| 1. |
- Axfors, Cathrine, et al.
(författare)
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Published registry-based pharmacoepidemiologic associations show limited concordance with agnostic medication-wide analyses
- 2023
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Ingår i: Journal of Clinical Epidemiology. - : Elsevier BV. - 0895-4356 .- 1878-5921. ; 160, s. 33-45
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: To assess how the results of published national registry-based pharmacoepidemiology studies (where select associations are of interest) compare with an agnostic medication-wide approach (where all possible drug associations are tested).Study Design and Setting: We systematically searched for publications that reported drug associations with any, breast, colon/colorectal, or prostate cancer in the Swedish Prescribed Drug Registry. Results were compared against a previously performed agnostic medication-wide study on the same registry. Protocol: https://osf.io/kqj8n.Results: Most published studies (25/32) investigated previously reported associations. 421/913 (46%) associations had statistically significant results. 134 of the 162 unique drug-cancer associations could be paired with 70 associations in the agnostic study (corresponding drug categories and cancer types). Published studies reported smaller effect sizes and absolute effect sizes than the agnostic study, and generally used more adjustments. Agnostic analyses were less likely to report statistically significant protective associations (based on a multiplicity-corrected threshold) than their paired associations in published studies (McNemar odds ratio 0.13, P = 0.0022). Among 162 published associations, 36 (22%) showed increased risk signal and 25 (15%) protective signal at P < 0.05, while for agnostic associations, 237 (11%) showed increased risk signal and 108 (5%) protective signal at a multiplicity-corrected threshold. Associations belonging to drug categories targeted by individual published studies vs. nontargeted had smaller average effect sizes; smaller P values; and more frequent risk signals.Conclusion: Published pharmacoepidemiology studies using a national registry addressed mostly previously proposed associations, were mostly “negative”, and showed only modest concordance with their respective agnostic analyses in the same registry.
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| 2. |
- Barbateskovic, Marija, et al.
(författare)
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A new tool to assess Clinical Diversity In Meta-analyses (CDIM) of interventions
- 2021
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Ingår i: Journal of Clinical Epidemiology. - : Elsevier BV. - 0895-4356 .- 1878-5921. ; 135, s. 29-41
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To develop and validate Clinical Diversity In Meta-analyses (CDIM), a new tool for assessing clinical diversity between trials in meta-analyses of interventions. Study design and setting: The development of CDIM was based on consensus work informed by empirical literature and expertise. We drafted the CDIM tool, refined it, and validated CDIM for interrater scale reliability and agreement in three groups. Results: CDIM measures clinical diversity on a scale that includes four domains with 11 items overall: setting (time of conduct/country development status/units type); population (age, sex, patient inclusion criteria/baseline disease severity, comorbidities); interventions (intervention intensity/strength/duration of intervention, timing, control intervention, cointerventions); and outcome (definition of outcome, timing of outcome assessment). The CDIM is completed in two steps: first two authors independently assess clinical diversity in the four domains. Second, after agreeing upon scores of individual items a consensus score is achieved. Interrater scale reliability and agreement ranged from moderate to almost perfect depending on the type of raters. Conclusion: CDIM is the first tool developed for assessing clinical diversity in meta-analyses of interventions. We found CDIM to be a reliable tool for assessing clinical diversity among trials in meta-analysis.
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| 3. |
- Bendtsen, Marcus, 1982-, et al.
(författare)
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Causal models accounted for research participation effects when estimating effects in a behavioral intervention trial
- 2021
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Ingår i: Journal of Clinical Epidemiology. - : Elsevier. - 0895-4356 .- 1878-5921. ; 136, s. 77-83
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Tidskriftsartikel (refereegranskat)abstract
- Objective Participants in intervention studies are asked to take part in activities linked to the conduct of research, including signing consent forms and being assessed. If participants are affected by such activities through mechanisms by which the intervention is intended to work, then there is confounding. We examine how to account for research participation effects analytically.Study design and setting Data from a trial of a brief alcohol intervention among Swedish university students is used to show how a proposed causal model can account for assessment effects.Results The proposed model can account for research participation effects as long as researchers are willing to use existing data to make assumptions about causal influences, for instance on the magnitude of assessment effects. The model can incorporate several research processes which may introduce bias.Conclusions As our knowledge grows about research participation effects, we may move away from asking if participants are affected by study design, toward rather asking by how much they are affected, by which activities and in which circumstances. The analytic perspective adopted here avoids assuming there are no research participation effects.
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| 4. |
- Berkelmans, G. F. N., et al.
(författare)
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Population median imputation was noninferior to complex approaches for imputing missing values in cardiovascular prediction models in clinical practice
- 2022
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Ingår i: Journal of Clinical Epidemiology. - : Elsevier BV. - 0895-4356. ; 145, s. 70-80
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: To compare the validity and robustness of five methods for handling missing characteristics when using cardiovascular disease risk prediction models for individual patients in a real-world clinical setting.& nbsp;Study design and setting: The performance of the missing data methods was assessed using data from the Swedish National Diabetes Registry (n = 419,533) with external validation using the Scottish Care Information ? diabetes database (n = 226,953). Five methods for handling missing data were compared. Two methods using submodels for each combination of available data, two imputation methods: conditional imputation and median imputation, and one alternative modeling method, called the naive approach, based on hazard ratios and populations statistics of known risk factors only. The validity was compared using calibration plots and c-statistics.& nbsp;Results: C-statistics were similar across methods in both development and validation data sets, that is, 0.82 (95% CI 0.82-0.83) in the Swedish National Diabetes Registry and 0.74 (95% CI 0.74-0.75) in Scottish Care Information-diabetes database. Differences were only observed after random introduction of missing data in the most important predictor variable (i.e., age).& nbsp;Conclusion: Validity and robustness of median imputation was not dissimilar to more complex methods for handling missing values, provided that the most important predictor variables, such as age, are not missing. (C)& nbsp;2022 Elsevier Inc. All rights reserved.
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| 5. |
- Cooray, Shamil D., et al.
(författare)
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Temporal validation and updating of a prediction model for the diagnosis of gestational diabetes mellitus
- 2023
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Ingår i: Journal of Clinical Epidemiology. - : Elsevier. - 0895-4356 .- 1878-5921. ; 164, s. 54-64
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The original Monash gestational diabetes mellitus (GDM) risk prediction in early pregnancy model is internationally externally validated and clinically implemented. We temporally validate and update this model in a contemporary population with a universal screening context and revised diagnostic criteria and ethnicity categories, thereby improving model performance and generalizability.Study Design and Setting: The updating dataset comprised of routinely collected health data for singleton pregnancies delivered in Melbourne, Australia from 2016 to 2018. Model predictors included age, body mass index, ethnicity, diabetes family history, GDM history, and poor obstetric outcome history. Model updating methods were recalibration-in-the-large (Model A), intercept and slope re-estimation (Model B), and coefficient revision using logistic regression (Model C1, original ethnicity categories; Model C2, revised ethnicity categories). Analysis included 10-fold cross-validation, assessment of performance measures (c-statistic, calibration-in-the-large, calibration slope, and expected-observed ratio), and a closed-loop testing procedure to compare models' log-likelihood and akaike information criterion scores.Results: In 26,474 singleton pregnancies (4,756, 18% with GDM), the original model demonstrated reasonable temporal validation (c-statistic = 0.698) but suboptimal calibration (expected-observed ratio = 0.485). Updated model C2 was preferred, with a high c-statistic (0.732) and significantly better performance in closed testing.Conclusion: We demonstrated updating methods to sustain predictive performance in a contemporary population, highlighting the value and versatility of prediction models for guiding risk-stratified GDM care.
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| 6. |
- Cord, Kimberly A. Mc., et al.
(författare)
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Reporting Transparency and Completeness in Trials : Paper 2 - Reporting of randomised trials using registries was often inadequate and hindered the interpretation of results
- 2022
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Ingår i: Journal of Clinical Epidemiology. - : Pergamon Press. - 0895-4356 .- 1878-5921. ; 141, s. 175-186
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: Registries are important data sources for randomised controlled trials (RCTs), but reporting of how they are used may be inadequate. The objective was to describe the current adequacy of reporting of RCTs using registries.STUDY DESIGN AND SETTING: We used a database of trials using registries from a scoping review supporting the development of the 2021 CONSORT extension for Trials Conducted Using Cohorts and Routinely Collected Data (CONSORT-ROUTINE). Reporting completeness of 13 CONSORT-ROUTINE items was assessed.RESULTS: We assessed reports of 47 RCTs that used a registry, published between 2011 and 2018. Of the 13 CONSORT-ROUTINE items, 6 were adequately reported in at least half of reports (2 in at least 80%). The 7 other items were related to routinely collected data source eligibility (32% adequate), data linkage (8% adequate), validation and completeness of data used for outcome assessment (8% adequate), validation and completeness of data used for participant recruitment (0% adequate), participant flow (9% adequate), registry funding (6% adequate) and interpretation of results in consideration of registry use (25% adequate).CONCLUSION: Reporting of trials using registries was often poor, particularly details on data linkage and quality. Better reporting is needed for appropriate interpretation of the results of these trials.
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| 10. |
- Dewidar, O., et al.
(författare)
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Methodological guidance for incorporating equity when informing rapid-policy and guideline development
- 2022
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Ingår i: Journal of Clinical Epidemiology. - : Elsevier BV. - 0895-4356. ; 150, s. 142-153
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: We provide guidance for considering equity in rapid reviews through examples of published COVID-19 rapid reviews. Study Design and Setting: This guidance was developed based on a series of methodological meetings, review of internationally renowned guidance such as the Cochrane Handbook and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for equity-focused systematic reviews (PRISMA-Equity) guideline. We identified Exemplar rapid reviews by searching COVID-19 databases and requesting examples from our team. Results: We proposed the following key steps: 1. involve relevant stakeholders with lived experience in the conduct and design of the review; 2. reflect on equity, inclusion and privilege in team values and composition; 3. develop research question to assess health inequities; 4. conduct searches in relevant disciplinary databases; 5. collect data and critically appraise recruitment, retention and attrition for populations experiencing inequities; 6. analyse evidence on equity; 7. evaluate the applicability of findings to populations experiencing inequities; and 8. adhere to reporting guidelines for communicating review findings. We illustrated these methods through rapid review examples. Conclusion: Implementing this guidance could contribute to improving equity considerations in rapid reviews produced in public health emergencies, and help policymakers better understand the distributional impact of diseases on the population.
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