Prespecification of subgroup analyses and examination of treatment-subgroup interactions in cancer individual participant data meta-analyses are suboptimal

Veroniki AA Straus SE Soobiah C Elliott MJ Tricco AC.

A scoping review of indirect comparison methods and applications using individual patient data.

BMC medical research methodology. 16: 47Simmonds M Stewart G Stewart L.

A decade of individual participant data meta-analyses: A review of current practice.

Contemporary clinical trials. 45: 76-83

To IPD or not to IPD? Advantages and disadvantages of systematic reviews using individual patient data.

Evaluation & the health professions. 25: 76-97Gao Y Shi S Li M Luo X Liu M Yang K et al.

Statistical analyses and quality of individual participant data network meta-analyses were suboptimal: a cross-sectional study.

BMC medicine. 18: 120Lambert PC Sutton AJ Abrams KR Jones DR.

A comparison of summary patient-level covariates in meta-regression with individual patient data meta-analysis.

Journal of clinical epidemiology. 55: 86-94Nevitt SJ Marson AG Davie B Reynolds S Williams L Smith CT

Exploring changes over time and characteristics associated with data retrieval across individual participant data meta-analyses: systematic review.

BMJ (Clinical research ed). 357: j1390Belias M Rovers MM Reitsma JB Debray TPA IntHout J.

Statistical approaches to identify subgroups in meta-analysis of individual participant data: a simulation study.

BMC medical research methodology. 19: 183Koopman L van der Heijden GJ Hoes AW Grobbee DE Rovers MM.

Empirical comparison of subgroup effects in conventional and individual patient data meta-analyses.

International journal of technology assessment in health care. 24: 358-361Riley RD Debray TPA Fisher D Hattle M Marlin N Hoogland J et al.

Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planning.

Statistics in medicine. 39: 2115-2137Tudur Smith C Marcucci M Nolan SJ Iorio A Sudell M Riley R et al.

Individual participant data meta-analyses compared with meta-analyses based on aggregate data.

The Cochrane database of systematic reviews. 9Mr000007D'Amico R Pifferi S Leonetti C Torri V Tinazzi A Liberati A.

Effectiveness of antibiotic prophylaxis in critically ill adult patients: systematic review of randomised controlled trials.

BMJ (Clinical research ed). 316: 1275-1285Riley RD Lambert PC Abo-Zaid G.

Meta-analysis of individual participant data: rationale, conduct, and reporting.

BMJ (Clinical research ed). 340: c221Sun X Briel M Walter SD Guyatt GH

Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses.

BMJ (Clinical research ed). 340: c117Kasenda B Schandelmaier S Sun X von Elm E You J Blumle A et al.

Subgroup analyses in randomised controlled trials: cohort study on trial protocols and journal publications.

BMJ (Clinical research ed). 349: g4539Kent DM van Klaveren D Paulus JK D'Agostino R Goodman S Hayward R et al.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration.

Annals of internal medicine. 172: W1-W25

Interpretation of subgroup analyses in randomized trials: heterogeneity versus secondary interventions.

Annals of internal medicine. 154: 680-683

Treating individuals 2. Subgroup analysis in randomised controlled trials: importance, indications, and interpretation.

Lancet (London, England). 365: 176-186

Cumulative subgroup analysis to reduce waste in clinical research for individualised medicine.

BMC medicine. 14: 197Schuit E Li AH Ioannidis JPA.

How often can meta-analyses of individual-level data individualize treatment? A meta-epidemiologic study.

International journal of epidemiology. 48: 596-608Sun X Briel M Busse JW You JJ Akl EA Mejza F et al.

The influence of study characteristics on reporting of subgroup analyses in randomised controlled trials: systematic review.

BMJ (Clinical research ed). 342: d1569Fan J Song F Bachmann MO.

Justification and reporting of subgroup analyses were lacking or inadequate in randomized controlled trials.

Journal of clinical epidemiology. 108: 17-25Pocock SJ Assmann SE Enos LE Kasten LE.

Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems.

Statistics in medicine. 21: 2917-2930Kasenda B Schandelmaier S Sun X von Elm E You J Blümle A et al.

Subgroup analyses in randomised controlled trials: cohort study on trial protocols and journal publications.

BMJ (Clinical research ed). 349: g4539Koopman L van der Heijden GJ Glasziou PP Grobbee DE Rovers MM.

A systematic review of analytical methods used to study subgroups in (individual patient data) meta-analyses.

Journal of clinical epidemiology. 60: 1002-1009Fisher DJ Copas AJ Tierney JF Parmar MK.

A critical review of methods for the assessment of patient-level interactions in individual participant data meta-analysis of randomized trials, and guidance for practitioners.

Journal of clinical epidemiology. 64: 949-967GBD 2017 Mortality Collaborators

Global, regional, and national age-sex-specific mortality and life expectancy, 1950-2017: a systematic analysis for the Global Burden of Disease Study 2017.

Lancet (London, England). 392: 1684-1735Gargon E Williamson PR Blazeby JM Kirkham JJ.

Improvement was needed in the standards of development for cancer core outcome sets.

Journal of clinical epidemiology. 112: 36-44Kent DM Paulus JK van Klaveren D D'Agostino R Goodman S Hayward R et al.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement.

Annals of internal medicine. 172: 35-45Schuit E Roes KC Mol BW Kwee A Moons KG Groenwold RH.

Meta-analyses triggered by previous (false-)significant findings: problems and solutions.

Systematic reviews. 4: 57Sun X Ioannidis JP Agoritsas T Alba AC Guyatt G.

How to use a subgroup analysis: users' guide to the medical literature.

Jama. 311: 405-411Schandelmaier S Briel M Varadhan R Schmid CH Devasenapathy N Hayward RA et al.

Development of the Instrument to assess the Credibility of Effect Modification Analyses (ICEMAN) in randomized controlled trials and meta-analyses.

CMAJ: Canadian Medical Association journal = journal de l'Association medicale canadienne. 192: E901-E9e6Kelly SE Moher D Clifford TJ.

Quality of conduct and reporting in rapid reviews: an exploration of compliance with PRISMA and AMSTAR guidelines.

Systematic reviews. 5: 79Koensgen N Rombey T Allers K Mathes T Hoffmann F Pieper D.

Comparison of non-Cochrane systematic reviews and their published protocols: differences occurred frequently but were seldom explained.

Journal of clinical epidemiology. 110: 34-41Page MJ McKenzie JE Forbes A.

Many scenarios exist for selective inclusion and reporting of results in randomized trials and systematic reviews.

Journal of clinical epidemiology. 66: 524-537Gao Y Cai Y Yang K Liu M Shi S Chen J et al.

Methodological and reporting quality in non-Cochrane systematic review updates could be improved: a comparative study.

Journal of clinical epidemiology. 119: 36-46Allers K Hoffmann F Mathes T Pieper D.

Systematic reviews with published protocols compared to those without: more effort, older search.

Journal of clinical epidemiology. 95: 102-110Varadhan R Segal JB Boyd CM Wu AW Weiss CO.

A framework for the analysis of heterogeneity of treatment effect in patient-centered outcomes research.

Journal of clinical epidemiology. 66: 818-825Liu P Ioannidis JPA Ross JS Dhruva SS Luxkaranayagam AT Vasiliou V et al.

Age-treatment subgroup analyses in Cochrane intervention reviews: a meta-epidemiological study.

BMC medicine. 17: 188Gao Y Yang K Cai Y Shi S Liu M Zhang J et al.

Updating systematic reviews can improve the precision of outcomes: a comparative study.

Journal of clinical epidemiology. 125: 108-119Hannink G Gooszen HG van Laarhoven CJ Rovers MM.

A systematic review of individual patient data meta-analyses on surgical interventions.

Systematic reviews. 2: 52Mistry D Stallard N Underwood M.

A recursive partitioning approach for subgroup identification in individual patient data meta-analysis.

Statistics in medicine. 37: 1550-1561Stewart LA Clarke M Rovers M Riley RD Simmonds M Stewart G et al.

Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: the PRISMA-IPD Statement.

Jama. 313: 1657-1665Shea BJ Reeves BC Wells G Thuku M Hamel C Moran J et al.

AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both.

BMJ (Clinical research ed). 358: j4008Berlin JA Santanna J Schmid CH Szczech LA Feldman HI

Anti-Lymphocyte Antibody Induction Therapy Study G. Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head.

Statistics in medicine. 21: 371-387Riley RD Lambert PC Staessen JA Wang J Gueyffier F Thijs L et al.

Meta-analysis of continuous outcomes combining individual patient data and aggregate data.

Statistics in medicine. 27: 1870-1893Fisher DJ Carpenter JR Morris TP Freeman SC Tierney JF.

Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach?.

BMJ (Clinical research ed). 356: j573Royston P Ambler G Sauerbrei W.

The use of fractional polynomials to model continuous risk variables in epidemiology.

International journal of epidemiology. 28: 964-974Royston P Altman DG Sauerbrei W.

Dichotomizing continuous predictors in multiple regression: a bad idea.

Statistics in medicine. 25: 127-141

Statistics notes - The cost of dichotomising continuous variables.

BMJ (Clinical research ed). 332: 1080

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