BMC Medical Research Methodology最新文献

{"title":"A pragmatic workflow for handling missing PROM data in registry studies: a methodological scoping review and simulated worked example.","authors":"Johan Eriksson","doi":"10.1186/s12874-026-03058-x","DOIUrl":"10.1186/s12874-026-03058-x","url":null,"abstract":"<p><strong>Background: </strong>Patient-reported outcome measures (PROMs) are increasingly integrated into clinical quality registries. Registry-based PROM data frequently exhibit structural non-response, including patients who never return PROMs (\"never-responders\"), and longitudinal attrition, making missing-not-at-random (MNAR) mechanisms plausible. Although MNAR sensitivity methods such as delta adjustment and tipping-point analysis are methodologically well-described, their uptake in registry-PROM practice is unknown.</p><p><strong>Methods: </strong>A structured search of PubMed (Nordic-specific terms), PubMed (global terms), Embase and CINAHL was conducted (2018-2024), yielding 425 records. After deduplication and screening, 61 registry-based observational PROM studies with explicit missing-data methods discussion were included as the primary analytic sample (17 Nordic, 44 global). The audit was conducted as a methodological scoping review and reported in accordance with the PRISMA extension for scoping reviews (PRISMA-ScR). A three-step pragmatic workflow is proposed and demonstrated: (1) non-response diagnostics separating never-responders from dropout, with standardized mean differences (SMD); (2) inverse probability weighting (IPW) as a default primary analysis strategy; and (3) MNAR sensitivity via delta adjustment with explicit tipping-point identification and a simple robustness classification. The workflow is demonstrated in a simulated registry-like cohort (N = 8,000).</p><p><strong>Results: </strong>In the primary analytic sample (n = 61), 24 studies (39%) stated no missing-data strategy. MNAR sensitivity analysis was reported in 18/61 (30%), but formal MNAR models were used in only 5/61 (8%). Critically, tipping-point analysis was not reported in any of the 61 included studies (0%); delta adjustment was reported in only 1/61 (2%). Never-responders were distinguished from dropout in only 17/61 (28%). In the simulated cohort, non-response was substantial and selective. In the primary scenario, the complete-case estimate was 1.21 PROM points (95% CI: 0.69-1.72), the single-stage IPW estimate was 1.22 (95% CI: 0.69-1.74), and the two-stage IPW estimate was 1.23 (95% CI: 0.67-1.79). The tipping point was δ* = 7.2 points (classified as sensitive under the illustrative thresholds). In the secondary scenario, the corresponding tipping point was δ* = 30.5 points (classified as stable).</p><p><strong>Conclusions: </strong>Tipping-point analysis was not reported in the accessible methods or supplementary material of any of the 61 included registry-PROM studies; delta adjustment was reported in only one study as a sensitivity check. The five formal MNAR analyses identified used methods that typically require specialist expertise and stronger modeling assumptions than are routinely feasible in many registry settings. A pragmatic workflow using IPW and tipping-point analysis can close this gap without specialist infrastructure.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13682039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Loss of power by degree of heaping in covariables.","authors":"Matthias Becher, Willi Sauerbrei, Heiko Becher","doi":"10.1186/s12874-026-03043-4","DOIUrl":"10.1186/s12874-026-03043-4","url":null,"abstract":"<p><p>Heaping or digit preference is frequently occurring in health research data as well as in deliberate data masking. We investigate the associated loss of power associated by degree of heaping by simulation and illustrate the effect with a real dataset. Moderate heaping leads to little power loss and may be tolerable. However, heaping in broad categories induces major power loss and should be avoided.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13676790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149215048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual extraction of individual patient data from published kaplan-meier survival plots using a web-based application offering different reconstruction methods (V-Exploder).","authors":"Fabian Acker, Lea Ruge, Maximilian Rost, Hanna Schulte, Sophie Heinzen, Friederike C Althoff, Khouloud Kouidri, Jan Stratmann, Eva Herrmann, Martin Sebastian, Sebastian E Koschade","doi":"10.1186/s12874-026-03036-3","DOIUrl":"10.1186/s12874-026-03036-3","url":null,"abstract":"<p><strong>Background: </strong>Individual patient-level survival data (IPD) is required for various types of medical research, such as meta-analyses and cost-effectiveness analyses. As IPD from clinical trials are usually not publicly available, secondary data utilization relies on IPD reconstruction from published Kaplan-Meier (KM) plots. In general, the established toolchains for IPD reconstruction are cumbersome and rarely offer possibilities to assess reconstruction accuracy.</p><p><strong>Implementation: </strong>We have therefore developed V-Exploder, an academic, non-profit, data-local tool that integrates the complete workflow of IPD reconstruction into a single browser application ( www.v-exploder.com ). Implemented methods include the iterative algorithms described by Guyot et al. and Rogula et al., and a novel, direct method for the positioning of individual events and censorings. A key feature of V-Exploder is its immediate visual feedback: recreated KM graphs are continuously constructed and superimposed onto the original figure. This enables more precise IPD reconstruction than previously possible.</p><p><strong>Methods: </strong>We conducted a study to evaluate the IPD reconstruction performance of V-Exploder. Ten investigators each reconstructed data from four of a total of twelve figures, each showing the survival of two randomly generated cohorts (20, 50, 100, or 200 subjects in each cohort). Metrics frequently used in survival analysis were calculated for the original and reconstructed data. Median absolute errors (AE), their interquartile ranges (IQR), and root square mean errors (RMSE) are reported.</p><p><strong>Results: </strong>Survival metrics from reconstructed IPD showed high similarity to metrics calculated from the reference data across all datasets, investigators and used methods of IPD reconstruction. Regarding log HRs, median AE (IQR) & RMSE with the direct, Rogula, and Guyot method were 5.93 × 10^- 4 (7.61 × 10^- 5, 1.65 × 10^- 3) & 6.26 × 10^- 3, 5.28 × 10^- 4 (3.21 × 10^- 5, 2.59 × 10^- 3) & 4.05 × 10^- 3, and 4.17 × 10^- 3 (1.84 × 10^- 3, 1.96 × 10^- 2) & 2.38 × 10^- 2, respectively. IPD reconstruction validity and reliability were lower with the Guyot method compared to the other methods, especially when used on smaller datasets. However, errors were considered acceptable for most research applications regardless of the method used.</p><p><strong>Conclusion: </strong>V-Exploder allows IPD reconstruction from published KM plots with high accuracy and reliability. Given its user-friendly visual interface, V-Exploder will facilitate secondary analysis of survival data and help improving analytic rigor.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13640098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying uncertainty: a practical comparison of Bayesian and frequentist meta-analytic frameworks applied to lead exposure and children's IQ.","authors":"Paulina Sell, Margaux Sanchez, Philippe Palmont, Simon Steiger, Dietrich Plass","doi":"10.1186/s12874-026-03010-z","DOIUrl":"10.1186/s12874-026-03010-z","url":null,"abstract":"<p><strong>Background: </strong>Uncertainty quantification is central to evidence synthesis for risk assessment and policy-making. Frequentist meta-analysis - currently predominant in epidemiology - quantifies uncertainty through confidence intervals, while Bayesian meta-analysis provides posterior distributions. We performed frequentist and Bayesian meta-analyses to compare uncertainty quantification and applicability for risk assessment using lead exposure and children's IQ as a case study.</p><p><strong>Methods: </strong>We systematically searched PubMed and Scopus for epidemiological studies on blood lead level (BLL) and IQ score in children. After screening, risk of bias (RoB) in the identified studies was assessed and regression coefficients were harmonised to the [Formula: see text] scale. In both meta-analyses, we included the same effect estimates, assumed a multilevel structure and used random-effects models. The Bayesian meta-analysis employed a hierarchical model with weakly-informative priors. Sensitivity analyses examined robustness to RoB and prior specification. [Formula: see text] and [Formula: see text] were estimated as measures of between-study heterogeneity.</p><p><strong>Results: </strong>Twelve studies were included in the meta-analysis. The frequentist model yielded a pooled estimate of -2.45 (95% confidence interval: -3.82 to -1.08) IQ points per unit increase in [Formula: see text]-transformed BLL (µg/dL) and a 95% predictive interval of -6.53 to 1.53. The Bayesian model yielded a mean pooled estimate of -2.28 (95% credible interval: -3.54 to -1.04) and a 95% predictive interval of -6.28 to 1.69. Between-study heterogeneity was substantial (frequentist: [Formula: see text] = 3.32, [Formula: see text] = 85.0%; Bayesian: posterior median [Formula: see text] = 3.26, [Formula: see text] = 84.7%). The Bayesian approach provided a full posterior distribution for heterogeneity, directly quantifying uncertainty from between-study variation. Sensitivity analyses showed both meta-analyses were robust to RoB, and Bayesian estimates were insensitive to tested priors. Depending on the specified prior configuration, the Bayesian model was less sensitive to studies reporting substantial uncertainty, resulting in a lower pooled effect compared to the frequentist model. This regularisation is particularly valuable in small meta-analyses, where frequentist heterogeneity estimates can be unstable.</p><p><strong>Conclusions: </strong>We provide updated pooled estimates for the effect of lead on children's IQ. The Bayesian framework provides posterior distributions, enabling direct probability statements, e.g. about exceeding risk thresholds, and comparative assessments between pollutants. These features are particularly valuable for risk assessment and policy-making.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13602614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149036371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods for analyzing recurrent and terminal events considering composite and competing endpoints in clinical trials: a practical overview.","authors":"Alidan Duoerkongjiang, Antonia Zapf, Paulus Kirchhof, Ann-Kathrin Ozga","doi":"10.1186/s12874-026-03008-7","DOIUrl":"10.1186/s12874-026-03008-7","url":null,"abstract":"<p><p>Time to event endpoints like time to myocardial infarction, relapse, or death are often of primary interest in clinical trials. To describe the whole disease process and gain a complete picture of treatment effects, methods for recurrent time to event analysis considering terminal events were proposed, i.e. methods considering all events that occur per individual. However, these methods are not often applied in clinical studies. This might be due to the researchers' uncertainties about underlying model assumptions and interpretation of results. We therefore aim to give a comprehensive overview of methods for the analysis of multiple time to events comparing model assumptions and interpretation. We illustrate the methods using a cardiovascular trial and provide the corresponding R code.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13571326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148954763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imputing longitudinal PROM data at the item and score level in clinical trials.","authors":"Carlo A G Veltri, Ulrike Grittner, Pimrapat Gebert","doi":"10.1186/s12874-026-02984-0","DOIUrl":"10.1186/s12874-026-02984-0","url":null,"abstract":"<p><strong>Background: </strong>Missing data in patient-reported outcome measures (PROMs) can affect estimation accuracy and inferential validity, particularly under missing-at-random (MAR) mechanisms. Multiple imputation (MI) is commonly recommended, but the relative performance of item-level and score-level imputation for longitudinal PROM composite scores remains insufficiently understood.</p><p><strong>Methods: </strong>We conducted a simulation study based on longitudinal clinical trial PROM data. Predictive mean matching (PMM) and random forest (RF) imputation were evaluated at both the item and score level under empirically calibrated MAR mechanisms. Simulation scenarios varied sample size, overall missingness, and the proportion of unit nonresponse. Performance was assessed using root mean squared error (RMSE), bias magnitude, relative efficiency compared with complete case analysis (CCA), conditional confidence interval coverage, and variance ratio estimates.</p><p><strong>Results: </strong>RMSE decreased with increasing sample size and increased with higher proportions of missingness across all methods. Both PMM and RF generally outperformed CCA in terms of RMSE and relative efficiency, particularly under higher missingness. Differences between item-level and score-level imputation were generally modest, although score-level imputation tended to yield slightly lower RMSE and bias magnitudes under more severe missingness conditions. Conditional coverage remained close to nominal levels across most MI settings. Variance tended to be overestimated in some scenarios, although sensitivity analyses with larger numbers of imputations substantially reduced this effect. PMM showed comparatively stable performance across simulation settings.</p><p><strong>Conclusions: </strong>Multiple imputation methods generally outperformed complete case analysis for handling longitudinal PROM data under MAR. While score-level imputation sometimes showed slightly more favorable performance than item-level imputation when targeting composite score means, the magnitude of these differences was frequently small in practical terms. PMM provided stable performance across a wide range of settings and represents a reasonable default approach for many applied PROM analyses.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13621587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149079697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is there a limit to limitations? A proposal for structured limitations reporting in scientific manuscripts.","authors":"Christos G Tsagkaris, Irem Sevik","doi":"10.1186/s12874-026-02992-0","DOIUrl":"10.1186/s12874-026-02992-0","url":null,"abstract":"<p><p>Reporting studies limitations is essential for transparent interpretation. While reporting guidelines such as CONSORT, STROBE, PRISMA mandate disclosure of limitations, no widely adopted framework standardizes how these are reported in a structured and comparable format. In practice, limitations sections often extend over several paragraphs reiterating predictable design-related constraints. This limits comparability, contributes to inefficiency and may overshadow genuine study-specific considerations. We propose a semi-structured checklist combining standardized limitation domains with a concise narrative component. This approach aims to ensure minimum systematic disclosure while preserving critical interpretation. Structured reporting may improve clarity, comparability, and enable meta-research on limitations across studies. Consensus-based refinement and pilot implementation are warranted to evaluate feasibility and impact.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13523189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simulation-based comparison of Boruta, LASSO, and Elastic Net for variable selection in logistic regression, with an ovarian cancer miRNA application.","authors":"Reza Arabi Belaghi, Hulya Yurekli, Farzaneh Hamidi, Neda Gilani","doi":"10.1186/s12874-026-02939-5","DOIUrl":"10.1186/s12874-026-02939-5","url":null,"abstract":"<p><strong>Background: </strong>Variable selection is a central challenge in logistic regression, particularly in high-dimensional biomedical applications where correlated predictors and limited sample sizes complicate reliable identification of relevant variables. This study aims to systematically compare three widely used variable selection approaches - Boruta, LASSO, and Elastic Net - under a range of data-generating conditions and to illustrate their performance using an ovarian cancer miRNA dataset.</p><p><strong>Methods: </strong>We conducted a simulation study across 36 logistic regression scenarios varying in sample size, dimensionality, predictor correlation, and effect magnitude. Performance was evaluated using true-positive and false-positive selection rates. In addition, all three methods were applied to a real-world serum miRNA expression dataset, and discriminative performance was assessed using the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>Boruta, Elastic Net, and LASSO exhibited distinct variable selection behaviors across simulation scenarios. Boruta maintained strong true-positive recovery while controlling false positives in most settings, particularly when predictors were highly correlated. Elastic Net consistently achieved high sensitivity but produced comparatively large false-positive rates. LASSO showed the most conservative behavior, recovering fewer true predictors while maintaining low false-positive rates across nearly all scenarios. In the ovarian cancer miRNA application, all three methods achieved similarly strong test-set AUC performance, despite marked differences in the size of the selected biomarker panels.</p><p><strong>Conclusions: </strong>The results demonstrate clear trade-offs among the three methods. Boruta offers a favorable balance between sensitivity and specificity in highly correlated settings. Elastic Net prioritizes sensitivity at the cost of increased false discoveries, whereas LASSO provides stricter false-positive control with reduced sensitivity. These findings offer practical guidance for selecting variable selection methods in logistic regression, particularly for high-dimensional biomedical applications.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13531732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148863657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reproducibility, transparency, and open science: a cultural evolution is needed to move from ideals we celebrate to implementation.","authors":"Livia Puljak, Vladimir Ruf, Josip Šimić, Melissa K Sharp, Ivan Buljan","doi":"10.1186/s12874-026-02964-4","DOIUrl":"10.1186/s12874-026-02964-4","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13499322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148788180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing patient-reported sociodemographic measures in health care - a national example of the development and evaluation of a practical standardized instrument for collecting patient data in studies.","authors":"Sigrid Boczor, Heike Hansen, Thomas Kloppe, Claudia Mews, Cathleen Muche-Borowski, Anja Rakebrandt, Ingmar Schäfer, Nadine Janis Pohontsch, Vivien Böttcher, Thorben W Fründt, Anna Sophie Hoffmann, Jan Dietrich Philipp Köster, Martin Scherer","doi":"10.1186/s12874-026-02977-z","DOIUrl":"https://doi.org/10.1186/s12874-026-02977-z","url":null,"abstract":"<p><strong>Background: </strong>The development of patient-oriented health care is continuous subject to old and new challenges. To better identify patient needs, studies are necessary and standardized data collection is essential to describe, analyze and compare data. Associated demographic variables remain a topic of discussion in health services research. Common approaches either differ in relevant sociodemographic aspects, or their standardization is too detailed to be feasibly used in studies with a clinical or health services research focus. Our aim was to develop and evaluate a comprehensive and practical instrument for collecting sociodemographic patient data in studies.</p><p><strong>Methods: </strong>A variant (interdisciplinary expert panel) of the nominal group technique was used as a consensus method, in addition, a focus group was held and a 2-phase pretest according to Prüfer and Rexroth (method: \"Think Aloud\") was used for evaluation. In the first phase we conducted 6 interviews with potential patients. The main phase was planned to enroll at least 200 patients to test the instrument with a quantitative sample as recommended for testing patient-reported outcome measures. It took place in eight departments of the University Medical Center Hamburg-Eppendorf. Exploratory analyses were conducted using SPSS. The interdisciplinary expert panel revised the instrument.</p><p><strong>Results: </strong>In the main phase, 290 (51%) of 574 approached patients participated [median (minimum; maximum) age: 40 (19; 86) years; 55% female]. Of these respondents 66%/ 31%/ 3% considered the length of the questionnaire to be appropriate/ too long/ too short; 96% found it understandable. Seven questions on living and income situation (commented at least three times) showed a need for clarification and modification. Non-responders optionally provided reasons for decline of which 7 clusters could be formed (predominantly: time). Final standardized variables: biological sex; year/country of birth; marital status/biological children; living/household type/persons/age; highest school/ qualification/current profession; household income/specialties/persons; country of birth mother/father; nationalities/residence status/native languages/German skills.</p><p><strong>Conclusions: </strong>Patient involvement is helpful in the development of patient survey instruments. Our instrument is based on the patients' perspective and understanding of these measurements. The aim is to further strengthen patient-centered health care. The revised instrument will be used in studies. The expert group will continue to identify and implement socio-demographic developments.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13491617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148788157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}