Background: Multiple Sclerosis (MS) is an auto immune disease, unpredictable in its symptoms, with uncertain prognosis.The most common phenotype is Relapsing-Remitting (RRMS).Despite remissions, relapses lead to CNS damage, and less CNS function recovery from recurrence of relapses, leading to increasing debilitation.There is no cure, and medicines are used for prevention of relapse, with intervention reserved generally for relief from serious inflammatory symptoms.Methods: This statistical study examines a 7-year span of very detailed medical records of one RRMS patient.Seven clinically observable MS disease responses to distress are identified by mapping the magnitude of distress against duration of symptom sets.Natural divisions were identified in this mapping.Probability distributions, within these natural divisions, were formally constructed.Further investigation is warranted as to how these relate to physiologic, histologic, and biochemical processes involved in MS pathology.Results: This study establishes that stressors exacerbate and expose the presence of the disease and that distress, is the missing consideration in many clinical studies as it is an intermediate outcome between stressors and symptoms.The statistical analysis documents 4 distress characteristics, and 7 Disease Response characteristics.Distress or its absence can predictably induce an MS relapse or remission, respectively.Mathematical and statistical models between distress and relapse are derived that characterize the RRMS disease response.These formulae facilitate managing other patients' symptoms.This study recommends several approaches to modeling symptom set data for the purpose of yielding better, more consistent models.For example, how to utilize the results of survival functions, and EDDS.A Stress-Disease Meta Model is proposed.That, Stressors cause Susceptible patients to exhibit a Stress Response (or distress) that leads to Effectors causing tissue Injury evidenced as Disease Responses.The Disease Processes determine how healing and/or deterioration is evidenced in the disease as Disease Responses.This facilitates the structuring and tracking of triggers, symptoms, other factors and/or comorbidities, to yield more usable data for statistical analysis.Finally, a multi-process disease-wellness approach is proposed that should open further avenues for research.Conclusion: MS is considered an unpredictable disease because (1) MS symptom sets are triggered within 3 days of stress triggers, (2) The random arrival of stress triggers causes the appearance of random symptom sets, and (3) MS is also unpredictable as the underlying disease processes are dynamic processes that keep switching.
Testing effect size homogeneity is an essential part when conducting a meta-analysis. Comparative studies of effect size homogeneity tests in case of binary outcomes are found in the literature, but no test has come out as an absolute winner. A alternative approach would be to carry out multiple effect size homogeneity tests on the same meta-analysis and combine the resulting dependent p-values. In this article we applied the correlated Lancaster method for dependent statistical tests. To investigate the proposed approach’s performance, we applied eight different effect size homogeneity tests on a case study and on simulated datasets, and combined the resulting p-values. The proposed method has similar performance to that of tests based on the score function in the presence of a effect size when the number of studies is small, but outperforms these tests as the number of studies increases. However, the method’s performance is sensitive to the correlation coefficient value assumed between dependent tests, and only performs well when this value is high. More research is needed to investigate the method’s assumptions on correlation in case of effect size homogeneity tests, and to study the method’s performance in meta-analysis of continuous outcomes.
Methodological development and applications of joint models for longitudinal and survival data have mostly coupled a single longitudinal outcome-based mixed-effects model with normal distribution and Cox proportional hazards model.In practice, however, (i) normality of model error in longitudinal sub-models is a routine assumption, but it may be unrealistically violating data features of subject variations.(ii) The data collected are often featured by multivariate longitudinal outcomes which are significantly correlated, ignoring their correlation may lead to biased estimation.Additionally, a parametric specification may be inflexible to capture the complicated longitudinal pattern of biomarkers.(iii) It is of importance to investigate how multivariate longitudinal outcomes are associated with an event time of interest.Multilevel item response theory (MLIRT) models have been increasingly used to analyze the multivariate longitudinal data of mixed types (e.g., continuous and categorical) in clinical studies.In this article, we develop a multivariate joint model that consists of an extended MLIRT model for the mixed types of multivariate longitudinal data and a Cox proportional hazards model, linked through random-effects.The proposed models and method are applied to analyze longitudinalsurvival data arising from a primary biliary cirrhosis study.Simulation studies are conducted to evaluate the performance of the proposed models and method.
The one-sample t test compares a sample to a known average. The standard deviation (SD) is known for the sample but not for the known average. This study compares p-values from one-sample versus twosample t testing where SD is
Background: Many clinical and public health researches collect data including multiple longitudinal measures and time-to-event outcomes, where characteristics of the pattern of exposure change and the association between features of longitudinal biomakers and the primary survival endpoint are of interest.Methods: Many existing statistical models for longitudinal-survival data might not provide robust inference when more than one longitudinal exposures which were significantly correlated and longitudinal measurements exhibit skewness and/or heavy tails; ignoring these data features may lead to biased estimation.In this article, we offered a multivariate joint model with the skew-normal (SN) distribution with application to the Mayo clinic primary biliary cirrhosis (PBC) study to assess simultaneous effects.Results: With the multivariate joint modeling associated with the skew-normal (SN) distribution, the subject-specific baseline (HR=2.390with 95% CI: (1.429, 4.112)) and change rate (HR=2.588with 95% CI: (1.845, 3.967)) of Bilirubin in natural log scale were positively associated with the risk of death; the higher the subject-specific change rate (HR=0.191with 95% CI: (0.037, 0.915)) of Albumin in natural log scale was associated with a decrease in mortality rate; the subject-specific of SGOT levels in natural log scale did not affect the risk of death for PBC patients significantly.The results of the skewness parameters of natural log-transformed Bilirubin (δ 1 =0.42),Albumin (δ 2 =-0.03) and SGOT (δ 3 =0.095)were estimated to be significant, indicating the skewness of three biomarkers existed.Conclusions: Our results revealed the Bilirubin and Albumin levels may be involved in predicting risk of death for PBC patients, except for SGOT.The multivariate joint modeling associated with SN distribution provides better fit to the data, gives less biased parameter estimates for those longitudinal biomarkers in comparison with its counterpart where the normal distribution is assumed (data not shown here).The introduced modeling approach is generally applicable to other situations where longitudinal measurements and time-to-event outcomes are available.
Abstract Background: The purpose of this study is to demonstrate the utility of generalizability theory in assessing the reliability of EEG data. Generalizability theory and its relevance for measurement are described,
Assessing agreement between the examiners, measurements and instruments are always of interest to health-care providers as the treatment of patients is highly dependent on the medical reports.Till now several agreement statistics have been developed and all of them have certain limitations.In 2002 Kilm Gwet introduced a more robust and unbiased agreement statistics named "Gwet's AC1 statistics".It has been shown by various researchers that AC1 statistics has the best statistical properties amongst all the other agreement statistics.Though it has been reported to be the better estimate still several inconsistencies existed in this agreement statistics.In this paper author aimed to develop a new formula that can overcome all the inconsistencies and dependencies of inter-rater agreement statistics.
Background: Clostridioides difficile infection causes chronic and sometimes life-threatening diarrhea in patients as a consequence of antibiotics overuse.A promising experimental procedure for recurrent and/ or refractory C. difficile infection is fecal microbiota transplantation therapy.The aim of this study was to analyze medical records of patients infected with recurrent and/or refractory C. difficile that were treated with fecal microbiota transplantation therapy to investigate the relationship between time to clinical resolution and explanatory variables.Methods: The analyses were based on a retrospective review of patients' data.Data of ninety-two patients between 24 and 95 years of age of which 43.6% were males were available for analyses.Three variables, age group, gender and hospitalization status, were included in the analyses.For time-to-event endpoints, the comparison between two groups was done with the Kaplan-Meier estimator.The nonparametric logrank test was used to compare the survival distributions between two age groups.The Cox proportional hazard model was used to analyze age, gender and hospitalization status as risk factors to clinical resolution.The most satisfactory model was selected based on the value of Akaike's information criterion.The proportional hazard assumptions and the overall model fit were assessed based on graphical evidence, hypothesis testing and residual analyses.Results: Overall, clinical resolution was achieved for 92% of the patients.In fact, 95.7% of them in the age group younger than 65 years and 83.1% in the age group 65 years and older achieved clinical resolution.We found that the hazard of fecal microbiota transplantation to C. difficile in patients younger than 65 is twice as high as in patients who were 65 and older.On average, the age group younger than 65 years received 1.3 fecal microbiota transplantations, while the older age group received 2.2 fecal microbiota transplantations.Results of analyses indicate that the used models were appropriate.Conclusion: Delivery of fecal microbiota transplantation via a retention enema is an effective alternative therapy for recurrent and/or refractory C. difficile infection.Age is strongly associated with clinical resolution, with older patients requiring more fecal microbiota transplantations and more time to be clinically resolved.The Kaplan-Meier estimators and the Cox proportional hazard model are adequate models to analyze data of patients infected with recurrent and/or refractory C. difficile.Randomized control trials with more variables are needed to confirm our findings and more deeply investigate the impact of other risk factors on clinical resolution.
A hybrid test statistics was proposed in the literature to analyze matched studies with non-normally distributed outcomes.In this article, we investigated and compared the hybrid statistic with the metaanalysis t test under commonly used interim analysis settings in clinical trials.We estimated the empirical powers and the empirical type I errors among the 10,000 simulated datasets with different sample sizes, different effect sizes, different correlation coefficients for matched pairs, and different data distributions, respectively, in the interim and final analysis with 4 different group sequential methods.Results from our simulation study show that, compared to the meta-analysis t-test commonly used for data with normally distributed observations, the hybrid statistic almost keeps the powerfor data observed from normally distributed random variables and generally achieves greater power for log-normally, and multinomially distributed random variables with matched and unmatched subjects as well as with outliers.Powers rose with the increase in sample size, effect size, and correlation coefficient for the matched pairs.In addition, lower type I errors were observed by using the hybrid statistic in most of the cases studied, which indicates that this test is also conservative for data with outliers in the interim analysis of clinical trials.
The objective of this work is to predict the spread of COVID-19 starting from observed data, using a forecast method inspired by probabilistic weather prediction systems operational today. Results show that this method works well for China: on day 25 we could have predicted well the outcome for the next 35 days. The same method has been applied to Italy and South Korea, and forecasts for the forthcoming weeks are included in this work. For Italy, forecasts based on data collected up to today (24 March) indicate that number of observed cases could grow from the current value of 69,176, to between 101k-180k, with a 50% probability of being between 110k-135k. For South Korea, it suggests that the number of observed cases could grow from the current value of 9,018 (as of the 23rd of March), to values between 8,500 and 9,300, with a 50% probability of being between 8,700 and 8,900. We conclude by suggesting that probabilistic disease prediction systems are possible and could be developed following key ideas and methods from weather forecasting. Having access to skilful daily updated forecasts could help taking better informed decisions on how to manage the spread of diseases such as COVID-19.
SAS/STAT® 14.1 released in SAS ® 9.4 TS1M3 can perform non-parametric method to calculate cumulative incidence function (CIF) and can create CIF plots by using PROC LIFETEST, but it can't directly show the information such as number at risk.And in PROC PHREG, both Fine and Gray's sub-distribution hazard model and cause-specific model are fitted when there is competing risk events.Up to date, there is no build-in variable selection procedure for the two kinds of models in SAS ® .To address this limitation, we developed SAS ® macros to apply Fine and Gray's approach and cause-specific method with backwards elimination.The two macros generate summary reports in the form of tables displaying results that include, Hazard Ratio in the case of cox regression with its 95% confidence intervals, sample size, and p-values.We also created the third SAS macro to show CIF plot.Comparing with the CIF plot generated by default SAS ® procedure, our SAS ® CIF Plot macro provides more information including Gray's test for the significant differences among the levels of a categorical covariate, a table with number at risk, accumulated number of events of interest, accumulated -events of competing for specified time-points at the bottom of the graph.And censored information also is shown in the CIF curve.The three macros used for competing risk survival data enable us to generate journal-quality summary tables and graphs, production of comprehension.
Background: The purpose of this research was to calculate the minimum sample size needed to obtain reliable results from crowdsourced retrospective online surveys of IVF clinics, where the sample was IVF cycles
Likelihood ratio test is widely used for detecting adverse reactions (ARs) of single drug in biomedical studies.However, it is difficult to detect adverse reactions of multiple drugs simultaneously.Corresponding to this, we consider a generalized likelihood ratio test procedure to detect adverse reactions simultaneously for all the drugs of the same class.An extensive simulation study is performed to evaluate the proposed test procedural as well as its power and sensitivity.The simulation study suggest that the proposed test procedure seems to work well for practical situations and an illustrative example is also provided.
Jointly modelling longitudinal parasite count and time-to-clinical malaria disease improves precision in log hazard ratio estimates compared to conventional time-dependent Cox PH model. The improved precision of joint modelling may improve study efficiency and allow for design of clinical trials with relatively lower sample sizes with increased power.
Abstract Background: The evaluation of the confounders is crucial to accurately estimate the association between environmental factors and diseases. The deterministic sensitivity analysis permits an external adjustment
Background:In the use of medical device procedures, learning effects have been shown to have a significant impact on the outcome, and are a critical component of medical device safety surveillance. To support estimation of these effects, we evaluated our methods for modeling these rates within several different actual datasets representing patients treated by physicians clustered within institutions to show the flexibility of this method across applications. Methods:In order to estimate the learning curve effects, we employed our unique modeling for the learning curves to incorporate the learning hierarchy between institution and physicians, and then modeled them within established methods that work with hierarchical data such as generalized estimating equations (GEE). Within the actual datasets, we looked at two device types and also two procedure types which had not been observed before: off pump coronary artery bypass (CABG) experience, and radial access experience. We also tried mediation analyses within the GEE framework for these various devices/procedures as well. Results:We found that the choice of shape used to produce the "learning-free" dataset would still be dataset specific depending upon needs for modeling fast or slow learning but that in general the power series or logarithmic shapes would be better for modeling slower learning while exponential may be better for faster learning. Mediation analysis also showed promise in adapting the modeling of the learning curve. Conclusions:In showing the flexibility of using our method in various applications; this time utilizing more than one possible procedure done per patient so that each physician had more volume, we were able to show the flexibility of applying our method in different data applications to allow for more accurately capturing the learning curve rates in physicians nested within institutions. This can, therefore, be used across the board for device and procedure safety.
The p-chart has traditionally been used to monitor processes that yield binary data.The p'-chart that adjusts the p-chart control limits for between subgroup variation was proposed in 2002 in an effort to reduce the p-chart's false alarm rate in the presence of large subgroup sizes.As illustrated with an example using real pharmacy data, the p-chart and p'-chart often yield very different results and how to decide which chart is appropriate for a given situation is not clear.A simulation study was undertaken to examine the phase II performance of the p-chart and p'-chart.With large subgroup sizes or when the between subgroup variation is high, p-charts have relatively high sensitivity to detect out-of-control shifts, but exhibit a high false alarm rate when the process is in-control, while p'-charts have low sensitivity to detect out-of-control shifts but have relatively low false alarm rates.For a specific situation, Youden's index can be used to decide whether to act on p-chart or p'-chart results while considering the relative costs of false positives and false negatives.
Objective: This study uses a flexible nonlinear approach, Fractional polynomial models (FPs), to examine the association between obesity and C-reactive protein to select the best fitted model within 44 potentially FP models.Methods: Data for 5 years (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) of the National Health Interview Survey (NHANES) was used.All respondents aged between 17 and 74 were included in the analysis.CRP was transformed to ln(CRP) to eliminate skewness and missing values were removed from the analysis.A fractional polynomial approach was applied to measure the relationship between elevated levels of CRP and obesity.A closed test was used to select the best model among the 44 models.Results: The best fitted fractional polynomial regression model contained the powers -2 and -2 for BMI.The association between the ln(CRP) and BMI when estimated using the FP approach exhibited a J-shaped pattern for women and men.Women have a higher risk of elevated CRP level compared to men.A deviance difference test yielded a significant improvement in model fit of -2 and -2 compared to other BMI functions. Conclusion:The fractional polynomial regression model is the most robust estimator of BMI compared to other linear or nonlinear models.
Background: A method comparison study is a topic of considerable interest in health and biomedical-related fields.The use of this study is to compare a new method with a standard established method.There are two typical steps in method comparison studies, namely modeling the data set and using the fitted model to analyze method comparison data.Methods: As a usual practice to model method comparison data, many recommend a mixed-effects model which assumes constant error variance (homoscedasticity) and normality of error terms.However, these assumptions are generally violated in practice.Thus, in this study, our main goal is to propose a copula based model to deal with non-replicated method comparison data.Moreover, a simulation procedure is carried out to validate the proposed model by means of different copula models.Results: Results indicate that the Normal and Gumbel copulas give more accurate results in terms of bias, mean-squared error and coverage probability values of estimators.Further it is confirmed that the accuracy of model increases with the Kendall tau (τ) correlation between methods and the number of observations.Furthermore, the proposed methodology is illustrated by analyzing finger and arm systolic blood pressure data.Besides the Total Deviation Index (TDI) and Concordance Correlation Coefficient (CCC) values are used to check the agreement between the two methods. Conclusion:The proposed model based on the Copula method can be used to model the method comparison data with balanced and unbalanced data designs.
