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

Minimum sample size for external validation of a clinical prediction model with a binary outcome

In prediction model research, external validation is needed to examine an existing model's performance using data independent to that for model development. Current external validation studies often suffer from small sample sizes and consequently imprecise predictive performance estimates. To address this, we propose how to determine the minimum sample size needed for a new external validation study of a prediction model for a binary outcome. Our calculations aim to precisely estimate calibration (Observed/Expected and calibration slope), discrimination (C-statistic), and clinical utility (net benefit). For each measure, we propose closed-form and iterative solutions for calculating the minimum sample size required. These require specifying: (i) target SEs (confidence interval widths) for each estimate of interest, (ii) the anticipated outcome event proportion in the validation population, (iii) the prediction model's anticipated (mis)calibration and variance of linear predictor values in the validation population, and (iv) potential risk thresholds for clinical decision-making. The calculations can also be used to inform whether the sample size of an existing (already collected) dataset is adequate for external validation. We illustrate our proposal for external validation of a prediction model for mechanical heart valve failure with an expected outcome event proportion of 0.018. Calculations suggest at least 9835 participants (177 events) are required to precisely estimate the calibration and discrimination measures, with this number driven by the calibration slope criterion, which we anticipate will often be the case. Also, 6443 participants (116 events) are required to precisely estimate net benefit at a risk threshold of 8%. Software code is provided.</p

Prognostic markers in cancer: the evolution of evidence from single studies to meta-analysis, and beyond

In oncology, prognostic markers are clinical measures used to help elicit an individual patient's risk of a future outcome, such as recurrence of disease after primary treatment. They thus facilitate individual treatment choice and aid in patient counselling. Evidence-based results regarding prognostic markers are therefore very important to both clinicians and their patients. However, there is increasing awareness that prognostic marker studies have been neglected in the drive to improve medical research. Large protocol-driven, prospective studies are the ideal, with appropriate statistical analysis and clear, unbiased reporting of the methods used and the results obtained. Unfortunately, published prognostic studies rarely meet such standards, and systematic reviews and meta-analyses are often only able to draw attention to the paucity of good-quality evidence. We discuss how better-quality prognostic marker evidence can evolve over time from initial exploratory studies, to large protocol-driven primary studies, and then to meta-analysis or even beyond, to large prospectively planned pooled analyses and to the initiation of tumour banks. We highlight articles that facilitate each stage of this process, and that promote current guidelines aimed at improving the design, analysis, and reporting of prognostic marker research. We also outline why collaborative, multi-centre, and multi-disciplinary teams should be an essential part of future studies

Differential expression of collectins in human placenta and role in inflammation during spontaneous Labor.

© 2014 Yadav et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Collectins, collagen-containing Ca2+ dependent C-type lectins and a class of secretory proteins including SP-A, SP-D and MBL, are integral to immunomodulation and innate immune defense. In the present study, we aimed to investigate their placental transcript synthesis, labor associated differential expression and localization at feto-maternal interface, and their functional implication in spontaneous labor. The study involved using feto-maternal interface (placental/decidual tissues) from two groups of healthy pregnant women at term (≥37 weeks of gestation), undergoing either elective C-section with no labor ('NLc' group, n = 5), or normal vaginal delivery with spontaneous labor ('SLv' group, n = 5). The immune function of SP-D, on term placental explants, was analyzed for cytokine profile using multiplexed cytokine array. SP-A, SP-D and MBL transcripts were observed in the term placenta. The 'SLv' group showed significant up-regulation of SP-D (p = 0.001), and down-regulation of SP-A (p = 0.005), transcripts and protein compared to the 'NLc' group. Significant increase in 43 kDa and 50 kDa SP-D forms in placental and decidual tissues was associated with the spontaneous labor (p<0.05). In addition, the MMP-9-cleaved form of SP-D (25 kDa) was significantly higher in the placentae of 'SLv' group compared to the 'NLc' group (p = 0.002). Labor associated cytokines IL-1α, IL-1β, IL-6, IL-8, IL-10, TNF-α and MCP-1 showed significant increase (p<0.05) in a dose dependent manner in the placental explants treated with nSP-D and rhSP-D. In conclusion, the study emphasizes that SP-A and SP-D proteins associate with the spontaneous labor and SP-D plausibly contributes to the pro-inflammatory immune milieu of feto-maternal tissues.Funding provided by BT/PR15227/BRB/10/906/2011) Department of Biotechnology (DBT), Government of India http://dbtindia.nic.in/index.asp (TM) and Indian Council of Medical Research (ICMR) Junior Research Fellowship (JRF)/Senior Research Fellowship (SRF), Government of India, www.icmr.nic.in (AKY)

Methodology of a novel risk stratification algorithm for patients with multiple myeloma in the relapsed setting

Introduction Risk stratification tools provide valuable information to inform treatment decisions. Existing algorithms for patients with multiple myeloma (MM) were based on patients with newly diagnosed disease, and these have not been validated in the relapsed setting or in routine clinical practice. We developed a risk stratification algorithm (RSA) for patients with MM at initiation of second-line (2L) treatment, based on data from the Czech Registry of Monoclonal Gammopathies. Methods Predictors of overall survival (OS) at 2L treatment were identified using Cox proportional hazards models and backward selection. Risk scores were obtained by multiplying the hazard ratios for each predictor. The K-adaptive partitioning for survival (KAPS) algorithm defined four groups of stratification based on individual risk scores. Results Performance of the RSA was assessed using Nagelkerke’s R2 test and Harrell’s concordance index through Kaplan–Meier analysis of OS data. Prognostic groups were successfully defined based on real-world data. Use of a multiplicative score based on Cox modeling and KAPS to define cut-off values was effective. Conclusion Through innovative methods of risk assessment and collaboration between physicians and statisticians, the RSA was capable of stratifying patients at 2L treatment by survival expectations. This approach can be used to develop clinical decision-making tools in other disease areas to improve patient management

Predicting the outcome of conservative treatment with physiotherapy in adults with shoulder pain associated with partial-thickness rotator cuff tears – a prognostic model development study

Model coefficient statistics (DOCX 21 kb

Predicting microbiologically defined infection in febrile neutropenic episodes in children : global individual participant data multivariable meta-analysis

BACKGROUND: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one. METHODS: The 'Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation. RESULTS: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically 'severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711-0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses. CONCLUSIONS: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making