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How to establish and evaluate clinical prediction models - Statswork

A clinical prediction model can be used in various clinical contexts, including screening for asymptomatic illness, forecasting future events such as disease, and assisting doctors in their decision-making and health education.

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How to establish and evaluate clinical prediction models - Statswork

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  1. HOW TO ESTABLISHAND EVALUATE CLINICAL PREDICTIONMODELS An Academic presentationby Dr. Nancy Agnes, Head, Technical Operations, Statswork Group www.statswork.com Email:info@statswork.com

  2. TODAY'SDISCUSSION Outline Introduction Clinical PredictionModel Steps to establishing a clinical prediction model Clinical prediction modelsCODE FutureScope:

  3. INTRODUCTION The use of a parametric/semi-parametric/non- parametric mathematical model to estimate the probability that a subject currently has a certain condition or the possibility of a certain outcome in the future is referred to as a clinical predictivemodel. Various regression analysis approaches are used to model clinical prediction models, and the statistical nature of regression analysis is to find "quantitative causality."

  4. To put it another way, regression analysis is a quantitative assessment of how much X impactsY. Multiple linear regression models, logistic regression models, and Cox regression models are all widely usedapproaches. 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 analysistechnology.

  5. CLINICAL PREDICTION MODEL A clinical prediction model is a tool used in healthcare to measure estimates of the likelihood of the future course of a specific patient outcome using multiple clinical or non- clinicalpredictors. A realistic checklist for developing a valid prediction model is presented in a clinical predictionmodel. A clinical prediction model can be used in various clinical contexts, including screening for asymptomatic illness, forecasting future events such as disease, and assisting doctors in their decision-making and healtheducation. Contd...

  6. Despite the positive effects of clinical prediction modelson practice, prediction modelling is a difficult process that necessitates meticulous statistical analysis and sound clinicaljudgments. Contd...

  7. STEPS TO ESTABLISHING A CLINICAL PREDICTION MODEL There exist several types of research detailing the methods to construct clinical predictionmodels. However, there is no proper method to construct the prediction model inmedicine. The construction and evaluation of prediction models are classified into fivesteps. Contd...

  8. STEP1:GATHERINGTHEIDEATIONSANDQUESTIONSFORENHANCINGTHE MODEL. It incorporates structuring the research questions, such as finding the target variable for predicting which age group of the targeted people you want to predict. etc. For instance, gathering one patient details and use it as a trained data set to test the other data set of another patient’s details.[1]. Contd...

  9. STEP 2: SELECTION OF DATA Data collectionis a vital part of statistical or clinicalresearch. Nevertheless, the perfect data and a perfect model can't exist. It would be nice to look for the mostappropriate. The primary dataset with the endpoint of the study and all key predictors may not be available at all thetime. Secondary or administrative data sources aremandatory. Contd...

  10. Based on the various data types of datasets, prediction models can beutilized. [2] For instance, the epidemiology study is based on the Data Mining systematic approach. STEP 3: WAYS TO HANDLE VARIABLES Most of the time, researchers may face challenging situations where the variables are highly correlated to each other, excluded in thestudy. Variables don't show statistical significance or the petite effectsize. But it will contribute to the predictive model. Researchers will handle the missing data problems, categorical data, etc., before getting theinterference. Contd...

  11. CLINICAL PREDICTION MODELSCODE Contd...

  12. The Bayesian network was implemented to manipulate the independent variables of some diseases in the crucial stage oftreatment. This model predicts and offers a way to handle the disease along with preventive measures[3]. Contd...

  13. STEP 4: GENERATING MODEL There are no proper rules to select a particular model for the statisticalanalysis. There are some standard methods to build a model using Linear regression analysis,logistic regression analysis, and Coxmodels. 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 InformationCriteria. Contd...

  14. The smaller AIC and BIC values result in a good fit for themodel. [4] Using Multivariate prediction models for analyzing the different characteristics of variouspatients. Contd...

  15. STEP 5: EVALUATION AND VALIDATION OF THEMODEL After building the model, it is necessary to evaluate and validate the predictive power of themodel. The key components that evaluate the model are calibration which plots the proportion, and discrimination classifies the events like success orfailure. There are two types of data validation, namely internal and external validation of the model. Internal validation evaluates the model within the data, whereas external validation can be done using the re-sampling technique, usually throughbootstrapping. Contd...

  16. It means 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 bootstrappeddata. Further, there are several statistical measures to evaluate themodel. Some of them are ROC curve, AUC curve, sensitivity and specificity, likelihood ratio, R square value, calibration plot, c-index, Hosmer-Lemeshow test, AIC, BIC,etc. Contd...

  17. Figure 1: Slope of Calibration plot – Source: Stevens and Poppe(2020) Contd...

  18. Besides,StevensandPoppe(2020)suggestedtheCox-calibrationslopeusingaBesides,StevensandPoppe(2020)suggestedtheCox-calibrationslopeusinga logistic regression modelinstead of using the predictive model's calibrationslope. This suggestion has been made after the scrutiny of around 33 research articles and found that most of the validation is external validation and identified the validity using the calibrationslope. Contd...

  19. Contd...

  20. Figure 2: This flow diagram illustrates the progress through the various phases of the CARDAMON phase II clinical trial, including the impact of COVID‐19 on the 70 patients on maintenance K across the two treatment arms at the start of the lockdownperiod. 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]. Contd...

  21. FUTURE SCOPE Based on the patient details, we can predict the further severe causation of disease in thefuture. By gathering the data from a single patient may help to predictother similar patients for bettertreatment. Big data support for manipulating vast amounts of clinical trials, without complexity simultaneously with highaccuracy. Contd...

  22. TABLE 1 Concepts and Techniques of Clinical predictionmodels:

  23. ContactUs UNITEDKINGDOM +44-1143520021 INDIA +91-4448137070 EMAIL info@statswork.com

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