19 Aug 2019
By Anatomise Biostats

Do I need a Biostatistician?

Biostatistics

Do I need a Biostatistician?

“…. half of current published peer-reviewed clinical research papers … contain at least one statistical error… When just surgical related papers were analysed, 78% were found to contain statistical errors.”

Peer reviewed published research is the go to source for clinicians and researchers to advance their knowledge on the topic at hand. It also currently the most reliable way available to do this. The rate of change in standard care and exponential development and implementation of innovative treatments and styles of patient involvement makes keeping up with the latest research paramount. (1)

Unfortunately, almost half of current published peer-reviewed clinical research papers have been shown to contain at least one statistical error, likely resulting in incorrect research conclusions being drawn from the results. When just surgical related papers were analysed, 78% were found to contain statistical errors due to incorrect application of statistical methods. (1)

Compared to 20 years ago all forms of medical research require the application of increasingly complex methodology, acquire increasingly varied forms of data, and require increasingly sophisticated approaches to statistical analysis. Subsequently the meta-analyses required to synthesise these clinical studies are increasingly advanced. Analytical techniques that would have previously sufficed and are still widely taught are now no longer sufficient to address these changes. (1)

The number of peer reviewed clinical research publications has increased over the past 12 years. Parallel to this, the statistical analyses contained in these papers are increasingly complex, as is the sophistication with which they are applied. For example, t tests and descriptive statistics were the go to statistical methodology for many highly regarded articles published in the 1970’s and 80’s. To rely on those techniques today would be insufficient, both in terms of being scientifically satisfying and in, in all likelihood, in meeting the current peer-review standards. (1)

Despite this, some concerning research has noted that these basic parametric techniques are actually currently still being misunderstood and misapplied reasonably frequently in contemporary research. They are also being increasingly relied upon (in line with the increase in research output) when in fact more sophisticated and modern analytic techniques would be better equipped and more robust in answering given research questions. (1)

Another contributing factor to statistical errors is of course ethical in nature. An recent online survey consulting biostatisticians in America revealed that inappropriate requests to change or delete data to support a hypothesis were common, as was the desire to mould the interpretation of statistical results of to fit in with expectations and established hypotheses, rather than interpreting results impartially. Ignoring violations of statistical assumptions that would deem to chosen statistical test inappropriate, and not reporting missing data that would bias results were other non-ethical requests that were reported. (2)

The use of incorrect statistical methodology and tests leads to incorrect conclusions being widely published in peer reviewed journals. Due to the reliance of clinical practitioners and researchers on these conclusions, to inform clinical practice and research directions respectively, the end result is a stunting of knowledge and a proliferation of unhelpful practices which can harm patients. (1)

Often these errors are a result of clinicians performing statistical analyses themselves without first consulting a biostatistician to design the study, assess the data and perform any analyses in an appropriately nuanced manner. Another problem can arise when researchers rely on the statistical techniques of a previously published peer-reviewed paper on the same topic. It is often not immediately apparent whether a statistician has been consulted on this established paper. Thus it is not necessarily certain whether the established paper has taken the best approach to begin with. This typically does not stop it becoming a benchmark for future comparable studies or deliberate replications. Further to this it can very often be the case that the statistical methods used have since been improved upon and other more advanced or more robust methods are now available. It can also be the case that small differences in the study design or collected data between the established study and the present study mean that the techniques used in the established study are not the most optimal techniques to address the statistical needs of present study, even if the research question is the same or very similar.

Another common scenario which can lead to the implementation of non-ideal statistical practices is under-budgeting for biostatisticians on research grant applications. Often biostatisticians are on multiple grants, each with a fairly low amount of funding allocated to the statistical component due to tight or under budgeting. This limits the statistician’s ability to focus substantially on a specific area and make a more meaningful contribution in that domain. A lack of focus prevents them from becoming a expert at this particular niche and engage in innovation.This in turn can limit the quality of the science as well as the career development of the statistician.

In order to reform and improve the state and quality of clinical and other research today, institutions and individuals must assign more value to the role of statisticians in all stages of the research process. Two ways to do this are increased budgeting for and in turn increased collaboration with statistical professionals.

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References:

(1) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6106004/

​(2) https://annals.org/aim/article-abstract/2706170/researcher-requests-inappropriate-analysis-reporting-u-s-survey-consulting-biostatisticians

28 Jun 2017
By Anatomise Biostats

Transforming Skewed Data: How to choose the right transformation for your distribution

All
​​Innumerable statistical tests exist for application in hypothesis testing based on the shape and nature of the pertinent variable’s distribution. If however the intention is to perform a parametric test – such as ANOVA, Pearson’s correlation or some types of regression – the results of such a test will be more valid if the distribution of the dependent variable(s) approximates a Gaussian (normal) distribution and the assumption of homoscedasticity is met. In reality data often fails to conform to this standard, particularly in cases where the sample size is not very large. As such, data transformation can serve as a useful tool in readying data for these types of analysis by improving normality, homogeneity of variance or both.For the purposes of Transforming Skewed Data, the degree of skewness of a skewed distribution can be classified as moderate, high or extreme. Skewed data will also tend to be either positively (right) skewed with a longer tail to the right, or negatively (left) skewed with a longer tail to the left. Depending upon the degree of skewness and whether the direction of skewness is positive or negative, a different approach to transformation is often required. As a short-cut, uni-modal distributions can be roughly classified into the following transformation categories:

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This article explores the transformation of a positively skewed distribution with a high degree of skewness. We will see how four of the most common transformations for skewness – square root, natural log, log to base 10, and inverse transformation – have differing degrees of impact on the distribution at hand. It should be noted that the inverse transformation is also known as the reciprocal transformation. In addition to the transformation methods offered in the table above Box-Cox transformation is also an option for positively skewed data that is >0. Further the Yeo-Johnson transformation is an extension of the Box-Cox transformation which does not require the original data values to be positive or >0.
The following example takes medical device sales in thousands for a sample of 2000 diverse companies. The histogram below indicates that the original data could be classified as “high(er)” positive skewed.
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​The skew is in fact quite pronounced – the maximum value on the x axis extends beyond 250 (the frequency of sales volumes beyond 60 are so sparse as to make the extent of the right tail imperceptible) – it is however the highly leptokurtic distribution that that lends this variable to be better classified as high rather than extreme. It is in fact log-normal – convenient for the present demonstration. From inspection it appears that the log transformation will be the best fit in terms of normalising the distribution.

​​Starting with a more conservative option, the square root transformation, a major improvement in the distribution is achieved already. The extreme observations contained in the right tail are now more visible. The right tail has been pulled in considerably and a left tail has been introduced. The kurtosis of the distribution has reduced by more than two thirds.

​A natural log transformation proves to be an incremental improvement yielding the following results:
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​This is quite a good outcome – the right tail has been reduced considerably while the left tail has extended along the number line to create symmetry. The distribution now roughly approximates a normal distribution. An outlier has emerged at around -4.25, while extreme values of the right tail have been eliminated. The kurtosis has again reduced considerably.
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Taking things a step further and apply a log to base 10 transformation yields the following:
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​In this case the right tail has been pulled in even further and the left tail extended less than the previous example. Symmetry has improved and the extreme value in the left tail has been bought closer in to around -2. The log to base ten transformation has provided an ideal result – successfully transforming the log normally distributed sales data to normal.
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In order to illustrate what happens when a transformation that is too extreme for the data is chosen, an inverse transformation has been applied to the original sales data below.
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​Here we can see that the right tail of the distribution has been brought in quite considerably to the extent of increasing the kurtosis. Extreme values have been pulled in slightly but still extend sparsely out towards 100. The results of this transformation are far from desirable overall.

Some thing to note is that in this case the log transformation has caused data that was previously greater than zero to now be located on both sides of the number line. ​Depending upon the context, data containing zero may become problematic when interpreting or calculating the confidence intervals of un-back-transformed data.  As  log(1)=0,  any data containing values <=1 can be made >0 by adding a constant to the original data so that the minimum raw value becomes >1 . Reporting un-back-transformed data can be fraught at the best of times so back-transformation of transformed data is recommended. Further information on back-transformation can be found here. 
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Adding a constant to data is not without it’s impact on the transformation. As the below example illustrates the effectiveness of the log transformation on the above sales data is effectively diminished in this case by the addition of a constant to the original data.
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​​​Depending on the subsequent intentions for analysis  this may be the preferred outcome for your data –  it is certainly an adequate improvement and has rendered the data approximately normal for most parametric testing purposes.

Taking the transformation a step further and applying the inverse transformation to the sales + constant data, again, leads to a less optimal result for this particular set of data – indicating that the skewness of the original data is not quite extreme enough to benefit from the inverse transformation.
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​​It is interesting to note that the peak of the distribution has been reduced whereas an increase in leptokurtosis occurred for the inverse transformation of the raw distribution. This serves to illustrate how a small alteration in the data can completely change the outcome of a data transformation without necessarily changing the shape of the original distribution.

There are many varieties of distribution, the below diagram depicting only the most frequently observed. If common data transformations have not adequately ameliorated your skewness, it may be more reasonable to select a non-parametric hypothesis test that is based on an alternate distribution.
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​Image credit: cloudera.com

02 Feb 2017
By Anatomise Biostats

Accessing Data Files when Using SAS via Citrix Receiver

All
 ​A SAS licence can be prohibitively expensive for many use cases. Installing the software can also take up a surprising amount of hard disk space and memory. For this reason many individuals with light or temporary usage needs choose to access a version of SAS which is licenced to their institution and therefore shared across many users. Are you trying unsuccessfully to access an SAS remotely via your institution using Citrix receiver? This step-by-step guide might help.

SAS syntax can differ based on whether a remote versus local server is used. An example of a local server is the computer you are physically using. When you have SAS installed on the PC you are using, you are accessing it locally. A remote server, on the other hand, allows you to access SAS without having SAS installed on your PC.

Client software such as Citrix Receiver, allows you to access SAS, and other software, from a remote server. Citrix Receiver is often used by university students, new and/or light users. SAS requires different syntax in order to enable the remote server to access data files on a local computer.
For the purpose of this example we are assuming that the data file we wish to access is located, locally, on a drive of the computer we are using.

It can be difficult to find the syntax for this on Google, where search results deal more with accessing remote data (libraries) using local SAS than the other way around. This can be a source of frustration for new users and SAS Technical Support are not always able to advise on the specifics of using SAS via Citrix Receiver.

The “INFILE”statement and the “PROC IMPORT” statement are two popular options for reading a data file into SAS. INFILE offers the greater flexibility and control, by way of manual specification of data characteristics. PROC IMPORT offers greater automation, in some cases at the risk of formatting error. The INFILE statement must always be used in the context of a DATA step, whereas PROC IMPORT acts as a stand-alone procedure. The ​document below shows syntax for the INFILE statement and PROC IMPORT procedure for local SAS compared to access via Citrix Receiver.

how to open a data file whe… by api-310702664

If you cannot see the document, please make sure that you are viewing the website in desktop mode.

​In SAS University Edition data file inputing difficulties can occur for a different reason. In order for the LIBNAME statement to run without error, a shared folder must first be defined. If you are using SAS University Edition, and experiencing an error when inputting data, the following videos may be helpful:
How to Set LIBNAME File Path (SAS University Edition) 
Accessing Data Files Via Citrix Receiver: for SAS University Edition

Troubleshooting check-list:

  • Was the “libref” appropriately assigned?
  • Was the file location referred to appropriately based on the user context?
  • ​Was the correct data file extension used?

While impractical for larger data sets, if all else fails, one can copy and paste the data from a data file into SAS using the ‘DATALINES’ function.

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