Explicit inclusion of treatment in prognostic modeling was recommended in observational and randomized settings

Groenwold, Rolf H.H.; Moons, Karel G.M.; Pajouheshnia, Romin; Altman, Doug; Collins, Gary S.; Debray, Thomas P A; Reitsma, Johannes B.; Riley, Richard D; Peelen, Linda M.

doi:10.1016/j.jclinepi.2016.03.017

Cited by 65 publications

(74 citation statements)

References 15 publications

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“…In this case, a prognostic model should estimate the risk of developing a certain outcome if individuals were to remain untreated with this particular treatment (so-called untreated risk prediction) [1,8,10]. If this particular, "guided" treatment is given to study participants after the predictors are measured but before the ascertainment of the outcome (henceforth, we refer to this as "treatment drop-in", see Fig.…”

Section: Guided Treatmentsmentioning

confidence: 99%

“…Crucially, the outcomes measured in the study will no longer represent the untreated outcomes that the model is designed to predict. It follows that models developed using data from individuals who received guided treatments will provide biased underestimates of (untreated) risks in future individuals, if treatment use is ignored [8]. In validation studies, models will incorrectly appear to overestimate risk if applied in individuals that receive the specific guided treatment [8,11].…”

Section: Guided Treatmentsmentioning

confidence: 99%

“…prognosis with or without treatment), as well as the types of treatments (guided or background) used in a data set or study, are key factors that determine how treatments may impact on prognostic model development or validation. For further details on the challenges of treatment use and how to account for them in prognostic model development and validation, see [8] and [11], respectively, and further guidance can be found in Table 1 (see below).…”

Section: Examplesmentioning

confidence: 99%

“…from a cohort or registry) is used to develop or validate a prognostic model [6][7][8]. In order to develop or validate prognostic models that predict an individual's probability of developing an outcome in the absence of a certain treatment (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, however, such prognostic models are often derived from or validated in data sets where a proportion of the individuals has received that specific treatment. If, for example, treatments were administered in a study according to individuals' predicted risks (either implicitly or explicitly), a model developed using this data will likely underestimate the risk of the predicted outcome in the absence of treatment and could thus lead to under-treatment when such a model is used in future individuals [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Treatment use in prognostic model research: a systematic review of cardiovascular prognostic studies

et al. 2017

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Background: Ignoring treatments in prognostic model development or validation can affect the accuracy and transportability of models. We aim to quantify the extent to which the effects of treatment have been addressed in existing prognostic model research and provide recommendations for the handling and reporting of treatment use in future studies. Methods: We first describe how and when the use of treatments by individuals in a prognostic study can influence the development or validation of a prognostic model. We subsequently conducted a systematic review of the handling and reporting of treatment use in prognostic model studies in cardiovascular medicine. Data on treatment use (e.g. medications, surgeries, lifestyle interventions), the timing of their use, and the handling of such treatment use in the analyses were extracted and summarised. Results: Three hundred two articles were included in the review. Treatment use was not mentioned in 91 (30%) articles. One hundred forty-six (48%) reported specific information about treatment use in their studies; 78 (26%) provided information about multiple treatments. Three articles (1%) reported changes in medication use ("treatment drop-in") during follow-up. Seventy-nine articles (26%) excluded treated individuals from their analysis, 80 articles (26%) modelled treatment as an outcome, and of the 155 articles that developed a model, 86 (55%) modelled treatment use, almost exclusively at baseline, as a predictor. Conclusions: The use of treatments has been partly considered by the majority of CVD prognostic model studies. Detailed accounts including, for example, information on treatment drop-in were rare. Where relevant, the use of treatments should be considered in the analysis of prognostic model studies, particularly when a prognostic model is designed to guide the use of certain treatments and these treatments have been used by the study participants. Future prognostic model studies should clearly report the use of treatments by study participants and consider the potential impact of treatment use on the study findings.

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Section: Guided Treatmentsmentioning

confidence: 99%

Section: Guided Treatmentsmentioning

confidence: 99%

Section: Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Treatment use in prognostic model research: a systematic review of cardiovascular prognostic studies

et al. 2017

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2021

Individual Participant Data Meta‐Analysis

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Systematic review of clinical prediction models for survival after surgery for resectable pancreatic cancer

Strijker

Chen

Mungroop

et al. 2019

British Journal of Surgery

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Background: As more therapeutic options for pancreatic cancer are becoming available, there is a need to improve outcome prediction to support shared decision-making. A systematic evaluation of prediction models in resectable pancreatic cancer is lacking.Methods: This systematic review followed the CHARMS and PRISMA guidelines. PubMed, Embase and Cochrane Library databases were searched up to 11 October 2017. Studies reporting development or validation of models predicting survival in resectable pancreatic cancer were included. Models without performance measures, reviews, abstracts or more than 10 per cent of patients not undergoing resection in postoperative models were excluded. Studies were appraised critically.Results: After screening 4403 studies, 22 (44 319 patients) were included. There were 19 model development/update studies and three validation studies, altogether concerning 21 individual models. Two studies were deemed at low risk of bias. Eight models were developed for the preoperative setting and 13 for the postoperative setting. Most frequently included parameters were differentiation grade (11 of 21 models), nodal status (8 of 21) and serum albumin (7 of 21). Treatment-related variables were included in three models. The C-statistic/area under the curve values ranged from 0⋅57 to 0⋅90. Based on study design, validation methods and the availability of web-based calculators, two models were identified as the most promising. Conclusion: Although a large number of prediction models for resectable pancreatic cancer have been reported, most are at high risk of bias and have not been validated externally. This overview of prognostic factors provided practical recommendations that could help in designing easily applicable prediction models to support shared decision-making.Prediction models enable clinicians and patients to calculate the risks of a specific endpoint individualized to a patient, using combinations of predictors. Such models are used widely in some fields, including the CHADS 2 -VASc score for estimation of stroke risk in atrial fibrillation 3 , and the Acute Physiology and Chronic Health Evaluation (APACHE) score 4 for mortality in intensive care. In breast cancer, the PREDICT 5 and Adjuvant! 6 models have been employed in the decision-making process to determine adjuvant treatment administration. Data analysisData extraction was performed independently by two authors. Articles were categorized into development/model update studies and validation studies 12 .

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Explicit inclusion of treatment in prognostic modeling was recommended in observational and randomized settings

Cited by 65 publications

References 15 publications

Treatment use in prognostic model research: a systematic review of cardiovascular prognostic studies

Treatment use in prognostic model research: a systematic review of cardiovascular prognostic studies

References

Systematic review of clinical prediction models for survival after surgery for resectable pancreatic cancer

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