BMC Medical Research Methodology最新文献

{"title":"Drivers of bias in diagnostic test accuracy estimates when using expert panels as a reference standard: a simulation study.","authors":"B E Kellerhuis, K Jenniskens, E Schuit, L Hooft, K G M Moons, J B Reitsma","doi":"10.1186/s12874-025-02557-7","DOIUrl":"https://doi.org/10.1186/s12874-025-02557-7","url":null,"abstract":"<p><strong>Background: </strong>Expert panels are often used as a reference standard when no gold standard is available in diagnostic test accuracy research. It is often unclear what study and expert panel characteristics produce the best estimates of diagnostic test accuracy. We simulated a large range of scenarios to assess the impact of study and expert panel characteristics on index test diagnostic accuracy estimates.</p><p><strong>Methods: </strong>Simulations were performed in which an expert panel was the reference standard to estimate the sensitivity and specificity of an index diagnostic test. Diagnostic accuracy was determined by combining probability estimates of target condition presence, provided by experts using four component reference tests, through a predefined threshold. Study and panel characteristics were varied in several scenarios: target condition prevalence, accuracy of component reference tests, expert panel size, study population size, and random or systematic differences between expert's probability estimates. The total bias in each scenario was quantified using mean squared error.</p><p><strong>Results: </strong>When estimating an index test with 80% sensitivity and 70% specificity, bias in estimates was hardly affected by the study population size or the number of experts. Prevalence had a large effect on bias, scenarios with a prevalence of 0.5 estimated sensitivity between 63.3% and 76.7% and specificity between 56.1% and 68.7%, whereas scenarios with a prevalence of 0.2 estimated sensitivity between 48.5% and 73.3% and specificity between 65.5% and 68.7%. Improved reference tests also reduced bias. Scenarios with four component tests of 80% sensitivity and specificity estimated index test sensitivity between 60.1% and 77.4% and specificity between 62.9% and 69.1%, whereas scenarios with four component tests of 70% sensitivity and specificity estimated index test sensitivity between 48.5% and 73.4% and specificity between 56.1% and 67.0%.</p><p><strong>Conclusions: </strong>Bias in accuracy estimates when using an expert panel will increase if the component reference tests are less accurate. Prevalence, the true value of the index test accuracy, and random or systematic differences between experts can also impact the amount of bias, but the amount and even direction will vary between scenarios.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"106"},"PeriodicalIF":3.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12016307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143965409","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":"Effect of methodological choices and inclusion criteria on network meta-analysis results in psoriasis.","authors":"R Guelimi, S Afach, V Chiocchia, E Sbidian, L Le Cleach, G Salanti","doi":"10.1186/s12874-025-02558-6","DOIUrl":"https://doi.org/10.1186/s12874-025-02558-6","url":null,"abstract":"<p><strong>Background: </strong>When conducting network meta-analysis (NMA), researchers need to make several methodological and analytical decisions, which can influence the results of NMAs. Our objective was to evaluate the impact of different methodological choices on the conclusions from the analyses of a network of 20 active treatments in patients with psoriasis.</p><p><strong>Methods: </strong>We re-analysed the available data of a living Cochrane NMA evaluating the systemic treatments in psoriasis under various analytical scenarios defined by the combination of pre-specified methodological choices. We performed NMAs on three outcomes: Psoriasis Area Severity Index (PASI) 90, PASI 100 and serious adverse events (SAEs). Variability of the effect estimates across NMAs was summarized using ratio of relative risks (RRR) and ratio of odds ratio (ROR). We estimated the level of agreement between the treatment hierarchies using the Average Overlap (AO).</p><p><strong>Results: </strong>Overall, 560 NMAs were conducted. The median number of included interventions was 18 (IQR 17-19), for a median number of included studies of 68 (IQR 57-93). The median RRR was 1.06 (IQR 1.06-1.08) for PASI 90, 1.07 (IQR 1.06-1.10) for early PASI 90, 1.14 (IQR 1.06-1.15) for late PASI 90, 1.04 (IQR 1.01-1.05) for PASI 100, and 1.02 (IQR 1.02-1.06) for SAEs. The criteria with the greatest impact on the effect estimates were the inclusion or exclusion of studies with biological-naïve patients, inclusion or exclusion of phase II trials, and the inclusion or exclusion of studies evaluating conventional treatments. The analysis choice with the greatest impact was the use of the Mantel-Haenszel method instead of the inverse variance method. There was a high agreement of treatment hierarchies between analyses. For the top 6 ranking treatments, the median AO across all scenarios for PASI 90 was 0.84 (IQR 0.72-0.97). For early PASI 90, late PASI 90, PASI 100, and SAE, the median AO were 0.94 (IQR 0.91-0.97), 0.75 (IQR 0.65-0.97), 0.94 (IQR 0.91-0.97), and 0.59 (IQR 0.59- 0.90), respectively.</p><p><strong>Conclusions: </strong>We found that different methodological choices could influence NMAs' results. However, even though moderate variation in effect estimates could be observed across the analyses, treatment hierarchies remained stable for the top-ranking drugs.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"110"},"PeriodicalIF":3.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958495","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":"Visualizing a marker's degrees of necessity and of sufficiency in the predictiveness curve.","authors":"Andreas Gleiss","doi":"10.1186/s12874-025-02544-y","DOIUrl":"10.1186/s12874-025-02544-y","url":null,"abstract":"<p><strong>Background: </strong>The degrees to which a factor is necessary or sufficient for an event have been proposed as generalizations of attributable risk based on simple functions of unconditional and conditional event probabilities. Predictiveness curves show the risk for an event, as derived by a model with one or more predictors, depending on risk percentiles that represent the predictors' distribution in the underlying population.</p><p><strong>Methods: </strong>Connections between the degrees of necessity and of sufficiency and explained variation on the one hand and the predictiveness curve on the other hand are mathematically proved and exemplified using data of in-hospital death of Covid- 19 patients.</p><p><strong>Results: </strong>We show that the degrees of necessity and of sufficiency can be represented as proportions of areas easily identifiable in the plot of the predictiveness curve. In addition, we show that the proportion of explained variation, a common measure of predictiveness and relative importance of prognostic factors, is also closely connected to these areas.</p><p><strong>Conclusion: </strong>Our investigations demonstrate that the predictiveness curve extended by these new interpretations of areas provides a comprehensive evaluation of markers or sets of markers for prediction.</p><p><strong>Trial registration: </strong>Austrian Coronavirus Adaptive Clinical Trial (ACOVACT); ClinicalTrials.gov, identifier NCT04351724.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"107"},"PeriodicalIF":3.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12016328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143959630","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":"Correction: Cause of death coding in asthma.","authors":"Alexandria Chung, George Addo Opoku-Pare, Holly Tibble","doi":"10.1186/s12874-025-02550-0","DOIUrl":"https://doi.org/10.1186/s12874-025-02550-0","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"105"},"PeriodicalIF":3.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12016139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143973875","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":"Causal inference methodologies to assess the effect of missed clinic visits on treatment success rate among people with tuberculosis in rural Uganda.","authors":"Jonathan Izudi, Adithya Cattamanchi, Francis Bajunirwe","doi":"10.1186/s12874-025-02553-x","DOIUrl":"https://doi.org/10.1186/s12874-025-02553-x","url":null,"abstract":"<p><strong>Background: </strong>Although randomized controlled trials are the gold standard design for cause-effect analysis, high costs and challenges around practicability, feasibility, and ethics may limit their use. In such situations, causal inference methods can improve the rigor of cause-effect analysis using observational data but such methods have infrequently been applied in tuberculosis (TB) research. We conducted a parallel comparison across three causal inference methods in order to assess the causal association between missed clinic visit/s and treatment success among people with drug-susceptible bacteriologically confirmed pulmonary TB.</p><p><strong>Methods: </strong>We used causal inference methods to analyze cross-sectional data of adults with drug-susceptible bacteriologically confirmed pulmonary TB at clinics in rural eastern Uganda. We compared effect estimates from three causal inference methods, namely instrumental variable analysis, propensity-score analysis (adjustment, matching, weighting, and stratification), and double-robust estimation for cause-effect analysis. The exposure was missing a TB clinic visit/s and the outcome was treatment success defined as cure or treatment completion, both measured on a binary scale. Covariates were selected based on the literature, and their social and biological relevance to the outcome. We report the odds ratio and 95% confidence interval from each causal analysis.</p><p><strong>Results: </strong>Of 762 participants (mean age of 39.3 ± 15.8 years) included, 186 (24.4%) had missed a clinic visit/s while 687 (90.2%) were successfully treated for TB. Missed clinic visit/s lowered treatment success across all analyses with instrumental variable analysis (OR 0.41, 95% CI 0.20-0.82), propensity-score analysis (adjustment [OR 0.49, 95% CI 0.30-0.82], matching [OR 0.43, 95% CI 0.21-0.91)], weighting [OR 0.52, 95% CI 0.30-0.91], and stratification [OR 0.34, 95% CI 0.19-0.62]), and double-robust estimation (OR 0.49, 95% CI 0.28-0.85).</p><p><strong>Conclusions: </strong>Missed clinic visit/s reduced the likelihood of TB treatment success rate across all causal inference methods, supporting a causal relationship. Studies are needed to examine interventions that enhance retention in TB treatment.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"104"},"PeriodicalIF":3.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969450","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 comparison of methods for analysing compositional data with fixed and variable totals: a simulation study using the examples of time-use and dietary data.","authors":"Georgia D Tomova, Rosemary Walmsley, Laurie Berrie, Michelle A Morris, Peter W G Tennant","doi":"10.1186/s12874-025-02509-1","DOIUrl":"https://doi.org/10.1186/s12874-025-02509-1","url":null,"abstract":"<p><strong>Background: </strong>Compositional data comprise the parts of a 'whole' (or 'total'), which sum to that 'whole'. The 'whole' may vary between units of analyses, or it may be fixed (constant). For example, total energy intake (a variable total) is the sum of intake from all foods or macronutrients. Total time in a day (a fixed total) is the sum of time spent engaging in various activities. There exist different approaches to analysing compositional data, such as the isocaloric or isotemporal model, ratio variables, and compositional data analysis (CoDA). Although the performance of the different approaches has been compared previously, this has only been conducted in real data. Since the true relationships are unknown in real data, it is difficult to compare model performance in estimating a known effect. We use data simulations of different parametric relationships, to explore and demonstrate the performance of each approach under various possible conditions.</p><p><strong>Methods: </strong>We simulated physical activity time-use and dietary data as examples of compositional data with fixed and variable totals, respectively, using different parametric relationships between the compositional components and the outcome (fasting plasma glucose): linear, log₂, and isometric log-ratios. We evaluated the performance of a range of generalised linear and additive models as well as CoDA, in estimating a 1-unit and either 10-unit (for physical activity) or 100-unit (for dietary data) reallocations under each parametric scenario. We simulated 10,000 datasets with 1,000 observations in each.</p><p><strong>Results: </strong>The performance of each approach to analysing compositional data depends on how closely its parameterisation matches the true data generating process. Overall, we demonstrated that the consequences of using an incorrect parameterisation (e.g. using CoDA when the true relationship is linear) are more severe for larger reallocations (e.g. 10-min or 100-kcal) than for 1-unit reallocations. The implications of choosing an unsuitable approach may be starker in compositional data with variable totals. For example, while models with ratio variables are mathematically equivalent to linear models in compositional data with fixed totals, their estimates may be radically different for variable totals.</p><p><strong>Conclusions: </strong>Compositional data with fixed and variable totals behave differently. All existing approaches to analysing such data have utility but need to be carefully selected. Investigators should explore the shape of the relationships between the compositional components and the outcome and chose an approach that matches it best.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"100"},"PeriodicalIF":3.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143978938","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":"Cohort data with dropout: a simulation study comparing five longitudinal analysis methods.","authors":"Rebecca K Stellato, Rutger M van den Bor, Maria Schipper, Maud Y A Lindeboom, Marinus J C Eijkemans","doi":"10.1186/s12874-025-02506-4","DOIUrl":"https://doi.org/10.1186/s12874-025-02506-4","url":null,"abstract":"<p><strong>Background: </strong>A simulation study was performed to visually demonstrate the problems with repeated measures ANOVA (RMA) and t-tests (TT) compared to linear mixed effects (LME), covariance pattern (CP) or generalized estimating equations (GEE) models in longitudinal cohort studies with dropout.</p><p><strong>Methods: </strong>Data were generated for a realistic, observational study on health-related quality of life (HRQoL) in a small, heterogeneous sample of children undergoing anti-reflux surgery. Each generated sample comprised two groups: one with low levels (4-10%) of random dropout (missing completely at random, MCAR); the other with higher levels (10-40%), where the chance of dropout depended on lower baseline HRQoL (missing at random, MAR). Outcome data were simulated for four time points in a one-year period, assuming in both groups small but meaningful increases in HRQoL between baseline and 3 months, and thereafter constant levels to 12 months. Five analysis methods were applied to the simulated datasets: LME; CP; GEE; RMA; and independent TT at all time points (between groups) or paired TT on the difference between 12 and 0 months (within groups). The bias in estimated marginal means was examined, and the coverage and width of 95% confidence intervals for, and the power of, three within- and between-group contrasts were examined.</p><p><strong>Results: </strong>In the group with MCAR, negligible bias was observed in all methods, coverage was close to 95%, and little difference was seen in power among methods. In the group with MAR dropout, independent and paired TT and RMA analyses displayed increasing bias and decreasing coverage and power for increasing levels of dropout. The paired TT also produced the widest confidence intervals on average, with the greatest variability. GEE displayed slightly lower coverage and higher power than LME and CP models, but bias and precision were further comparable to LME and CP. LME and CP models produced unbiased results and close to 95% coverage, even in the case of 40% MAR dropout.</p><p><strong>Conclusions: </strong>As expected, LME and CP models performed best in terms of bias and coverage even in the case of higher levels of MAR data. Paired TT and RMA produce biased results and poor coverage and precision in the presence of MAR data.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"103"},"PeriodicalIF":3.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143972067","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":"Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial.","authors":"Lu Mao","doi":"10.1186/s12874-025-02554-w","DOIUrl":"https://doi.org/10.1186/s12874-025-02554-w","url":null,"abstract":"<p><strong>Background: </strong>The win ratio has been widely used in the analysis of hierarchical composite endpoints, which prioritize critical outcomes such as mortality over nonfatal, secondary events. Although a regression framework exists to incorporate covariates, it is limited to low-dimensional datasets and may struggle with numerous predictors. This gap necessitates a robust variable selection method tailored to the win ratio framework.</p><p><strong>Methods: </strong>We propose an elastic net-type regularization approach for win ratio regression, extending the proportional win-fractions (PW) model in low-dimensional settings. The method addresses key challenges, including adapting pairwise comparisons to penalized regression, optimizing model selection through subject-level cross-validation, and defining performance metrics via a generalized concordance index. The procedures are implemented in the wrnet R-package, publicly available at https://lmaowisc.github.io/wrnet/ .</p><p><strong>Results: </strong>Simulation studies demonstrate that wrnet outperforms traditional (regularized) Cox regression for time-to-first-event analysis, particularly in scenarios with differing covariate effects on mortality and nonfatal events. When applied to data from the HF-ACTION trial, the method identified prognostic variables and achieved superior predictive accuracy compared to regularized Cox models, as measured by overall and component-specific concordance indices.</p><p><strong>Conclusion: </strong>The wrnet approach combines the interpretability and clinical relevance of the win ratio with the scalability and robustness of elastic net regularization. The accompanying R-package provides a user-friendly interface for routine application of the procedures, whenever appropriate. Future research could explore additional applications or refine the methodology to address non-proportionalities in win-loss risks and nonlinearities in covariate effects.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"102"},"PeriodicalIF":3.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143967929","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":"The conclusiveness of trial sequential analysis varies with estimation of between-study variance: a case study.","authors":"Enoch Kang, James S Hodges, Yu-Chieh Chuang, Jin-Hua Chen, Chiehfeng Chen","doi":"10.1186/s12874-025-02545-x","DOIUrl":"https://doi.org/10.1186/s12874-025-02545-x","url":null,"abstract":"<p><strong>Background: </strong>Trial sequential methods have been introduced to address issues related to increased likelihood of incorrectly rejecting the null hypothesis in meta-analyses due to repeated significance testing. Between-study variance (τ²) and its estimate ( <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ²) play a crucial role in both meta-analysis and trial sequential analysis with the random-effects model. Therefore, we investigated how different <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ² impact the results of and quantities used in trial sequential analysis.</p><p><strong>Methods: </strong>This case study was grounded in a Cochrane review that provides data for smaller (< 10 randomized clinical trials, RCTs) and larger (> 20 RCTs) meta-analyses. The review compared various outcomes between video-laryngoscopy and direct laryngoscopy for tracheal intubation, and we used outcomes including hypoxemia and failed intubation, stratified by difficulty, expertise, and obesity. We calculated odds ratios using inverse variance method with six estimators for τ², including DerSimonian-Laird, restricted maximum-likelihood, Paule-Mandel, maximum-likelihood, Sidik-Jonkman, and Hunter-Schmidt. Then we depicted the relationships between <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ² and quantities in trial sequential analysis including diversity, adjustment factor, required information size (RIS), and α-spending boundaries.</p><p><strong>Results: </strong>We found that diversity increases logarithmically with <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ², and that the adjustment factor, RIS, and α-spending boundaries increase linearly with <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ². Also, the conclusions of trial sequential analysis can differ depending on the estimator used for between-study variance.</p><p><strong>Conclusion: </strong>This study highlights the importance of <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ² in trial sequential analysis and underscores the need to align the meta-analysis and the trial sequential analysis by choosing estimators to avoid introducing biases and discrepancies in effect size estimates and uncertainty assessments.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"101"},"PeriodicalIF":3.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969578","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":"Interpretation of coefficients in segmented regression for interrupted time series analyses.","authors":"Yongzhe Wang, Narissa J Nonzee, Haonan Zhang, Kimlin T Ashing, Gaole Song, Catherine M Crespi","doi":"10.1186/s12874-025-02556-8","DOIUrl":"https://doi.org/10.1186/s12874-025-02556-8","url":null,"abstract":"<p><strong>Background: </strong>Segmented regression, a common model for interrupted time series (ITS) analysis, primarily utilizes two equation parametrizations. Interpretations of coefficients vary between the two segmented regression parametrizations, leading to occasional user misinterpretations.</p><p><strong>Methods: </strong>To illustrate differences in coefficient interpretation between two common parametrizations of segmented regression in ITS analysis, we derived analytical results and present an illustration evaluating the impact of a smoking regulation policy in Italy using a publicly accessible dataset. Estimated coefficients and their standard errors were obtained using two commonly used parametrizations for segmented regression with continuous outcomes. We clarified coefficient interpretations and intervention effect calculations.</p><p><strong>Results: </strong>Our investigation revealed that both parametrizations represent the same model. However, due to differences in parametrization, the immediate effect of the intervention is estimated differently under the two approaches. The key difference lies in the interpretation of the coefficient related to the binary indicator for intervention implementation, impacting the calculation of the immediate effect.</p><p><strong>Conclusions: </strong>Two common parametrizations of segmented regression represent the same model but have different interpretations of a key coefficient. Researchers employing either parametrization should exercise caution when interpreting coefficients and calculating intervention effects.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"98"},"PeriodicalIF":3.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12001611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143968600","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}