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Blanes-Vidal V, Tashk A, Cantuaria ML, Hansen RS, Poulsen CA, Poulsen MH, Krogh ML, Sheikh SP, Nadimi ES. Epidemiological description and trajectories of patients with prostate cancer in Denmark: an observational study of 7448 patients. BMC Res Notes 2023; 16:341. [PMID: 37974202 PMCID: PMC10655388 DOI: 10.1186/s13104-023-06599-2] [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: 03/02/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
OBJECTIVE Identification of patients at high risk of aggressive prostate cancer is a major clinical challenge. With the view of developing artificial intelligence-based methods for identification of these patients, we are constructing a comprehensive clinical database including 7448 prostate cancer (PCa) Danish patients. In this paper we provide an epidemiological description and patients' trajectories of this retrospective observational population, to contribute to the understanding of the characteristics and pathways of PCa patients in Denmark. RESULTS Individuals receiving a PCa diagnosis during 2008-2014 in Region Southern Denmark were identified, and all diagnoses, operations, investigations, and biochemistry analyses, from 4 years prior, to 5 years after PCa diagnosis were obtained. About 85.1% were not diagnosed with metastatic PCa during the study period (unaggressive PCa); 9.2% were simultaneously diagnosed with PCa and metastasis (aggressive-advanced PCa), while 5.7% were not diagnosed with metastatic PCa at first, but they were diagnosed with metastasis at some point during the 5 years follow-up (aggressive-not advanced PCa). Patients with unaggressive PCa had more clinical investigations directly related to PCa detection (prostate ultrasounds and biopsies) during the 4 years prior to PCa diagnosis, compared to patients with aggressive PCa, which may have contributed to the early detection of PCa.
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Affiliation(s)
- Victoria Blanes-Vidal
- Applied AI and Data Science Unit, The Maersk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark.
- Danish Centre for Clinical Artificial Intelligence (CAI-X), University of Southern Denmark, Odense University Hospital, Odense, Denmark.
| | - Ashkan Tashk
- Applied AI and Data Science Unit, The Maersk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark
| | - Manuella Lech Cantuaria
- Applied AI and Data Science Unit, The Maersk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark
| | - Rasmus Søgaard Hansen
- Clinical Biochemistry Unit, Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | | | - Mads H Poulsen
- Department of Urology, Odense University Hospital, Odense, Denmark
- Department of Urology, Hospital South West Jutland, Esbjerg, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | | | - Søren P Sheikh
- Blue-Cell Therapeutics, Ole Maaloes Vej 3, Copenhagen, Denmark
| | - Esmaeil S Nadimi
- Danish Centre for Clinical Artificial Intelligence (CAI-X), University of Southern Denmark, Odense University Hospital, Odense, Denmark
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Dhiman P, Ma J, Qi C, Bullock G, Sergeant JC, Riley RD, Collins GS. Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review. BMC Med Res Methodol 2023; 23:188. [PMID: 37598153 PMCID: PMC10439652 DOI: 10.1186/s12874-023-02008-1] [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: 05/23/2023] [Accepted: 08/04/2023] [Indexed: 08/21/2023] Open
Abstract
BACKGROUND Having an appropriate sample size is important when developing a clinical prediction model. We aimed to review how sample size is considered in studies developing a prediction model for a binary outcome. METHODS We searched PubMed for studies published between 01/07/2020 and 30/07/2020 and reviewed the sample size calculations used to develop the prediction models. Using the available information, we calculated the minimum sample size that would be needed to estimate overall risk and minimise overfitting in each study and summarised the difference between the calculated and used sample size. RESULTS A total of 119 studies were included, of which nine studies provided sample size justification (8%). The recommended minimum sample size could be calculated for 94 studies: 73% (95% CI: 63-82%) used sample sizes lower than required to estimate overall risk and minimise overfitting including 26% studies that used sample sizes lower than required to estimate overall risk only. A similar number of studies did not meet the ≥ 10EPV criteria (75%, 95% CI: 66-84%). The median deficit of the number of events used to develop a model was 75 [IQR: 234 lower to 7 higher]) which reduced to 63 if the total available data (before any data splitting) was used [IQR:225 lower to 7 higher]. Studies that met the minimum required sample size had a median c-statistic of 0.84 (IQR:0.80 to 0.9) and studies where the minimum sample size was not met had a median c-statistic of 0.83 (IQR: 0.75 to 0.9). Studies that met the ≥ 10 EPP criteria had a median c-statistic of 0.80 (IQR: 0.73 to 0.84). CONCLUSIONS Prediction models are often developed with no sample size calculation, as a consequence many are too small to precisely estimate the overall risk. We encourage researchers to justify, perform and report sample size calculations when developing a prediction model.
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Affiliation(s)
- Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Cathy Qi
- Population Data Science, Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea University, Singleton Park, Swansea, SA2 8PP, UK
| | - Garrett Bullock
- Department of Orthopaedic Surgery, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, UK
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PT, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, B15 2TT, Birmingham, UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
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Naye F, Décary S, Houle C, LeBlanc A, Cook C, Dugas M, Skidmore B, Tousignant-Laflamme Y. Six Externally Validated Prognostic Models Have Potential Clinical Value to Predict Patient Health Outcomes in the Rehabilitation of Musculoskeletal Conditions: A Systematic Review. Phys Ther 2023; 103:7066982. [PMID: 37245218 DOI: 10.1093/ptj/pzad021] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/21/2022] [Accepted: 01/06/2023] [Indexed: 05/30/2023]
Abstract
OBJECTIVE The purpose of this systematic review was to identify and appraise externally validated prognostic models to predict a patient's health outcomes relevant to physical rehabilitation of musculoskeletal (MSK) conditions. METHODS We systematically reviewed 8 databases and reported our findings according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020. An information specialist designed a search strategy to identify externally validated prognostic models for MSK conditions. Paired reviewers independently screened the title, abstract, and full text and conducted data extraction. We extracted characteristics of included studies (eg, country and study design), prognostic models (eg, performance measures and type of model) and predicted clinical outcomes (eg, pain and disability). We assessed the risk of bias and concerns of applicability using the prediction model risk of bias assessment tool. We proposed and used a 5-step method to determine which prognostic models were clinically valuable. RESULTS We found 4896 citations, read 300 full-text articles, and included 46 papers (37 distinct models). Prognostic models were externally validated for the spine, upper limb, lower limb conditions, and MSK trauma, injuries, and pain. All studies presented a high risk of bias. Half of the models showed low concerns for applicability. Reporting of calibration and discrimination performance measures was often lacking. We found 6 externally validated models with adequate measures, which could be deemed clinically valuable [ie, (1) STart Back Screening Tool, (2) Wallis Occupational Rehabilitation RisK model, (3) Da Silva model, (4) PICKUP model, (5) Schellingerhout rule, and (6) Keene model]. Despite having a high risk of bias, which is mostly explained by the very conservative properties of the PROBAST tool, the 6 models remain clinically relevant. CONCLUSION We found 6 externally validated prognostic models developed to predict patients' health outcomes that were clinically relevant to the physical rehabilitation of MSK conditions. IMPACT Our results provide clinicians with externally validated prognostic models to help them better predict patients' clinical outcomes and facilitate personalized treatment plans. Incorporating clinically valuable prognostic models could inherently improve the value of care provided by physical therapists.
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Affiliation(s)
- Florian Naye
- School of Rehabilitation, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Clinical Research of the Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, Quebec, Canada
| | - Simon Décary
- Department of Family Medicine and Emergency Medicine, Pavillon Ferdinand-Vandry, Université Laval, Quebec, Quebec, Canada
- Tier 1 Canada Research Chair in Shared Decision Making and Knowledge Translation, Centre de recherche sur les soins et les services de première ligne de l'Université Laval (CERSSPL-UL), Quebec, Quebec, Canada
| | - Catherine Houle
- School of Rehabilitation, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Clinical Research of the Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, Quebec, Canada
| | - Annie LeBlanc
- Department of Family Medicine and Emergency Medicine, Pavillon Ferdinand-Vandry, Université Laval, Quebec, Quebec, Canada
| | - Chad Cook
- Physical Therapy Division, Duke University, Durham, North Carolina, USA
| | - Michèle Dugas
- VITAM Research Center, Centre Intégré Universitaire de Santé et de Services Sociaux de la Capitale-Nationale, Quebec, Quebec, Canada
| | - Becky Skidmore
- Independent Information Specialist, Ottawa, Ontario, Canada
| | - Yannick Tousignant-Laflamme
- School of Rehabilitation, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Clinical Research of the Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, Quebec, Canada
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Powel JE, Zantow EW, Bialko MF, Farley LG, Lawlor ML, Mullan SJ, Vricella LK, Tomlinson TM. Predictive index for adverse perinatal outcome in pregnancies complicated by fetal growth restriction. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 61:367-376. [PMID: 36856169 DOI: 10.1002/uog.26044] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 07/01/2022] [Accepted: 07/25/2022] [Indexed: 06/18/2023]
Abstract
OBJECTIVES To develop and validate an index predictive of adverse perinatal outcome (APO) in pregnancies meeting the consensus-based criteria for fetal growth restriction (FGR) endorsed by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG). METHODS This was a retrospective analysis of consecutive singleton non-anomalous gestations meeting the ISUOG-endorsed criteria for FGR at a single tertiary care center from November 2010 to August 2020. The dataset was divided randomly into a development set (two-thirds) and a validation set (one-third). The primary composite APO comprised one or more of: perinatal demise, Grade III-IV intraventricular hemorrhage (IVH), periventricular leukomalacia (PVL), seizures, hypoxic ischemic encephalopathy (HIE), necrotizing enterocolitis (NEC), sepsis, bronchopulmonary dysplasia (BPD) and length of stay in the neonatal intensive care unit (NICU) > 7 days. Regression analysis incorporated clinical factors readily available at the time of FGR diagnosis. The sum of β coefficient-based weights yielded an index score, the performance of which was assessed in the validation set. Score cut-offs were selected to identify 'high-risk' and 'low-risk' ranges for which positive (PPV) and negative (NPV) predictive values and positive (LR+) and negative (LR-) likelihood ratios were calculated. RESULTS Of the 875 consecutive pregnancies that met the criteria for FGR and were included in the study cohort, 405 (46%) were complicated by one or more components of the composite APO, including 54 (6%) perinatal deaths, 22 (3%) neonates with Grade III-IV IVH and/or PVL, nine (1%) with seizures and/or HIE, 91 (10%) with BPD, 57 (7%) with sepsis, 21 (2%) with NEC, and 361 (41%) who remained in the NICU > 7 days. In addition, 270 (31%) pregnancies were delivered by Cesarean section for non-reassuring fetal status, 43 (5%) were admitted to the NICU for < 7 days, 79 (9%) had 5-min Apgar score < 7, 125/631 (20%) had a cord gas pH ≤ 7.1 and 35/631 (6%) had a base excess ≥ 12 mmol/L. The predictive index we developed included seven factors available at the time of FGR diagnosis: hypertensive disorder of pregnancy (HDP) (+8 points), chronic hypertension without HDP (+4 points), gestational age ≤ 32 weeks (+5 points), absent or reversed end-diastolic flow in the umbilical artery (+8 points), prepregnancy body mass index ≥ 35 kg/m2 (+3 points), isolated abdominal circumference < 3rd percentile (-4 points) and non-Hispanic black race (-2 points). The bias-corrected bootstrapped (1000 replicates) area under the receiver-operating-characteristics curve (AUC) of the predictive index for composite APO in the validation group was 0.88 (95% CI, 0.84-0.92), which was similar to that in the development group (AUC, 0.86 (95% CI, 0.82-0.89); P = 0.34). In the total cohort, 40% of pregnancies had a low-risk index score (≤ 2), associated with a NPV of 85% (95% CI, 81-88%) and a LR- of 0.21 (95% CI, 0.16-0.27), and 23% had a high-risk index score (≥ 10), associated with a PPV of 96% (95% CI, 93-98%) and a LR+ of 27.36 (95% CI, 14.33-52.23). Of the remaining pregnancies that had an intermediate-risk score, 50% were complicated by composite APO. CONCLUSION An easy-to-use index incorporating seven clinical factors readily available at the time of FGR diagnosis is predictive of APO and may prove useful in counseling and management of pregnancies meeting the ISUOG-endorsed criteria for FGR. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- J E Powel
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology & Women's Health, Saint Louis University School of Medicine, St Louis, MO, USA
| | - E W Zantow
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology & Women's Health, Saint Louis University School of Medicine, St Louis, MO, USA
| | - M F Bialko
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology & Women's Health, Saint Louis University School of Medicine, St Louis, MO, USA
| | - L G Farley
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology & Women's Health, Saint Louis University School of Medicine, St Louis, MO, USA
| | - M L Lawlor
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology & Women's Health, Saint Louis University School of Medicine, St Louis, MO, USA
| | - S J Mullan
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology & Women's Health, Saint Louis University School of Medicine, St Louis, MO, USA
| | - L K Vricella
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology & Women's Health, Saint Louis University School of Medicine, St Louis, MO, USA
| | - T M Tomlinson
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology & Women's Health, Saint Louis University School of Medicine, St Louis, MO, USA
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Parr H, Hall E, Porta N. Joint models for dynamic prediction in localised prostate cancer: a literature review. BMC Med Res Methodol 2022; 22:245. [PMID: 36123621 PMCID: PMC9487103 DOI: 10.1186/s12874-022-01709-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 08/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Prostate cancer is a very prevalent disease in men. Patients are monitored regularly during and after treatment with repeated assessment of prostate-specific antigen (PSA) levels. Prognosis of localised prostate cancer is generally good after treatment, and the risk of having a recurrence is usually estimated based on factors measured at diagnosis. Incorporating PSA measurements over time in a dynamic prediction joint model enables updates of patients' risk as new information becomes available. We review joint model strategies that have been applied to model time-dependent PSA trajectories to predict time-to-event outcomes in localised prostate cancer. METHODS We identify articles that developed joint models for prediction of localised prostate cancer recurrence over the last two decades. We report, compare, and summarise the methodological approaches and applications that use joint modelling accounting for two processes: the longitudinal model (PSA), and the time-to-event process (clinical failure). The methods explored differ in how they specify the association between these two processes. RESULTS Twelve relevant articles were identified. A range of methodological frameworks were found, and we describe in detail shared-parameter joint models (9 of 12, 75%) and joint latent class models (3 of 12, 25%). Within each framework, these articles presented model development, estimation of dynamic predictions and model validations. CONCLUSIONS Each framework has its unique principles with corresponding advantages and differing interpretations. Regardless of the framework used, dynamic prediction models enable real-time prediction of individual patient prognosis. They utilise all available longitudinal information, in addition to baseline prognostic risk factors, and are superior to traditional baseline-only prediction models.
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Affiliation(s)
- Harry Parr
- Clinical Trials and Statistics Unit at The Institute of Cancer Research, London, UK
| | - Emma Hall
- Clinical Trials and Statistics Unit at The Institute of Cancer Research, London, UK
| | - Nuria Porta
- Clinical Trials and Statistics Unit at The Institute of Cancer Research, London, UK
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Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, Moons KGM, Collins GS. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med Res Methodol 2022; 22:101. [PMID: 35395724 PMCID: PMC8991704 DOI: 10.1186/s12874-022-01577-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 03/18/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology. METHODS We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models. RESULTS Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available. CONCLUSIONS The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.
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Affiliation(s)
- Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Benjamin Speich
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
- Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Garrett Bullock
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Shona Kirtley
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, ST5 5BG, UK
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
- EPI-centre, KU Leuven, Leuven, Belgium
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Sample size justifications in Gait & Posture. Gait Posture 2022; 92:333-337. [PMID: 34920357 DOI: 10.1016/j.gaitpost.2021.12.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/17/2021] [Accepted: 12/03/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND Context regarding how researchers determine the sample size of their experiments is important for interpreting the results and determining their value and meaning. Between 2018 and 2019, the journal Gait & Posture introduced a requirement for sample size justification in their author guidelines. RESEARCH QUESTION How frequently and in what ways are sample sizes justified in Gait & Posture research articles and was the inclusion of a guideline requiring sample size justification associated with a change in practice? METHODS The guideline was not in place prior to May 2018 and was in place from 25th July 2019. All articles in the three most recent volumes of the journal (84-86) and the three most recent, pre-guideline volumes (60-62) at time of preregistration were included in this analysis. This provided an initial sample of 324 articles (176 pre-guideline and 148 post-guideline). Articles were screened by two authors to extract author data, article metadata and sample size justification data. Specifically, screeners identified if (yes or no) and how sample sizes were justified. Six potential justification types (Measure Entire Population, Resource Constraints, Accuracy, A priori Power Analysis, Heuristics, No Justification) and an additional option of Other/Unsure/Unclear were used. RESULTS In most cases, authors of Gait & Posture articles did not provide a justification for their study's sample size. The inclusion of the guideline was associated with a modest increase in the percentage of articles providing a justification (16.6-28.1%). A priori power calculations were the dominant type of justification, but many were not reported in enough detail to allow replication. SIGNIFICANCE Gait & Posture researchers should be more transparent in how they determine their sample sizes and carefully consider if they are suitable. Editors and journals may consider adding a similar guideline as a low-resource way to improve sample size justification reporting.
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Kuroda N, Shiroshita A, Tsujimoto Y. Room for methodological improvement in gait speed study for COPD patients. Eur Respir J 2021; 58:13993003.01445-2021. [PMID: 34413150 DOI: 10.1183/13993003.01445-2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 05/30/2021] [Indexed: 11/05/2022]
Affiliation(s)
- Naoto Kuroda
- Dept of Pediatrics, Wayne State University, Detroit, MI, USA .,Dept of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Akihiro Shiroshita
- Dept of Respiratory Medicine, Ichinomiyanishi Hospital, Aichi, Japan.,Systematic Review Workshop Peer Support Group (SRWS-PSG), Osaka, Japan
| | - Yasushi Tsujimoto
- Systematic Review Workshop Peer Support Group (SRWS-PSG), Osaka, Japan.,Dept of Nephrology and Dialysis, Kyoritsu Hospital, Kawanishi, Japan.,Dept of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan
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