Distribution Of Covariates Research Articles

Laparoscopic Heller-Dor myotomy in elderly achalasia patients: a single center experience with PSM analysis

Introduction Achalasia is a rare esophageal motility disorder of unknown etiology. With the ageing of the general population, treatment in elderly patients has become increasingly common; however, the gold standard treatment in this population remains unclear. The aim of this study was to evaluate the outcomes of laparoscopic Heller-Dor myotomy (LHM) in geriatric patients. Material and methods In this study, consecutive achalasia patients undergoing LHM at the University Hospital ‘Federico II’ of Naples from November 2018 to November 2022 were prospectively enrolled. Patients were divided into two groups based on their age at intervention: elderly (≥70 years) and younger (<70 years). The two study groups were compared by minimizing the different distribution of covariates through a propensity score matching analysis (PSM). Results In both populations, there was a significant improvement in terms of manometric parameters and symptoms after surgery. After applying one-on-one PSM, we obtained a total population of 48 achalasia patients divided into two groups (24 patients each). No significant differences were found in terms of demographic characteristics as well as preoperative and intraoperative variables between two groups. At 12 months from surgery, integrated relaxation pressure (IRP) was significantly lower in patients ≥ 70 years (p = 0.032), while younger patients scored significantly less at the post-operative Eckardt score (p = 0.047). Conclusions Laparoscopic Heller-Dor myotomy is a safe and effective treatment even in elderly patients with rapid post-operative recovery, improvement of symptoms and manometric parameters.

Trial arm outcome variance difference after dropout as an indicator of missing-not-at-random bias in randomized controlled trials.

Randomized controlled trials (RCTs) are vulnerable to bias from missing data. When outcomes are missing not at random (MNAR), estimates from complete case analysis (CCA) and multiple imputation (MI) may be biased. There is no statistical test for distinguishing between outcomes missing at random (MAR) and MNAR. Current strategies rely on comparing dropout proportions and covariate distributions, and using auxiliary information to assess the likelihood of dropout being associated with the outcome. We propose using the observed variance difference across trial arms as a tool for assessing the risk of dropout being MNAR in RCTs with continuous outcomes. In an RCT, at randomization, the distributions of all covariates should be equal in the populations randomized to the intervention and control arms. Under the assumption of homogeneous treatment effects and homoskedastic outcome errors, the variance of the outcome will also be equal in the two populations over the course of follow-up. We show that under MAR dropout, the observed outcome variances, conditional on the variables included in the model, are equal across trial arms, whereas MNAR dropout may result in unequal variances. Consequently, unequal observed conditional trial arm variances are an indicator of MNAR dropout and possible bias of the estimated treatment effect. Heterogeneous treatment effects or heteroskedastic outcome errors are another potential cause of observing different outcome variances. We show that for longitudinal data, we can isolate the effect of MNAR outcome-dependent dropout by considering the variance difference at baseline in the same set of patients who are observed at final follow-up. We illustrate our method in simulation for CCA and MI, and in applications using individual-level data and summarydata.

Optimal sampling using Conditioned Latin Hypercube for digital soil mapping: An approach using Bhattacharyya distance

Soil properties are important because they determine the soil’s suitability for different types of plant growth, ecosystems and biota functioning. Soil properties influence nutrient cycling, carbon sequestration and soil management. Digital Soil Mapping (DSM) is a procedure to map soil properties. Soil sampling for DSM is a foundational step in building prediction accuracy and essential for incorporating variability in terms of environmental covariates (ancillary variables). Conditioned Latin Hypercube (CLH) sampling is a method for generating a sample of points from a multivariate distribution that has been conditioned on one or more covariates. It is an extension of Latin Hypercube sampling, which is a popular technique for generating samples from a multivariate distribution in a way that ensures that each dimension is sampled uniformly. CLH sampling carries the benefit of selecting sampling locations covering the feature space and forming a Hypercube of the original sample. However, determining the optimum sample size is crucial in soil survey exercises constrained by budget and time limits. For this purpose, a study was carried out on Scotland's Finzean Estate (44.8 km2) location. A dataset of 21 independent features (16 continuous and five categorical) and 17,932 sampling locations was created using Digital Elevation Model (DEM) derivatives, soil classes map, land cover map, the peat depth map and parent material map to further generate sub-samples and compare the generated sub-samples with the original population. Two hundred CLH sampling datasets were extracted from the original population (17932 data points) with different sizes (5, 10, 15, 20, …, 100) and each size was given 10 repetitions e.g. (5_1, 5_2, …, 5_10). The sample datasets were analysed by comparing the mean, standard deviation, boxplot and estimates of the probability density function (pdf) for all the 16 continuous independent features. All the mentioned comparisons suggest that the impact of increasing sample size on the distribution of covariates can be observed up to a certain point, beyond which further increases in sample size may not yield noticeable differences. Bhattacharyya distance, a statistical measurement that quantifies the similarity between two probability distributions, was calculated between every quantitative and qualitative element of respective sampling size and for the original population. In contrast, as the CLH sample dataset size increased, the Bhattacharyya distance value decreased and became constant.. The optimum number of samples based on the study was determined for the spatial extent of the Finzean Estate in Scotland and a range of 25–50 CLH samples was suggested based on the study. This work, therefore, achieved both reductions in sampling location numbers compared to classical approaches and identification of the precise location of these sample location to achieve optimal DSM.

Reproducible learning for accelerated failure time models via deep knockoffs

Selecting truly relevant variables contributing to the response is a fundamental problem in many scientific fields. One of major challenges in variable selection is to effectively control the false discovery rate (FDR). Most existing variable selection procedures in survival analysis neglect the FDR control. In this article, we fill such a gap and propose a new and flexible variable selection method with guaranteed FDR control for accelerated failure time models. The proposed method combines the strengths of deep knockoffs and the weighted M-estimation procedure and enjoys the FDR control for arbitrarily high dimension with finite samples. More importantly, our method does not require prior knowledge about the joint distribution of covariates. Extensive simulation studies confirm the generality, effectiveness, and power of the proposed method. Finally, the proposed method is used to analyze a primary biliary cirrhosis data to demonstrate its practical utility.

Genetic matching for time-dependent treatments: a longitudinal extension and simulation study

BackgroundLongitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching.MethodsTo evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In simulation, we generated 1,000 datasets, each consisting of 1,000 subjects, and applied: (1) nearest neighbour matching on time-invariant, baseline propensity scores; (2) sequential risk set matching on time-dependent propensity scores; and (3) longitudinal genetic matching on time-dependent covariates. To measure comparative performance, we estimated covariate balance, efficiency, bias, and root mean squared error (RMSE) of treatment effect estimates. In scenario analysis, we varied underlying assumptions for assumed covariate distributions, correlations, treatment assignment models, and outcome models.ResultsIn all scenarios, baseline propensity score matching resulted in biased effect estimation in the presence of time-dependent confounding, with mean bias ranging from 29.7% to 37.2%. In contrast, time-dependent propensity score matching and longitudinal genetic matching achieved stronger covariate balance and yielded less biased estimation, with mean bias ranging from 0.7% to 13.7%. Across scenarios, longitudinal genetic matching achieved similar or better performance than time-dependent propensity score matching without requiring manual re-specifications or normality of covariates.ConclusionsWhile the most appropriate longitudinal method will depend on research questions and underlying data patterns, our study can help guide these decisions. Simulation results demonstrate the validity of our longitudinal genetic matching approach for supporting future real-world assessments of treatments accessible at multiple time points.

Heterogeneous interventional effects with multiple mediators: Semiparametric and nonparametric approaches

Abstract We propose semiparametric and nonparametric methods to estimate conditional interventional indirect effects in the setting of two discrete mediators whose causal ordering is unknown. Average interventional indirect effects have been shown to decompose an average treatment effect into a direct effect and interventional indirect effects that quantify effects of hypothetical interventions on mediator distributions. Yet these effects may be heterogeneous across the covariate distribution. We consider the problem of estimating these effects at particular points. We propose an influence function-based estimator of the projection of the conditional effects onto a working model, and show under some conditions that we can achieve root-n consistent and asymptotically normal estimates. Second, we propose a fully nonparametric approach to estimation and show the conditions where this approach can achieve oracle rates of convergence. Finally, we propose a sensitivity analysis that identifies bounds on both the average and conditional effects in the presence of mediator-outcome confounding. We show that the same methods easily extend to allow estimation of these bounds. We conclude by examining heterogeneous effects with respect to the effect of COVID-19 vaccinations on depression during February 2021.

Additional adjuvant radiotherapy improves survival at 1 year after surgical treatment for pancreatic cancer patients with T4, N2 disease, positive resection margin, and receiving adjuvant chemotherapy.

While adjuvant chemotherapy has been established as standard practice following radical resection of pancreatic ductal adenocarcinoma (PDAC), the role of adjuvant radiation therapy (RT) and which patients may benefit remains unclear. This retrospective study included PDAC patients who received pancreatic surgery from April 2012 to December 2019 in Zhongshan Hospital Fudan University. Patients with carcinoma in situ, distant metastasis, and without adjuvant chemotherapy were excluded. Cox proportional hazards modeling of survival were constructed to find potential prognostic factors. Propensity score matching (PSM) and exploratory subgroup analyses were used to create a balanced covariate distribution between groups and to investigate therapeutic effect of radiotherapy in certain subgroups. A total of 399 patients were finally included, 93 of them receiving adjuvant chemoradiotherapy (C+R+) and 306 of them receiving chemotherapy only. Patients in C+R+ group were more likely to be male patients with T3-4 disease. Lymph node metastases was the only negative prognostic factor associated with overall survival (OS). Additional adjuvant RT was not associated with an OS benefit both before and after PSM. Surprisingly, a trend towards improved OS with RT among patients with either T4, N2 disease or R1 resection becomes significant in patients alive more than 1 year after surgery. Adjuvant RT was not associated with an OS benefit across all patients, though did show a possible OS benefit for the subgroup with T4N2 disease or R1 resection at 1 year after surgery.

The nano-micellar curcumin improves International Prostate Symptoms Score (IPSS) in patients with benign prostatic hyperplasia: a randomized clinical trial.

Benign prostatic hyperplasia (BPH) is the main prevalent disorder in men over forty years, usually revealing itself with lower urinary tract symptoms. Despite the existence of different treatments, the incidence of BPH is increasing, so further studies for better management are a necessity. This research was designed to assay the effectiveness of nano-micellar curcumin on biomedical indicators of patients with BPH. The present research was a double-blind, randomized, and placebo-controlled trial that enrolled fifty-two patients with BPH between June 2021 and December 2021. Participants were randomized to receive 160mg/d nano-micellar curcumin (n = 26) or placebo (n = 26) as soft gel during 3months. Primary end point was changes in International Prostate Symptoms Score (IPSS). Data gathering was occurred using a standard inquiry form and measuring other biomedical parameters based on routine laboratory techniques. To compare the distribution of demographics and covariates, independent t-test and Chi-square were used. Nano-micellar curcumin had significant effect on IPSS (p value: 0.010), low effect on high-sensitive C-reactive protein (hs-CRP) (p value: 0.032), and low to intermediate effect on malondialdehyde (MDA) (p value: 0.014) level as secondary end points after the intervention. The effect of nano-micellar curcumin on other parameters was negligible. Overall, this trial indicated 3-month intake of nano-micellar curcumin had considerable effects on IPSS as the most common clinical symptom and also two biomedical parameters including serum hs-CRP and MDA. http://www.irct.ir : IRCT20170430033730N3.

Determining the effect of one decade on fitness of elite Austrian youth soccer players using propensity score matching.

Current trends in attacking strategies and increases in external workload have led to a need for fast and well-conditioned athletes in modern soccer. More recently, progressions in speed, coordination, power and endurance were found over a decade in elite Austrian youth players. However, possible confounders such as relative age, maturation, learning effects, and academy philosophy may have influenced these changes. The present study aimed to determine the decade effect on fitness under statistical control of players' exact age, height, body mass, test location as well as total number of pretests and time interval between test and pretest. Players annually completed a battery of anthropometric, general and soccer-specific fitness tests. MANCOVA was calculated to identify the overall impacts of the covariates on fitness. To balance the covariates of initially 2,530 "former" (2002 to 2005) and 2,611 "recent" (2012 to 2015) players, 1:1 nearest neighbor propensity score (PS) matching was used, resulting in 587 U13, 573 U14, 475 U15, 325 U16, 262 U17, and 129 U18 matched pairs. The decade effect on fitness was assessed by independent t-tests and Cohen's d separately at each age group. Superior performances of recent players were found for linear sprint across all age categories (d = 0.154-0.476) as well as for agility (d = 0.125-0.340) and change-of-direction speed (d = 0.172-0.466) in U15 to U18. Reaction speed increased in U13 (d = 0.288) and U15 (d = 0.310). Flexibility reduced over the decade in all age categories (d = -0.151 to -0.589) and upper-limb power decreased (d = -0.278 to -0.347) in U13 and U14. Balancing the covariate distribution via PS matching generally confirmed previous findings, with fitness decade effects reflecting the athletic needs for modern soccer. Since fitness performance changed over time, reference values should be periodically updated. Coaches favor both physical and cognitive fast players nowadays. Thus, training should target all aspects of speed, without disregarding flexibility, upper-limb power and other preventive strategies that keep the players on the pitch.

Rerandomization and optimal matching

AbstractOn average, randomization achieves balance in covariate distributions between treatment groups; yet in practice, chance imbalance exists post randomization, which increases the error in estimating treatment effects. This is an important issue, especially in cluster randomized trials, where the experimental units (the clusters) are highly heterogeneous and relatively few in number. To address this, several restricted randomization designs have been proposed to balance on a few covariates of particular interest. More recently, approaches involving rerandomization have been proposed that aim to achieve simultaneous balance on several important prognostic factors. In this article, we comment on some properties of rerandomized designs and propose a new design for comparing two or more treatments. This design combines optimal nonbipartite matching of the subjects together with rerandomization, both aimed at minimizing a measure of distance between elements in blocks to achieve reductions in the mean squared error of estimated treatment effects. Compared with the existing alternatives, the proposed design can substantially reduce the mean squared error of the estimated treatment effect. This enhanced efficiency is evaluated both theoretically and empirically, and robustness properties are also noted. The design is generalized to three or more treatment arms.

Calculating the power of a planned individual participant data meta-analysis of randomised trials to examine a treatment-covariate interaction with a time-to-event outcome.

Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers should consider the power of their planned IPDMA conditional on the studies promising their IPD and their characteristics. Such power estimates help inform whether the IPDMA project is worth the time and funding investment, before IPD are collected. Here, we suggest how to estimate the power of a planned IPDMA of randomised trials aiming to examine treatment-covariate interactions at the participant-level (i.e., treatment effect modifiers). We focus on a time-to-event (survival) outcome with a binary or continuous covariate, and propose an approximate analytic power calculation that conditions on the actual characteristics of trials, for example, in terms of sample sizes and covariate distributions. The proposed method has five steps: (i) extracting the following aggregate data for each group in each trial-the number of participants and events, the mean and SD for each continuous covariate, and the proportion of participants in each category for each binary covariate; (ii) specifying a minimally important interaction size; (iii) deriving an approximate estimate of Fisher's information matrix for each trial and the corresponding variance of the interaction estimate per trial, based on assuming an exponential survival distribution; (iv) deriving the estimated variance of the summary interaction estimate from the planned IPDMA, under a common-effect assumption, and (v) calculating the power of the IPDMA based on a two-sided Wald test. Stata and R code are provided and a real example provided for illustration. Further evaluation in real examples and simulations is needed.

Covariate-Shift Generalization via Random Sample Weighting

Shifts in the marginal distribution of covariates from training to the test phase, named covariate-shifts, often lead to unstable prediction performance across agnostic testing data, especially under model misspecification. Recent literature on invariant learning attempts to learn an invariant predictor from heterogeneous environments. However, the performance of the learned predictor depends heavily on the availability and quality of provided environments. In this paper, we propose a simple and effective non-parametric method for generating heterogeneous environments via Random Sample Weighting (RSW). Given the training dataset from a single source environment, we randomly generate a set of covariate-determining sample weights and use each weighted training distribution to simulate an environment. We theoretically show that under appropriate conditions, such random sample weighting can produce sufficient heterogeneity to be exploited by common invariance constraints to find the invariant variables for stable prediction under covariate shifts. Extensive experiments on both simulated and real-world datasets clearly validate the effectiveness of our method.

Calibrated meta-analysis to estimate the efficacy of mental health treatments in target populations: an application to paliperidone trials for treatment of schizophrenia

BackgroundsMeta-analyses can be a powerful tool but need to calibrate potential unrepresentativeness of the included trials to a target population. Estimating target population average treatment effects (TATE) in meta-analyses is important to understand how treatments perform in well-defined target populations. This study estimated TATE of paliperidone palmitate in patients with schizophrenia using meta-analysis with individual patient trial data and target population data.MethodsWe conducted a meta-analysis with data from four randomized clinical trials and target population data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Efficacy was measured using the Positive and Negative Syndrome Scale (PANSS). Weights to equate the trial participants and target population were calculated by comparing baseline characteristics between the trials and CATIE. A calibrated weighted meta-analysis with random effects was performed to estimate the TATE of paliperidone compared to placebo.ResultsA total of 1,738 patients were included in the meta-analysis along with 1,458 patients in CATIE. After weighting, the covariate distributions of the trial participants and target population were similar. Compared to placebo, paliperidone palmitate was associated with a significant reduction of the PANSS total score under both unweighted (mean difference 9.07 [4.43, 13.71]) and calibrated weighted (mean difference 6.15 [2.22, 10.08]) meta-analysis.ConclusionsThe effect of paliperidone palmitate compared with placebo is slightly smaller in the target population than that estimated directly from the unweighted meta-analysis. Representativeness of samples of trials included in a meta-analysis to a target population should be assessed and incorporated properly to obtain the most reliable evidence of treatment effects in target populations.

Comparative Efficacy of Infliximab Versus Tofacitinib for Inducing Remission in Biologic Naive Ulcerative Colitis: A Propensity Matched Study.

In the absence of head-to-head clinical trials, indirect comparative studies are needed to help position therapies in ulcerative colitis (UC). We aimed to compare the efficacy of infliximab vs. tofacitinib for moderate-severe UC among biologic-naïve participants at post-induction. This was a post-hoc analysis of patient-level data from four clinical trials including 659 biologic-naïve UC participants. We compared proportions of patients achieving week 8 clinical remission (CR), endoscopic improvement, and endoscopic remission. Clinical response at week 2 was also assessed. Multiple logistic regression models were adjusted for potential confounders identified as having an association with the outcome of interest on univariate analysis. Propensity scores were calculated to create a cohort of participants with similar distribution of baseline co-variates. Patients treated with infliximab had significantly greater odds of CR at week 8 compared to tofacitinib [88/242 (36.4%) vs. 100/417 (24.0%), aOR: 1.65 (95% CI 1.11-2.44), p = 0.013]. Endoscopic improvement at week 8 was also significantly greater among infliximab-treated patients [149/242 (61.6%) vs. 159/417 (38.1%), aOR: 2.12 (95% CI 1.45-3.10), p < 0.001]. Similar findings were observed with week 8 endoscopic remission [61/242 (25.2%) vs. 43/417 (10.3%); aOR: 2.72 (95% CI 1.66-4.46), p < 0.001]. A similar proportion of participants attained clinical response at week 2 [205/242 (84.7%) vs. 334/417 (80.1%), aOR: 1.48 (95% CI 0.93-2.37), p = 0.101]. Similar results were observed among the propensity score matched cohort. Based on the efficacy observed in this post-hoc analysis, consideration should be given to use of infliximab over tofacitinib for treatment of moderate to severe biologic-naïve UC. However, baseline characteristic mismatches persisted despite propensity score matching, and further studies are needed to confirm our findings.

Allogeneic transplantation in advanced cutaneous T-cell lymphomas (CUTALLO): a propensity score matched controlled prospective study

Advanced-stage cutaneous T-cell lymphomas (CTCLs) are rare, usually refractory, and fatal diseases. Case series have suggested that allogeneic haematopoietic stem cell transplantation (HSCT) might improve the prognosis of advanced-stage CTCLs. The objective of this study was to investigate the effect of allogeneic HSCT compared with non-HSCT therapy on the outcome of individuals with advanced-stage CTCLs. In this prospective, multicentre, matched controlled trial, conducted at 30 hospitals, participants with advanced CTCLs were allocated treatment: if they had an available compatible related donor they were assigned to allogeneic HSCT, or if not they were allocated to non-allogeneic HSCT therapy. Key inclusion criteria were participants aged 18-70 years, with advanced stage mycosis fungoides or Sézary syndrome, and at least one poor prognostic criteria. Participants were excluded if they were not in complete or partial remission of the disease. Propensity score 1:1 matching with replacement (ie, that each participant treated with HSCT was matched to the participant with the closest propensity score treated with non-HSCT therapy, even if they had already been matched) was used to handle confounding factors, with the balance of covariate distribution between HSCT and non-HSCT groups assessed using standardised mean differences. The primary endpoint was progression-free survival in the matched intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT02520908), and is currently active but not recruiting. From June 1, 2016, to March 3, 2022, total of 99 participants were enrolled at 17 centres in France. Participants with a sibling or matched unrelated donor were assigned to allogeneic HSCT (HSCT group, n=55 [56%]) and participants without a donor were assigned to non-allogeneic HSCT treatment (non-HSCT group, n=44 [44%]). The median follow-up among survivors was 12·6 months (IQR 11·0-35·2). In the HSCT group, 51 participants (93%) were 1:1 matched to participants from the non-HSCT group. In the intention-to-treat analysis, median progression-free survival was significantly longer in the HSCT group (9·0 months [95% CI 6·6-30·5]) than in the non-HSCT group (3·0 months [2·0-6·3]), with a hazard ratio of 0·38 (95% CI 0·21-0·69; p<0·0001). In the per-protocol population, 40 participants (78%) in the HSCT group had 101 serious events and 29 participants (67%) in the non-HSCT group had 70 serious adverse events. The most common serious adverse event other than graft-versus-host disease in both groups was infections, occurring in 30 participants (59%) in the HSCT group and in 19 participants (44%) in the non-HSCT group. Allogeneic HSCT was associated with significantly longer progression-free survival in participants with advanced-stage CTCLs. These results indicate that allogeneic HSCT treatment should be made available to individuals with high-risk, advanced-stage mycosis fungoides or Sézary syndrome who achieve pre-transplant disease remission. French Ministry of Health, National Cancer Institute, Programme Hospitalier de Recherche Clinique en Cancérologie.

Randomizing a clinical trial in neuro-degenerative disease

The paper studies randomization rules for a sequential two-treatment, two-site clinical trial in Parkinson’s disease. An important feature is that we have values of responses and five potential prognostic factors from a sample of 144 patients similar to those to be enrolled in the trial. Analysis of this sample provides a model for trial analysis. The comparison of allocation rules is made by simulation yielding measures of loss due to imbalance and of potential bias. A major novelty of the paper is the use of this sample, via a two-stage algorithm, to provide an empirical distribution of covariates for the simulation; sampling of a correlated multivariate normal distribution is followed by transformation to variables following the empirical marginal distributions. Six allocation rules are evaluated. The paper concludes with some comments on general aspects of the evaluation of such rules and provides a recommendation for two allocation rules, one for each site, depending on the target number of patients to be enrolled.

Association of Perioperative Use of Epidural Analgesia with Disease Free Survival in Epithelial Ovarian Cancer- A Retrospective Cohort Observational Study with Propensity Score Matched Analysis

Background: Recurrence is common after surgery for epithelial ovarian cancer and is multifactorial. Perioperative factors affecting stress and inflammation have an influence on immunity and thus cancer recurrence. The effect of peri-operative epidural anesthesia has been quoted to be beneficial by decreasing stress. A retrospective analysis of perioperative Epidural analgesia use (EA) was compared to general anesthesia (GA) without epidural, with disease free survival (DFS)as the outcome.&#x0D; Methods: We did a retrospective observational study of patients with epithelial ovarian malignancy who had undergone surgery between 2013 and 2017. Cohorts were primarily divided based on receipt of epidural analgesia. Perioperative patient, anesthetic, and tumor related data were collected. The aim was to estimate the association of epidural analgesia with disease free survival (DFS). To prevent bias due to skewed covariate distribution propensity score match was done matching all covariates.&#x0D; Results: We had 312 patients with age ranging from 18 to 77. The median DFS in months was 32.73±25.6 for the entire cohort while it was 54.81±28.69 for the GA group and 27.23±21.84 for EA group. After PSM median DFS was 35.33 for the full cohort and it was 46 and 34 months for the GA and EA groups, respectively. Multivariate Cox regression analysis demonstrated that only patients who were undergoing primary surgery had survival advantage (P&lt;0.013). Perioperative epidural analgesia was not associated with survival benefit (P=0.480).&#x0D; Conclusion: Perioperative use of epidural analgesia did not offer survival benefit in epithelial ovarian cancer as measured by disease free survival.

Sample size calculations for indirect standardization

Indirect standardization, and its associated parameter the standardized incidence ratio, is a commonly-used tool in hospital profiling for comparing the incidence of negative outcomes between an index hospital and a larger population of reference hospitals, while adjusting for confounding covariates. In statistical inference of the standardized incidence ratio, traditional methods often assume the covariate distribution of the index hospital to be known. This assumption severely compromises one’s ability to compute required sample sizes for high-powered indirect standardization, as in contexts where sample size calculation is desired, there are usually no means of knowing this distribution. This paper presents novel statistical methodology to perform sample size calculation for the standardized incidence ratio without knowing the covariate distribution of the index hospital and without collecting information from the index hospital to estimate this covariate distribution. We apply our methods to simulation studies and to real hospitals, to assess both its capabilities in a vacuum and in comparison to traditional assumptions of indirect standardization.

Changes in the Frequency and Type of Barriers to Reproductive Health Care Between 2017 and 2021

Previous research has documented individual-level barriers to reproductive health services, but few studies have examined national trends. To determine whether the number and type of barriers to reproductive health care experienced by US women of reproductive age changed from 2017 to 2021. This cross-sectional study used serial survey data, weighted to be nationally representative, collected in August 2017 and December 2021 from members of Ipsos's KnowledgePanel who were aged 18 to 49 years and assigned female at birth. Having experienced barriers to reproductive health care over the past 3 years. The primary outcome was number and type of barriers to reproductive health care services, including Papanicolaou tests or birth control, experienced in the past 3 years. Increases in barriers to reproductive health care were measured using multivariable linear regressions adjusted for age, race and ethnicity, education level, employment status, metropolitan area, geographic region, household income, and language (English vs Spanish). Of 29 496 KnowledgePanel members invited, 7022 (mean [SD] age, 33.9 [9.0] years) and 6841 (mean [SD] age, 34.2 [8.6] years) completed the 2017 and 2021 surveys (50% and 45% response rates, respectively). Among 12 351 participants who indicated that they had ever tried accessing reproductive health services, 35.9% (95% CI, 34.8%-37.0%) were aged 30 to 39 years; 5.5% (95% CI, 4.9%-6.2%) were Asian or Pacific Islander, 13.7% (95% CI, 12.8%-14.6%) were Black, 19.1% (95% CI, 18.1%-20.1%) were Hispanic, 58.2% (95% CI, 57.0%-59.5%) were White, and 3.5% (95% CI, 3.1%-4.0%) were multiracial or of other race or ethnicity; and 11.7% (95% CI, 11.0%-12.5%) were living below 100% of the federal poverty level. Covariate distribution was similar across years. In bivariable analyses, participants were more likely to report experiencing a given barrier in the past 3 years in 2021 than in 2017 for all but 2 barriers. More people experienced 3 or more barriers in 2021 (18.6%; 95% CI, 17.3%-20.0%) than in 2017 (16.1%; 95% CI, 14.9%-17.4%) (P = .008). In multivariable analyses, the mean number of barriers increased significantly from 1.09 (95% CI, 1.02-1.14) in 2017 to 1.29 (95% CI, 1.22-1.37) (P < .001) in 2021. Participants who were aged 25 to 29 years (0.42; 95% CI, 0.37-0.47), identified as Hispanic (0.41; 95% CI, 0.38-0.45), had no high school diploma or General Educational Development test (0.62; 95% CI, 0.53-0.72), lived below 100% of the federal poverty level (0.65; 95% CI, 0.55-0.73), and took the survey in Spanish (0.87; 95% CI, 0.73-1.01) saw the greatest increases in mean number of barriers between 2017 and 2021. The study findings suggest that barriers to reproductive health care increased between 2017 and 2021, with the largest increases observed among individuals from historically disadvantaged populations. Efforts are needed to ensure that reproductive health care access remains a priority.

Tailored inference for finite populations: conditional validity and transfer across distributions

Summary Parameters of subpopulations can be more relevant than those of superpopulations. For example, a healthcare provider may be interested in the effect of a treatment plan for a specific subset of their patients; policymakers may be concerned with the impact of a policy in a particular state within a given population. In these cases, the focus is on a specific finite population, as opposed to an infinite superpopulation. Such a population can be characterized by fixing some attributes that are intrinsic to them, leaving unexplained variations like measurement error as random. Inference for a population with fixed attributes can then be modelled as inferring parameters of a conditional distribution. Accordingly, it is desirable that confidence intervals are conditionally valid for the realized population, instead of marginalizing over many possible draws of populations. We provide a statistical inference framework for parameters of finite populations with known attributes. Leveraging the attribute information, our estimators and confidence intervals closely target a specific finite population. When the data are from the population of interest, our confidence intervals attain asymptotic conditional validity, given the attributes, and are shorter than those for superpopulation inference. In addition, we develop procedures to infer parameters of new populations with differing covariate distributions; the confidence intervals are also conditionally valid for the new populations under mild conditions. Our methods extend to situations where the fixed information has a weaker structure or is only partially observed. We demonstrate the validity and applicability of our methods using simulated data and a real-word dataset for predicting car prices.