Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines.
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ABSTRACT:
SUBMITTER: Burton A
PROVIDER: S-EPMC2364743 | biostudies-other | 2004 Jul
REPOSITORIES: biostudies-other
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Project description:Aims: We aimed to review prison healthcare expenditure internationally. Objectives: To systematically review healthcare spending on prisoners worldwide, examine comparability between countries, and develop guidelines to improve reporting. Methods: Five bibliographic indexes (International Monetary Fund, ProQuest: Statistical Abstracts of the World, PubMed, Google Scholar, and JSTOR) were searched for the costs of prison and prison healthcare, supplemented with country-specific searches for the 20 countries with the highest prison populations. Information on overall healthcare costs, their breakdown by categories, and their proportion to overall prison expenditure was extracted. PRISMA guidelines were followed. Results: Prison healthcare expenditure data was identified for 10 countries, and overall operating costs were reported for 12 countries. The most commonly reported healthcare cost was for primary medical care. Healthcare costs reporting varied widely, and few countries were comparable. We developed a set of guidelines for consistent and transparent reporting of healthcare costs. Conclusions: Few countries report the costs of healthcare services in prison. When reported, there is a lack of clarity and consistency as to what is included. Using the proposed reporting guidelines would enable national trends and international comparisons to be investigated, and any recommended benchmarks to be monitored.
| S-EPMC6306428 | biostudies-literature
Project description:BackgroundPoor reporting compromises the reliability and clinical value of prognostic tumour marker studies. We review articles to assess the reporting of patients and events using REMARK guidelines, at the time of guideline publication.MethodsWe sampled 50 prognostic tumour marker studies from higher impact cancer journals between 2006 and 2007. The inclusion criteria were cancer; focus on single biological tumour marker; survival analysis; multivariable analysis; and not gene array or proteomic data. Articles were assessed for the REMARK profile and other REMARK guideline items. We propose a reporting aid, the REMARK profile, motivated by the CONSORT flowchart.ResultsIn 50 studies assessed for the REMARK profile, the number of eligible patients (56% of articles), excluded patients (54%) and patients in analyses (98%) was reported. Only 50% of articles reported the number of outcome events. In multivariable analyses, 54% and 30% of articles reported patient and event numbers for all variables. Of the studies, 66% used archival samples, indicating a potentially biased patient selection. Only 36% of studies reported clearly defined outcomes.ConclusionsGood reporting is critical for the interpretability and clinical applicability of prognostic studies. Current reporting of key information, such as the number of outcome events in all patients and subgroups, is poor. Use of the REMARK profile would greatly improve reporting and enhance prognostic research.
| S-EPMC2795163 | biostudies-literature
Project description:BackgroundThe reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand and replicate such studies. To address this issue, multiple consensus and expert reporting guidelines for ML studies have been published. However, these guidelines cover different parts of the analytics lifecycle, and individually, none of them provide a complete set of reporting requirements.ObjectiveWe aimed to consolidate the ML reporting guidelines and checklists in the literature to provide reporting items for prognostic and diagnostic ML in in-silico and shadow mode studies.MethodsWe conducted a literature search that identified 192 unique peer-reviewed English articles that provide guidance and checklists for reporting ML studies. The articles were screened by their title and abstract against a set of 9 inclusion and exclusion criteria. Articles that were filtered through had their quality evaluated by 2 raters using a 9-point checklist constructed from guideline development good practices. The average κ was 0.71 across all quality criteria. The resulting 17 high-quality source papers were defined as having a quality score equal to or higher than the median. The reporting items in these 17 articles were consolidated and screened against a set of 6 inclusion and exclusion criteria. The resulting reporting items were sent to an external group of 11 ML experts for review and updated accordingly. The updated checklist was used to assess the reporting in 6 recent modeling papers in JMIR AI. Feedback from the external review and initial validation efforts was used to improve the reporting items.ResultsIn total, 37 reporting items were identified and grouped into 5 categories based on the stage of the ML project: defining the study details, defining and collecting the data, modeling methodology, model evaluation, and explainability. None of the 17 source articles covered all the reporting items. The study details and data description reporting items were the most common in the source literature, with explainability and methodology guidance (ie, data preparation and model training) having the least coverage. For instance, a median of 75% of the data description reporting items appeared in each of the 17 high-quality source guidelines, but only a median of 33% of the data explainability reporting items appeared. The highest-quality source articles tended to have more items on reporting study details. Other categories of reporting items were not related to the source article quality. We converted the reporting items into a checklist to support more complete reporting.ConclusionsOur findings supported the need for a set of consolidated reporting items, given that existing high-quality guidelines and checklists do not individually provide complete coverage. The consolidated set of reporting items is expected to improve the quality and reproducibility of ML modeling studies.
| S-EPMC10502599 | biostudies-literature
Project description:Multiple imputation (MI) is a well-established method for dealing with missing data. MI is computationally intensive when imputing missing covariates with high-dimensional outcome data (e.g., DNA methylation data in epigenome-wide association studies (EWAS)), because every outcome variable must be included in the imputation model to avoid biasing associations towards the null. Instead, EWAS analyses are reduced to only complete cases, limiting statistical power and potentially causing bias. We used simulations to compare 5 MI methods for high-dimensional data under 2 missingness mechanisms. All imputation methods had increased power over complete-case (C-C) analyses. Imputing missing values separately for each variable was computationally inefficient, but dividing sites at random into evenly sized bins improved efficiency and gave low bias. Methods imputing solely using subsets of sites identified by the C-C analysis suffered from bias towards the null. However, if these subsets were added into random bins of sites, this bias was reduced. The optimal methods were applied to an EWAS with missingness in covariates. All methods identified additional sites over the C-C analysis, and many of these sites had been replicated in other studies. These methods are also applicable to other high-dimensional data sets, including the rapidly expanding area of "-omics" studies.
| S-EPMC6825836 | biostudies-literature
Project description:In individually matched case-control studies, when some covariates are incomplete, an analysis based on the complete data may result in a large loss of information both in the missing and completely observed variables. This usually results in a bias and loss of efficiency. In this article, we propose a new method for handling the problem of missing covariate data based on a missing-data-induced intensity approach when the missingness mechanism does not depend on case-control status and show that this leads to a generalization of the missing indicator method. We derive the asymptotic properties of the estimates from the proposed method and, using an extensive simulation study, assess the finite sample performance in terms of bias, efficiency, and 95% confidence coverage under several missing data scenarios. We also make comparisons with complete-case analysis (CCA) and some missing data methods that have been proposed previously. Our results indicate that, under the assumption of predictable missingness, the suggested method provides valid estimation of parameters, is more efficient than CCA, and is competitive with other, more complex methods of analysis. A case-control study of multiple myeloma risk and a polymorphism in the receptor Inter-Leukin-6 (IL-6-α) is used to illustrate our findings.
| S-EPMC2910202 | biostudies-literature
Project description:Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. Importantly, the lack of transparency around methodological decisions is threatening the validity and reproducibility of modern research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records' analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits and whether a sensitivity analysis regarding the missingness mechanism is required; 2) Examine the data, checking the methods outlined in the analysis plan are appropriate, and conduct the preplanned analysis; and 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data and transparently reporting the potential effect on the study results, therefore increasing the confidence in and reproducibility of research findings.
| S-EPMC8168830 | biostudies-literature
Project description:Prognostic markers help to stratify patients for treatment by identifying patients with different risks of outcome (e.g. recurrence of disease), and are important tools in the management of cancer and many other diseases. Systematic review and meta-analytical approaches to identifying the most valuable prognostic markers are needed because (sometimes conflicting) evidence relating to markers is often published across a number of studies. To investigate the practicality of this approach, an empirical investigation of a systematic review of tumour markers for neuroblastoma was performed; 260 studies of prognostic markers were identified, which considered 130 different markers. The reporting of these studies was often inadequate, in terms of both statistical analysis and presentation, and there was considerable heterogeneity for many important clinical/statistical factors. These problems restricted both the extraction of data and the meta-analysis of results from the primary studies, limiting feasibility of the evidence-based approach.Guidelines for reporting the results of primary prognostic marker studies in cancer, and other diseases, are given in order to facilitate both the interpretation of individual studies and the undertaking of systematic reviews, meta-analysis and, ultimately, evidence-based practice. General availability of full individual patient data is a necessary step forward and would overcome the majority of problems encountered, including poorly reported summary statistics and variability in cutoff level, outcome assessed and adjustment factors used. It would also limit the problem of reporting bias, although publication bias will remain a concern until studies are prospectively registered. Such changes in practice would help important evidence-based reviews to be conducted in order to establish the most appropriate prognostic markers for clinical use, which should ultimately improve patient care.
| S-EPMC2747576 | biostudies-other
Project description:BackgroundDevelopment of prognostic models enables identification of variables that are influential in predicting patient outcome and the use of these multiple risk factors in a systematic, reproducible way according to evidence based methods. The reliability of models depends on informed use of statistical methods, in combination with prior knowledge of disease. We reviewed published articles to assess reporting and methods used to develop new prognostic models in cancer.MethodsWe developed a systematic search string and identified articles from PubMed. Forty-seven articles were included that satisfied the following inclusion criteria: published in 2005; aiming to predict patient outcome; presenting new prognostic models in cancer with outcome time to an event and including a combination of at least two separate variables; and analysing data using multivariable analysis suitable for time to event data.ResultsIn 47 studies, prospective cohort or randomised controlled trial data were used for model development in only 33% (15) of studies. In 30% (14) of the studies insufficient data were available, having fewer than 10 events per variable (EPV) used in model development. EPV could not be calculated in a further 40% (19) of the studies. The coding of candidate variables was only reported in 68% (32) of the studies. Although use of continuous variables was reported in all studies, only one article reported using recommended methods of retaining all these variables as continuous without categorisation. Statistical methods for selection of variables in the multivariate modelling were often flawed. A method that is not recommended, namely, using statistical significance in univariate analysis as a pre-screening test to select variables for inclusion in the multivariate model, was applied in 48% (21) of the studies.ConclusionsWe found that published prognostic models are often characterised by both use of inappropriate methods for development of multivariable models and poor reporting. In addition, models are limited by the lack of studies based on prospective data of sufficient sample size to avoid overfitting. The use of poor methods compromises the reliability of prognostic models developed to provide objective probability estimates to complement clinical intuition of the physician and guidelines.
| S-EPMC2856521 | biostudies-literature
Project description:The objective of this study was to assess the impact of a 2010 community-based participatory research (CBPR) reporting guideline on the quality of reporting a CBPR on smoking cessation. We searched the MEDLINE, Embase, the Cochrane Central Register for Controlled Trials (CENTRAL), PsycINFO, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases and included articles published up to December 2019 (PROSPERO: CRD42019111668). We assessed reporting quality using the 13-item checklist. Of the 80 articles identified, 42 (53%) were published after 2010. The overall reporting quality before and after 2010 was poor and did not differ significantly (mean difference: 0.66, 95% confidence interval [CI]: -0.21 to 1.53). The total reporting scores of the studies did not differ significantly according to the effect size of the intervention (beta coefficient: -2.86, 95% CI: -5.77 to 0.04). This study demonstrates the need to improve the quality of reporting CBPRs. We recommend that journal editors endorse the CBPR reporting guideline to encourage its use by more researchers.
| S-EPMC7312250 | biostudies-literature
Project description:To assess the quality of reported consent processes of cluster-randomised trials conducted in residential facilities for older people and to explore whether the focus on improving the general conduct and reporting of cluster-randomised trials influenced the quality of conduct and reporting of ethical processes in these trials. Systematic review of cluster-randomised trials reports, published up to the end of 2010. National Library of Medicine (Medline) via PubMed, hand-searches of BMJ, Journal of the American Medical Association, BMC Health Services Research, Age and Ageing and Journal of the American Geriatrics Society, reference search in Web of Knowledge and consultation with experts. Published cluster-randomised trials where the unit of randomisation is a part or the whole of a residential facility for older people, without language or year of publication restrictions. We included 73 trials. Authors reported ethical approval in 59, obtaining individual consent in 51, and using proxies for this consent in 37, but the process to assess residents' capacity to consent was clearly reported in only eight. We rated only six trials high for the quality of consent processes. We considered that individual informed consent could have been waived legitimately in 14 of 22 trials not reporting obtaining consent. The proportions reporting ethical approval and quality of consent processes were higher in recent trials. Recently published international recommendations regarding ethical conduct in cluster-randomised trials are much needed. In relation to consent processes when cognitively impaired individuals are included in these trials, we provide a six-point checklist and recommend the minimum information to be reported. Those who lack capacity in trials with complex designs should be afforded the same care in relation to consent as competent adults in trials with simpler designs.
| S-EPMC3710983 | biostudies-literature