Counterfactual Clinical Prediction Models Could help to Infer Individualised Treatment Effects in Randomised Controlled Trials - an Illustration with the International Stroke Trial.
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[1] Sally Morton,et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration , 2019, Annals of Internal Medicine.
[2] Sally Morton,et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement , 2019, Annals of Internal Medicine.
[3] Ewout W Steyerberg,et al. Models with interactions overestimated heterogeneity of treatment effects and were prone to treatment mistargeting. , 2019, Journal of clinical epidemiology.
[4] J. Jacot,et al. Identifying treatment responders using counterfactual modeling and potential outcomes , 2018, Statistical methods in medical research.
[5] Daniel L. Oberski,et al. Identification of predicted individual treatment effects in randomized clinical trials , 2018, Statistical methods in medical research.
[6] P. Austin,et al. Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models , 2014, Statistical methods in medical research.
[7] Issa J Dahabreh,et al. Risk and treatment effect heterogeneity: re-analysis of individual participant data from 32 large clinical trials. , 2016, International journal of epidemiology.
[8] Richard D Riley,et al. Explicit inclusion of treatment in prognostic modeling was recommended in observational and randomized settings. , 2016, Journal of clinical epidemiology.
[9] Lihui Zhao,et al. A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies. , 2016, Biometrics.
[10] Gary S Collins,et al. Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model , 2016, Statistics in medicine.
[11] J. Spertus,et al. Development and Validation of a Prediction Rule for Benefit and Harm of Dual Antiplatelet Therapy Beyond 1 Year After Percutaneous Coronary Intervention. , 2016, JAMA.
[12] Gary S Collins,et al. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study , 2015, Statistics in medicine.
[13] Yvonne Vergouwe,et al. Estimates of absolute treatment benefit for individual patients required careful modeling of statistical interactions. , 2015, Journal of clinical epidemiology.
[14] G. Collins,et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement , 2015, BMJ : British Medical Journal.
[15] Gary S Collins,et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.
[16] N. Cook,et al. Individualised prediction of alternate-day aspirin treatment effects on the combined risk of cancer, cardiovascular disease and gastrointestinal bleeding in healthy women , 2014, Heart.
[17] C. Adams. Estimating Heterogeneous Treatment Effects in Randomized Control Trials , 2014 .
[18] Holly Janes,et al. Combining biomarkers to optimize patient treatment recommendations , 2014, Biometrics.
[19] Frank L J Visseren,et al. Personalized cardiovascular disease prevention by applying individualized prediction of treatment effects. , 2014, European heart journal.
[20] P. Sandercock,et al. Oral antiplatelet therapy for acute ischaemic stroke. , 2014, The Cochrane database of systematic reviews.
[21] David M. Kent,et al. Using Internally Developed Risk Models to Assess Heterogeneity in Treatment Effects in Clinical Trials , 2014, Circulation. Cardiovascular quality and outcomes.
[22] Ewout W. Steyerberg,et al. Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers , 2013, Statistics in medicine.
[23] C. Sudlow,et al. Epidemiology of stroke and its subtypes in Chinese vs white populations , 2013, Neurology.
[24] M. Wintermark,et al. Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association , 2013, Stroke.
[25] Lu Tian,et al. Effectively Selecting a Target Population for a Future Comparative Study , 2013, Journal of the American Statistical Association.
[26] E. Steyerberg,et al. Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research , 2013, PLoS medicine.
[27] Holly Janes,et al. Assessing Treatment‐Selection Markers using a Potential Outcomes Framework , 2012, Biometrics.
[28] J. M. Taylor,et al. Subgroup identification from randomized clinical trial data , 2011, Statistics in medicine.
[29] Ewout W Steyerberg,et al. Estimating treatment effects for individual patients based on the results of randomised clinical trials , 2011, BMJ : British Medical Journal.
[30] P. Sandercock,et al. The International Stroke Trial database , 2011, Trials.
[31] L. Tian,et al. Analysis of randomized comparative clinical trial data for personalized treatment selections. , 2011, Biostatistics.
[32] A. Alwan. Global status report on noncommunicable diseases 2010. , 2011 .
[33] Judea Pearl,et al. On the Consistency Rule in Causal Inference: Axiom, Definition, Assumption, or Theorem? , 2010, Epidemiology.
[34] David M Kent,et al. Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal , 2010, Trials.
[35] Peter Schlattmann,et al. Medical Applications of Finite Mixture Models , 2009 .
[36] G. Guyatt,et al. Evidence-based medicine targets the individual patient, part 2: guides and tools for individual decision-making , 2008, Evidence-based medicine.
[37] G. Guyatt,et al. Evidence-based practice targets the individual patient. Part 1: how clinicians can use study results to determine optimal individual care , 2008, Evidence-based nursing.
[38] G. Guyatt,et al. Evidence-based medicine targets the individual patient, part 1: how clinicians can use study results to determine optimal individual care , 2008, Evidence-based medicine.
[39] Gary A. Ford,et al. Guidelines for management of ischaemic stroke and transient ischaemic attack 2008. , 2008, Cerebrovascular diseases.
[40] Gordon H Guyatt,et al. GrADe : what is “ quality of evidence ” and why is it important to clinicians ? rATING quALITY of evIDeNCe AND STreNGTH of reCommeNDATIoNS , 2022 .
[41] David M Kent,et al. Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification. , 2007, JAMA.
[42] P. Rothwell. Subgroup analysis in randomised controlled trials: importance, indications, and interpretation , 2005, The Lancet.
[43] P. Rothwell,et al. External validity of randomised controlled trials: “To whom do the results of this trial apply?” , 2005, The Lancet.
[44] Sara T Brookes,et al. Subgroup analyses in randomized trials: risks of subgroup-specific analyses; power and sample size for the interaction test. , 2004, Journal of clinical epidemiology.
[45] Thompson G. Robinson,et al. National Clinical Guidelines for Stroke. , 2001 .
[46] T. Peters,et al. Subgroup analyses in randomised controlled trials: quantifying the risks of false-positives and false-negatives. , 2001, Health technology assessment.
[47] C Warlow,et al. Indications for early aspirin use in acute ischemic stroke : A combined analysis of 40 000 randomized patients from the chinese acute stroke trial and the international stroke trial. On behalf of the CAST and IST collaborative groups. , 2000, Stroke.
[48] Peter Sandercock,et al. The International Stroke Trial (IST): a randomised trial of aspirin, subcutaneous heparin, both, or neither among 19 435 patients with acute ischaemic stroke , 1997, The Lancet.
[49] P. Rothwell,et al. Can overall results of clinical trials be applied to all patients? , 1995, The Lancet.
[50] J. Robins. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .
[51] P. Holland. Statistics and Causal Inference , 1985 .
[52] A. Dawid. Conditional Independence in Statistical Theory , 1979 .
[53] D. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .