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Marlin N, Godolphin PJ, Hooper RL, Riley RD, Rogozińska E. Nonlinear effects and effect modification at the participant-level in IPD meta-analysis part 2: methodological guidance is available. J Clin Epidemiol 2023; 159:319-329. [PMID: 37146657 DOI: 10.1016/j.jclinepi.2023.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/20/2023] [Accepted: 04/26/2023] [Indexed: 05/07/2023]
Abstract
OBJECTIVES To review methodological guidance for nonlinear covariate-outcome associations (NL), and linear effect modification and nonlinear effect modification (LEM and NLEM) at the participant level in individual participant data meta-analyses (IPDMAs) and their power requirements. STUDY DESIGN AND SETTING We searched Medline, Embase, Web of Science, Scopus, PsycINFO and the Cochrane Library to identify methodology publications on IPDMA of LEM, NL or NLEM (PROSPERO CRD42019126768). RESULTS Through screening 6,466 records we identified 54 potential articles of which 23 full texts were relevant. Nine further relevant publications were published before or after the literature search and were added. Of these 32 references, 21 articles considered LEM, 6 articles NL or NLEM and 6 articles described sample size calculations. A book described all four. Sample size may be calculated through simulation or closed form. Assessments of LEM or NLEM at the participant level need to be based on within-trial information alone. Nonlinearity (NL or NLEM) can be modeled using polynomials or splines to avoid categorization. CONCLUSION Detailed methodological guidance on IPDMA of effect modification at participant-level is available. However, methodology papers for sample size and nonlinearity are rarer and may not cover all scenarios. On these aspects, further guidance is needed.
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Affiliation(s)
- Nadine Marlin
- Methodology Research Unit, Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London E1 2AB, UK.
| | - Peter J Godolphin
- MRC Clinical Trials Unit at University College London, Institute of Clinical Trials and Methodology, 90 High Holborn, London WC1V 6LJ, UK
| | - Richard L Hooper
- Methodology Research Unit, Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London E1 2AB, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Ewelina Rogozińska
- MRC Clinical Trials Unit at University College London, Institute of Clinical Trials and Methodology, 90 High Holborn, London WC1V 6LJ, UK
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2
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Lehnert T, Röver C, Köpke S, Rio J, Chard D, Fittipaldo AV, Friede T, Heesen C, Rahn AC. Immunotherapy for people with clinically isolated syndrome or relapsing-remitting multiple sclerosis: treatment response by demographic, clinical, and biomarker subgroups (PROMISE)-a systematic review protocol. Syst Rev 2022; 11:134. [PMID: 35778721 PMCID: PMC9250266 DOI: 10.1186/s13643-022-01997-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 05/28/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Multiple sclerosis (MS) is an inflammatory and degenerative disease of the central nervous system with an increasing worldwide prevalence. Since 1993, more than 15 disease-modifying immunotherapies (DMTs) have been licenced and have shown moderate efficacy in clinical trials. Based on the heterogeneity of the disease and the partial effectiveness of therapies, a personalised medicine approach would be valuable taking individual prognosis and suitability of a chosen therapy into account to gain the best possible treatment effect. The primary objective of this review is to assess the differential treatment effects of all approved DMTs in subgroups of adults with clinically isolated syndrome or relapsing forms of MS. We will analyse possible treatment effect modifiers (TEM) defined by baseline demographic characteristics (gender, age), and diagnostic (i.e. MRI measures) and clinical (i.e. relapses, disability level) measures of MS disease activity. METHODS We will include all published and accessible unpublished primary and secondary analyses of randomised controlled trials (RCTs) with a follow-up of at least 12 months investigating the efficacy of at least one approved DMT, with placebo or other approved DMTs as control intervention(s) in subgroups of trial participants. As the primary outcome, we will address disability as defined by the Expanded Disability Status Scale or multiple sclerosis functional composite scores followed by relapse frequency, quality of life measures, and side effects. MRI data will be analysed as secondary outcomes. MEDLINE, EMBASE, CINAHL, LILACS, CENTRAL and major trial registers will be searched for suitable studies. Titles and abstracts and full texts will be screened by two persons independently using Covidence. The risk of bias will be analysed based on the Cochrane "Risk of Bias 2" tool, and the certainty of evidence will be assessed using GRADE. Treatment effects will be reported as rate ratio or odds ratio. Primary analyses will follow the intention-to-treat principle. Meta-analyses will be carried out using random-effects models. DISCUSSION Given that individual patient data from clinical studies are often not available, the review will allow to analyse the evidence on TEM in MS immunotherapy and thus support clinical decision making in individual cases. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42021279665 .
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Affiliation(s)
- Thomas Lehnert
- Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Christian Röver
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Sascha Köpke
- Institute of Nursing Science, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jordi Rio
- Neurology/Neuroimmunology, Centre d’Esclerosi Multiple de Catalunya (Cemcat), Hospital Universitari Vall d’Hebron, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Declan Chard
- Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre, London, UK
| | - Andrea V. Fittipaldo
- Department of Oncology, Istituto Ricerche Farmacologiche “Mario Negri” IRCCS, Milano, Italy
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Christoph Heesen
- Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Anne C. Rahn
- Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Institute for Social Medicine and Epidemiology, Nursing Research Unit, University of Lübeck, Lübeck, Germany
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Meid AD, Ruff C, Wirbka L, Stoll F, Seidling HM, Groll A, Haefeli WE. Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data. Clin Epidemiol 2020; 12:1223-1234. [PMID: 33173350 PMCID: PMC7646479 DOI: 10.2147/clep.s274466] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 10/08/2020] [Indexed: 01/02/2023] Open
Abstract
When healthcare professionals have the choice between several drug treatments for their patients, they often experience considerable decision uncertainty because many decisions simply have no single “best” choice. The challenges are manifold and include that guideline recommendations focus on randomized controlled trials whose populations do not necessarily correspond to specific patients in everyday treatment. Further reasons may be insufficient evidence on outcomes, lack of direct comparison of distinct options, and the need to individually balance benefits and risks. All these situations will occur in routine care, its outcomes will be mirrored in routine data, and could thus be used to guide decisions. We propose a concept to facilitate decision-making by exploiting this wealth of information. Our working example for illustration assumes that the response to a particular (drug) treatment can substantially differ between individual patients depending on their characteristics (heterogeneous treatment effects, HTE), and that decisions will be more precise if they are based on real-world evidence of HTE considering this information. However, such methods must account for confounding by indication and effect measure modification, eg, by adequately using machine learning methods or parametric regressions to estimate individual responses to pharmacological treatments. The better a model assesses the underlying HTE, the more accurate are predicted probabilities of treatment response. After probabilities for treatment-related benefit and harm have been calculated, decision rules can be applied and patient preferences can be considered to provide individual recommendations. Emulated trials in observational data are a straightforward technique to predict the effects of such decision rules when applied in routine care. Prediction-based decision rules from routine data have the potential to efficiently supplement clinical guidelines and support healthcare professionals in creating personalized treatment plans using decision support tools.
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Affiliation(s)
- Andreas D Meid
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany
| | - Carmen Ruff
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany
| | - Lucas Wirbka
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany
| | - Felicitas Stoll
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany
| | - Hanna M Seidling
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany.,Cooperation Unit Clinical Pharmacy, University of Heidelberg, Heidelberg 69120, Germany
| | - Andreas Groll
- Department of Statistics, TU Dortmund University, Dortmund 44227, Germany
| | - Walter E Haefeli
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany.,Cooperation Unit Clinical Pharmacy, University of Heidelberg, Heidelberg 69120, Germany
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4
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Brunette CA, Miller SJ, Majahalme N, Hau C, MacMullen L, Advani S, Ludin SA, Zimolzak AJ, Vassy JL. Pragmatic Trials in Genomic Medicine: The Integrating Pharmacogenetics In Clinical Care (I-PICC) Study. Clin Transl Sci 2020; 13:381-390. [PMID: 31808996 PMCID: PMC7070795 DOI: 10.1111/cts.12723] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 10/31/2019] [Indexed: 12/19/2022] Open
Abstract
Pragmatic clinical trials (PCTs) have an established presence in clinical research and yet have only recently garnered attention within the landscape of genomic medicine. Using the PRagmatic-Explanatory Continuum Indicator Summary 2 (PRECIS-2) as a framework, this paper illustrates the application of PCT principles to The Integrating Pharmacogenetics In Clinical Care (I-PICC) Study, a trial of pharmacogenetic testing prior to statin initiation for cardiovascular disease prevention in primary care. The trial achieved high engagement with providers (85% enrolled of those approached) and enrolled a representative sample of participants for which statin therapy would be recommended. The I-PICC Study has a high level of pragmatism, which should enhance the generalizability of its findings. The PRECIS-2 may be useful in the design and evaluation of PCTs of genomic medicine interventions, contributing to the generation of evidence that can bridge the gap between genomics innovation and clinical adoption.
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Affiliation(s)
| | | | | | - Cynthia Hau
- VA Boston Healthcare SystemBostonMassachusettsUSA
| | | | | | - Sophie A. Ludin
- VA Boston Healthcare SystemBostonMassachusettsUSA
- Cornell UniversityIthacaNew YorkUSA
| | - Andrew J. Zimolzak
- VA Boston Healthcare SystemBostonMassachusettsUSA
- Baylor College of MedicineHoustonTexasUSA
- Michael E. DeBakey VA Medical CenterHoustonTexasUSA
| | - Jason L. Vassy
- VA Boston Healthcare SystemBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
- Division of General Internal Medicine and Primary CareBrigham and Women's HospitalBostonMassachusettsUSA
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5
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When drug treatments bias genetic studies: Mediation and interaction. PLoS One 2019; 14:e0221209. [PMID: 31461463 PMCID: PMC6713387 DOI: 10.1371/journal.pone.0221209] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 08/01/2019] [Indexed: 11/19/2022] Open
Abstract
Background Increasingly, genetic analyses are conducted using information from subjects with established disease, who often receive concomitant treatment. We determined when treatment may bias genetic associations with a quantitative trait. Methods Graph theory and simulated data were used to explore the impact of drug prescriptions on (longitudinal) genetic effect estimates. Analytic derivations of longitudinal genetic effects are presented, accounting for the following scenarios: 1) treatment allocated independently of a genetic variant, 2) treatment that mediates the genetic effect, 3) treatment that modifies the genetic effect. We additionally evaluate treatment modelling strategies on bias, the root mean squared error (RMSE), coverage, and rejection rate. Results We show that in the absence of treatment by gene effect modification or mediation, genetic effect estimates will be unbiased. In simulated data we found that conditional models accounting for treatment, confounding, and effect modification were generally unbiased with appropriate levels of confidence interval coverage. Ignoring the longitudinal nature of treatment prescription, however (e.g. because of incomplete records in longitudinal data), biased these conditional models to a similar degree (or worse) as simply ignoring treatment. Conclusion The mere presence of (drug) treatment affecting a GWAS phenotype is insufficient to bias genetic associations with quantitative traits. While treatment may bias associations through effect modification and mediation, this might not occur frequently enough to warrant general concern at the presence of treated subjects in GWAS. Should treatment by gene effect modification or mediation be present however, current GWAS approaches attempting to adjust for treatment insufficiently account for the multivariable and longitudinal nature of treatment trajectories and hence genetic estimates may still be biased.
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6
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Prognosis, Effect Modification, and Mediation. Eur Urol 2018; 74:243-245. [PMID: 29908877 DOI: 10.1016/j.eururo.2018.05.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 05/25/2018] [Indexed: 11/20/2022]
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7
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Schmidt AF, Finan C. Linear regression and the normality assumption. J Clin Epidemiol 2017; 98:146-151. [PMID: 29258908 DOI: 10.1016/j.jclinepi.2017.12.006] [Citation(s) in RCA: 204] [Impact Index Per Article: 29.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 12/05/2017] [Accepted: 12/12/2017] [Indexed: 02/06/2023]
Abstract
OBJECTIVES Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. STUDY DESIGN AND SETTING Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. RESULTS Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. CONCLUSION Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations.
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Affiliation(s)
- Amand F Schmidt
- Faculty of Population Health, Institute of Cardiovascular Science, University College London, London WC1E 6BT, United Kingdom; Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands; Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Chris Finan
- Faculty of Population Health, Institute of Cardiovascular Science, University College London, London WC1E 6BT, United Kingdom
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Oude Rengerink K, Kalkman S, Collier S, Ciaglia A, Worsley SD, Lightbourne A, Eckert L, Groenwold RHH, Grobbee DE, Irving EA. Series: Pragmatic trials and real world evidence: Paper 3. Patient selection challenges and consequences. J Clin Epidemiol 2017; 89:173-180. [PMID: 28502808 DOI: 10.1016/j.jclinepi.2016.12.021] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 12/02/2016] [Accepted: 12/12/2016] [Indexed: 12/29/2022]
Abstract
This paper addresses challenges of identifying, enrolling, and retaining participants in a trial conducted within a routine care setting. All patients who are potential candidates for the treatments in routine clinical practice should be considered eligible for a pragmatic trial. To ensure generalizability, the recruited sample should have a similar distribution of the treatment effect modifiers as the target population. In practice, this can be best achieved by including-within the selected sites-all patients without further selection. If relevant heterogeneity between subgroups is expected, increasing the relative proportion of the subgroup of patients in the heterogeneous trial could be considered (oversampling) or a separate trial in this subgroup can be planned. Selection will nevertheless occur. Low enrollment and loss to follow-up can introduce selection and can jeopardize validity as well as generalizability. Pragmatic trials are conducted in clinical practice rather than in a dedicated research setting, which could reduce recruitment rates. However, if a trial poses a minimal burden to the physician and the patient and routine clinical practice is maximally adhered to, the participation rate may be high and loss to follow-up will not be a specific problem for pragmatic trials.
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Affiliation(s)
- Katrien Oude Rengerink
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, Utrecht 3584 CG, The Netherlands.
| | - Shona Kalkman
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, Utrecht 3584 CG, The Netherlands
| | - Susan Collier
- RD Respiratory Fibrosis DPU Clinical Development Pharma Research and Development GSK Stockley Park West, 1-3 Ironbridge Road, Uxbridge, Middlesex UB11 1BT, UK
| | - Antonio Ciaglia
- International Alliance of Patients' Organizations, 49-51 East Road, London N1 6AH, UK
| | - Sally D Worsley
- Real World Study Delivery, GSK Research & Development, Gunnels Wood Rd, Stevenage SG1 2NY, UK
| | - Alison Lightbourne
- International Alliance of Patients' Organizations, 49-51 East Road, London N1 6AH, UK
| | - Laurent Eckert
- Health Economics and Outcome Research, Sanofi Global Maket Access Center of Excellence, Chilly-Mazarin, France
| | - Rolf H H Groenwold
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, Utrecht 3584 CG, The Netherlands
| | - Diederick E Grobbee
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, Utrecht 3584 CG, The Netherlands; Julius Clinical, Broederplein 41-43, Zeist 3703 CD, The Netherlands
| | - Elaine A Irving
- Real World Study Delivery, GSK Research & Development, Gunnels Wood Rd, Stevenage SG1 2NY, UK
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Schmidt AF, Pearce LS, Wilkins JT, Overington JP, Hingorani AD, Casas JP. PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease. Cochrane Database Syst Rev 2017; 4:CD011748. [PMID: 28453187 PMCID: PMC6478267 DOI: 10.1002/14651858.cd011748.pub2] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Despite the availability of effective drug therapies that reduce low-density lipoprotein (LDL)-cholesterol (LDL-C), cardiovascular disease (CVD) remains an important cause of mortality and morbidity. Therefore, additional LDL-C reduction may be warranted, especially for patients who are unresponsive to, or unable to take, existing LDL-C-reducing therapies. By inhibiting the proprotein convertase subtilisin/kexin type 9 (PCSK9) enzyme, monoclonal antibodies (PCSK9 inhibitors) may further reduce LDL-C, potentially reducing CVD risk as well. OBJECTIVES Primary To quantify short-term (24 weeks), medium-term (one year), and long-term (five years) effects of PCSK9 inhibitors on lipid parameters and on the incidence of CVD. Secondary To quantify the safety of PCSK9 inhibitors, with specific focus on the incidence of type 2 diabetes, cognitive function, and cancer. Additionally, to determine if specific patient subgroups were more or less likely to benefit from the use of PCSK9 inhibitors. SEARCH METHODS We identified studies by systematically searching the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and Web of Science. We also searched Clinicaltrials.gov and the International Clinical Trials Registry Platform and screened the reference lists of included studies. We identified the studies included in this review through electronic literature searches conducted up to May 2016, and added three large trials published in March 2017. SELECTION CRITERIA All parallel-group and factorial randomised controlled trials (RCTs) with a follow-up time of at least 24 weeks were eligible. DATA COLLECTION AND ANALYSIS Two review authors independently reviewed and extracted data. When data were available, we calculated pooled effect estimates. MAIN RESULTS We included 20 studies with data on 67,237 participants (median age 61 years; range 52 to 64 years). Twelve trials randomised participants to alirocumab, three trials to bococizumab, one to RG7652, and four to evolocumab. Owing to the small number of trials using agents other than alirocumab, we did not differentiate between types of PCSK9 inhibitors used. We compared PCSK9 inhibitors with placebo (thirteen RCTs), ezetimibe (two RCTs) or ezetimibe and statins (five RCTs).Compared with placebo, PCSK9 inhibitors decreased LDL-C by 53.86% (95% confidence interval (CI) 58.64 to 49.08; eight studies; 4782 participants; GRADE: moderate) at 24 weeks; compared with ezetimibe, PCSK9 inhibitors decreased LDL-C by 30.20% (95% CI 34.18 to 26.23; two studies; 823 participants; GRADE: moderate), and compared with ezetimibe and statins, PCSK9 inhibitors decreased LDL-C by 39.20% (95% CI 56.15 to 22.26; five studies; 5376 participants; GRADE: moderate).Compared with placebo, PCSK9 inhibitors decreased the risk of CVD events, with a risk difference (RD) of 0.91% (odds ratio (OR) of 0.86, 95% CI 0.80 to 0.92; eight studies; 59,294 participants; GRADE: moderate). Compared with ezetimibe and statins, PCSK9 inhibitors appeared to have a stronger protective effect on CVD risk, although with considerable uncertainty (RD 1.06%, OR 0.45, 95% CI 0.27 to 0.75; three studies; 4770 participants; GRADE: very low). No data were available for the ezetimibe only comparison. Compared with placebo, PCSK9 probably had little or no effect on mortality (RD 0.03%, OR 1.02, 95% CI 0.91 to 1.14; 12 studies; 60,684 participants; GRADE: moderate). Compared with placebo, PCSK9 inhibitors increased the risk of any adverse events (RD 1.54%, OR 1.08, 95% CI 1.04 to 1.12; 13 studies; 54,204 participants; GRADE: low). Similar effects were observed for the comparison of ezetimibe and statins: RD 3.70%, OR 1.18, 95% CI 1.05 to 1.34; four studies; 5376 participants; GRADE: low. Clinical event data were unavailable for the ezetimibe only comparison. AUTHORS' CONCLUSIONS Over short-term to medium-term follow-up, PCSK9 inhibitors reduced LDL-C. Studies with medium-term follow-up time (longest median follow-up recorded was 26 months) reported that PCSK9 inhibitors (compared with placebo) decreased CVD risk but may have increased the risk of any adverse events (driven by SPIRE-1 and -2 trials). Available evidence suggests that PCSK9 inhibitor use probably leads to little or no difference in mortality. Evidence on relative efficacy and safety when PCSK9 inhibitors were compared with active treatments was of low to very low quality (GRADE); follow-up times were short and events were few. Large trials with longer follow-up are needed to evaluate PCSK9 inhibitors versus active treatments as well as placebo. Owing to the predominant inclusion of high-risk patients in these studies, applicability of results to primary prevention is limited. Finally, estimated risk differences indicate that PCSK9 inhibitors only modestly change absolute risks (often to less than 1%).
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Affiliation(s)
- Amand F Schmidt
- University College LondonInstitute of Cardiovascular Science222 Euston Road, Room 206LondonUKNW1 2DA
| | - Lucy S Pearce
- London School of Hygiene & Tropical MedicineDepartment of Non‐communicable Disease EpidemiologyKeppel StreetLondonUKWC1 E7HT
| | - John T Wilkins
- Northwestern University Feinberg School of MedicineThe Department of Medicine (Cardiology) and the Department of Preventive MedicineSuite 1400 680 N. Lakeshore DriveChicagoUSA60611
| | | | - Aroon D Hingorani
- University College LondonInstitute of Cardiovascular Science222 Euston Road, Room 206LondonUKNW1 2DA
| | - Juan P Casas
- University College LondonFarr Institute of Health Informatics Research222 Euston RoadLondonUKNW1 2DA
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10
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Schmidt AF, Groenwold R. Adjusting for bias in unblinded randomized controlled trials. Stat Methods Med Res 2016; 27:2413-2427. [PMID: 27932664 DOI: 10.1177/0962280216680652] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
It may not always be possible to blind participants of a randomized controlled trial for treatment allocation. As a result, estimators of the actual treatment effect may be biased. In this paper, we will extend a novel method, originally introduced in genetic research, for instrumental variable meta-analysis, adjusting for bias due to unblinding of trial participants. Using simulation studies, this novel method, "Egger Correction for non-Adherence", is introduced and compared to the performance of the "intention-to-treat," "as-treated," and conventional "instrumental variable" estimators. Scenarios considered (time-varying) non-adherence, confounding, and between-study heterogeneity. The effect of treatment on a binary endpoint was quantified by means of a risk difference. In all scenarios with unblinded treatment allocation, the Egger Correction for non-Adherence method was the least biased estimator. However, unless the variation in adherence was relatively large, precision was lacking, and power did not surpass 0.50. As a comparison, in a meta-analysis of blinded randomized controlled trials, power of the conventional IV estimator was 1.00 versus at most 0.14 for the Egger Correction for non-Adherence estimator. Due to this lack of precision and power, we suggest to use this method mainly as a sensitivity analysis.
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Affiliation(s)
- A F Schmidt
- 1 Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
| | - Rhh Groenwold
- 2 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
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Schmidt AF, Nielen M, Withrow SJ, Selmic LE, Burton JH, Klungel OH, Groenwold RHH, Kirpensteijn J. Chemotherapy effectiveness and mortality prediction in surgically treated osteosarcoma dogs: A validation study. Prev Vet Med 2016; 125:126-34. [PMID: 26827107 DOI: 10.1016/j.prevetmed.2016.01.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Revised: 12/17/2015] [Accepted: 01/03/2016] [Indexed: 11/29/2022]
Abstract
Canine osteosarcoma is the most common bone cancer, and an important cause of mortality and morbidity, in large purebred dogs. Previously we constructed two multivariable models to predict a dog's 5-month or 1-year mortality risk after surgical treatment for osteosarcoma. According to the 5-month model, dogs with a relatively low risk of 5-month mortality benefited most from additional chemotherapy treatment. In the present study, we externally validated these results using an independent cohort study of 794 dogs. External performance of our prediction models showed some disagreement between observed and predicted risk, mean difference: -0.11 (95% confidence interval [95% CI]-0.29; 0.08) for 5-month risk and 0.25 (95%CI 0.10; 0.40) for 1-year mortality risk. After updating the intercept, agreement improved: -0.0004 (95%CI-0.16; 0.16) and -0.002 (95%CI-0.15; 0.15). The chemotherapy by predicted mortality risk interaction (P-value=0.01) showed that the chemotherapy compared to no chemotherapy effectiveness was modified by 5-month mortality risk: dogs with a relatively lower risk of mortality benefited most from additional chemotherapy. Chemotherapy effectiveness on 1-year mortality was not significantly modified by predicted risk (P-value=0.28). In conclusion, this external validation study confirmed that our multivariable risk prediction models can predict a patient's mortality risk and that dogs with a relatively lower risk of 5-month mortality seem to benefit most from chemotherapy.
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Affiliation(s)
- A F Schmidt
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, P.O. Box 85500, 3508 GA Utrecht, The Netherlands; Division of Pharmacoepidemiology and Clinicl Pharmacology, Utrecht Institute for Pharmaceutical Sciences, P.O. Box 80082, 3508 TB Utrecht, The Netherlands; Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 7, 3584 CL Utrecht, The Netherlands; Institute of Cardiovascular Science, Faculty of Population Health, University College London, London WC1E 6BT, UK.
| | - M Nielen
- Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 7, 3584 CL Utrecht, The Netherlands
| | - S J Withrow
- Flint Animal Cancer Center, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA
| | - L E Selmic
- Flint Animal Cancer Center, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA
| | - J H Burton
- Flint Animal Cancer Center, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA; University of California, Davis, Department of Surgical and Radiological Sciences, School of Veterinary Medicine, Davis, CA, USA
| | - O H Klungel
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, P.O. Box 85500, 3508 GA Utrecht, The Netherlands; Division of Pharmacoepidemiology and Clinicl Pharmacology, Utrecht Institute for Pharmaceutical Sciences, P.O. Box 80082, 3508 TB Utrecht, The Netherlands
| | - R H H Groenwold
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, P.O. Box 85500, 3508 GA Utrecht, The Netherlands; Division of Pharmacoepidemiology and Clinicl Pharmacology, Utrecht Institute for Pharmaceutical Sciences, P.O. Box 80082, 3508 TB Utrecht, The Netherlands
| | - J Kirpensteijn
- Department of Clinical Sciences of Companion Animals, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 8, Utrecht 3584 CM, The Netherlands
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