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More patient-specific medical care is demanded as further is discovered regarding variations in patient responses to medical practices.
The recent developments in patient specific medical care
has generated a lot of variations in medical treatments. Analytical tools
establish a link between RCTs and observational studies. This cluster analysis
provides the data from different studies which were collected and integrated to
examine patient responses to pregabalin in patients with diabetic neuropathy.
More patient-specific medical care is demanded as further
is discovered regarding variations in patient responses to medical practices.
Analytical tools allow insights by combining treatment responses from various
types of studies, such as observational studies and randomised controlled
trials (RCTs). This study aimed to combine such types of data into a single
predictive program to anticipate the response to Pregabalin among subjects with
painful diabetic peripheral neuropathy (pDPN).
One largest observational study of Pregabalin comprising
3159 German patients and three pivotal RCTs of Pregabalin including 398 North
American patients were selected for the analysis. A hierarchical cluster analysis
was implemented to distinguish patient clusters in the Observational Study to
which RCT subjects could be matched by using the coarsened exact matching (CEM)
method, thereby performing a matched dataset.
Autoregressive moving average models (ARMAXs) were developed to measure
weekly pain scores for Pregabalin-treated individuals within each cluster in
the matched dataset applying the maximum likelihood approach. Lastly, ARMAX
models validation was done by utilising Observational Study subjects who had
not matched with RCT subjects, using t-tests between predicted and observed
pain scores.
Cluster analysis capitulated six clusters with the
subsequent clustering variables: age, body mass index, pDPN duration, gender,
baseline sleep interference, depression history, baseline pain score,
Pregabalin monotherapy, and prior Gabapentin use. CEM obtained 1528 individual
patients during the matched dataset. The decline in global imbalance scores for
the clusters after incorporating the RCT patients showed that the method
decreased the bias of covariates in five of the six clusters. ARMAX models of
pain score executed well. Further, t-tests did not reveal differences within
predicted and observed pain scores in the 1955 subjects who had not matched
with RCT patients.
The combination of CEM, ARMAX modelling, and cluster
analyses allowed strong predictive abilities with respect to pain scores.
Combining Observational Study and RCT data employing CEM allowed efficient
utility of Observational Study data to predict patient responses.
Journal of Pain Research
Integrating data from randomized controlled trials and observational studies to predict the response to pregabalin in patients with painful diabetic peripheral neuropathy
Joe Alexander et al.
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