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1. Doubly robust estimation of marginal cumulative incidence curves for competing risk analysis

Author

van Hage, Patrick, Cessie, Saskia le, van Maaren, Marissa C., Putter, Hein, and van Geloven, Nan

Subjects
Statistics - Methodology
Abstract

Covariate imbalance between treatment groups makes it difficult to compare cumulative incidence curves in competing risk analyses. In this paper we discuss different methods to estimate adjusted cumulative incidence curves including inverse probability of treatment weighting and outcome regression modeling. For these methods to work, correct specification of the propensity score model or outcome regression model, respectively, is needed. We introduce a new doubly robust estimator, which requires correct specification of only one of the two models. We conduct a simulation study to assess the performance of these three methods, including scenarios with model misspecification of the relationship between covariates and treatment and/or outcome. We illustrate their usage in a cohort study of breast cancer patients estimating covariate-adjusted marginal cumulative incidence curves for recurrence, second primary tumour development and death after undergoing mastectomy treatment or breast-conserving therapy. Our study points out the advantages and disadvantages of each covariate adjustment method when applied in competing risk analysis., Comment: 19 pages, 2 figures

Published
2024

2. The risks of risk assessment: causal blind spots when using prediction models for treatment decisions

Author

van Geloven, Nan, Keogh, Ruth H, van Amsterdam, Wouter, Cinà, Giovanni, Krijthe, Jesse H., Peek, Niels, Luijken, Kim, Magliacane, Sara, Morzywołek, Paweł, van Ommen, Thijs, Putter, Hein, Sperrin, Matthew, Wang, Junfeng, Weir, Daniala L., and Didelez, Vanessa

Subjects
Statistics - Methodology
Abstract

Prediction models are increasingly proposed for guiding treatment decisions, but most fail to address the special role of treatments, leading to inappropriate use. This paper highlights the limitations of using standard prediction models for treatment decision support. We identify `causal blind spots' in three common approaches to handling treatments in prediction modelling: including treatment as a predictor, restricting data based on treatment status and ignoring treatments. When predictions are used to inform treatment decisions, confounders, colliders and mediators, as well as changes in treatment protocols over time may lead to misinformed decision-making. We illustrate potential harmful consequences in several medical applications. We advocate for an extension of guidelines for development, reporting and evaluation of prediction models to ensure that the intended use of the model is matched to an appropriate risk estimand. When prediction models are intended to inform treatment decisions, prediction models should specify upfront the treatment decisions they aim to support and target a prediction estimand in line with that goal. This requires a shift towards developing predictions under the specific treatment options under consideration (`predictions under interventions'). Predictions under interventions need causal reasoning and inference techniques during development and validation. We argue that this will improve the efficacy of prediction models in guiding treatment decisions and prevent potential negative effects on patient outcomes.

Published
2024

3. When accurate prediction models yield harmful self-fulfilling prophecies

Author

van Amsterdam, Wouter A. C., van Geloven, Nan, Krijthe, Jesse H., Ranganath, Rajesh, and Ciná, Giovanni

Subjects
Statistics - Methodology, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisions, and are often hailed as the poster children for personalized, data-driven healthcare. We show however, that using prediction models for decision making can lead to harmful decisions, even when the predictions exhibit good discrimination after deployment. These models are harmful self-fulfilling prophecies: their deployment harms a group of patients but the worse outcome of these patients does not invalidate the predictive power of the model. Our main result is a formal characterization of a set of such prediction models. Next we show that models that are well calibrated before and after deployment are useless for decision making as they made no change in the data distribution. These results point to the need to revise standard practices for validation, deployment and evaluation of prediction models that are used in medical decisions.

Published
2023

4. Author Correction: Prospective individual patient data meta-analysis of two randomized trials on convalescent plasma for COVID-19 outpatients

Author

Millat-Martinez, Pere, Gharbharan, Arvind, Alemany, Andrea, Rokx, Casper, Geurtsvankessel, Corine, Papageorgiou, Grigorios, van Geloven, Nan, Jordans, Carlijn, Groeneveld, Geert, Swaneveld, Francis, van der Schoot, Ellen, Corbacho-Monné, Marc, Ouchi, Dan, Piccolo Ferreira, Francini, Malchair, Pierre, Videla, Sebastian, García García, Vanesa, Ruiz-Comellas, Anna, Ramírez-Morros, Anna, Rodriguez Codina, Joana, Amado Simon, Rosa, Grifols, Joan-Ramon, Blanco, Julian, Blanco, Ignacio, Ara, Jordi, Bassat, Quique, Clotet, Bonaventura, Baro, Bàrbara, Troxel, Andrea, Zwaginga, Jaap Jan, Mitjà, Oriol, and Rijnders, Bart J. A.

Published
2024
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5. Prediction under interventions: evaluation of counterfactual performance using longitudinal observational data

Author

Keogh, Ruth H. and van Geloven, Nan

Subjects
Statistics - Methodology
Abstract

Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision making. However, evaluating predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different to those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting which involves creating a validation dataset that mimics the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUCt) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation.

Published
2023

8. Cumulative incidence and risk factors for cutaneous squamous cell carcinoma metastases in organ transplant recipients: The Skin Care in Organ Transplant Patients in Europe-International Transplant Skin Cancer Collaborative metastases study, a prospective multicenter study

Author

de Jong, Estella, Genders, Roel, Harwood, Catherine A., Green, Adèle C., Plasmeijer, Elsemieke I., Proby, Charlotte, Geissler, Edward, Ferrándiz-Pulido, Carla, Ducroux, Emilie, Euvrard, Sylvie, Geusau, Alexandra, Jahn-Bassler, Karin, Borik-Heil, Liliane, Rácz, Emõke, Nägeli, Mirjam, Hofbauer, Günther F.L., Piaserico, Stefano, Russo, Irene, Mackintosh, Lorna, Borges-Costa, João, Angeliki-Gkini, Maria, Zavattaro, Elisa, Savoia, Paola, Imko-Walszuk, Beata, Dębska-Slizień, Alicja, Garmyn, Marjan, van Kelst, Sofie, Ricar, Jan, Cetkovska, Petra, Matin, Rubeta, Güleç, Ayşe Tülin, Seçkin, Deniz, Anene, Chinedu Anthony, Oliveira, Walmar R.P., Rademaker, Marius, Goeman, Jelle, van Geloven, Nan, Ruiz, Emily, Murad, Fadi, Karn, Emily, Schmults, Chrysalyne D., and Bouwes Bavinck, Jan Nico

Published
2024
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9. Time-lag bias induced by unobserved heterogeneity: comparing treated patients to controls with a different start of follow-up

Author

van Eekelen, Rik, Bossuyt, Patrick M. M., and van Geloven, Nan

Subjects
Statistics - Methodology
Abstract

In comparative effectiveness research, treated and control patients might have a different start of follow-up as treatment is often started later in the disease trajectory. This typically occurs when data from treated and controls are not collected within the same source. Only patients who did not yet experience the event of interest whilst in the control condition end up in the treatment data source. In case of unobserved heterogeneity, these treated patients will have a lower average risk than the controls. We illustrate how failing to account for this time-lag between treated and controls leads to bias in the estimated treatment effect. We define estimands and time axes, then explore five methods to adjust for this time-lag bias by utilising the time between diagnosis and treatment initiation in different ways. We conducted a simulation study to evaluate whether these methods reduce the bias and then applied the methods to a comparison between fertility patients treated with insemination and similar but untreated patients. We conclude that time-lag bias can be vast and that the time between diagnosis and treatment initiation should be taken into account in the analysis to respect the chronology of the disease and treatment trajectory.

Published
2021

10. Prediction meets causal inference: the role of treatment in clinical prediction models

Author

van Geloven, Nan, Swanson, Sonja, Ramspek, Chava, Luijken, Kim, van Diepen, Merel, Morris, Tim, Groenwold, Rolf, van Houwelingen, Hans, Putter, Hein, and Cessie, Saskia le

Subjects
Statistics - Methodology
Abstract

In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medicines Agency for clinical trials, we propose a `predictimand' framework of different questions that may be of interest when predicting risk in relation to treatment started after baseline. We provide a formal definition of the estimands matching these questions, give examples of settings in which each is useful and discuss appropriate estimators including their assumptions. We illustrate the impact of the predictimand choice in a dataset of patients with end-stage kidney disease. We argue that clearly defining the estimand is equally important in prediction research as in causal inference., Comment: under review

Published
2020
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12. Outpatient convalescent plasma therapy for high-risk patients with early COVID-19: a randomized placebo-controlled trial

Author

Gharbharan, Arvind, Jordans, Carlijn, Zwaginga, Lisa, Papageorgiou, Grigorios, van Geloven, Nan, van Wijngaarden, Peter, den Hollander, Jan, Karim, Faiz, van Leeuwen-Segarceanu, Elena, Soetekouw, Robert, Lammers, Jolanda, Postma, Douwe, Kampschreur, Linda, Groeneveld, Geert, Swaneveld, Francis, van der Schoot, C. Ellen, Götz, Hannelore, Haagmans, Bart, Koopmans, Marion, Bogers, Susanne, Geurtsvankessel, Corine, Zwaginga, Jaap Jan, Rokx, Casper, and Rijnders, Bart

Published
2023
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13. Risk‐Based Decision Making: Estimands for Sequential Prediction Under Interventions.

Author

Luijken, Kim, Morzywołek, Paweł, van Amsterdam, Wouter, Cinà, Giovanni, Hoogland, Jeroen, Keogh, Ruth, Krijthe, Jesse H., Magliacane, Sara, van Ommen, Thijs, Peek, Niels, Putter, Hein, van Smeden, Maarten, Sperrin, Matthew, Wang, Junfeng, Weir, Daniala L., Didelez, Vanessa, and van Geloven, Nan

Abstract

Prediction models are used among others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: for example, an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred, and reevaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Prospective individual patient data meta-analysis of two randomized trials on convalescent plasma for COVID-19 outpatients

Author

Millat-Martinez, Pere, Gharbharan, Arvind, Alemany, Andrea, Rokx, Casper, Geurtsvankessel, Corine, Papageorgiou, Grigorios, van Geloven, Nan, Jordans, Carlijn, Groeneveld, Geert, Swaneveld, Francis, van der Schoot, Ellen, Corbacho-Monné, Marc, Ouchi, Dan, Piccolo Ferreira, Francini, Malchair, Pierre, Videla, Sebastian, García García, Vanesa, Ruiz-Comellas, Anna, Ramírez-Morros, Anna, Rodriguez Codina, Joana, Amado Simon, Rosa, Grifols, Joan-Ramon, Blanco, Julian, Blanco, Ignacio, Ara, Jordi, Bassat, Quique, Clotet, Bonaventura, Baro, Bàrbara, Troxel, Andrea, Zwaginga, Jaap Jan, Mitjà, Oriol, and Rijnders, Bart J. A.

Published
2022
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15. Efficacy and Safety Analyses of Recombinant Factor VIIa in Severe Post-Partum Hemorrhage

Author

Caram-Deelder, Camila, primary, McKinnon Edwards, Hellen, additional, Zdanowicz, Jarmila A., additional, van den Akker, Thomas, additional, Birkegård, Camilla, additional, Blatný, Jan, additional, van der Bom, Johanna G., additional, Colucci, Giuseppe, additional, van Duuren, Derek, additional, van Geloven, Nan, additional, Henriquez, Dacia D. C. A., additional, Knight, Marian, additional, Korsholm, Lars, additional, Landorph, Andrea, additional, Lavigne Lissalde, Géraldine, additional, McQuilten, Zoe K., additional, Surbek, Daniel, additional, Wellard, Cameron, additional, Wood, Erica M., additional, and Mercier, Frederic J., additional

Published
2024
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20. Cumulative incidence and risk factors for cutaneous squamous-cell carcinoma metastases in organ transplant recipients: the SCOPE-ITSCC metastases study, a prospective multi-center study.

Author

de Jong, Estella, primary, Genders, Roel, additional, Harwood, Catherine A., additional, Green, Adèle C., additional, Plasmeijer, Elsemieke I., additional, Proby, Charlotte, additional, Geissler, Edward, additional, Ferrándiz-Pulido, Carla, additional, Ducroux, Emilie, additional, Euvrard, Sylvie, additional, Geusau, Alexandra, additional, Jahn-Bassler, Karin, additional, Borik-Heil, Liliane, additional, Rácz, Emõke, additional, Nägeli, Mirjam, additional, Hofbauer, Günther FL., additional, Piaserico, Stefano, additional, Russo, Irene, additional, Mackintosh, Lorna, additional, Borges-Costa, João, additional, Angeliki-Gkini, Maria, additional, Zavattaro, Elisa, additional, Savoia, Paola, additional, Imko-Walszuk, Beata, additional, Dębska-Slizień, Alicja, additional, Garmyn, Marjan, additional, van Kelst, Sofie, additional, Ricar, Jan, additional, Cetkovska, Petra, additional, Matin, Rubeta, additional, Güleç, A. Tülin, additional, Seçkin, Deniz, additional, Anene, Chinedu Anthony, additional, Oliveira, Walmar R.P., additional, Rademaker, Marius, additional, Goeman, Jelle, additional, van Geloven, Nan, additional, Ruiz, Emily, additional, Murad, Fadi, additional, Karn, Emily, additional, Schmults, Chrysalyne D., additional, and Bouwes Bavinck, Jan Nico, additional

Published
2024
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24. Hemostatic efficacy of pathogen-inactivated vs untreated platelets: a randomized controlled trial

Author

van der Meer, Pieter F., Ypma, Paula F., van Geloven, Nan, van Hilten, Joost A., van Wordragen-Vlaswinkel, Rinie J., Eissen, Okke, Zwaginga, Jaap J., Trus, Michael, Beckers, Erik A.M., te Boekhorst, Peter, Tinmouth, Alan, Lin, Yulia, Hsia, Cyrus, Lee, David, Norris, Philip J., Goodrich, Raymond P., Brand, Anneke, Hervig, Tor, Heddle, Nancy M., van der Bom, Johanna G., and Kerkhoffs, Jean-Louis H.

Published
2018
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26. Accuracy of Pulse Oximetry Screening for Critical Congenital Heart Defects after Home Birth and Early Postnatal Discharge

Author

van den Akker, E.S.A., van den Berg, E., de Boer, M.A., Bouwmeester, S., van Dorp, M.C., de Graaf, I.M., Haak, M.C., Hermsen, B.B.J., Hummel, P., Kok, B., Medema, T.J., Mérelle, M.E., Mooren, G.C.A.H.C.N., Onland, W., Pernet, P.J., von Rosenstiel, I.A., Schrijver-Levie, N., Six, S., Williams, J.M., Narayen, Ilona C., Blom, Nico A., van Geloven, Nan, Blankman, Ellen I.M., van den Broek, Annique J.M., Bruijn, Martijn, Clur, Sally-Ann B., van den Dungen, Frank A., Havers, Hester M., van Laerhoven, Henriëtte, Mir, Shahryar E., Muller, Moira A., Polak, Odette M., Rammeloo, Lukas A.J., Ramnath, Gracita, van der Schoor, Sophie R.D., van Kaam, Anton H., and te Pas, Arjan B.

Published
2018
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27. Season of sampling and season of birth influence serotonin metabolite levels in human cerebrospinal fluid.

Author

Luykx, Jurjen J, Bakker, Steven C, Lentjes, Eef, Boks, Marco PM, van Geloven, Nan, Eijkemans, Marinus JC, Janson, Esther, Strengman, Eric, de Lepper, Anne M, Westenberg, Herman, Klopper, Kai E, Hoorn, Hendrik J, Gelissen, Harry PMM, Jordan, Julian, Tolenaar, Noortje M, van Dongen, Eric PA, Michel, Bregt, Abramovic, Lucija, Horvath, Steve, Kappen, Teus, Bruins, Peter, Keijzers, Peter, Borgdorff, Paul, Ophoff, Roel A, and Kahn, René S

Subjects
Humans, Serotonin, Anesthesia, Spinal, Models, Statistical, Prospective Studies, Seasons, Parturition, Adult, Female, Male, Elective Surgical Procedures, Anesthesia, Spinal, Surgical Procedures, Elective, Models, Statistical, General Science & Technology
Abstract

BackgroundAnimal studies have revealed seasonal patterns in cerebrospinal fluid (CSF) monoamine (MA) turnover. In humans, no study had systematically assessed seasonal patterns in CSF MA turnover in a large set of healthy adults.Methodology/principal findingsStandardized amounts of CSF were prospectively collected from 223 healthy individuals undergoing spinal anesthesia for minor surgical procedures. The metabolites of serotonin (5-hydroxyindoleacetic acid, 5-HIAA), dopamine (homovanillic acid, HVA) and norepinephrine (3-methoxy-4-hydroxyphenylglycol, MPHG) were measured using high performance liquid chromatography (HPLC). Concentration measurements by sampling and birth dates were modeled using a non-linear quantile cosine function and locally weighted scatterplot smoothing (LOESS, span = 0.75). The cosine model showed a unimodal season of sampling 5-HIAA zenith in April and a nadir in October (p-value of the amplitude of the cosine = 0.00050), with predicted maximum (PC(max)) and minimum (PC(min)) concentrations of 173 and 108 nmol/L, respectively, implying a 60% increase from trough to peak. Season of birth showed a unimodal 5-HIAA zenith in May and a nadir in November (p = 0.00339; PC(max) = 172 and PC(min) = 126). The non-parametric LOESS showed a similar pattern to the cosine in both season of sampling and season of birth models, validating the cosine model. A final model including both sampling and birth months demonstrated that both sampling and birth seasons were independent predictors of 5-HIAA concentrations.ConclusionIn subjects without mental illness, 5-HT turnover shows circannual variation by season of sampling as well as season of birth, with peaks in spring and troughs in fall.

Published
2012

28. Subcutaneous immunoglobulin for maintenance treatment in chronic inflammatory demyelinating polyneuropathy (PATH): a randomised, double-blind, placebo-controlled, phase 3 trial

Author

Sabet, A., George, K., Roberts, L., Carne, R., Blum, S., Henderson, R., Van Damme, P., Demeestere, J., Larue, S., D'Amour, C., Bril, V., Breiner, A., Kunc, P., Valis, M., Sussova, J., Kalous, T., Talab, R., Bednar, M., Toomsoo, T., Rubanovits, I., Gross-Paju, K., Sorro, U., Saarela, M., Auranen, M., Pouget, J., Attarian, S., Le Masson, G., Wielanek-Bachelet, A., Desnuelle, C., Delmont, E., Clavelou, P., Aufauvre, D., Schmidt, J., Zschuentssch, J., Sommer, C., Kramer, D., Hoffmann, O., Goerlitz, C., Haas, J., Chatzopoulos, M., Yoon, R., Gold, R., Berlit, P., Jaspert-Grehl, A., Liebetanz, D., Kutschenko, A., Stangel, M., Trebst, C., Baum, P., Bergh, F., Klehmet, J., Meisel, A., Klostermann, F., Oechtering, J., Lehmann, H., Schroeter, M., Hagenacker, T., Mueller, D., Sperfeld, A., Bethke, F., Drory, V., Algom, A., Yarnitsky, D., Murinson, B., Di Muzio, A., Ciccocioppo, F., Sorbi, S., Mata, S., Schenone, A., Grandis, M., Lauria, G., Cazzato, D., Antonini, G., Morino, S., Cocito, D., Zibetti, M., Yokota, T., Ohkubo, T., Kanda, T., Kawai, M., Kaida, K., Onoue, H., Kuwabara, S., Mori, M., Iijima, M., Ohyama, K., Baba, M., Tomiyama, M., Nishiyama, K., Akutsu, T., Yokoyama, K., Kanai, K., van Schaik, I.N., Eftimov, F., Notermans, N.C., Visser, N., Faber, C., Hoeijmakers, J., Rejdak, K., Chyrchel-Paszkiewicz, U., Casanovas Pons, C., Alberti Aguiló, M., Gamez, J., Figueras, M., Marquez Infante, C., Benitez Rivero, S., Lunn, M., Morrow, J., Gosal, D., Lavin, T., Melamed, I., Testori, A., Ajroud-Driss, S., Menichella, D., Simpson, E., Chi-Ho Lai, E., Dimachkie, M., Barohn, R.J., Beydoun, S., Johl, H., Lange, D., Shtilbans, A., Muley, S., Ladha, S., Freimer, M., Kissel, J., Latov, N., Chin, R., Ubogu, E., Mumfrey, S., Rao, T., MacDonald, P., Sharma, K., Gonzalez, G., Allen, J., Walk, D., Hobson-Webb, L., Gable, K., van Schaik, Ivo N, Bril, Vera, van Geloven, Nan, Hartung, Hans-Peter, Lewis, Richard A, Sobue, Gen, Lawo, John-Philip, Praus, Michaela, Mielke, Orell, Durn, Billie L, Cornblath, David R, and Merkies, Ingemar S J

Published
2018
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30. Prediction Under Interventions: Evaluation of Counterfactual Performance Using Longitudinal Observational Data

Author

Keogh, Ruth H. and Van Geloven, Nan

Abstract

Predictions under interventions are estimates of what a person’s risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision-making. However, evaluating the predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different from those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting that involves creating a validation dataset mimicking the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUCt) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation.

Published
2024
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34. Tell me what you want, what you really really want: Estimands in observational pharmacoepidemiologic comparative effectiveness and safety studies

Author

Luijken, Kim, van Eekelen, Rik, Gardarsdottir, Helga, Groenwold, Rolf H H, van Geloven, Nan, Luijken, Kim, van Eekelen, Rik, Gardarsdottir, Helga, Groenwold, Rolf H H, and van Geloven, Nan

Abstract

PURPOSE: Ideally, the objectives of a pharmacoepidemiologic comparative effectiveness or safety study should dictate its design and data analysis. This paper discusses how defining an estimand is instrumental to this process.METHODS: We applied the ICH-E9 (Statistical Principles for Clinical Trials) R1 addendum on estimands - which originally focused on randomized trials - to three examples of observational pharmacoepidemiologic comparative effectiveness and safety studies. Five key elements specify the estimand: the population, contrasted treatments, endpoint, intercurrent events, and population-level summary measure.RESULTS: Different estimands were defined for case studies representing three types of pharmacological treatments: (1) single-dose treatments using a case study about the effect of influenza vaccination versus no vaccination on mortality risk in an adult population of ≥60 years of age; (2) sustained-treatments using a case study about the effect of dipeptidyl peptidase 4 inhibitor versus glucagon-like peptide-1 agonist on hypoglycemia risk in treatment of uncontrolled diabetes; and (3) as needed treatments using a case study on the effect of nitroglycerin spray as-needed versus no nitroglycerin on syncope risk in treatment of stabile angina pectoris.CONCLUSIONS: The case studies illustrated that a seemingly clear research question can still be open to multiple interpretations. Defining an estimand ensures that the study targets a treatment effect that aligns with the treatment decision the study aims to inform. Estimand definitions further help to inform choices regarding study design and data-analysis and clarify how to interpret study findings.

Published
2023

35. Assessing Performance and Clinical Usefulness in Prediction Models with Survival Outcomes: Practical Guidance for Cox Proportional Hazards Models

Author

Infection & Immunity, Epi Methoden Team 3, McLernon, David J., Giardiello, Daniele, Van Calster, Ben, Wynants, Laure, van Geloven, Nan, van Smeden, Maarten, Therneau, Terry, Steyerberg, Ewout W., Topic Groups 6 and 8 of The STRATOS Initiative, Infection & Immunity, Epi Methoden Team 3, McLernon, David J., Giardiello, Daniele, Van Calster, Ben, Wynants, Laure, van Geloven, Nan, van Smeden, Maarten, Therneau, Terry, Steyerberg, Ewout W., and Topic Groups 6 and 8 of The STRATOS Initiative

Published
2023

36. Allogeneic Serum Eye Drops: A Randomized Clinical Trial to Evaluate the Clinical Effectiveness of Two Drop Sizes

Author

Oogheelkunde Onderzoek, Infection & Immunity, MS Oogheelkunde, Zorgeenheid Oogheelkunde Medisch, Other research (not in main researchprogram), Vermeulen, Christie, van der Burg, Lars L.J., van Geloven, Nan, Eggink, Catharina A., Cheng, Yanny Y.Y., Nuijts, Rudy M.M.A., Wisse, Robert P.L., van Luijk, Chantal M., Nieuwendaal, Carla, Remeijer, Lies, van der Meer, Pieter F., de Korte, Dirk, Klei, Thomas R.L., Oogheelkunde Onderzoek, Infection & Immunity, MS Oogheelkunde, Zorgeenheid Oogheelkunde Medisch, Other research (not in main researchprogram), Vermeulen, Christie, van der Burg, Lars L.J., van Geloven, Nan, Eggink, Catharina A., Cheng, Yanny Y.Y., Nuijts, Rudy M.M.A., Wisse, Robert P.L., van Luijk, Chantal M., Nieuwendaal, Carla, Remeijer, Lies, van der Meer, Pieter F., de Korte, Dirk, and Klei, Thomas R.L.

Published
2023

37. Tell me what you want, what you really really want: Estimands in observational pharmacoepidemiologic comparative effectiveness and safety studies

Author

Afd Pharmacoepi & Clinical Pharmacology, Pharmacoepidemiology and Clinical Pharmacology, PECP - Centre for Pharmacoepidemiology, Luijken, Kim, van Eekelen, Rik, Gardarsdottir, Helga, Groenwold, Rolf H H, van Geloven, Nan, Afd Pharmacoepi & Clinical Pharmacology, Pharmacoepidemiology and Clinical Pharmacology, PECP - Centre for Pharmacoepidemiology, Luijken, Kim, van Eekelen, Rik, Gardarsdottir, Helga, Groenwold, Rolf H H, and van Geloven, Nan

Published
2023

38. Outpatient convalescent plasma therapy for high-risk patients with early COVID-19:a randomized placebo-controlled trial

Author

Gharbharan, Arvind, Jordans, Carlijn, Zwaginga, Lisa, Papageorgiou, Grigorios, van Geloven, Nan, van Wijngaarden, Peter, den Hollander, Jan, Karim, Faiz, van Leeuwen-Segarceanu, Elena, Soetekouw, Robert, Lammers, Jolanda, Postma, Douwe, Kampschreur, Linda, Groeneveld, Geert, Swaneveld, Francis, van der Schoot, C. Ellen, Götz, Hannelore, Haagmans, Bart, Koopmans, Marion, Bogers, Susanne, Geurtsvankessel, Corine, Zwaginga, Jaap Jan, Rokx, Casper, Rijnders, Bart, Gharbharan, Arvind, Jordans, Carlijn, Zwaginga, Lisa, Papageorgiou, Grigorios, van Geloven, Nan, van Wijngaarden, Peter, den Hollander, Jan, Karim, Faiz, van Leeuwen-Segarceanu, Elena, Soetekouw, Robert, Lammers, Jolanda, Postma, Douwe, Kampschreur, Linda, Groeneveld, Geert, Swaneveld, Francis, van der Schoot, C. Ellen, Götz, Hannelore, Haagmans, Bart, Koopmans, Marion, Bogers, Susanne, Geurtsvankessel, Corine, Zwaginga, Jaap Jan, Rokx, Casper, and Rijnders, Bart

Abstract

Objectives: The potential benefit of convalescent plasma (CP) therapy for coronavirus disease 2019 (COVID-19) is highest when administered early after symptom onset. Our objective was to determine the effectiveness of CP therapy in improving the disease course of COVID-19 among high-risk outpatients. Methods: A multicentre, double-blind randomized trial was conducted comparing 300 mL of CP with non-CP. Patients were ≥50 years, were symptomatic for <8 days, had confirmed RT-PCR or antigen test result for COVID-19 and had at least one risk factor for severe COVID-19. The primary endpoint was the highest score on a 5-point ordinal scale ranging from fully recovered (score = 1) or not (score = 2) on day 7, over hospital admission (score = 3), intensive care unit admission (score = 4) and death (score = 5) in the 28 days following randomization. Secondary endpoints were hospital admission, symptom duration and viral RNA excretion. Results: After the enrolment of 421 patients and the transfusion in 416 patients, recruitment was discontinued when the countrywide vaccination uptake in those aged >50 years was 80%. Patients had a median age of 60 years, symptoms for 5 days, and 207 of 416 patients received CP therapy. During the 28 day follow-up, 28 patients were hospitalized and two died. The OR for an improved disease severity score with CP was 0.86 (95% credible interval, 0.59–1.22). The OR was 0.58 (95% CI, 0.33–1.02) for patients with ≤5 days of symptoms. The hazard ratio for hospital admission was 0.61 (95% CI, 0.28–1.34). No difference was found in viral RNA excretion or in the duration of symptoms. Conclusions: In patients with early COVID-19, CP therapy did not improve the 5-point disease severity score.

Published
2023

39. Assessing Performance and Clinical Usefulness in Prediction Models With Survival Outcomes: Practical Guidance for Cox Proportional Hazards Models

Author

McLernon, David J, Giardiello, Daniele, Van Calster, Ben, Wynants, Laure, van Geloven, Nan, van Smeden, Maarten, Therneau, Terry, Steyerberg, Ewout W, topic groups 6 and 8 of the STRATOS Initiative, McLernon, David J, Giardiello, Daniele, Van Calster, Ben, Wynants, Laure, van Geloven, Nan, van Smeden, Maarten, Therneau, Terry, Steyerberg, Ewout W, and topic groups 6 and 8 of the STRATOS Initiative

Abstract

Risk prediction models need thorough validation to assess their performance. Validation of models for survival outcomes poses challenges due to the censoring of observations and the varying time horizon at which predictions can be made. This article describes measures to evaluate predictions and the potential improvement in decision making from survival models based on Cox proportional hazards regression. As a motivating case study, the authors consider the prediction of the composite outcome of recurrence or death (the "event") in patients with breast cancer after surgery. They developed a simple Cox regression model with 3 predictors, as in the Nottingham Prognostic Index, in 2982 women (1275 events over 5 years of follow-up) and externally validated this model in 686 women (285 events over 5 years). Improvement in performance was assessed after the addition of progesterone receptor as a prognostic biomarker. The model predictions can be evaluated across the full range of observed follow-up times or for the event occurring by the end of a fixed time horizon of interest. The authors first discuss recommended statistical measures that evaluate model performance in terms of discrimination, calibration, or overall performance. Further, they evaluate the potential clinical utility of the model to support clinical decision making according to a net benefit measure. They provide SAS and R code to illustrate internal and external validation. The authors recommend the proposed set of performance measures for transparent reporting of the validity of predictions from survival models.

Published
2023

42. Prediction under hypothetical interventions: evaluation of counterfactual performance using longitudinal observational data

Author

Keogh, Ruth H. and van Geloven, Nan

Subjects
Methodology (stat.ME), FOS: Computer and information sciences, Statistics - Methodology
Abstract

Predictions under hypothetical interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision making. However, evaluating predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different to those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual predictive performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting which involves creating a validation dataset that mimics the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUC) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation.

Published
2023

46. Development of the OPAL prediction model for prediction of live birth in couples with recurrent pregnancy loss: protocol for a prospective and retrospective cohort study in the Netherlands

Author

Youssef, Angelos, primary, van der Hoorn, Marie-Louise, additional, van Eekelen, Rik, additional, van Geloven, Nan, additional, van Wely, Madelon, additional, Smits, Myrthe A J, additional, Mulders, Annemarie, additional, van Lith, Jan MM, additional, Goddijn, Mariëtte, additional, and Lashley, Eileen, additional

Published
2022
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47. Postponing Early intrauterine Transfusion with Intravenous immunoglobulin Treatment; the PETIT study on severe hemolytic disease of the fetus and newborn

Author

Zwiers, Carolien, van der Bom, Johanna G., van Kamp, Inge L., van Geloven, Nan, Lopriore, Enrico, Smoleniec, John, Devlieger, Roland, Sim, Pauline E., Ledingham, Marie Anne, Tiblad, Eleonor, Moise, Kenneth J., Jr., Gloning, Karl-Philip, Kilby, Mark D., Overton, Timothy G., Jørgensen, Ditte S., Schou, Katrine V., Paek, Bettina, Walker, Martin, Parry, Emma, Oepkes, Dick, and de Haas, Masja

Published
2018
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50. Timing of Angiography With a Routine Invasive Strategy and Long-Term Outcomes in Non–ST-Segment Elevation Acute Coronary Syndrome: A Collaborative Analysis of Individual Patient Data From the FRISC II (Fragmin and Fast Revascularization During Instability in Coronary Artery Disease), ICTUS (Invasive Versus Conservative Treatment in Unstable Coronary Syndromes), and RITA-3 (Intervention Versus Conservative Treatment Strategy in Patients With Unstable Angina or Non-ST Elevation Myocardial Infarction) Trials

Author

Damman, Peter, van Geloven, Nan, Wallentin, Lars, Lagerqvist, Bo, Fox, Keith A.A., Clayton, Tim, Pocock, Stuart J., Hirsch, Alexander, Windhausen, Fons, Tijssen, Jan G.P., and de Winter, Robbert J.

Published
2012
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