Percentile Bootstrap Confidence Interval Research Articles

Sensitivity analysis for unmeasured confounding in estimating the difference in restricted mean survival time.

The difference in restricted mean survival time has been increasingly used as an alternative measure to the hazard ratio in survival analysis. Although some statistical methods have been developed for estimating the difference in restricted mean survival time adjusted for measured confounders in observational studies, the impact of unmeasured confounding on the estimate has rarely been assessed. We develop a novel sensitivity analysis for the estimate of the difference in restricted mean survival time with respect to unmeasured confounding. After formulating the sensitivity analysis problem as an optimization problem, we explain how to obtain the sensitivity range of the difference in restricted mean survival time efficiently and assess its uncertainty using the percentile bootstrap confidence interval. Analytic results are provided for some important survival settings. Simulation studies show that the proposed methods perform well in various settings. We illustrate the proposed sensitivity analysis method by analyzing data from the German Breast Cancer Study Group study.

Confidence Intervals for the Mean and Difference of Means of Birnbaum-Saunders Distributions with Application to Wind Speed Data

This paper proposes the confidence intervals for the mean and difference of means of Birnbaum-Saunders (BirSau) distributions based on the Bootstrap confidence interval (BCI), Percentile bootstrap confidence interval (PBCI), Generalized confidence interval (GCI), Bayesian credible interval (BayCrI) and the highest posterior density (HPD). The simulation study used R statistical software to evaluate the coverage probabilities and average lengths. The concerning results of the mean suggest that HPD is the recommended method for constructing confidence intervals in the BirSau distributions, except for small sample sizes where the GCI method proves more efficient. For the difference of means, PBCI emerges as the preferred way to construct confidence intervals, except in some cases where small sample sizes with the HPD method are more efficient. Moreover, the average lengths of these proposed confidence intervals decreased as both sample size and shape parameters increased. To illustrate the effectiveness of the suggested confidence intervals, we applied them to wind speed datasets collected in Ayutthaya and Ratchaburi provinces, Thailand.

The generalized order statistics arising from three populations with the lower truncated proportional hazard rate models and its application to the sensitivity to the early disease stage

ABSTRACT In this paper, we present some results to make inference about the parameters of lower truncated proportional hazard rate models with the same baseline distributions based on three independent generalized order statistics samples. Then, especially by considering the results of the diagnostic tests for the non-diseased, early-diseased stage and fully diseased populations, we make inference about sensitivity to the early disease stage parameter. The maximum likelihood estimator, a generalized pivotal estimator and some Bayes estimators are obtained for different structures of prior distributions. The percentile bootstrap confidence interval, a generalized pivotal confidence interval and some Bayesian credible intervals are also presented. A Monte Carlo simulation study is used to evaluate the performances of the obtained point estimators and confidence/credible intervals and two competitors. We use two real datasets to illustrate the proposed methods.

Does Social Support Play a Role in the Expression and Everyday Functioning Impact of Apathy in HIV Disease?

Clinically notable apathy occurs in approximately one-third of persons living with HIV (PLWH). Drawing from psychological theory, this cross-sectional study examined the interplay between apathy and social support in persons with (n = 143) and without (n = 61) HIV disease. Analyses were conducted using multiple regression and mediation procedures with 95th percentile bootstrap confidence intervals. Positive HIV serostatus and lower social support were associated with more frequent apathy, independent of other mood symptoms. Social support did not moderate apathy's associations with everyday functioning among PLWH, but post hoc analyses revealed that apathy mediated the relationship between social support and everyday functioning among PLWH. Stronger social support may provide a buffer against the frequency of apathy symptoms in persons with and without HIV disease. The relationship between lower social support and poorer everyday functioning in HIV might be partly explained by apathy. Longitudinal research is needed to examine the mechanisms of these relationships.

Abstract PO5-14-08: External Reproducibility of PD-L1 IHC 22C3 pharmDx on Breast Cancer Specimens at CPS ≥ 1 and CPS ≥ 10 Cutoffs

Abstract Background: PD-L1 IHC 22C3 pharmDx is an approved companion diagnostic for pembrolizumab treatment in triple-negative breast cancer (TNBC). Clinical evidence for pembrolizumab approval in TNBC was demonstrated by KEYNOTE-355 (NCT02819518), which investigated the clinical validity of PD-L1 IHC 22C3 pharmDx in identifying patients with PD-L1 expressing Combined Positive Score (CPS) ≥ 10 in previously untreated locally recurrent unresectable or metastatic TNBC.1 The role of immune checkpoint inhibitors for breast cancer as a whole is not as well defined as it is for TNBC. In this study, we show evidence of the reproducibility of PD-L1 IHC 22C3 pharmDx in PD-L1 expression determination in breast cancer specimens at the CPS ≥ 1 and CPS ≥ 10 cutoffs documenting robust assay performance in breast cancer. Methods: A blinded, randomized study was conducted at three external sites using formalin-fixed, paraffin-embedded breast cancer specimens. Reproducibility of PD-L1 IHC 22C3 pharmDx was assessed at the inter-site, intra-site/inter-day, inter-observer, and intra-observer endpoints at the CPS ≥ 1 and CPS ≥ 10 cutoffs. To assess inter-site and intra-site reproducibility, five replicate sets of pre-qualified unstained specimens were stained and evaluated at each of the three external sites. To assess inter-observer and intra-observer reproducibility, one set of pre-stained specimens was evaluated three times by each of three external pathologists. Negative percent agreement (NPA), positive percent agreement (PPA), and overall percent agreement (OA), based on comparisons to consensus diagnostic outcome, were computed with corresponding two-sided 95% percentile bootstrap confidence intervals. Acceptance criteria required at least 85.0% for the lower-bound (LB) of the two-sided 95% confidence interval (CI) on NPA, PPA, and OA for all endpoints. Results: All but one endpoint in the external reproducibility study met pre-determined acceptance criteria. Both the inter- and intra-site reproducibility endpoints had 95% CI LB values of 98.8% for NPA, 95.9% for PPA, and 97.9% OA at the CPS ≥ 1 cutoff. At the CPS ≥ 10 cutoff, the inter-site reproducibility endpoint had 95% CI LB values of 88.0% for NPA, 87.1% for PPA, and 89.5% OA, and the intra-site reproducibility endpoint had 95% CI LB values of 96.7% for NPA, 92.2% for PPA, and 95.6% OA. The inter-observer reproducibility endpoint had 95% CI LB values of 95.3% for NPA, 92.3% for PPA, and 94.7% OA at the CPS ≥ 1 cutoff. At the CPS ≥ 10 cutoff, the inter-observer reproducibility endpoint had 95% CI LB values of 84.9% for NPA, 86.8% for PPA, and 87.3% OA. The intra-observer reproducibility endpoint had 95% CI LB values of 97.0% for NPA, 95.0% for PPA, and 96.6% OA at the CPS ≥ 1 cutoff. At the CPS ≥ 10 cutoff, the intra-observer reproducibility endpoint had 95% CI LB values of 92.2% for NPA, 89.2% for PPA, and 91.7% OA. Conclusions: Acceptance criteria were met for all endpoints in the breast cancer external reproducibility study of PD-L1 IHC 22C3 pharmDx at the CPS ≥ 1 and CPS ≥ 10 cutoffs, except for inter-observer at the CPS ≥ 10 cutoff, which had a 95% CI LB value of 84.9% for NPA.

Functional limitations 3 and 12 months after venous thromboembolism: a cohort study

BackgroundVenous thromboembolism (VTE) is associated with various long-term complications. ObjectivesWe aimed to investigate the association of clinical characteristics at VTE diagnosis with functional limitations 3 and 12 months afterward. MethodsWe conducted a prospective cohort study of VTE patients, excluding patients with cancer, pregnancy, and postpartum period. Functional limitations were assessed with the post-VTE functional status (PVFS) scale (range, 0-4) within 21 days of diagnosis, after 3 and 12 months (prospectively), and 1 month before diagnosis (retrospectively). Twelve-month follow-up was only performed in patients on anticoagulation. We fitted 2 proportional odds logistic regression models for the 3- and 12-month follow-ups and computed odds ratios (ORs) with 95% bootstrap percentile confidence intervals (CIs). ResultsWe included 307 patients (42% female, median age 55.6 years) with a median (IQR) PVFS scale grade of 2 (2-3) at study inclusion and 0 (0-0) before diagnosis. After 3 months, PVFS scale grade in 269 patients was 1 (0-2). Female sex (OR, 2.15; 95% CI, 1.26-4.14), body mass index (OR per 1 kg/m2 increase, 1.05; 95% CI, 1.00-1.10), functional limitations at baseline, and older age were associated with functional limitations. After 12 months, PVFS scale grade in 124 patients was 1 (0-2). Female sex (OR, 4.47; 95% CI, 2.11-16.00), history of cardiovascular/pulmonary disease (OR, 2.36; 95% CI, 1.01-6.89), and functional limitations at baseline were associated with functional limitations. ConclusionFunctional limitations in VTE patients improved 3 and 12 months after diagnosis but did not return to pre-VTE values. We identified clinical characteristics that could help identify patients at risk of persisting functional limitations after VTE.

A generating family of unit-Garima distribution: Properties, likelihood inference, and application

This article proposes the unit Garima (UGa) distribution for analysing proportion data. Some statistical properties of the UGa distribution are investigated, including survival and hazard functions, order statistics, quantile function, and stress-strength reliability measure. Next, a new family of continuous distributions, called the unit Garima-generated (UGa-G) family of distributions, is studied. The UGa-G family of distributions has the feature to use the UGa distribution as the main generator and the concept of the T-X family of distributions. Some UGa-G family sub-models are provided, such as the UGa-Beta, UGa-Weibull, and UGa-normal distributions. The maximum likelihood method is used to estimate the model parameters for the statistical aspect. A Monte Carlo simulation for the percentile bootstrap confidence intervals for each parameter of the proposed distributions is provided. Applications to eight practical data sets are given to demonstrate the usefulness of the proposed distributions.

The moderating role of perceived brand globalness on the effects of consumers’ attitude during periods of political controversies: three interconnected studies following marketing analytics’ best practices

AbstractThe purpose of this research is to test the moderating role of perceived brand globalness (PBG) on consumer unfavourable attitudes and buying intention towards brands associated with a conflicting political belief. Three studies were conducted. Study 1 (N = 228) examines the effect of a brand being associated with a conflicting political belief on attitude and buying intention. Study 2 (N = 212) tests the moderating role of PBG among these relationships. Study 3 (N = 126) tests the moderating role of PBG for a service brand and adds the mediating role of self-congruity. We followed marketing analytics best practices to examine causal effects of manipulated stimuli. We also examined the psychometric characteristics of the data-collection tool and tested direct relationships between variables. Additionally, we used percentile bootstrap confidence intervals, with PROCESS-Macro, to test mediating effects in the theoretical model. The studies include moderating analyses and analysis of variance. The results show that the association of a brand with a conflicting political belief negatively influences perceived self-congruity, which leads to an unfavourable attitude and, in turn, to a lower buying intention. Presenting a brand as global mitigates such unfavourable effects. The moderating effect of PBG is confirmed for a tangible product and for a service brand.

COGNITIVE AND FUNCTIONAL IMPACTS OF CHILD LABOR IN INDIA: THE MEDIATING ROLE OF EDUCATION

Abstract This study aims to examine how entry into the labor market in childhood is associated with deficits in late-life cognitive and functional health. Using the Cumulative Advantage/Disadvantage Theory, it further explores how educational attainment, an indicator of socio-economic status, mediates the associations between child labor and cognitive and functional health among older adults in India. Lastly, it investigates if the results vary by gender. We use the nationally representative World Health Organization Study on global AGEing and Health (SAGE), and select respondents aged 50 years or older (n = 6,215 for Mini-Mental State Examination (MMSE); n=6,408 for the Functional Limitation (FL)). Mediation models were performed for each dependent variable using the sgmediation2 program, bootstrapped 5,000 resamples, and percentile bootstrap confidence interval. The indirect and total effects of child labor on MMSE were significant, while the direct effect of child labor was not. For FL, the indirect effect was significant, but the direct and total effects were not. Gender difference was found in terms of the direct effect, child work was directly associated with a lower MMSE score in older women (p < .01), whereas it was not significant for older men. Findings suggest that early entry into the labor market (as a child) has a negative impact on older adults’ MMSE and FL through deprivation of educational opportunities. Providing opportunities for completing certain level of education can mitigate the negative impact of child labor on health in old age.

Comparative performance of Algeria's education districts: The Influence of colonial legacy through cultural capital

This article aims to analyze the comparative performance of Algeria's education districts using a two-stage DEA methodology. Modeling their functioning as non-homogeneous parallel sub-unit production systems, districts are considered Decision-Making Units with three educational subunits. This type of modeling provides an overall regional efficiency analysis of the entire education sector. Furthermore, the current research links early colonial policy of educational discrimination across regions to present-day regional educational performance. The main DEA-like model evaluates the average overall efficiency at 74.5% and identifies no efficient district while the standard DEA overestimates this average by 20.8% and identifies five efficient districts with no efficient subunits. Moreover, IV truncated regression estimates the impact of one standard deviation increase in cultural capital to be a 0.81 standard deviation increase in the efficiency score with a 95% bootstrap percentile confidence interval covering estimations of all alternative methods. The findings confirm that history matters and seem to fit the narrative of the link between colonial legacy and present-day educational performance through the cultural capital transmission channel.

Statistical inference for the partial area under ROC curve for the lower truncated proportional hazard rate models based on progressive Type-II censoring

This paper considers inference on the partial area under the receiver operating characteristic curve based on two independent progressively Type-II censored samples from the populations that are belonging to the lower truncated proportional hazard rate models with the same baseline distributions. The maximum likelihood estimator, a generalized pivotal estimator and some Bayes estimators are obtained for three structures of prior distributions. The percentile bootstrap confidence interval, a generalized pivotal confidence interval and some Bayesian credible intervals are also presented. A Monte-Carlo simulation study is used to evaluate the performances of the obtained point estimators and confidence and credible intervals. Finally, a real data set is applied for illustrative purposes.

Impact of clinical note format on diagnostic accuracy and efficiency.

Clinician notes are structured in a variety of ways. This research pilot tested an innovative study design and explored the impact of note formats on diagnostic accuracy and documentation review time. To compare two formats for clinical documentation (narrative format vs. list of findings) on clinician diagnostic accuracy and documentation review time. Participants diagnosed written clinical cases, half in narrative format, and half in list format. Diagnostic accuracy (defined as including correct case diagnosis among top three diagnoses) and time spent processing the case scenario were measured for each format. Generalised linear mixed regression models and bias-corrected bootstrap percentile confidence intervals for mean paired differences were used to analyse the primary research questions. Odds of correctly diagnosing list format notes were 26% greater than with narrative notes. However, there is insufficient evidence that this difference is significant (75% CI 0.8-1.99). On average the list format notes required 85.6 more seconds to process and arrive at a diagnosis compared to narrative notes (95% CI -162.3, -2.77). Of cases where participants included the correct diagnosis, on average the list format notes required 94.17 more seconds compared to narrative notes (75% CI -195.9, -8.83). This study offers note format considerations for those interested in improving clinical documentation and suggests directions for future research. Balancing the priority of clinician preference with value of structured data may be necessary. This study provides a method and suggestive results for further investigation in usability of electronic documentation formats.

When to Use Different Inferential Methods for Power Analysis and Data Analysis for Between-Subjects Mediation

Several options exist for conducting inference on indirect effects in mediation analysis. Although methods that use bootstrapping are the preferred inferential approach for testing mediation, they are time-consuming when the test must be performed many times for a power analysis. Alternatives that are more computationally efficient are not as robust, meaning accuracy of the inferences from these methods is more affected by nonnormal and heteroskedastic data. Previous research has shown that different sample sizes are needed to achieve the same amount of statistical power for different inferential approaches with data that meet all the statistical assumptions of linear regression. By contrast, we explore how similar power estimates are at the same sample size, including when assumptions are violated. We compare the power estimates from six inferential methods for between-subjects mediation using a Monte Carlo simulation study. We varied the path coefficients, inferential methods for the indirect effect, and degree to which assumptions are met. We found that when the assumptions of linear regression are met, three inferential methods consistently perform similarly: the joint significance test, the Monte Carlo confidence interval, and the percentile bootstrap confidence interval. When the assumptions were violated, the nonbootstrapping methods tended to have vastly different power estimates compared with the bootstrapping methods. On the basis of these results, we recommend using the more computationally efficient joint significance test for power analysis only when no assumption violations are hypothesized a priori. We also recommend the joint significance test to pick an optimal starting sample size value for power analysis using the percentile bootstrap confidence interval when assumption violations are suspected.

Abundance of Bering-Chukchi-Beaufort bowhead whales (Balaena mysticetus) in 2004 estimated from photo-identification data

Ice-based surveys near Point Barrow, Alaska, have been used to obtain most estimates of abundance for the Bering-Chukchi-Beaufort (B-C-B) stock of bowhead whales, but global warming has raised concerns that ice-based surveys may not be practical in the future. Aerial photographic surveys provide an alternative method for obtaining abundance estimates and may replace ice-based surveys. Aerial photographic surveys were conducted near Point Barrow during the spring migrations of bowhead whales in 2003 and 2004 and, in 2005, in the northern Bering Sea in spring and near Barrow in fall. The 2003 survey was the most complete photographic survey of the population conducted to date. These surveys provided photo-identification data for use in capture-recapture analyses. A screening procedure was used to define which whales captured in 2003, 2004 and/or 2005 were marked and could be reidentified if photographed on another occasion. An estimate of the number of marked whales was obtained using a closed population model for capture-recapture data. Several models were investigated, including models that accounted for heterogeneity in capture probabilities, but a simple model with no covariates produced the most precise estimate. To account for unmarked whales, the estimate of marked whales was divided by an estimate of the proportion of the bowhead population that was marked based on the 1989–2004 spring photographic surveys near Point Barrow. Abundance of the B-C-B bowhead population in 2004 (excluding calves) was estimated to be 12,631 with CV 0.2442, 95% bootstrap percentile confidence interval (7,900; 19,700) and 5% lower limit 8,400. These results were compared with results that used approximate variance expressions for the estimates of the number of marked whales, the proportion of the population that was marked and population abundance instead of using the bootstrap. The estimates of abundance in 2004 computed for comparison included one based on a modified Petersen estimate of the number of marked whales that omitted the 2005 data as well as the estimate of 12,631 described above. The comparison estimates also included estimates of abundance in 1985 computed from 1984–87 photographic survey data using the same methods. All the abundance estimates computed from photographic data were consistent with expectations based on independent abundance and trend estimates from the ice-based surveys conducted from 1978 to 2001.

Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction

Machine learning (ML) models have recently shown potential for predicting kidney allograft outcomes. However, their ability to outperform traditional approaches remains poorly investigated. Therefore, using large cohorts of kidney transplant recipients from 14 centers worldwide, we developed ML-based prediction models for kidney allograft survival and compared their prediction performances to those achieved by a validated Cox-Based Prognostication System (CBPS). In a French derivation cohort of 4000 patients, candidate determinants of allograft failure including donor, recipient and transplant-related parameters were used as predictors to develop tree-based models (RSF, RSF-ERT, CIF), Support Vector Machine models (LK-SVM, AK-SVM) and a gradient boosting model (XGBoost). Models were externally validated with cohorts of 2214 patients from Europe, 1537 from North America, and 671 from South America. Among these 8422 kidney transplant recipients, 1081 (12.84%) lost their grafts after a median post-transplant follow-up time of 6.25 years (Inter Quartile Range 4.33-8.73). At seven years post-risk evaluation, the ML models achieved a C-index of 0.788 (95% bootstrap percentile confidence interval 0.736-0.833), 0.779 (0.724-0.825), 0.786 (0.735-0.832), 0.527 (0.456-0.602), 0.704 (0.648-0.759) and 0.767 (0.711-0.815) for RSF, RSF-ERT, CIF, LK-SVM, AK-SVM and XGBoost respectively, compared with 0.808 (0.792-0.829) for the CBPS. In validation cohorts, ML models' discrimination performances were in a similar range of those of the CBPS. Calibrations of the ML models were similar or less accurate than those of the CBPS. Thus, when using a transparent methodological pipeline in validated international cohorts, ML models, despite overall good performances, do not outperform a traditional CBPS in predicting kidney allograft failure. Hence, our current study supports the continued use of traditional statistical approaches for kidney graft prognostication.

Fast estimation and choice of confidence interval methods for step regression

In this paper we propose a new fast grid search algorithm for finding the least square estimators of a step regression model. This algorithm makes it practical to compute resampling-based confidence intervals for step regression models. We introduce five data generating models, including one where the mean model is a step model (model correctly specified) and four where the mean models are not step models (model misspecified), and use them to study the coverage probabilities of two new types of resampling-based confidence intervals for step regression: symmetric percentile bootstrap confidence intervals and subsampling confidence intervals using a new set of rules-of-thumb to select block size. Our results show that when the model is correctly specified, the symmetric percentile Efron bootstrap confidence intervals provide close-to-nominal coverage and have shorter intervals than the subsampling methods; when the model is misspecified, the subsampling method using the rules-of-thumb provides good coverage and shorter confidence intervals than the symmetric percentile Efron bootstrap method and the subsampling method using a double bootstrap-like procedure for block size selection. Finally, we apply the proposed methods to a real world environmental dataset on the relationship between grassland productivity, soil moisture anomalies and other hydro-climatic and land use variables to provide inference for the threshold in soil moisture anomalies, across which there is a jump in grassland productivity.

Accommodating a Latent XM Interaction in Statistical Mediation Analysis

Statistical mediation analysis is used in the social sciences and public health to uncover potential mechanisms, known as mediators, by which a treatment led to a change in an outcome. Recently, the estimation of the treatment-by-mediator interaction (i.e., the XM interaction) has been shown to play a pivotal role in understanding the equivalence between the traditional mediation effects in linear models and the causal mediation effects in the potential outcomes framework. However, there is limited guidance on how to estimate the XM interaction when the mediator is latent. In this article, we discuss eight methods to accommodate latent XM interactions in statistical mediation analysis, which fall in two categories: using structural models (e.g., latent moderated structural equations, Bayesian mediation, unconstrained product indicator method, multiple-group models) or scoring the mediator prior to estimating the XM interaction (e.g., summed scores and factor scores, with and without attenuation correction). Simulation results suggest that finite-sample bias is low, type 1 error rates and coverage of percentile bootstrap confidence intervals and Bayesian credible intervals are close to the nominal values, and statistical power is similar across approaches. The methods are demonstrated with an applied example, syntax is provided for their implementation, and general considerations are discussed.

Comparing methods for assessing a difference in correlations with dependent groups, measurement error, nonnormality, and incomplete data.

I compared multiple methods to estimate and test a difference in correlations (ρdiff) between two variables that are repeatedly measured or originate from dyads. Fisher's z transformed correlations are often used for testing ρdiff. However, raw scores are typically used directly to compute correlations under this popular method, whose performance has not been evaluated with measurement error or nonnormality in data. Structural equation modeling (SEM) can handle measurement error via latent-variable modeling. To handle nonnormality for testing a difference in correlations in dependent groups, when measurement error was of no concern, I discussed SEM using maximum likelihood estimation (ML) with sandwich-type standard errors, ML with bootstrap percentile or bias-corrected confidence intervals, and Bayesian credible intervals. In addition, I also examined a mean- and variance-adjusted likelihood ratio test and the Bollen-Stine bootstrapping model fit test. When measurement error existed, ρdiff was tested under a latent variable model. Furthermore, under the same SEM frameworks with complete data, I also assessed ρdiff with ignorable missing data. A simulation study was conducted considering whether measurement error existed, whether data were normal, and whether data were complete. Empirical examples were used for illustration. Recommendations were then given based on the findings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Two Instrument Comparison of Reagents From a US FDA-Approved Assay for the Assessment of Ki-67 in High-Risk Early Breast Cancer.

The objective of this study was to measure concordance of results obtained from the US Food and Drug Administration–approved Ki-67 immunohistochemistry MIB-1 pharmDx assay performed on the Dako Omnis automated staining instrument (Omnis) versus results produced from the assay reagents applied using an optimized protocol on the more widely available Autostainer Link 48 (ASL48) platform. Tissue sections obtained from 40 formalin-fixed paraffin-embedded breast carcinoma samples, with available Oncotype DX Breast Recurrence Score (RS) results, were stained. Three certified pathologists scored slides at 3 timepoints, totaling 360 observations for each instrument (N=720 total) using the approved scoring approach. Using the ≥20% cutoff, agreement was calculated with corresponding 2-sided 95% percentile bootstrap confidence intervals (CIs). Pairwise comparisons (N=360) from the interinstrument evaluation, performed with all observers, resulted in 325 (90.3%) concordant outcomes (244 negative and 81 positive) and 35 (9.7%) discordant outcomes. The overall agreement was 90.3% (95% confidence interval, 85.6% to 94.4%). No significant systematic differences were observed between instruments. Specimens scored from the Omnis were on average <1% higher than ASL48, with high correlation and little bias between the continuous Ki-67 scores (concordance correlation coefficient=0.916). Most specimens with a Ki-67 score ≥20% had a RS >25. This study demonstrated that good concordance can be achieved with the reagents run on the ASL48 instrument when using an optimized protocol and standardized scoring.

ALTERNATIVES TO THE CLASSICAL FREQUENTIST CONFIDENCE INTERVAL FOR DESCRIBING ZERO-INFLATED LEAF DISEASE SEVERITY

This paper presents the bootstrap percentile interval and the Bayesian credible interval as alternatives to the classical frequentist confidence interval for analysis of zero-inflated data. The indicated methods were applied to soybean downy mildew severity data obtained by stratified sampling in two municipalities in the state of São Paulo: Estiva Gerbi and Piracicaba. The amplitudes of the frequentist and bootstrap percentile confidence intervals were similar. For the Bayesian approach, the credible intervals of the posterior predictive distribution were considered using the zero-inflated beta distribution as likelihood. The credible intervals showed a wider range and included values in the upper bounds of the intervals greater than those observed in the data. We conclude that Bayesian inference is more complex, but allows incorporation of prior information regarding regional and seasonal aspects, contributing to better disease management in the field. When this information is not known, nonparametric bootstrap resampling is a simple alternative to construct intervals for zero-inflated data without assuming the distribution function.