
standard drug or it is just due to chance ( random variation
due to sampling) .This is achieved by applying appropriate
statistical test of significance .
If the response variable is discrete, the test to be applied is
Chi-square and if it is continuous, the test is Student's' t ' test /
Analysis of variance or an appropriate Non- Parametric test
Chi-Square ( ²)tests
Eg.
Improvement rate with the Standard drug ( SD) = 60 % nl = 50
Improvement rate with the New drug ( ND ) = 80 % n2 = 50
Drug
Improvement
Yes
No
Standard
30
20
New
40
10
. ² = 3.85 , with 1 degree of fteedom (> 3.84 with 95 %
confidence)
ie; The difference observed in the improvement rate is not by
chance and it is because of the additional effect of the new drug
over the standard one.
The same test can be applied in case of more than two treatments
also.
In case of a Cross-over / Matched Trial, the corresponding test is
McNemar's' ²
Comparison of two groups
For comparison of two groups t test is applied if the
groups are independents
For paired observations paired t test is applied
ANOVA For more than two groups.
Non Parametric Test
The Non- Parametric test corresponding to Student's t test (
for independent samples) is -Wilcoxon's Rank Sum Test
and to One-Way Analysis of Variance is KruskalWally's
One-Way Analysis of Variance.