How To Establish And Evaluate Clinical Prediction Models
Dr. Nancy Agnes, Head,
Technical Operations, Tutorsindia
info@ tutorsindia.com
models are all widely used approaches.
Keywords:
Statistical analysis help, clinical research
analysis, data collection services, clinical
prediction
models,
multiple
linear
regression analysis, logistic regression
analysis, Clinical Research & Analytics,
The secret to statistical analysis, data
modelling, and project design is assessing
and verifying prediction models' efficacy.
It is also the most difficult aspect of data
analysis technology.
statistics services, clinical trial data
analysis, External Validation Of Clinical
II. CLINICAL PREDICTION MODEL
Prediction Models
A clinical prediction model is a tool used
I. INTRODUCTION
The
use
of
a
in healthcare to measure estimates of the
parametric/semi-
parametric/non-parametric
mathematical
likelihood of the future course of a specific
patient outcome using multiple clinical or
model to estimate the probability that a
non-clinical
subject currently has a certain condition or
checklist for developing a valid prediction
the possibility of a certain outcome in the
model is presented in a clinical prediction
future is referred to as a clinical predictive
model. A clinical prediction model can be
model.
used in various clinical contexts, including
Various
regression
analysis
predictors.
A
realistic
approaches are used to model clinical
screening
prediction models, and the statistical
forecasting future events such as disease,
nature of regression analysis is to find
and assisting doctors in their decision-
"quantitative causality." To put it another
making and health education. Despite the
way, regression analysis is a quantitative
positive effects of clinical prediction
assessment of how much X impacts Y.
models on practice, prediction modelling
Multiple linear regression models, logistic
is a difficult process that necessitates
for
asymptomatic
illness,
regression models, and Cox regression
Copyright © 2021 TutorsIndia. All rights
1
meticulous statistical analysis and sound
clinical judgments.
III. STEPS TO ESTABLISHING A
CLINICAL PREDICTION MODEL
There exist several types of research
S.NO
DISEASE
SYMPTOMS
1
CANCER
Unusual lump,
changes in the
detailing the methods to construct clinical
mole,
prediction models. However, there is no
cough
and
proper method to construct the prediction
hoarseness,
model in medicine. The construction and
unusual
evaluation
diarrhoea
of
prediction
models
are
constipation
classified into five steps.
Step
1:Gathering
the
and
ideations
and
2
questions for enhancing the model.
CARDIOVASCULAR
Chest
pain,
DISEASE
chest tightness,
shortness
of
It incorporates structuring the research
breath,
questions, such as finding the target
numbness and
variable for predicting which age group of
weakness.
the targeted people you want to predict.
3
ARTHRITIS
Pain in hip or
For instance, gathering one patient details
joint, swelling,
and use it as a trained data set to test the
colour changes
in
other data set of another patient's details.
the
skin,
loss
[1].
of
appetite.
Step 2: Selection of data
4
DIABETES
Darkened area
Data collection is a vital part of statistical
of skin, High
or clinical research. Nevertheless, the
blood pressure
perfect data and a perfect model can't
and cholesterol
levels
exist. It would be nice to look for the most
appropriate.
The primary dataset with the endpoint of
the study and all key predictors may not be
Copyright © 2021 TutorsIndia. All rights
2
available at all the time. Secondary or
The Bayesian network was implemented to
administrative data sources are mandatory.
manipulate the independent variables of
Based on the various data types of
some diseases in the crucial stage of
datasets, prediction models can be utilized.
treatment. This model predicts and offers a
[2] For instance, the epidemiology study is
based on the Data Mining systematic
way to handle the disease along with
preventive measures [3].
approach.
Step 4: Generating model
Step 3: Ways to handle variables
There are no proper rules to select a
Most of the time, researchers may face
challenging situations where the variables
are highly correlated to each other,
excluded in the study. Variables don't
show statistical significance or the petite
effect size. But it will contribute to the
predictive model. Researchers will handle
the missing data problems, categorical
data, etc., before getting the interference.
particular model for the statistical analysis.
There are some standard methods to build
a model using Linear regression analysis,
logistic regression analysis, and Cox
models. Sometimes the clinical data
encounters over-fitting of the model and
its results in as estimates. This over-fitting
issue can be detected using Akaike
Information
Criteria
or
Bayesian
Information Criteria. The smaller AIC and
BIC values result in a good fit for the
IV. CLINICAL PREDICTION MODELS
CODE:
model. [4] Using Multivariate prediction
models
for
analyzing
the
different
characteristics of various patients.
Code number
Disease/
Deficiency
Step 5: Evaluation and validation of the
model After building the model, it is
ICD-10-R50
fever
ICD-R05
cough
necessary to evaluate and validate the
predictive power of the model. The key
components that evaluate the model are
ICD-10-CM-
pain
R52
ICD-9-CM-
calibration which plots the proportion, and
discrimination classifies the events like
headache
784.0
success or failure. There are two types of
data validation, namely internal and
external validation of the model. Internal
Copyright © 2021 TutorsIndia. All rights
3
validation evaluates the model within the
the scrutiny of around 33 research articles
data, whereas external validation can be
and found that most of the validation is
done using the re-sampling technique,
external validation and identified the
usually through bootstrapping. It means
validity using the calibration slope.
you are creating or generating new data
sets with similar characteristics to the
original data and validating the study's
method through the newly created or
bootstrapped data. Further, there are
several statistical measures to evaluate the
model. Some of them are ROC curve,
AUC curve, sensitivity and specificity,
Figure 2: This flow diagram illustrates the
likelihood
ratio,
value,
progress through the various phases of the
calibration
plot,
Hosmer-
CARDAMON phase II clinical trial,
R
square
c-index,
Lemeshow test, AIC, BIC, etc.
including the impact of COVID‐19 on the
70 patients on maintenance K across the
two treatment arms at the start of the
lockdown period. The 15 patients who
stopped K maintenance joined the 170
patients who were already on long‐term
follow‐up on 24 March 2020, bringing the
number up to a total of 185. SCT, stem cell
transplantation;
K,
carfilzomib;
C,
cyclophosphamide; d, dexamethasone [6].
Figure 1: Slope of Calibration plot –
V. FUTURE SCOPE:
Source:
Stevens
and
Poppe
(2020)
Besides,
Stevens
and
Poppe
(2020)
Based on the patient details, we can
suggested the Cox- calibration slope using
predict the further severe causation of
a logistic regression model instead of
disease in the future. By gathering the data
using the predictive model's calibration
from a single patient may help to predict
slope. This suggestion has been made after
other similar patients for better treatment.
Copyright © 2021 TutorsIndia. All rights
4
prediction models: feature selection methods in
Big data support for manipulating vast
amounts
of
clinical
trials,
complexitsimultaneously
data mining could improve the results."
without
with
Journal of clinical epidemiology 71 (2016): 76-
high
85.
accuracy.
3.
Chowdhury, Mohammad Ziaul Islam, and
Tanvir C. Turin. "Variable selection strategies
and their importance in clinical prediction
modeling." Family medicine and community
TABLE 1 Concepts and Techniques of
health 8.1 (2020).
Clinical prediction models:
4.
S.NO
METHODS
1
Data
comparative effectiveness of bootstrap-based
PURPOSES
REFERENCES
optimism
correction
development
of
Collection To train and test the [1]
methods
multivariable
in
the
clinical
prediction models." BMC Medical Research
using Surveys
data
between
two 21.1 (2021): 1-14.
Methodology
patients
5.
2
Iba, Katsuhiro, et al. "Re-evaluation of the
Epidemiology study
Stevens, R. J. and Poppe, K. K. (2020).
Validation of Clinical Prediction Models: What
Data mining
of data [2]
does the "Calibration Slope" Really Measure?.
sets
Journal of clinical epidemiology, 118, pp. 93–
99.
3
Bayesian Network
To
predict
6.
the [3]
Camilleri, Marquita, et al. "COVID‐19 and
characteristics
based
myeloma
clinical research–experience from the
CARDAMON clinical trial." British Journal of
on the independent
variable
4
Haematology 192.1 (2021): e14.
Multivariate analysis To manipulate the [4]
independent
variables
REFERENCES:
1.
Schmidt, André, et al. "Improving prognostic
accuracy in subjects at clinical high risk for
psychosis: systematic review of predictive
models and meta-analytical sequential testing
simulation."
Schizophrenia
Bulletin
43.2
(2017): 375-388.
2.
Bagherzadeh-Khiabani, Farideh, et al. "A
tutorial on variable selection for clinical
Copyright © 2021 TutorsIndia. All rights
5