Analysis of ordinal categorical data with misclassification

Association of Stool Frequency and Consistency with the Risk of All-Cause and Cause-Specific Mortality among U.S. Adults: Results from NHANES 2005-2010

BACKGROUND

Prior studies on the relationship between bowel health and mortality have generally focused on the individual association of stool frequency or consistency with mortality but did not present a joint association. Therefore, we aimed to systematically evaluate the individual and joint associations of stool frequency and consistency with all-cause and cause-specific mortality in this study.

METHODS

A total of 14,574 participants from the National Health and Nutrition Examination Survey 2005-2010 were incorporated in this analysis. Survey sample-weighted Cox proportional hazards models adjusted for potential confounders were used to estimate hazard ratios (HRs) between bowel health measures and mortality risks.

RESULTS

During a median of 7.6 years of follow-up, 1502 deaths occurred, including 357 cancer deaths and 284 cardiovascular disease (CVD) deaths. The bowel habit of the most participants was 7 times/week (50.7%), and the most common type was "Like a sausage or snake, smooth and soft" (51.8%). Stool frequency displayed a parabolic relationship with all-cause mortality, and less than 7 times/week is a significant risk factor for mortality (HR for 1 time/week: 1.43, p-values = 0.04. HR for 6 times/week: 1.05, p-value = 0.03). Analyzing the joint association of stool frequency and consistency on mortality clarified the limitations of only inspecting the effects of either individual factor. Compared with 7 times/week of normal stool, infrequent soft stools at 4 times/week were associated with 1.78-, 2.42-, and 2.27-times higher risks of all-cause, cancer, and CVD mortality, respectively.

CONCLUSION

Analyses of bowel health should consider the joint effects of stool frequency and stool consistency. Self-appraisal of stool frequency and consistency may be a simple but useful tool for informing about major chronic illnesses.

Confidence interval construction for proportion difference from partially validated series with two fallible classifiers

This article investigates the confidence interval (CI) construction of proportion difference for two independent partially validated series under the double-sampling scheme in which both classifiers are fallible. Several CIs based on the variance estimates recovery method of combining confidence limits from asymptotic, bootstrap, and Bayesian methods for two independent binomial proportions are developed under two models. Simulation results show that all CIs except for the bootstrap percentile-t CI and Bayesian credible interval with uniform prior under the independence model and all CIs under the dependence model generally perform well and are recommended. Two examples are used to illustrate the methodologies.

Psychological distress and health-related quality of life among women with breast cancer: a descriptive cross-sectional study

PURPOSE

Breast cancer is the most common type of cancer found in women in Sweden and worldwide. Treatment leads to increased survival of patients, but they are at risk to experience psychological distress, including anxiety and depressive symptoms, and decreased health-related quality of life (HRQoL). This study investigated the relationship between psychological distress and HRQoL and related factors among women with breast cancer in Sweden.

METHODS

This descriptive cross-sectional study was conducted in Sweden. A total of 481 women with breast cancer answered voluntarily a questionnaire about sociodemographic and support factors, psychological distress, and HRQoL. Data were subjected to Pearson's correlation and linear regression analyses.

RESULTS

Psychological distress was related to HRQoL in terms of body image, future perspective, side effects of systemic therapy, breast symptoms, arm symptoms, and hair loss. Women with lower age were associated with increased symptoms of anxiety, while those having undergone breast reconstruction were associated with increased symptoms of depression. Breast reconstruction and chemotherapy worsened body image, low support from partner decreased sexual functioning and enjoyment, and low support from physicians and nurses worsened future perspective, side effects of systemic therapy, breast symptoms, and indignation about hair loss.

CONCLUSIONS

Psychological distress was correlated with the HRQoL. Increased support from physicians, nurses, and husband/partner may increase the HRQoL among women with breast cancer. Breast cancer treatments such as breast reconstruction and chemotherapy were factors that decreased the psychological distress and increased the HRQoL.

Spinal versus general anesthesia for patients undergoing outpatient total knee arthroplasty: a national propensity matched analysis of early postoperative outcomes

BACKGROUND

A comparison of different anesthetic techniques to evaluate short term outcomes has yet to be performed for patients undergoing outpatient knee replacements. The aim of this investigation was to compare short term outcomes of spinal (SA) versus general anesthesia (GA) in patients undergoing outpatient total knee replacements.

METHODS

The ACS NSQIP datasets were queried to extract patients who underwent primary, elective, unilateral total knee arthroplasty (TKA) between 2005 and 2018 performed as an outpatient procedure. The primary outcome was a composite score of serious adverse events (SAE). The primary independent variable was the type of anesthesia (e.g., general vs. spinal).

RESULTS

A total of 353,970 patients who underwent TKA procedures were identified comprising of 6,339 primary, elective outpatient TKA procedures. Of these, 2,034 patients received GA and 3,540 received SA. A cohort of 1,962 patients who underwent outpatient TKA under GA were propensity matched for covariates with patients who underwent outpatient TKA under SA. SAE rates at 72 h after surgery were not greater in patients receiving GA compared to SA (0.92%, 0.66%, P = 0.369). In contrast, minor adverse events were greater in the GA group compared to SA (2.09%, 0.51%), P < 0.001. The rate of postoperative transfusion was greater in the patients receiving GA.

CONCLUSIONS

The type of anesthetic technique, general or spinal anesthesia does not alter short term SAEs, readmissions and failure to rescue in patients undergoing outpatient TKR surgery. Recognizing the benefits of SA tailored to the anesthetic management may maximize the clinical benefits in this patient population.

Noninferiority testing for matched-pair ordinal data with misclassification

New treatments that are noninferior or equivalent to-but not necessarily superior to-the reference treatment may still be beneficial to patients because they have fewer side effects, are more convenient, take less time, or cost less. The noninferiority test is widely used in medical research to provide guidance in such situation. In addition, categorical variables are frequently encountered in medical research, such as in studies involving patient-reported outcomes. In this paper, we develop a noninferiority testing procedure for correlated ordinal categorical variables based on a paired design with a latent normal distribution approach. Misclassification is frequently encountered in the collection of ordinal categorical data; therefore, we further extend the procedure to account for misclassification using information in the partially validated data. Simulation studies are conducted to investigate the accuracy of the estimates, the type I error rates, and the power of the proposed procedure. Finally, we analyze one substantive example to demonstrate the utility of the proposed approach.

A Bayesian approach for analysis of ordered categorical responses subject to misclassification

Ordinal categorical responses are frequently collected in survey studies, human medicine, and animal and plant improvement programs, just to mention a few. Errors in this type of data are neither rare nor easy to detect. These errors tend to bias the inference, reduce the statistical power and ultimately the efficiency of the decision-making process. Contrarily to the binary situation where misclassification occurs between two response classes, noise in ordinal categorical data is more complex due to the increased number of categories, diversity and asymmetry of errors. Although several approaches have been presented for dealing with misclassification in binary data, only limited practical methods have been proposed to analyze noisy categorical responses. A latent variable model implemented within a Bayesian framework was proposed to analyze ordinal categorical data subject to misclassification using simulated and real datasets. The simulated scenario consisted of a discrete response with three categories and a symmetric error rate of 5% between any two classes. The real data consisted of calving ease records of beef cows. Using real and simulated data, ignoring misclassification resulted in substantial bias in the estimation of genetic parameters and reduction of the accuracy of predicted breeding values. Using our proposed approach, a significant reduction in bias and increase in accuracy ranging from 11% to 17% was observed. Furthermore, most of the misclassified observations (in the simulated data) were identified with a substantially higher probability. Similar results were observed for a scenario with asymmetric misclassification. While the extension to traits with more categories between adjacent classes is straightforward, it could be computationally costly. For traits with high heritability, the performance of the methodology would be expected to improve.

Test procedures for disease prevalence with partially validated data

Investigating the prevalence of a disease is an important topic in medical studies. Such investigations are usually based on the classification results of a group of subjects according to whether they have the disease. To classify subjects, screening tests that are inexpensive and nonintrusive to the test subjects are frequently used to produce results in a timely manner. However, such screening tests may suffer from high levels of misclassification. Although it is often possible to design a gold-standard test or device that is not subject to misclassification, such devices are usually costly and time-consuming, and in some cases intrusive to the test subjects. As a compromise between these two approaches, it is possible to use data that are obtained by the method of double-sampling. In this article, we derive and investigate four test statistics for testing a hypothesis on disease prevalence with double-sampling data. The test statistics are implemented through both the asymptotic method suitable for large samples and approximate unconditional method suitable for small samples. Our simulation results show that the approximate unconditional method usually produces a more satisfactory empirical type I error rate and power than its asymptotic counterpart, especially for small to moderate sample sizes. The results also suggest that the score test and the Wald test based on an estimate of variance with parameters estimated under the null hypothesis outperform the others. An real example is used to illustrate the proposed methods.

Sample size determination for disease prevalence studies with partially validated data

Summary

Disease prevalence is an important topic in medical research, and its study is based on data that are obtained by classifying subjects according to whether a disease has been contracted. Classification can be conducted with high-cost gold standard tests or low-cost screening tests, but the latter are subject to the misclassification of subjects. As a compromise between the two, many research studies use partially validated datasets in which all data points are classified by fallible tests, and some of the data points are validated in the sense that they are also classified by the completely accurate gold-standard test. In this article, we investigate the determination of sample sizes for disease prevalence studies with partially validated data. We use two approaches. The first is to find sample sizes that can achieve a pre-specified power of a statistical test at a chosen significance level, and the second is to find sample sizes that can control the width of a confidence interval with a pre-specified confidence level. Empirical studies have been conducted to demonstrate the performance of various testing procedures with the proposed sample sizes. The applicability of the proposed methods are illustrated by a real-data example.