How many longitudinal covariate measurements are needed for risk prediction?

Reinikainen, Jaakko; Karvanen, Juha; Tolonen, Hanna

doi:10.1016/j.jclinepi.2015.06.022

Cited by 2 publications

(3 citation statements)

References 56 publications

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“…Record linkage was performed by the Australian Institute of Health and Welfare utilizing an established probabilistic record linkage algorithm. 10 We obtained ethical approval from the Australian Institute of Health and Welfare Institutional Review Board (IRB) (EC2008- [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and all other relevant IRBs.…”

Section: Data Linkagementioning

confidence: 99%

“…3,4 The use of discharge immunosuppression data may therefore result in exposure misclassification and loss of accuracy in predicting immunosuppression-health outcome relationships over time. 5,6 The potential of drug exposure misclassification to bias the drugoutcome association has been demonstrated in pharmacoepidemiology, 7,8 and the need to determine the impact of such misclassification on study results has been acknowledged. 9 Nevertheless, there is a lack of real-world evidence of the impact of exposure misclassification on the many cohort-based analyses that are undertaken using immunosuppression data in transplantation research.…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal immunosuppression data can minimize misclassification bias in solid organ transplantation cohorts

Laaksonen

Webster

McCaughan

et al. 2019

Clinical Transplantation

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Background: Most cohort studies investigating the effect of immunosuppression on transplant outcomes use drugs at first hospital discharge. We evaluated the extent of drug exposure misclassification and its impact on outcome prediction. Methods:We retrospectively collected longitudinal immunosuppression data, at discharge and at 1, 5, 10, and 15 years after transplantation, and outcomes for solid organ transplant recipients 1984-2006 (n = 3133). We compared the risk of death from exposure to individual immunosuppressive drugs (cyclosporine, tacrolimus, azathioprine, and mycophenolate) and dual therapies, as defined by discharge only vs longitudinal immunosuppression data, using adjusted Cox proportional hazards models. Results: During a median follow-up of 5.2 years, immunosuppressive drugs were altered for 947 (30%) recipients and 955 recipients died. Longitudinal receipt of cyclosporine and azathioprine were associated with an increased risk (HR 1.41, 95% CI 1.07-1.89, and HR 1.34, 95% CI 1.00-1.80), and mycophenolate with a reduced risk (HR 0.35, 0.16-0.78), of death. Recipients on mycophenolate and tacrolimus dual therapy had a lower risk of death compared to those on azathioprine and cyclosporine dual therapy (HR 0.30, 0.10-0.93). The increased risk of death associated with the receipt of cyclosporine or azathioprine was not shown in the analyses based on drugs allocated at discharge, and all of the associations between immunosuppressive regimens and death were strengthened in the analyses based on longitudinal immunosuppression data. Conclusions: Cohort findings based on immunosuppressive drugs allocated at discharge should be interpreted with caution due to potential exposure misclassification. The use of granular, longitudinal data on immunosuppressive regimens could improve prediction. K E Y W O R D S cohort, immunosuppression, longitudinal, misclassification, prediction, transplantation S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section at the end of the article. How to cite this article: Laaksonen MA, Webster AC, McCaughan GW, Keogh AM, Grulich AE, Vajdic CM. Longitudinal immunosuppression data can minimize misclassification bias in solid organ transplantation cohorts.

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Section: Data Linkagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Longitudinal immunosuppression data can minimize misclassification bias in solid organ transplantation cohorts

Laaksonen

Webster

McCaughan

et al. 2019

Clinical Transplantation

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“…The IDI is known to be very sensitive detecting changes in prediction probabilities in a new model compared to an old model, and therefore, it quickly became popular in the field of medical research (for example, the works of Zethelius et al, Tangri et al, and Reinikainen et al). However, unfortunately, the IDI can falsely detect a significant improvement in the new model even if no new information has been provided by the additional covariates.…”

Section: Introductionmentioning

confidence: 99%

The power‐integrated discriminant improvement: An accurate measure of the incremental predictive value of additional biomarkers

Hayashi

Eguchi

2019

Statistics in Medicine

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The predictive performance of biomarkers is a central concern in biomedical research. This is often evaluated by comparing two statistical models: a "new" model incorporating additional biomarkers and an "old" model without them.In 2008, the integrated discrimination improvement (IDI) was proposed for cases when the response variable is binary, and it is now widely applied as a promising alternative to conventional measures, such as the difference of the area under the receiver operating characteristic curve. However, the IDI can erroneously identify a significant improvement in the new model even if no additional information has been provided by new biomarkers. In order to overcome problems with existing measures, in this study, we propose the power-IDI as a measure of incremental predictive value. Our study explains why the IDI cannot avoid false detection of apparent improvements in a new model and we show that our proposed measure is better able to capture improvements in prediction. Numerical simulations and examples using real empirical data reveal that the power-IDI is not only more powerful but also incurs fewer false detections of improvement. KEYWORDS area under the ROC curve, Bayes-risk consistency, fisher consistency, integrated discrimination improvement, logistic regression, net reclassification index Statistics in Medicine. 2019;38:2589-2604.wileyonlinelibrary.com/journal/sim

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How many longitudinal covariate measurements are needed for risk prediction?

Abstract: Decisions about the study design have significant effects on the costs. The cost-efficiency can be improved by applying the measures of model discrimination to data from previous studies and simulations.

Cited by 2 publications

References 56 publications

Longitudinal immunosuppression data can minimize misclassification bias in solid organ transplantation cohorts

Longitudinal immunosuppression data can minimize misclassification bias in solid organ transplantation cohorts

The power‐integrated discriminant improvement: An accurate measure of the incremental predictive value of additional biomarkers

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