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International Clinical Trials
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The occurrence of missing data has been a prevalent issue for
clinicians, pharmacists and statisticians for many years. While there
are requirements in place to reduce non-response and missing data, one
question that has continually arisen is whether there is information
contained within the non-response.
Missing or incomplete responses are a common feature with many clinical
trials and observational studies. The problem created by trial
non-response is that data values intended by the trial design to be
observed are, in fact, missing. These missing values mean not only less
efficient estimates due to the reduction in sample size, but also that
standard complete-data methods cannot be used to analyse the data.
Furthermore, bias could become an issue in the analysis due to responses
often being systematically different to non-responses.
What are the Regulators Saying?
Currently, regulatory agencies such as the FDA in the US and EMA in
Europe are exploring various options to approach missing data in
clinical trials. The EMA have released a report in 2011 expressing their
views on handling missing data in confirmatory clinical trials (1).
Similarly, in 2010, the FDA commissioned a special panel from the US
National Academy of Science in Statistics (NAS) to draw up “a report
with recommendations that would be useful for FDA’s development of a
guidance for clinical trials on appropriate study design and follow-up
methods to reduce missing data and appropriate statistical methods to
address missing data analysis of results”
(2). Additionally, there was a report by the International Conference on
Harmonization (ICH) in 1998 addressing the issue of non-response in
clinical trials and asking some tough questions about how this needs to
be dealt with (3). Both the FDA and the EMA recognise how missing data
can affect the analysis and any interpretations that are made, due to it
reducing sample size and injecting potential bias.
In the NAS report the panel makes 18 recommendations to the FDA for
consideration, spanning areas such as: trial objectives; reducing
dropout through trial design and trial conduct; treating missing data;
and understanding the causes of dropouts in clinical trials (2). Of
these 18 recommendations, seven are focused on reducing missing data and
a further seven are aimed at dealing with missing data. Furthermore
both the NAS and the EMA reports suggest similar approaches to handling
missing data (1-2). As a result, it appears that a certain trend is
surfacing and a particular direction in this field is beginning to gain
traction among the regulatory agencies.
What are the Statisticians Saying?
Historically, many statistical techniques were developed in an attempt
to gain a wealth of knowledge from very limited sources of information,
hence the birth of the ‘sample’. By taking a sample of an overall
population, it is possible to make certain inferences about that
population. Thus, a natural progression of opinion was whether any
information could be salvaged from missing data that can so easily creep
into any clinical trial. This debate led to the question of whether the
observed data is biased due to lack of complete data. In other words,
if the missing data were observed, would the results from any analysis
differ?
Traditionally, there have been various ways of dealing with non-response
and missing data; the most common of which is referred to as ‘complete
case analysis’. This is where all the cases with incomplete responses
are systematically deleted before any analysis is performed. One opinion
that has gained a lot of momentum and support is the idea that deleting
the incomplete cases is wrong for two main reasons: firstly, by
deleting the incomplete cases you are still throwing away some
information that you already have; and secondly, you may be injecting
bias into the sample you have collected. To tackle this dilemma,
statisticians and clinicians decided to replace the missing data with
the worst possible (or best possible) response to purposely bias the
data. Therefore, if you could prove that a particular drug or device can
perform even under the worst/ best case scenario, then that result can
be extrapolated to the masses.
In longitudinal trials, or trials conducted over time, the single
imputation approach is usually implemented to fill in the missing data,
using the last value recorded, or the last observation made about a
particular case (where single imputation can be defined as the procedure
of entering a single value for a specific data point that is missing).
This common method is referred to as Last Value Carried Forward (LVCF),
Last Observation Carried Forward (LOCF) or Baseline Observation Carried
Forward (BOCF) depending on the values in question. Both the EMA and NAS
reports were critical of these single imputation methods as they felt
this approach can result in “confidence intervals for the treatment
effect … may be too narrow and give an artificial impression of
precision” and “not conform to well-recognised statistical principles
for drawing inferences” (1-2).
A modern and more novel approach that is gaining much interest in the
clinical and pharmaceutical circles currently is the multiple imputation
approach. Originally proposed by Donald Rubin of Harvard University,
multiple imputation is the technique that replaces each missing or
deficient value with two or more acceptable values that represent a
distribution of the possibilities (4). Using this method, you start out
with one incomplete database and you end up with multiple complete
databases.
Why so Many and What to Do with Them?
An important question to pose is whether non-response is an issue or an
inconvenience. In terms of the inconvenience, when incorporating
multiple imputation, we start with one database containing all of the
information gathered throughout the trial and, subsequently, we generate
several – and generally the more the better. An overview of the
multiple imputation process is displayed in Figure 1. Beginning with the
original data collected from the study, there are missing data points
displayed in red. Inserting multiple possible values for each missing
data point generates a number of databases. Finally the various
characteristics of the databases are examined.
The reason for this course of action is that simply inserting one value
to replace a missing data point can lack the depth required to fully
encompass all the information that is missing. In other words, inserting
one value may be too extreme while inserting a whole range of values
generates a more complete picture. Once we have generated several
complete databases, we approach them in the same way as with any
database. This is where the inconvenience comes into play; where there
was only one database and one set of analysis to be done, we now have to
look at and analyse several databases. Once this is done, a comparison
is undertaken to contrast the results across all the newly created
databases and examine the findings. So when adopting multiple
imputation, there is a considerable amount of extra work involved, but
the return is far greater than the investment.
So Why Bother with It?
Turning to the ‘issue’ side of the story, the argument for dealing with
the issue of missing data, and not simply ignoring it, is that there
could be substantial bias attributed to the absence of the data in
question. This means that there could be a very specific reason (or
several reasons) why certain information is missing, and to simply
ignore that could be detrimental to a study and its outcomes. For
example, if a specific type of patients’ measurement could not be taken,
then this group of patients are essentially excluded from the study. If
their measurements were recorded, they might influence the outcome
significantly. Based on the observed data, making accurate imputations
can help to achieve a much more representative insight into the group
being studied. As it has been already outlined, Table 1 summarises the
main advantages and disadvantages to incorporating multiple imputation
into the data analysis and study process. Bias reduction, increase study
power and more efficient sample size are clear advantages to
implementing multiple imputation. The side effects of using this method
are that there is a requirement for additional analysis time and
technological issues such as computation speed and memory.
Conclusion
In statistical terms, multiple imputation is still a relatively young
methodology and certainly a very new approach to handling data in the
clinical trial setting. It is a novel approach and appears to be gaining
a significant amount of momentum with both regulatory agencies and
pharmaceutical companies. A point was made that is a very prevalent
issue in clinical trials as missing data can occur so easily. Using
multiple imputation as a solution to handling missing data may at first
appear as an inconvenience due to the extra analysis required. With that
in mind, advancements in technology and software are reducing that
inconvenience at an incredible pace. Therefore, to answer the question
of whether non-response in clinical trials is an issue or an
inconvenience, the answer is a bit of both, but while the inconvenience
will subside, the issue will always remain.
References
- European Medicines Agency, Guideline on Missing Data in
Confirmatory Clinical Trials, www.ema.europa.eu/docs/en_GB/
document_library/Scientific_guideline/2010/09/WC500096793. pdf, accessed
on 11th May 2012
- National Research Council, The Prevention and Treatment of Missing Data in Clinical Trials, 2010
- International Conference of Harmonisation, Topic E9: Statistical
Principles for Clinical Trials, www.emea.europa.eu/docs/en_GB/
document_library/Scientific_guideline/2009/09/WC500002928. pdf, accessed
on 11th May 2012
- Rubin D, Multiple Imputation for Survey Non-Response, 1987
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