Prognosis and prognostic research validating a prognostic model

12-Apr-2020 23:18

Members of the cohort answered questionnaires every 6 months for 3.5 years.

Depressive symptoms were assessed by the Hospital Anxiety and Depression Scale (HADS) and a single item from the SF-12 (MH4) health survey.

Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature.

Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample.

Four approaches were compared in a simulation study in which the prognosis for each member of the cohort was individually assessed.

The mean standard deviations were 40% to 70% higher in simulated scores.

Prognostic models, like the one we developed in the previous article in this series,1 yield scores to enable the prediction of the risk of future events in individual patients or groups and the stratification of patients by these risks.2 A good model may allow the reasonably reliable classification of patients into risk groups with different prognoses.

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In order to assess reliability and generalizability for use, models need to have been validated and measures of model performance reported.Clinical prediction models are formal combinations of historical, physical examination and laboratory or radiographic test data elements designed to accurately estimate the probability that a specific illness is present (diagnostic model), will respond to a form of treatment (therapeutic model) or will have a well-defined outcome (prognostic model) in an individual patient.They are derived and validated using empirical data and used to assist physicians in their clinical decision-making that requires a quantitative assessment of diagnostic, therapeutic or prognostic probabilities at the bedside.Non-random splitting (for example, by centre) may be preferable as it reduces the similarity of the two sets of patients.1 4 If the available data are limited, the model can be developed on the whole dataset and techniques of data re-use, such as cross validation and bootstrapping, applied to assess performance.1 Internal validation is helpful, but it cannot provide information about the model’s performance elsewhere.—An alternative is to evaluate the performance of a model on subsequent patients from the same centre(s).6 10 Temporal validation is no different in principle from splitting a single dataset by time.There will clearly be many similarities between the two sets of patients and between the clinical and laboratory techniques used in evaluating them.

In order to assess reliability and generalizability for use, models need to have been validated and measures of model performance reported.

Clinical prediction models are formal combinations of historical, physical examination and laboratory or radiographic test data elements designed to accurately estimate the probability that a specific illness is present (diagnostic model), will respond to a form of treatment (therapeutic model) or will have a well-defined outcome (prognostic model) in an individual patient.

They are derived and validated using empirical data and used to assist physicians in their clinical decision-making that requires a quantitative assessment of diagnostic, therapeutic or prognostic probabilities at the bedside.

Non-random splitting (for example, by centre) may be preferable as it reduces the similarity of the two sets of patients.1 4 If the available data are limited, the model can be developed on the whole dataset and techniques of data re-use, such as cross validation and bootstrapping, applied to assess performance.1 Internal validation is helpful, but it cannot provide information about the model’s performance elsewhere.—An alternative is to evaluate the performance of a model on subsequent patients from the same centre(s).6 10 Temporal validation is no different in principle from splitting a single dataset by time.

There will clearly be many similarities between the two sets of patients and between the clinical and laboratory techniques used in evaluating them.

Information to the patient about the long-term prognosis of symptom burden and functioning is an integrated part of clinical practice, but relies mostly on the clinician’s personal experience.