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Using R Programming for Clinical Trial Data Analysis
Oct 30, · Filling this gap Clinical Trial Data Analysis Using R provides a thorough presentation of biostatistical analyses of clinical trial data and shows step by step how to implement the statistical methods using R. The book’s practical detailed approach draws on the authors’ 30 years of real-world experience in biostatistical research and Estimated Reading Time: 50 secs Dec 18, · R for the Analysis of Clinical Data •Data analysis using R: •The NHS was able to identify potential savings of over GB£ 1 billion •By providing accurate, reliable data back to clinicians and policy makers it has enabled antibiotic prescribing to be reduced by 7%
Clinical trial data analysis using r pdf download
The use of R programming in clinical trials has not been the most popular and obvious, despite its recent growth over the past few years, its practical use still seems to be hindered by several factors, sometimes due to misunderstandings, e. g validation but also because of a lack of knowledge of its capabilities.
Despite these bottlenecks, though, R is doubtlessly creating its own larger by the day niche in the pharmaceutical industry. Watch our statistical consultant explore the most suitable manner of creating TLFs, be it R or SAS in the presentation below:. The most famous of these is most certainly the open software Rwhich has a long track record of usage in academia, both for applied and theoretical statistical research, and has been used to date also in the pharma industry, though its adoption in submissions has clinical trial data analysis using r pdf download hindered by the above described misconception.
Setting aside the validation part of the flow which is a current hot area of discussion when it comes to R but will not be discussed heremoving from SAS to R for such a consolidated task as Tables, Listings and Figures TLFs creation does require some re-thinking. How the actual reporting works is a challenge as there is not a direct PROC REPORT counterpart that can be used straight away to export results to an output file, but that is the only real challenge.
No matter the software we use, i. One key thing to decide is the actual format of the output table or listing: in many cases tables are created as plain text documents. txt which are then collated to a Word or PDF document using tools that are often external to the statistical software e. VBA macros, UNIX scriptsbut other viable options include either creating tables as Rich Text Format. rtf or.
docx files to read into Microsoft Word straight away and also in this case collation for reporting purposes is done outside of the software as well as Portable Document Format PDF or html files. For the sake of illustration, we will use data from a study on leprosy treatment described in Snedecor and Cochrane seminal Statistical Methods book [1]. The goal is to produce a table so that for each of the 3 treatment arms, basic summary statistics are displayed as well as a section with a statistical analysis that compares drugs A and D to drug F the control arm.
Whichever the output destination, the core PROC REPORT specifications will be pretty much the same, the main difference being the need to add some style commands when e. outputting to RTF to ensure the specifications are followed. The syntax for a plain text file here would look as follows:. title3 "Table 1. sas" ;, clinical trial data analysis using r pdf download. endcomp. run. The nice thing about the above commands is that the input dataset matches the shells only partially and full adherence can be obtained by using the plethora of built-in options that PROC REPORT features.
Finally, in order to have the above sent to a. txt file we can wrap the PROC REPORT call between two PROC PRINTTO calls the first starting clinical trial data analysis using r pdf download to an output file, the second resuming printing to SAS listingwhereas for a. rtf file we could use the ODS RTF command syntax skipped hereand for a.
Figure 2 shows the resulting. txt file using the above PROC REPORT syntax, clinical trial data analysis using r pdf download. This can in fact be a great advantage when it comes to manipulating data frames to calculate simple summary statistics as well as fitting complex statistical models.
Before moving onto the reporting bit, we will see how the summaries and ANCOVA section of the table can be quickly created using R to illustrate how this flexibility can be leveraged to achieve our purposes. The summary statistics sections of the table are usually created in SAS by passing the source dataset to one of PROC MEANS, PROC SUMMARY or PROC UNVARIATE and the resulting dataset has to be re-formatted for our purposes.
In order to do the same in R we can either manually write a function that calculates each summary and then arrange it, using e. tapply or by :. frame t sapply mean01, I. Note in the above the second and third column in the data are the baseline and post treatment values, respectively. Also we can re-arrange the source data in long-format i.
adding numeric VISITN and character VISIT variables identifying the time-point being summarized and then rely on one of the many wrapper functions such as ddply from the plyr package to calculate everything for us and put it in a nicely arranged dataset similar to a PROC MEANS output :. visitn, visit, Drugsummarize.
This latter option was chosen here for it returns a readily usable data frame with already formatted summary statistics that then needs to undergo the usual post-processing steps to obtain an object that mimics the table shell.
By using some functionalities of the tidyr package this can be easily achieved with no more programming efforts than it would using SAS. Once this section is post- processed using whichever combination of SQL-like language using sqldf or base R functionalities, we end up having the following data frame:.
setting of libnames and options for SAS and loading of required libraries for Rand using a consistent formatting style although R functions lend themselves more to compactness, i. one line of code for a function, much more than what SAS procedure statements need for ease of reading. Whilst this per se does not necessarily mean that R is more efficient than SAS runtime on a large dataset or a more complicated table would be a much better indicatorit suggests that R offers an elegant and compact way of summarizing and analyzing data which is perfectly comparable to what SAS offers and this is by no means a small thing!
Linked to this, the white lines between each section have been effectively added in as empty rows to mimic the table shell, something that in SAS we accomplished via the SKIP option in a BREAK BEFORE command.
subject ID in listings where only the first occurrence row-wise is populated and the other are blanks. At this point, having created the dataset in a rather simple manner, is it as simple to export it for reporting? Whilst there are many options to achieve this, not all of them are as seamless to use as PROC REPORT and the resulting output is sometimes not fully fit for purpose, clinical trial data analysis using r pdf download.
The combination of the officer and flextable packages, nevertheless, provides the user with a rich variety of functionalities to create nice-looking tables in Word format in a flexible manner. The first step is to convert the data frame to report into an object of class flextable using the flextable function and then apply all styling and formatting functions that are needed to match the required layout.
This translates in the below syntax:, clinical trial data analysis using r pdf download. Summary and Analysis of. time" by ", work, "Table1. The actual reporting is then done using the standard print function:. Figure 3. docx table file created using flextable and officer.
In the above program there are a few things worth mentioning before we do an actual compare with SAS:. As introduced at the beginning of this blog, the reporting of Tables using R is a conceptually different process compared to SAS, however we clinical trial data analysis using r pdf download see that the key options that exist in PROC REPORT also exist in the flextable package, as mentioned in Table 1.
There are other features e, clinical trial data analysis using r pdf download. the possibility to change font, clinical trial data analysis using r pdf download size, etc. that can be mapped between the two software, clinical trial data analysis using r pdf download, but those mentioned below cover the most important ones.
Table 1. Key reporting features — comparison between PROC REPORT and flextable. FLEXTABLE function. Aligning columns. align function. Width of columns. width option in the define statement. width function. Line above column names.
Line below column names. headline option in proc report statement. Line at the bottom of the table. Multicolumn header. colwidths option. Skip lines, clinical trial data analysis using r pdf download. Page break. officer package. One specific mention is needed for the last two rows here, i. Fixing the first issue in R is relatively easy, as we mentioned before, whereas for the second one a different approach needs to be taken that involves writing a general function that iteratively prints pages to the reference Word document and then adds page breaks.
Identify which variable stores the page number and how many pages. Define the flextable object separately for each page. for i in npage {. Create the Page x of y object for the title and identify which rows to display based on page number. flextable object definition use the npg object to ensure the correct Page x of y header line is added.
Print the object out: if it clinical trial data analysis using r pdf download the first one add to base. docx otherwise add to the document with previous pages printed. Once a page variable is added to the reporting dataset to have the ANCOVA section displayed on a separate page and the above clinical trial data analysis using r pdf download is applied, the result is the one displayed in Figure 4 the actual spaces between tables have been reduced to fit this blog.
Figure 4. Multipage table using flextable and officer. In general, anyway, the two packages combined offer a great degree of flexibility that allows one to reproduce a table as with SAS using a somehow more explicit report definition compared to PROC REPORT, and the actual amount of programming required is similar to Clinical trial data analysis using r pdf download. In the introduction we clearly stated that moving from SAS to R for TLFs implies some rethinking of the way we approach clinical trial reporting.
There are certainly some cases where attempting to recreate SAS results using R and vice versa has generated more than one migraine, but in most cases this occurs because the assumptions and default implementations differ e. the optimization method for a given regression rather than because of real differences. Many of these have been documented on the internet in mailing lists and help pages, and no doubt that the more the industry starts making more consistent use of R more scenarios will emerge, thus prompting better knowledge of both software.
The uptake of R as software of choice within pharmaceutical companies and Contract Research Organizations CROs is something that many doubted to see during their lifespan, however things are rapidly moving even in the pharmaceutical industry.
In this blog we have seen that R can be an extremely powerful tool to create Tables and Listings using the officer and flextable packages and tools already available and for great figures ggplot2 is availableand that by leveraging its high flexibility it is possible to obtain high- quality results with comparable efficiency and quality to standard SAS code.
Hopefully the above example and considerations have provided a different angle to this quarrel and why it does not need to be a quarrel at all. Turn your validated trial data into interpretable information ready for biostatistical analysis.
Clinical data analysis in R
, time: 26:50Clinical trial data analysis using r pdf download
Oct 27, · R versus SAS for TLFs. Everyone involved in any capacity in clinical trials reporting is aware of a sort-of self-proclaimed truth that can be broadly summarized as ‘Thou shall not have any software other than SAS’.Whilst this has been practically true for quite some time, somehow based on the misconception that regulators ‘strictly’ requested submissions to be made using SAS, other Dec 18, · Using R for Data Analysis and Graphics Introduction, Code and Commentary J H Maindonald Centre for Mathematics and Its Applications, Australian National University. ©J. H. Maindonald , , A licence is granted for personal study and classroom use. Redistribution in any other form is prohibited
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