A Review of Published Studies Looking At Statistical Models And Methods And Their Applications by Statswork

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

Dr. Nancy Agnes, Head, Technical Operations, Tutorsindia info@ tutorsindia.com

or age category. The correlation technique

Keywords: statistical regression

meta-analysis analysis,

factor

service, analysis,

Confirmatory Factor Analysis, clinical trial analysis, data mining services, biostatistics services, Time series analysis

will be useful to identify the relationship between these two variables. And suppose the researcher wants to predict the effectiveness of future outcomes. In that case, the regression analysis will be useful as

using R.

identifies

the

average

linear

relationship between the dependent and independent variables. Apart from the I. INTRODUCTION TO HEALTH

usual correlation and regression analysis,

SCIENCE:

many researchers adopt dimensionality reduction

Health science research is the most

techniques

such

factor

analysis.

interesting research area as we identify the III. FACTOR ANALYSIS

pattern of Genomic diseases and various other kinds of diseases.

Factor analysis reduces the dimensions and

II. STATISTICAL MODELS IN HUMAN

creates the latent variables. Each latent

HEALTH SCIENCE:

variable acts as another variable in the study. With those latent variables, one can

The most common statistical approach for any human health studies is correlation and regression analysis. Suppose, consider a vaccine effectiveness researcher

wants

study, to

and the

identify

the

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

effectiveness of vaccine among the gender

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. Thus, the dimensionality reduction

technique

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

IV. BAYESIAN META-ANALYSIS 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.

conducted a survey and investigated the properties of the KAP questionnaire by

Apart from the viral infectious disease,

adopting Confirmatory Factor Analysis

numerous diseases are of interest to the

(CFA) and RASCH model and the results

researchers

of these analyses revealed that each of the

remedies, risk factors, etc. One such

items in the questionnaire possesses

increasing research area is cancer studies.

unique qualities and this questionnaire is

Calster et al. (2020) considered a cohort

adequate enough to measure the student's

study on ovarian cancer and identified the

knowledge, attitude and practice during

best model to detect cancer and properly

COVID19. Further, Siemieniuk et al.

distinguish cancer types. The dataset has

(2020) compared the effects of COVID19

been collected from IOTA and selected a

treatments from literature using Meta-

proper sample for the analysis. Five

analysis. Data for this study has been

different models have been conducted, and

collected daily from different sources such

the results revealed that SRRisk and

as the WHO website, Centre for Disease

ADNEX

Control and Prevention in the U.S.,

classifying the type of cancer. Healthcare

PubMed, etc. The data includes detailed

research is to diagnose the disease or find

information of the patient affected with

the risk factor associated with the disease.

COVID19, like the length of stay in ICU,

Statistical techniques can be used to

duration of ventilation, etc.

analyse the causes of the diseases. In that

models

finding

the

performed

causes,

well

sense, Tian et al. (2019) estimated the risk

80%.The fifth model uses a mobile

factors of hospital admission related to

application to collect data and to risk-

cardiovascular disease. A total of 184

stratify patients. It uses demographics,

cities in China are included in the study,

symptoms, and contact history of users.It

and the information related to pollution

further expanded into two more models:

and hospital admissions are collected.

blood values and blood values plus

They adopted Time series analysis to

computed tomography (C.T.) images.

investigate

the

association

between

pollution and disease. The results showed

Table: Overview of prediction models for

that short-term exposure to pollution leads

diagnosis and prognosis of covid-1911

increased

cardiovascular

hospital

admissions

disease.

for

Statswork

provides high quality biostatistics services which helps precise estimation of the effect

size

and

increases

the

generalizability of the results of individual studies. V. MODELS TO FORECAST THE RISK OF COVID-19 IN THE GENERAL POPULATION

VI. DIAGNOSTIC MODELS TO DISCOVER COVID-19 IN PATIENTS WITH SUSPECTED INFECTION

They acknowledged seven models that

It is a type of method or test used to

help in predicting the risk of covid-19 in

help diagnose a disease or condition. It

the general population. Three models from

includes imaging tests and tests to measure

one study used hospital admission based

blood pressure, pulse, and temperature are

on non-tuberculosis pneumonia, influenza,

examples

acute bronchitis, or upper respiratory tract

Diagnosis has

infections as substitution outcomes in a

for patient care, research, and policy.

of diagnostic

techniques.

significant

implications

dataset without any patients with covid191. The fourth model uses a deep learning technique detecting thermal video from the

VII .PREDICTIVE MODELS TO DIAGNOSE COVID-19

faces of people wearing facemasks to

A predictive

detecting abnormal breathing (not covid

combining at least two prognostic factors,

related) with a reported sensitivity of

based

model was

multivariable

defined

analysis,

REFERENCES

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.

VIII. 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 metaanalysis. 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

recommended companies

meta-analysis. that

provide

the

They

corporate

transparency

providing funds to patients, and this type of funding can be seen in high-income countries. If you are struggling with metaanalysis you can reach our statistical metaanalysis service.

Dauda Goni M, Hasan H, Naing N.N., et al. Assessment of Knowledge, Attitude and Practice towards Prevention of Respiratory Tract Infections among Hajj and Umrah Pilgrims from Malaysia in 2018. International Journal of Environmental Research and Public Health. 2019 Nov;16(22). 2. Saefi, M., Fauzi, A., Kristiana, E., Adi, W. C., Muchson, M., Setiawan, M. E., Ramadhani, M. (2020). Validating of Knowledge, Attitudes, and Practices Questionnaire for Prevention of COVID-19 infections among Undergraduate Students: A RASCH and Factor Analysis. Eurasia Journal of Mathematics, Science and Technology Education, 16(12), em1926. 3. Siemieniuk R A, Bartoszko J J, Ge L, Zeraatkar D, Izcovich A, Kum E et al. Drug treatments for covid-19: living systematic review and network meta-analysis BMJ 2020; 370 :m2980 4. Van Calster B, Valentin L, Froyman W, Landolfo C, Ceusters J, Testa A C et al. Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study BMJ 2020; 370 :m2614 5. Whaley C M, Arnold D R, Gross N, Jena A B. Practice composition and sex differences in physician income: observational study BMJ 2020; 370 :m2588 6. Tian Y, Liu H, Wu Y, Si Y, Song J, Cao Y et al. Association between ambient fine particulate pollution and hospital admissions for cause specific cardiovascular disease: time series study in 184 major Chinese cities BMJ 2019; 367 :l6572 7. Forbes H, Douglas I, Finn A, Breuer J, Bhaskaran K, Smeeth L et al. Risk of herpes zoster after exposure to varicella to explore the exogenous boosting hypothesis: self controlled case series study using U.K. electronic healthcare data BMJ 2020; 368 :l6987 8. Moustgaard H, Clayton G L, Jones H E, Boutron I, JÃ¸rgensen L, Laursen D R T et al. Impact of blinding on estimated treatment effects in randomised clinical trials: metaepidemiological study BMJ 2020; 368 :l6802 9. Fabbri A, Parker L, Colombo C, Mosconi P, Barbara G, Frattaruolo M P et al. Industry funding of patient and health consumer organisations: systematic review with metaanalysis BMJ 2020; 368 :l6925 10. Xu X, Wu X, Jiang X, Xu K, Ying L, Ma C et al. Clinical findings in a group of patients infected with the 2019 novel corona virus (SARS-Cov-2) outside of Wuhan, China: retrospective case series BMJ 2020; 368 :m606 11. Wynants, L., Van Calster, B., Collins, G. S., Riley, R. D., Heinze, G., Schuit, E., ... & van

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