A REVIEW OF PUBLISHED STUDIES
LOOKING AT STATISTICAL MODELS
AND METHODS AND
THEIR APPLICATION TO PROBLEMS
OF INFECTIOUS DISEASES SUCH AS
COVID-19 IN BMJ
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Statswork
Group www.statswork.com
Email: info@statswork.com
TODAY'S DISCUSSION
Outline
Introduction
Statistical models in human health science
Factor analysis
Bayesian meta-analysis
Models to forecast the risk of covid-19 in the general population
Diagnostic models to discover covid-19 in patients with
suspected infection
Predictive models to diagnose covid-19
Conclusion
INTRODUCTION
Health science research is the most interesting research
area as we identify the pattern of Genomic diseases and
various other kinds of diseases.
STATISTICAL
MODELS IN
H U M A N HEALTH
SCIENCE
The most common statistical approach for any
human health studies is correlation and regression
analysis.
Suppose, consider a vaccine effectiveness study,
and the researcher wants to identify the
effectiveness of vaccine among the gender or age
category.
The correlation technique will be useful to identify
the relationship between these two variables.
Contd...
And suppose the researcher wants to predict the effectiveness of future outcomes.
In that case, the regression analysis will be useful as it identifies the average linear
relationship between the dependent and independent variables.
Apart from the usual correlation and regression analysis, many researchers adopt
dimensionality reduction techniques such as factor analysis.
FACTOR
AN ALYSIS
Factor analysis reduces the dimensions and creates the latent
variables. Each latent variable acts as another variable in the study.
With those latent variables, one can construct linear regression
analysis and predict future outcomes or simply identify the
variables' linear relationship.
For example, Goni et al. (2020) considered a Confirmatory factor
analysis to study respiratory tract infections in Hajj and Umrah.
They collected the data in the form survey involving 72 variables. In
practice, analysing the entire 72 variables will yield poor results.
Contd...
Thus, the dimensionality reduction technique is adopted and measured by the
confirmatory factor analysis, which uses the chi-square statistic.
Also, Saefi et al. (2020) studied the undergraduate student's knowledge about
COVID19, measures taken by them to prevent the disease, and maintaining the health
style during COVID19.
They conducted a survey and investigated the properties of the KAP questionnaire by
adopting Confirmatory Factor Analysis (CFA) and RASCH model and the results of
these analyses revealed that each of the items in the questionnaire possesses unique
qualities and this questionnaire is adequate enough to measure the student's
knowledge, attitude and practice during COVID19.
Contd...
Further, Siemieniuk et al. (2020) compared the effects of COVID19 treatments from
literature using Meta-analysis.
Data for this study has been collected daily from different sources such as the WHO
website, Centre for Disease Control and Prevention in the U.S., PubMed, etc.
The data includes detailed information of the patient affected with COVID19, like the
length of stay in ICU, duration of ventilation, etc.
BAYESIAN
METAANALYSIS
With this information, they conducted a Bayesian meta-analysis
and performed 10000 Markov Chain iterations using fixed effects
and random effects separately and found no statistical
incoherence in the analysis.
Furthermore, Xu et al. (2020) studied the characteristics of
patients affected by COVID19 outside Wuhan in China.
The study revealed that people affected with COVID outside
Wuhan city are very mild than the people affected in Wuhan.
Apart from the viral infectious disease, numerous diseases are of
interest to the researchers in finding the causes, remedies, risk
factors, etc.
Contd...
One such increasing research area is cancer studies.
Calster et al. (2020) considered a cohort study on ovarian cancer and identified the
best model to detect cancer and properly distinguish cancer types.
The dataset has been collected from IOTA and selected a proper sample for the
analysis.
Five different models have been conducted, and the results revealed that SRRisk and
ADNEX models performed well in classifying the type of cancer.
Healthcare research is to diagnose the disease or find the risk factor associated with
the disease.
Statistical techniques can be used to analyse the causes of the diseases.
Contd...
In that sense, Tian et al. (2019) estimated the risk factors of hospital admission related
to cardiovascular disease.
A total of 184 cities in China are included in the study, and the information related to
pollution and hospital admissions are collected.
They adopted Time series analysis to investigate the association between pollution
and disease.
The results showed that short-term exposure to pollution leads to increased hospital
admissions for cardiovascular disease.
Statswork provides high quality biostatistics services which helps precise estimation of
the effect size and increases the generalizability of the results of individual studies.
MODELS TO
FORECAST
THE RISK OF
COVID-19 IN
THE GENERAL
POPULATION
They acknowledged seven models that help in predicting the
risk of covid-19 in the general population.
Three models from one study used hospital admission based
on non-tuberculosis pneumonia, influenza, acute bronchitis, or
upper respiratory tract infections as substitution outcomes in a
dataset without any patients with covid-191.
The fourth model uses a deep learning technique detecting
thermal video from the faces of people wearing facemasks to
detecting abnormal breathing (not covid related) with a
reported sensitivity of 80%.
Contd...
The fifth model uses a mobile application to collect data and to risk-stratify patients.
It uses demographics, symptoms, and contact history of users.
It further expanded into two more models: blood values and blood values plus
computed tomography (C.T.) images.
Table: Overview of prediction models for diagnosis and prognosis of covid-1911
DIAGNOSTIC
MODELS TO
DISCOVER
COVID-19 IN
PATIENTS WITH
SUSPECTED
INFECTION
It is a type of method or test used to help diagnose a
disease or condition.
It includes imaging tests and tests to measure blood
pressure, pulse, and temperature are examples of
diagnostic techniques.
Diagnosis has significant implications for patient care,
research, and policy.
PREDICTIVE
MODELS TO
DIAGNOSE
COVID-19
A predictive model was defined as combining at least two
prognostic factors, based on multivariable analysis, as estimating
the individual risk of a specific outcome, presented as regression
formula, nomogram, or in a simplified form, such as risk score.
A predictive model is a formal grouping of multiple predictors from
which a particular endpoint's risks can be calculated for individual
patients.
Other names for a predictive model include prognostic (or
prediction) index or rule, risk (or clinical) prediction model, and
predictive model.
CONCLUSION
Further, statistical techniques have been widely used in
epidemiological research.
Moustgaard et al. (2020) studied the impact of treatment and
therapeutically effects in clinical trials using meta-analysis.
The results showed no difference in the effects of treatments of
patients from the healthcare providers with and without blinding.
Furthermore, Fabbri et al. (2020) presented a review on the
health care providers and South African patients' funding using
meta-analysis.
They recommended that the corporate companies provide transparency in
providing funds to patients, and this type of funding can be seen in high-income
countries.
If you are struggling with meta-analysis you can reach our statistical meta-analysis
service.
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