Sample size considerations and predictive performance of multinomial logistic prediction models

Jong, Valentijn M. T. de; Eijkemans, Marinus J.C.; Calster, Ben Van; Timmerman, Dirk; Moons, Karel G.M.; Steyerberg, Ewout W.; Smeden, Maarten van

doi:10.1002/sim.8063

Cited by 97 publications

(88 citation statements)

References 56 publications

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“…To select best goodness of fit, we compared the Bayesian Information Criteria (BIC) of the main effect model and with interaction variables included; the main effect model had a lower BIC indicating better fit. To reduce the risk of overfitting, events per variable (EPV) was also calculated based on the smallest number of observations in the outcome categories divided by the number of effective regression coefficients (De Jong et al, 2019). Multivariable multinomial logistic regression analyses were then performed to determine whether the strength of data sharing policies can be predicted from the aforementioned factors.…”

Section: Discussionmentioning

confidence: 99%

Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports

Kim

Cho³

et al. 2020

PeerJ

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Background Many scholarly journals have established their own data-related policies, which specify their enforcement of data sharing, the types of data to be submitted, and their procedures for making data available. However, except for the journal impact factor and the subject area, the factors associated with the overall strength of the data sharing policies of scholarly journals remain unknown. This study examines how factors, including impact factor, subject area, type of journal publisher, and geographical location of the publisher are related to the strength of the data sharing policy. Methods From each of the 178 categories of the Web of Science’s 2017 edition of Journal Citation Reports, the top journals in each quartile (Q1, Q2, Q3, and Q4) were selected in December 2018. Of the resulting 709 journals (5%), 700 in the fields of life, health, and physical sciences were selected for analysis. Four of the authors independently reviewed the results of the journal website searches, categorized the journals’ data sharing policies, and extracted the characteristics of individual journals. Univariable multinomial logistic regression analyses were initially conducted to determine whether there was a relationship between each factor and the strength of the data sharing policy. Based on the univariable analyses, a multivariable model was performed to further investigate the factors related to the presence and/or strength of the policy. Results Of the 700 journals, 308 (44.0%) had no data sharing policy, 125 (17.9%) had a weak policy, and 267 (38.1%) had a strong policy (expecting or mandating data sharing). The impact factor quartile was positively associated with the strength of the data sharing policies. Physical science journals were less likely to have a strong policy relative to a weak policy than Life science journals (relative risk ratio [RRR], 0.36; 95% CI [0.17–0.78]). Life science journals had a greater probability of having a weak policy relative to no policy than health science journals (RRR, 2.73; 95% CI [1.05–7.14]). Commercial publishers were more likely to have a weak policy relative to no policy than non-commercial publishers (RRR, 7.87; 95% CI, [3.98–15.57]). Journals by publishers in Europe, including the majority of those located in the United Kingdom and the Netherlands, were more likely to have a strong data sharing policy than a weak policy (RRR, 2.99; 95% CI [1.85–4.81]). Conclusions These findings may account for the increase in commercial publishers’ engagement in data sharing and indicate that European national initiatives that encourage and mandate data sharing may influence the presence of a strong policy in the associated journals. Future research needs to explore the factors associated with varied degrees in the strength of a data sharing policy as well as more diverse characteristics of journals related to the policy strength.

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Section: Discussionmentioning

confidence: 99%

Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports

Kim

Cho³

et al. 2020

PeerJ

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“…In this article we provide practical guidance for calculating the sample size required for the development of clinical prediction models, which builds on our recent methodology papers 1314151618. We suggest that current minimum sample size rules of thumb are too simplistic and outline a more scientific approach that tailors sample size requirements to the specific setting of interest.…”

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Calculating the sample size required for developing a clinical prediction model

Riley

Ensor

Snell

et al. 2020

BMJ

1,173

947

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Calculating the sample size required for developing a clinical prediction model Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a clinical prediction model.

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“…The authors took the investigation further by comparing the baseline category model to a logistic regression model fitted to dichotomized ordinal responses which demonstrated that the baseline category model was a superior fit. At least 50 multinomial events per variable was recommended leading to the MLR predictive performance gradually improving as the number of multinomial events per variable increases [22]. Our study results show that this could be the possible reason for the MLR model estimated by maximum likelihood being the most unlikely choice among the 3 predictive mechanisms.…”

Section: Application To Surgically-treated Cervical Cancer Patientsmentioning

confidence: 70%

Assessing Efficient Risk Ratios: An Application to Surgical Stage Prediction in Cervical Cancer

Jesang¹,

Odhiambo²

2020

OJS

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Background: Cervical cancer remains the second most commonly diagnosed cancer and the third leading cause of cancer death in developing countries. Improving clinicians' knowledge and understanding of surgical staging is critical in the fight against the disease. However, a systematic evaluation of different ordinal regression models based on diverse predicted outcomes has not been given its due share in literature. Objective: To systematically assess the flexibility of odds ratios for three popular ordinal regression models i.e. the Multinomial Logistic (ML) model, the Continuation Ratio (CR) model and Adjacent Category Logistic (ACL) model when applying cervical cancer data in surgical stage prediction. Method: We systematically, compared the performance of CR, ML and the ACL as the predictive mechanisms, and evaluate the most appropriate model in the cervical cancer setting. The study considered women who visited the Oncology department at the Moi Teaching and Referral Hospital's Chandaria Cancer and Chronic Diseases Center and were diagnosed and surgically treated for cervical cancer from January 2014 to December 2018. Results and Conclusion: We presented the comparison between 3 different regression models for ordinal data within the cervical cancer setting. We found that the CR model without proportional odds yielded better results comparing Akaike Information Criterion (AIC), log likelihood ratio and residual deviance. In addition, the key prognostic factor associated with invasive cervical cancer was the (International Federation of Gynecology and Obstetrics) FIGO clinical stage which in particular, had a higher influence on the surgical Stage 2 outcomes compared to the lesser surgical stage categories. All the 5 independent features selected for classifying the patients into surgical stages were the FIGO clinical stage and partly, the presence or absence of symptomatic vaginal discharge.

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Sample size considerations and predictive performance of multinomial logistic prediction models

Cited by 97 publications

References 56 publications

Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports

Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports

Calculating the sample size required for developing a clinical prediction model

Assessing Efficient Risk Ratios: An Application to Surgical Stage Prediction in Cervical Cancer

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