Counterfactual Clinical Prediction Models Could help to Infer Individualised Treatment Effects in Randomised Controlled Trials - an Illustration with the International Stroke Trial.

[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 .