An annoying period (.) appeared in the p-value column when the break statement was applied in proc REPORT. How can we eliminate this period (.) from the report (rtf file)?
Solution: Firstly, the data type of p-value variable is numeric then the break option will replace the missing values with a period. To avoid this situation change the data type of p-value variable to character. Then the missing values will be replaced by blank space only. Eg: /* Create dataset with 5 variables and 8 observations*/ data test; input usubjid $ parameter $ visit $ trtgrp result; datalines; 001 FEV1 VISIT1 1 2.34 002 FEV1 VISIT1 3 0.98 001 FEV1 VISIT4 1 2.04 002 FEV1 VISIT4 3 1.98 001 FVC VISIT1 1 2.34 002 FVC VISIT1 3 2.98 001 FVC VISIT4 1 2.44 002 FVC VISIT4 3 1.99 ; run; /* Carrying out ANOVA*/ ods output ModelAnova= test1; proc glm data=test; by parameter visit; class trtgrp; model result=trtgrp/ss3; lsmeans trtgrp/adjust=t pdiff; run;
data test2; set test1; test=' '; /* dummy variable*/ keep parameter visit probf Test; run;
proc report data = test2 nowd spacing = 2 headline headskip split = '*' missing; column parameter visit test,(probf); define parameter / group order=data left 'Parameter' ; define visit / display left 'Timepoint' ; define Test / across center "Test" ; define probf/ display left "p-value"; break after parameter/summarize suppress; run;
ods _all_ close; /* Closing all ODS outputs statements*/
In the above example probf is a numeric variable within the across variable ‘test’. So in the output a period occurred when the break statement active. Eliminate this we add one more statement in the dataset Test2. probf1=put (probf, pvalue6.); /* for converting numeric type to character*/
Generally we can say that the numeric variables within the across variable in REPORT procedure should make a period in the blank space and to avoid this convert numeric variables to character by using PUT function.
SAS with mySQL Compiled by Soumya Gopinath How to transfer the MySQL Databases named dbformo and dbformo_add to SAS datasets library names formoSQL and AddSQL respectively?
Solution: libname libnameODBC datasrc=sourceuser=user name password=passwordschema=database;
By using the procedure COPY we can copy the library formoSQL and AddSQL to our permanent SAS libraries FormoDM and FormoAdd respectively. Before executing the connection string add an ODBC Data source name in the current system, which should be the same as given in datasrc= option. The process,
1) Settings -> Control Panel ->Administrative Tools ->Data Source (ODBC) 2) Click on the Add button. 3) Select the driver for which you want to set up a data source (MySQL ODBC 3.51 Driver) and click Finish button. 4) Add required information in Connector/ODBC window. Click OK button. Then the process is completed.
As part of the efficacy analysis of a recent Phase III study , it was planned to determine the significant difference in efficacy of treatments over the weeks. Each treatment group was analyzed separately. The paired t-test was employed to carry out the analysis on the parametric data.The Wilcoxon Signed Rank test was used for analysis of efficacy of treatments with respect to non-parametric data. The null hypothesis was that in the underlying population of differences among pairs the median difference is equal to 0. The alternate hypothesis may be one sided or two sided.
In SAS, this test is performed using the proc univariate procedure. Before applying the procedure, the difference between the value of parameter at the two time points that are to be compared (usually pre and post dose values) has to be computed. This difference is then defined in the “var” statement of the procedure. The syntax is as followed
The output will contain p-value corresponding to Student’s t, Sign and Signed Rank tests. The p-value corresponding to the signed rank test will be considered. Further, For a 1-sided p-value, we would divide the 2-sided p-value by 2.The ‘ods output statement’ is used to extract descriptive statistics and the p value to two different datasets by using the as shown in the above example
data transpose; set transpose; label _name_ ="Parameter" ; rename _name_=Parameter; run;
25.7.07
Asthma Studies
For the analysis of any clinical trial on asthma inhalers, your efficacy parameters are most probably going to be related to the spirometry measures and will almost always include the following FEV1 - Forced expiratory volume in 1 secondThe FEV1 is the volume exhaled during the first second of a forced expiratory maneuver started from the level of total lung capacity. PEF - Peak expiratory flowExpiratory peak flow (PEF) is the maximum flow generated during expiration performed with maximal force and started after a full inspiration .
FVC - Forced expiratory vital capacityThe volume change of the lung between a full inspiration to total lung capacity and a maximal expiration to residual volume.Here are more details on Spirometry. Also check out GINA for more Asthma information
Blind Review
First of all the ICH definition - Blind Review is the checking and assessment of data during the period of time between trial completion (the last observation on the last subject) and the breaking of the blind, for the purpose of finalizing the planned analysis.
So how do we do it ? We look at the Clinical Protocol and create a Blind Review Criteria document which will list out all the conditions that needs to be checked against the data to make sure that there is no violations. For eg. if Visit 1 were to happen with in 15-20 days of the screening visit, then we compare the dates of screening and visit1 and if the difference falls below 15 and above 20, then boom we have a violation. Then we write a SAS program to check all those conditions and run it to create a protocol violation document.
Easily said. But to do it and to make sure that all the violations are caught is one hell of a task Johnny.
Hodges-Lehmann ∆
A parametric test, such as the t-test, compares the means of the two samples. A nonparametric method, such as the Wilcoxon Rank Sum Test compares the entire distributions of the two independent samples. The null hypothesis of the Wilcoxon Rank Sum test says the two samples can be viewed as a single sample from one population. The alternative hypothesis is that the first treatment group has a different different distribution (or location) than the second treatment group.
The treatment effect, denoted as ∆, is the difference between treatment groups. If parametric methods were used, means could be calculated for each treatment group, and a subtraction of the means can be used to estimate ∆. However, when the data are not normally distributed and the median value of the response variable of interest is calculated for each treatment group, the estimate of the difference in treatment groups is not as straightforward as subtracting one median from the other.
The difference in medians is estimated using the methodology of Hodges-Lehmann. It is a very simple approach. The following steps can be used to estimate ∆:
• form all possible differences between the first treatment group and the second treatment group, in the response variable of interest. For example, if there are 100 patients in each group then 10,000 (100*100) differences would be calculated. • the estimator ∆ is the median of those 10,000 differences.
In clinical trials, data are often collected over a period of time from participating patients. In many situations, however, analyses are only based on data from the last time point (the end of the study) or change from the baseline to the last time point. It is often the case that patients drop out before the completion of the study.
So the question arises on how to perform analysis of the last observations, which are defined as observations from the last time point for patients who completed the study and the last observations prior to the dropout for patients who did not complete the study.
An analysis based only on data from patients who completed the study is called a completers analysis. Although a completers analysis is sufficient in some situations, it is often more desirable to perform an all randomized subjects analysis.
The analysis based on all randomized subjects is usually referred to as an intention-to-treat (ITT) analysis. Regulatory agencies generally consider the ITT analysis as the primary analysis for evaluation of efficacy and safety in clinical trials with informative dropout.
When the dropout is informative, the target populations of a completers analysis and an ITT analysis are different. Suppose that the population of patients under certain treatment is stratified according to the time of the last observations; then the target population of a completers analysis is only the subpopulation of patients who completed the study under different treatments while the target populations of an ITT analysis include all the subpopulations under different treatments.
For the last observation carry-forward (LOCF) analysis based on ITT population, the last observations are carried forward to the last time point for patients who dropped out. The LOCF analysis treats the carried-forward data as observed data at the last time point. Therefore when the dropout is informative, the LOCF analysis may introduce biases to the statistical inference, which has been speculated upon by the Food and Drug Administration (FDA) it is still unknown whether or not the LOCF test is asymptotically correct
An analysis of published papers in the field of medical research has found that Thiruvananthapuram accounts for 64 percent of the total papers published in the state. Something that make Kreara proud about our location
In the field of pharmacokinetics, the area under the curve (AUC) is the area under the curve in a plot of concentration of drug in plasma against time.
In real-world terms the AUC (from zero to infinity) represents the total amount of drug absorbed by the body, irrespective of the rate of absorption. This is useful when trying to determine whether two formulations of the same dose (for example a capsule and a tablet) release the same dose of drug to the body.
AUC becomes useful for knowing the average concentration over a time interval, AUC/t. Also, AUC is referenced when talking about elimination. The amount eliminated by the body = clearance (volume/time) * AUC (mass*time/volume).
Another application that we recently found is when we try to establish the equivalence of two asthma formulations and their superiority over placebo. Here are some links if you
a. want to learn how we can use integration to find the AUC b. A small SAS macro to calculate AUC using trapezoidal rule c. Use proc expand to find out the AUC
2.7.07
Wilcoxon rank-sum test
The Wilcoxon rank-sum test is a non-parametric analog of the two-sample t-test. It is based on ranks of the data, and is used to compare location parameters, such as the mean or median, between two independent populations without the assumption of normally distributed data.
DATA RNKSM; INPUT TRT $ PAT SCORE @@; DATALINES; SER 2 0 SER 3 2 SER 5 3 SER 6 3 SER 8 -2 SER 10 1 SER 12 3 SER 14 3 SER 16 -1 SER 17 2 SER 20 -3 SER 21 3 SER 22 3 SER 24 0 SER 26 2 SER 27 -1 PBO 1 3 PBO 4 -1 PBO 7 2 PBO 9 3 PBO 11 -2 PBO 13 1 PBO 15 0 PBO 18 -1 PBO 19 -3 PBO 23 -2 PBO 25 1 PBO 28 0 ;
PROC NPAR1WAY WILCOXON DATA = RNKSM; CLASS TRT; VAR SCORE; TITLE1 'The Wilcoxon Rank-Sum Test'; RUN;
1.7.07
Van Elteren's test
You can think of van Elteren's test as analogous to an analysis of variance with a blocking factor. One common use of the van Elteren test is with clinic as the stratification variable in a multicenter clinical study. van Elteren's test is used in lieu of randomized blocks ANOVA with clinics as blocks. In the case of a geographically distributed study we could use this test with country as a blocking factor
Sample Size Calculations for Comparisons BetweenProportions
Sample Size Calculations for Comparisons Between Means
27.6.07
mySQL with SAS
Using MySQL came out handy for us while designing databases for Clinical Trials where in the scalability is not much of an issue. The performance turns poor when a large number of users are simultaneously accessing the database. We are yet to do some benchmarking on a database with around 24 standard CDISC tables, where data will be entered for close to 200 patients.
Here is the small and handy piece of sas code to extract data off of a mySQL database to SAS
/*Importing datasets from SQL to SAS*/ libname sqllibrary ODBC datasrc='DBDRIVER' user=root password=root schema="testdb"; proccopyout=workdb in=testdb; exclude validation schema_info; run;
You will need to install the ODBC driver for MySQL before you venture into running this code
Hypothesis testing
In studies concerned with detecting an effect (e.g. a difference between two treatments, or relative risk of a diagnosis if a certain risk factor is present versus absent), sample size calculations are important to ensure that if an effect deemed to be clinically meaningful exists,then there is a high chance of it being detected, i.e. that the analysis will be statistically significant. If the sample is too small, then even if large differences are observed, it will be impossible to show that these are due to anything more than sampling variation. There are different types of hypothesis testing problems depending on the goal of the research.
Let μS = mean of standard treatment, μT = mean of new treatment, and δ = the minimumclinically important difference.
1. Test for Equality: Here the goal is to detect a clinically meaningful difference/effects is such a difference/effects exists 2. Test for Non-inferiority: To demonstrate that the new drug is as less effective as the standard treatment (ie the difference between the new treatment and the standard is less than the smallest clinically meaningful difference) 3. Test for Superiority: To demonstrate that the new treatment is more superior that standard treatment (ie the difference between the new treatment and the standard is greater than the smallest clinically meaningful difference). 4. Test for equivalence: To demonstrate the difference between the new treatment and standard treatment has no clinical importance
It is important to note that the test for superiority is often referred to as the test for clinical superiority If δ = 0, it is called the test of statistical superiority Equivalence is taken to be the alternative hypothesis, and the null hypothesis is nonequivalence
For the analysis of any clinical trial on asthma inhalers, your efficacy parameters are most probably going to be related to the spirometry measures and will almost always include the following 
For the analysis of any clinical trial on asthma inhalers, your efficacy parameters are most probably going to be related to the spirometry measures and will almost always include the following 
In clinical trials, data are often collected over a period of time from participating patients. In many situations, however, analyses are only based on data from the last time point (the end of the study) or change from the baseline to the last time point. It is often the case that patients drop out before the completion of the study.
In clinical trials, data are often collected over a period of time from participating patients. In many situations, however, analyses are only based on data from the last time point (the end of the study) or change from the baseline to the last time point. It is often the case that patients drop out before the completion of the study.
An analysis of published papers in the field of medical research has found that Thiruvananthapuram accounts for 64 percent of the total papers published in the state. Something that make Kreara proud about our location
An analysis of published papers in the field of medical research has found that Thiruvananthapuram accounts for 64 percent of the total papers published in the state. Something that make Kreara proud about our location
In the field of pharmacokinetics, the area under the curve (AUC) is the area under the curve in a plot of concentration of drug in plasma against time.
In the field of pharmacokinetics, the area under the curve (AUC) is the area under the curve in a plot of concentration of drug in plasma against time.
It is important to note that
It is important to note that