QuestionAre spinal manipulation therapy and clinician-supported biopsychosocial self-management provided by physical therapists or chiropractors more effective than guideline-based medical care for preventing chronic impactful low back pain (LBP) at 1 year?FindingsIn this randomized clinical trial including 1000 adults, supported self-management resulted in a small reduction in LBP impact score at 1 year with a higher proportion of responders with at least a 50% reduction compared to medical care. Reductions in LBP impact scores did not differ between spinal manipulation and medical care, and supported self-management performed better on most secondary outcomes compared to medical care.MeaningGiven the reductions in LBP impact score and consistent results of the responder analyses and multiple measures of chronic LBP burden, the effects of clinician-supported biopsychosocial self-management compared with guideline-based medical care appear to be clinically relevant. ImportanceAcute and subacute low back pain (LBP) often progresses to a chronic impactful back problem in patients with elevated risk. The most effective way to prevent this progression is unknown.ObjectiveTo determine the effectiveness of spinal manipulation and clinician-supported biopsychosocial self-management vs medical care for preventing chronic impactful LBP.Design, Setting, and ParticipantsThis 2 & times; 2 factorial randomized clinical trial was conducted in research clinics at the University of Minnesota and the University of Pittsburgh, Pennsylvania, from November 2018 to May 2023, with follow-up concluding in June 2024. Adults with acute or subacute LBP with a moderate to high risk of chronicity were included.InterventionsFour interventions were applied for 8 weeks: spinal manipulation therapy; supported self-management; combined spinal manipulation therapy and supported self-management; and guideline-based medical care. Spinal manipulation and supported self-management were provided by physical therapists and chiropractors.Main Outcomes and MeasuresMean LBP impact score per the US National Institutes of Health Task Force on Chronic LBP scale (8 [best] to 50 [worst]) during 10 to 12 months, responder analyses of group differences in the proportion of participants with at least 50% reductions. A reduction of 30% was considered the minimal clinically important within-patient difference. Secondary outcomes included measures of chronicity and LBP burden (ie, health care and medication use, productivity), important patient-reported outcomes (eg, improvement, satisfaction), biopsychosocial measures (eg, Patient-Reported Outcomes Measurement Information System), and potential mediating psychosocial measures (eg, self-efficacy, kinesiophobia, pain catastrophizing).ResultsOf the 1000 participants (mean [SD] age, 47 [16] years; 577 females [58%]) randomized, 928 (93%) completed the trial. An omnibus test of the primary outcome was statistically significant (P = .006). Group differences in mean LBP impact scores were small but statistically significant: supported self-management vs medical care, -1.7 (95% CI, -2.7 to -0.6); combined self-management and spinal manipulation vs medical care, -1.3 (95% CI, -2.5 to 0). Spinal manipulation therapy and medical care did not differ: -0.3 (95% CI, -1.5 to 1.0). Adding spinal manipulation to supported self-management did not provide additional benefit. The supported self-management group had a significantly higher proportion with at least 50% reduction in LBP impact vs medical care (64% vs 55%). Supported self-management also performed better on most secondary outcomes compared to medical care, including 12% fewer reporting chronic pain that frequently interfered with regular activities. Mediation analyses showed changes in psychosocial factors at 6 months and explained 76% of supported self-management effects at 1 year.Conclusions and RelevanceThis randomized clinical trial found that for patients with acute or subacute LBP at increased risk of chronic impactful LBP, clinician-supported biopsychosocial self-management resulted in a lower mean LBP impact score at 10 to 12 months vs medical care; spinal manipulation and medical care did not differ. While the LBP impact difference was small, the consistent results of the responder analyses and most secondary outcomes suggest differences between clinician-supported self-management and medical care are clinically relevant.Trial RegistrationClinicalTrials.gov Identifier: NCT03581123 This randomized clinical trial compares the effectiveness of spinal manipulation and clinician-supported biopsychosocial self-management compared with medical care for preventing chronic low back pain.

As artificial intelligence becomes embedded in clinical workflows, trials must accommodate ongoing monitoring and updates.

Background Many patients with lung cancer receive guideline-discordant nodal staging. However, existing studies lack clinical details—such as imaging indications for biopsy—that obscure attempts to characterize guideline-discordant nodal staging. Research question What are the frequency and types of guideline-discordant nodal staging, and what factors are associated with discordance? Study design and methods We conducted a cohort study of patients diagnosed with non-metastatic non-small cell lung cancer (2010-2021) staged by computed tomography and positron emission tomography four months prior to initiating treatment. We linked data from administrative, cancer, and vital status registries to electronic health records from two health systems. Guideline-discordant nodal staging was defined as no biopsy when one was indicated (e.g. tumor >3cm, central tumor, or lymphadenopathy on imaging), or a non-diagnostic, non-thorough, or negative needle biopsy despite high suspicion for nodal disease. We used generalized estimating equations to investigate imaging factors associated with guideline-discordant nodal staging. We performed a sensitivity analysis to evaluate the robustness of our findings across two national practice guidelines. Results Among 5,582 patients, 3,580 (64%) had an indication for biopsy, of which 2,798 (78%) experienced guideline-discordant nodal staging. Types of guideline-discordant nodal staging included no biopsy despite an indication for one (82%), negative needle biopsy despite a high suspicion of nodal disease (9%), a non-thorough procedure (7%), a non-diagnostic biopsy (1%), and other (1%). These findings were robust across sensitivity analyses—the only factor consistently associated with a higher risk of guideline-discordant nodal staging was higher standardized uptake values for the primary tumor. Interpretation When estimated among patients with an indication for biopsy, rates of guideline-discordant nodal staging were in the upper range of prior reports. The predominant type of guideline-discordant staging was no biopsy despite an indication. These findings further motivate the need for improved adoption and performance of nodal staging.

Motivation Although common data models for electronic health record (EHR) data can facilitate multi-site data organization and querying, the same medical event may still be coded differently between healthcare systems. In this paper, we present statistical methods to identify and mitigate coding discrepancies using summary-level data, and demonstrate these methods using data from two FDA Sentinel data partners: Kaiser Permanente Washington and Kaiser Permanente Northwest.Results We first characterize differences in coding patterns, then compute a code mapping matrix to harmonize data between systems. Our findings reveal significant heterogeneity in coded EHR data, even after adopting a common data model with the same coding system, highlighting the importance of data harmonization before downstream analyses. Our study also demonstrates the effectiveness of the data harmonization approaches, which provide a foundational data quality step to promote semantic interoperability, enhance data integration, and improve the integrity of study conclusions.Availability and implementation Computation prototypes, including R/Python codes and examples, are included in Section 7, available as supplementary data at Bioinformatics online and will be posted on GitHub upon publication.

BackgroundStepped wedge cluster randomized trials (SW-CRTs) have historically been analyzed using immediate treatment (IT) models, which assume the effect of the treatment is immediate after treatment initiation and subsequently remains constant over time. However, recent research has shown that this assumption can lead to severely misleading results if treatment effects vary with exposure time, i.e., time since the intervention started. Models that account for time-varying treatment effects, such as the exposure time indicator (ETI) model, allow researchers to target estimands such as the time-averaged treatment effect (TATE) over an interval of exposure time, or the point treatment effect (PTE) representing a treatment contrast at one time point. However, this increased flexibility results in reduced power.MethodsIn this paper, we use public power calculation software and simulation to characterize factors affecting SW-CRT power. Key elements include choice of estimand, study design considerations, and analysis model selection.ResultsFor common SW-CRT designs, the sample size (clusters per sequence or individuals per cluster-period) must be increased substantially, commonly by a factor of 1.5 to 3, but often by much more, to maintain 90% power when switching from an IT model to an ETI model (targeting the TATE over the study). However, the inflation factor is lower for TATE estimands over shorter periods that exclude longer exposure times. In general, SW-CRT designs (including the "staircase" variant) have much greater power for estimating "short-term effects" relative to "long-term effects." For an ETI model targeting a TATE estimand, substantial power can be gained by adding time points to the start of the study or increasing baseline sample size, but surprisingly, little power is gained from adding time points to the end of the study. More restrictive choices for modeling the exposure time or calendar time trends (e.g., splines or linear terms) have little effect on power for TATE estimands but increases power for PTE estimands. If the effect curve is constant after a washout period, a "delayed constant treatment" model that uses exposure time indicators during the washout period but assumes a constant effect thereafter can slightly increase power relative to an IT model that discards washout period data.

Stepped-wedge cluster-randomized trials (SW-CRTs) are widely used in healthcare and implementation science, enabling all clusters to receive the intervention through a staggered rollout. Traditional model-based methods, including generalized estimating equations and mixed models, yield estimates that depend on implicit weighting schemes and parametric assumptions, and therefore may target ambiguous estimands under model misspecification. In this article, we propose a model-robust standardization framework for SW-CRTs that generalizes existing methods from parallel-arm CRTs to address informative sizes. We define causal estimands including horizontal-individual, horizontal-cluster, vertical-individual, and vertical-cluster average treatment effects under a super population framework and introduce a simple procedure that standardizes parametric and semiparametric working models for estimand-aligned analysis. For any specified working model, the resulting estimators remain consistent for their target estimands even if the working regression model is misspecified; moreover, their efficiency improves as the working model more closely approximates the true data-generating process. We evaluate the finite-sample properties of our proposed estimators through extensive simulations. The proposed methods are implemented in the MRStdLCRT R package on CRAN. Finally, we illustrate the application of our methods through reanalyses of two real-world SW-CRTs.

QuestionHow effective are spinal manipulation and clinician-supported self-management relative to guideline-based medical care for adults with acute or subacute low back pain (LBP) at increased risk of chronicity?FindingsThis randomized clinical trial found statistically significant but small differences between supported self-management and medical care for reducing disability over a follow-up time of 1 year. There were no differences between spinal manipulation and medical care for disability and no group differences in pain intensity.MeaningFor acute or subacute LBP, clinician-supported self-management resulted in a small reduction in disability, but not pain, over a follow-up time of 1 year vs medical care; spinal manipulation showed no difference for either outcome. ImportanceLow back pain (LBP) is influenced by interrelated physical, psychological, and social factors. However, most treatments focus on symptom reduction without addressing the underlying biopsychosocial needs of patients.ObjectiveTo determine the effectiveness of spinal manipulation and clinician-supported biopsychosocial self-management vs medical care for adults with increased risk of chronic disabling LBP.Design, Setting, and ParticipantsThis 2 x 2 factorial randomized clinical trial enrolled participants in 3 research clinics at the Universities of Minnesota and Pittsburgh from November 2018 to May 2023; final follow-up was in June 2024. Adults with acute or subacute LBP at moderate to high risk of chronicity based on the STarT Back tool were randomized to 1 of 4 groups, with interventions lasting up to 8 weeks. Statistical analysis was conducted from November 2024 to June 2025.InterventionsSpinal manipulation therapy (n = 201), supported self-management (n = 305), or combined supported self-management with spinal manipulation (n = 193) compared with guideline-based medical care (n = 301). Physical therapists and chiropractors provided spinal manipulation and supported self-management.Main Outcomes and MeasuresThe 2 primary outcomes averaged over a follow-up of 1 year were monthly low back disability (Roland-Morris Disability Questionnaire) and weekly pain intensity (numerical rating scale). Secondary analysis examined the proportion of participants achieving a 50% or higher reduction in the primary outcome measures.ResultsAmong the 1000 participants randomized (mean [SD] age, 47 [16] years; 58% female), 93% completed the trial. The omnibus test for differences across the 4 treatment groups was statistically significant for disability (P = .001; supported self-management, 4.7; spinal manipulation, 5.5; combined supported self-management with spinal manipulation, 4.8; medical care, 5.9) but not pain intensity (P = .16; supported self-management, 2.8; spinal manipulation, 3.0; combined supported self-management with spinal manipulation, 2.8; medical care, 3.0). Averaged over 12 months, LBP disability was significantly lower compared with medical care for supported self-management (mean difference, -1.2 [95% CI, -1.9 to -0.5]) and supported self-management with spinal manipulation (mean difference, -1.1 [95% CI, -1.9 to -0.3]) but not spinal manipulation alone (mean difference, -0.4 [95% CI, -1.2 to 0.4]). Group differences in pain intensity were not statistically significant; point estimates ranged from -0.2 to 0. Both supported self-management groups had higher proportions of patients achieving a 50% or greater reduction in disability (supported self-management, 67%; spinal manipulation, 54%; combined supported self-management with spinal manipulation, 65%; medical care, 54%).Conclusions and RelevanceFor patients with acute or subacute LBP at increased risk of chronic disabling LBP, clinician-supported biopsychosocial self-management showed statistically significant but small reductions in disability, but not pain, vs medical care over 1-year follow-up, and spinal manipulation alone showed no significant difference for either outcome.Trial RegistrationClinicalTrials.gov Identifier: NCT03581123 This randomized clinical trial examines the efficacy of spinal manipulation therapy combined with clinician-supported self-management vs guideline-directed medical care in patients with acute or subacute low back pain.

Cluster-randomized trials (CRTs) are a well-established class of designs for evaluating community-based interventions. An essential task in planning these trials is determining the number of clusters and cluster sizes needed to achieve sufficient statistical power for detecting a clinically relevant effect size. While methods for evaluating the average treatment effect (ATE) for the entire study population are well-established, sample size methods for testing heterogeneity of treatment effects (HTEs), i.e. treatment-covariate interaction or difference in subpopulation-specific treatment effects, in CRTs have only recently been developed. For pre-specified analyses of HTEs in CRTs, effect-modifying covariates should, ideally, be accompanied by sample size or power calculations to ensure the trial has adequate power for the planned analyses. Power analysis for testing HTEs is more complex than for ATEs due to the additional design parameters that must be specified. Power and sample size formulas for testing HTEs via linear mixed-effects models have been separately derived for different cluster-randomized designs, including single and multi-period parallel designs, crossover designs, and stepped-wedge designs, and for continuous and binary outcomes. This tutorial provides a consolidated reference guide for these methods and enhances their accessibility through an online R Shiny Calculator. We further discuss key considerations for conducting sample size and power calculations to test pre-specified HTE hypotheses in CRTs, highlighting the importance of specifying advanced estimates of intracluster correlation coefficients for both outcomes and covariates and their implications for power. The sample size methodology and calculator functionality are demonstrated through a real CRT example.

BACKGROUND AND OBJECTIVES:Data sharing enhances transparency, facilitates reproducibility, and promotes innovation in health research. For statisticians, access to data from real trials is essential to develop, validate, and refine statistical methods. Within a collection of published stepped-wedge cluster randomized trials (SW-CRTs), we aimed to describe the prevalence and types of data sharing statements, the actual availability of data after emailing authors, and factors associated with data obtainment. METHODS:We identified SW-CRTs published between 2016 and 2022 from a previous systematic review and updated that search to include studies published through December 31, 2023. Data sharing statements, when provided, were classified as indicating data were publicly available, available upon request, or not available. Authors were emailed to request datasets. Associations between trial characteristics and data obtainment were explored using bivariable logistic regression, and the results reported as odds ratio (OR) with 95% confidence interval (CI). RESULTS:Of 217 SW-CRTs identified, 98 (45%) had no clear data sharing statements, 89 (41%) indicated data were available upon request, 16 (7%) indicated data were not available, and 14 (7%) indicated data were publicly available. Datasets were ultimately obtained for 76 (35%) SW-CRTs. Data obtainment did not differ between studies with no data sharing statement and those indicating data were available upon request (both 34%). The odds of data obtainment were significantly higher among trials conducted in low- and middle-income countries (odds ratio [OR] = 2.9, 95% CI 1.5-5.4). The odds of data obtainment increased with years since publication (OR = 1.13; 95% CI 0.99-1.29) and years since trial initiation (OR = 1.11; 95% CI 1.00-1.23), although confidence intervals overlapped with the null. There was no clear evidence of an association either with having positive primary trial results (OR = 0.62; 95% CI 0.35-1.10) or with journal impact factor, trial size, type of design, region of corresponding author, and funding source. CONCLUSION:Data sharing practices in SW-CRTs are suboptimal. The presence of a data sharing statement is not predictive of actual data availability. There is significant regional variation in whether data were obtained but few other characteristics explain variation in data obtainment. Clear guidance and dedicated resources to facilitate data sharing in research are required.

In orderto assess the relevance of trial results or the appropriate trials methods, interest holders (eg, clinicians, patients, and policy makers) need a clear description of the trial's research question. To improve clarity and consistency in clinical trials, the International Conference on Harmonisation (ICH) released the ICH E9(R1) addendum, which set out a framework for defining estimands-a precise description of the treatment effect to be estimated. While the ICH E9(R1) addendum has been widely adopted, it primarily focused on individually randomised trials. In contrast, cluster randomised trials, where groups of individuals are randomised, present additional challenges for defining estimands. Therefore, the CRT-Estimands Frameworkwas developed as a consensus based extension of the ICH E9(R1) addendum for cluster randomised trials. This framework provides a set of attributes that should be described when defining estimands in cluster randomised trials, with the objective of improving the clarity of the estimands, and consequently, the research questions, in these trials. This article presents the CRT-Estimands Framework with explanations and examples of how it can be implemented. Adopting the framework will improve the clarity of estimands in cluster randomised trials and facilitate interest holders to make informed decisions from these trials.

Irregular monitoring and missing data limit the utility of longitudinal biomarkers in real-world practice. We developed a generalizable framework that combines interval-aligned preprocessing, localized multiple imputation, and machine-learning forecasting to generate complete trajectories and predict future biomarker values under routine clinical conditions. Using BCR::ABL1 monitoring in chronic myeloid leukemia as a case study, we aligned measurements to 90-day intervals, applied a windowed, uncertainty-propagating imputation strategy, and trained recurrent neural network (RNN) and XGBoost models to forecast values three and six months ahead. Full Information models achieved RMSEs of 1.22-1.24 for 3-month predictions-well below the biomarker's observed variability-and maintained accuracy even when the most recent visit was intentionally omitted, simulating extended follow-up. This framework preserves local temporal structure, supports individualized monitoring decisions, and is directly adaptable to other continuous biomarkers measured under irregular real-world schedules.

OBJECTIVE:This study describes the enrollment and baseline characteristics of participants in the Lumbar Stenosis Prognostic Subgroups for Personalizing Care and Treatment Study (PROSPECTS) cohort and explores subgroups of patients presenting for nonoperative care. DESIGN:Cross-sectional study. SETTING AND SUBJECTS:We enrolled adults ≥50 years of age initiating nonoperative care for symptomatic lumbar spinal stenosis. We excluded those with serious spinal pathology, conditions limiting ambulation, and prior or planned lumbar surgery. METHODS:We collected demographics, the Patient-Reported Outcomes Measurement Information System (PROMIS) 29, pain intensity, Oswestry Disability Index, Swiss Spinal Stenosis Questionnaire, chronicity of symptoms, pain sites, comorbidities, falls, and opioid use. We used descriptive statistics to characterize the sample and latent class analysis to derive subgroups with distinct phenotypes. The best model was selected on the basis of model fit statistics, class separation, and clinical interpretability. RESULTS:We enrolled 598 participants. The mean age was 67 (SD = 9), and 61% were female. Back and leg pain had been present for ≥1 year in 65% of participants. Multiple pain sites were common, with a mean of 4.3 sites (SD = 2.2), and a majority of patients had multiple comorbidities (54%). We selected a 4-class solution as the best model from the latent class analysis. These phenotypes were described as (1) "high pain impact, low psychosocial features" (n = 233; 39%), (2) "mild pain impact, low psychosocial features" (n = 218; 36%), (3) "high pain impact, complex health needs" (n = 95; 16%), and (4) "acute, intermittent, moderate-severe leg pain with high pain impact" (n = 52; 9%). CONCLUSIONS:These phenotypes reflect distinct profiles that could inform health needs and patient-centered care. Future studies should examine longitudinal outcomes to establish their clinical utility and prognostic value.

Importance:Acute and subacute low back pain (LBP) often progresses to a chronic impactful back problem in patients with elevated risk. The most effective way to prevent this progression is unknown. Objective:To determine the effectiveness of spinal manipulation and clinician-supported biopsychosocial self-management vs medical care for preventing chronic impactful LBP. Design, Setting, and Participants:This 2 × 2 factorial randomized clinical trial was conducted in research clinics at the University of Minnesota and the University of Pittsburgh, Pennsylvania, from November 2018 to May 2023, with follow-up concluding in June 2024. Adults with acute or subacute LBP with a moderate to high risk of chronicity were included. Interventions:Four interventions were applied for 8 weeks: spinal manipulation therapy; supported self-management; combined spinal manipulation therapy and supported self-management; and guideline-based medical care. Spinal manipulation and supported self-management were provided by physical therapists and chiropractors. Main Outcomes and Measures:Mean LBP impact score per the US National Institutes of Health Task Force on Chronic LBP scale (8 [best] to 50 [worst]) during 10 to 12 months, responder analyses of group differences in the proportion of participants with at least 50% reductions. A reduction of 30% was considered the minimal clinically important within-patient difference. Secondary outcomes included measures of chronicity and LBP burden (ie, health care and medication use, productivity), important patient-reported outcomes (eg, improvement, satisfaction), biopsychosocial measures (eg, Patient-Reported Outcomes Measurement Information System), and potential mediating psychosocial measures (eg, self-efficacy, kinesiophobia, pain catastrophizing). Results:Of the 1000 participants (mean [SD] age, 47 [16] years; 577 females [58%]) randomized, 928 (93%) completed the trial. An omnibus test of the primary outcome was statistically significant (P = .006). Group differences in mean LBP impact scores were small but statistically significant: supported self-management vs medical care, -1.7 (95% CI, -2.7 to -0.6); combined self-management and spinal manipulation vs medical care, -1.3 (95% CI, -2.5 to 0). Spinal manipulation therapy and medical care did not differ: -0.3 (95% CI, -1.5 to 1.0). Adding spinal manipulation to supported self-management did not provide additional benefit. The supported self-management group had a significantly higher proportion with at least 50% reduction in LBP impact vs medical care (64% vs 55%). Supported self-management also performed better on most secondary outcomes compared to medical care, including 12% fewer reporting chronic pain that frequently interfered with regular activities. Mediation analyses showed changes in psychosocial factors at 6 months and explained 76% of supported self-management effects at 1 year. Conclusions and Relevance:This randomized clinical trial found that for patients with acute or subacute LBP at increased risk of chronic impactful LBP, clinician-supported biopsychosocial self-management resulted in a lower mean LBP impact score at 10 to 12 months vs medical care; spinal manipulation and medical care did not differ. While the LBP impact difference was small, the consistent results of the responder analyses and most secondary outcomes suggest differences between clinician-supported self-management and medical care are clinically relevant. Trial Registration:ClinicalTrials.gov Identifier: NCT03581123.

Health information exchanges (HIEs) provide the capability to electronically move health care information among different health care information systems and store these data for downstream use cases. However, use of data from HIEs for postmarketing surveillance of medical products has not previously been explored. To conduct a pilot descriptive study characterizing data from MyHealth Access Network, a statewide HIE for Oklahoma, to understand its utility for conducting pharmacoepidemiology studies. MyHealth Access Network connects 95

BACKGROUND:Packed red blood cell (pRBC) transfusions are often required in extremely premature infants but are associated with increased pro-inflammatory cytokines and adverse neurodevelopment, which may differ by sex. METHODS:In this post-hoc analysis of the Preterm Erythropoietin Neuroprotection (PENUT) Trial, associations between pRBC transfusion volume and cytokines at 0-7 and 7-14 days, MRI injury, and Bayley Scales of Infant Development (BSID-III) scores at 24 months corrected age were evaluated. Graphical network and generalized estimating equation models were used to examine interactions by sex as well as the influence of hematocrit level. RESULTS:182 and 164 infants were assessed with biomarkers at 0-7 and 7-14 days, 220 infants had MRIs, and 692 infants had at least one BSID-III assessment. Infant sex modified the association between pRBC transfusion volume and IL-6 at 7-14 days but did not impact the association between transfusion volume or hematocrit and BSID-III scores. Total pRBC transfusion volume was significantly negatively associated with all BSID-III subscales after accounting for anemia and severity of illness. CONCLUSION:Infant sex may impact short-term cytokine responses to transfusions but not the association between transfusion volume and long-term outcomes. IMPACT:In a post hoc analysis of extremely preterm infants from the PENUT Trial, the relationship between transfusion exposure and pro-inflammatory cytokines, MRI scores and neurodevelopment were evaluated by sex. The impact of transfusions on inflammatory cytokines may vary by sex. However, this does not appear to lead to differences in neurodevelopmental outcomes. Based on current evidence, providers should not alter their transfusion practices based on sex of the infant.

Exacerbations of existing low back pain (LBP) or new LBP episodes are colloquially referred to as "flares." Although the experience of flares is common to many people with LBP, few validated measures enable people to self-report if they are experiencing a flare. This study examined the convergent validity of a person-dependent definition of flares ("a worsening of your low back pain that lasts from hours to weeks") as compared with (1) LBP intensity, (2) LBP-related pain interference, and (3) analgesic use, in a large, prospective research study of Veterans with LBP. Veterans seeking care for LBP (n = 465) were followed prospectively for up to 1 year. Participants completed up to 36 scheduled surveys and additional patient-initiated surveys (triggered by the onset of new flares) over follow-up. Each survey inquired about current flare status, pain intensity measured on a 0 to 10 numeric rating scale (NRS), LBP-related pain interference, and analgesic use. Linear mixed-effects models estimated the association between current flare status and pain intensity, with and without adjustment for potential confounding factors; secondary analyses examined associations with pain interference and analgesic use. In longitudinal analyses of 11,817 surveys, flare status was significantly associated with a 2.8-NRS point greater pain intensity (P < 0.0001), with and without adjustment for other factors. Statistically significant associations were found between flare status and LBP-related pain interference and analgesic use. New flare periods were associated with impacts on coping, functional limitations, and mood/emotions. These findings support the convergent validity of a person-dependent flare definition.

This Guide to Statistics and Methods describes design, interpretation, and examples of a stepped-wedge cluster randomized trial.

Stepped wedge designs (SWDs) are increasingly used to evaluate longitudinal cluster-level interventions but pose substantial challenges for valid inference. Because crossover times are randomized, intervention effects are intrinsically confounded with secular time trends, while heterogeneity across clusters, complex correlation structures, baseline covariate imbalances, and small numbers of clusters further complicate inference. We propose a unified semiparametric framework for estimating possibly time-varying intervention effects in SWDs. Under a semiparametric model on treatment contrast, we develop a nonstandard semiparametric efficiency theory that accommodates correlated observations within clusters, varying cluster-period sizes, and weakly dependent treatment assignments. The resulting estimator is consistent and asymptotically normal even under misspecified covariance structure and control cluster-period means, and is efficient when both are correctly specified. To enable inference with few clusters, we exploit the permutation structure of treatment assignment to propose a standard error estimator that reflects finite-sample variability, with a leave-one-out correction to reduce plug-in bias. The framework also allows incorporation of effect modification and adjustment for imbalanced precision variables through design-based adjustment or double adjustment that additionally incorporates an outcome-based component. Simulations and application to a public health trial demonstrate the robustness and efficiency of the proposed method relative to standard approaches.

To examine associations between lumbar intervertebral disc degeneration (LDD) and type II Modic changes (MC) when retaining information at each interspace (“interspace-level analysis”), as compared to aggregating information across interspaces as is typically done in spine research (“person-level analysis”). The study compared results from (1) interspace-level analyses assuming a common relationship across interspaces (the “interspace-level, common-relationship” approach), (2) interspace-level analyses allowing for interspace-specific associations (an “interspace-level, interspace-specific” approach), (3) and a conventional person-level analytic approach. Adults in primary care (n = 147) received lumbar spine magnetic resonance imaging and neuroradiologist-evaluated assessments of prevalent disc height narrowing (DHN), type II MC, and other LDD parameters. Analyses examined associations between DHN and type II MC in interspace-level, common-relationship analyses, interspace-level, interspace-specific analyses, and conventional person-level analyses. Cross-sectional, interspace-level, common-relationship analyses found large-magnitude DHN-type II MC associations (adjusted OR [aOR] = 6.5, 95