Avoiding Blunders When Analyzing Correlated Data, Clustered Data, or Repeated Measures

Rheumatology research often involves correlated and clustered data. A common error when analyzing these data occurs when instead we treat these data as independent observations. This can lead to incorrect statistical inference. The data used are a subset of the 2017 study from Raheel et al consisting of 633 patients with rheumatoid arthritis (RA) between 1988 and 2007. RA flare and the number of swollen joints served as our binary and continuous outcomes, respectively. Generalized linear models (GLM) were fitted for each, while adjusting for rheumatoid factor (RF) positivity and sex. Additionally, a generalized linear mixed model with a random intercept and a generalized estimating equation were used to model RA flare and the number of swollen joints, respectively, to take additional correlation into account. The GLM's β coefficients and their 95% confidence intervals (CIs) are then compared to their mixed-effects equivalents. The β coefficients compared between methodologies are very similar. However, their standard errors increase when correlation is accounted for. As a result, if the additional correlations are not considered, the standard error can be underestimated. This results in an overestimated effect size, narrower CIs, increased type I error, and a smaller P value, thus potentially producing misleading results. It is important to model the additional correlation that occurs in correlated data.

Free software applications for authors for writing a research paper

Basic computer skills are essential for authors writing research papers as it has the potential to make the task easier for a researcher. This article provides a glimpse about the essential software programs for a novice author writing a research paper. These software applications help streamline the writing process, improve the quality of work, and ensure that papers are formatted correctly. It covers word processing software, grammar correction software, bibliography management software, paraphrasing tool, writing tools, and statistical software. All of the tools described are free to use. Hence, it would help researchers from resource-limited settings or busy physicians who get lesser time for research writing. We presume this review paper would help provide valuable insights and guidance for novice authors looking to write a high-quality research paper.

prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

Background: Surgical site infections (SSIs) have a major role in the evolution of medical care. Despite centuries of medical progress, the management of surgical infection remains a pressing concern. Nowadays, the SSIs continue to be an important factor able to increase the hospitalization duration, cost, and risk of death, in fact, the SSIs are a leading cause of morbidity and mortality in modern health care. Methods: A study based on statistical test and logistic regression for unveiling the association between SSIs and different risk factors was carried out. Successively, a predictive analysis of SSIs on the basis of risk factors was performed. Results: The obtained data demonstrated that the level of surgery contamination impacts significantly on the infection rate. In addition, data also reveals that the length of postoperative hospital stay increases the rate of surgical infections. Finally, the postoperative length of stay, surgery department and the antibiotic prophylaxis with 2 or more antibiotics are a significant predictor for the development of infection. Conclusions: The data report that the type of surgery department and antibiotic prophylaxis there are a statistically significant predictor of SSIs. Moreover, KNN model better handle the imbalanced dataset (48 infected and 3983 healthy), observing highest accuracy value.

Conducting Bayesian-Classical Hybrid Power Analysis with R Package Hybridpower

There are several approaches to incorporating uncertainty in power analysis. We review these approaches and highlight the Bayesian-classical hybrid approach that has been implemented in the R package hybridpower. Calculating Bayesian-classical hybrid power circumvents the problem of local optimality in which calculated power is valid if and only if the specified inputs are perfectly correct. hybridpower can compute classical and Bayesian-classical hybrid power for popular testing procedures including the t-test, correlation, simple linear regression, one-way ANOVA (with equal or unequal variances), and the sign test. Using several examples, we demonstrate features of hybridpower and illustrate how to elicit subjective priors, how to determine sample size from the Bayesian-classical approach, and how this approach is distinct from related methods. hybridpower can conduct power analysis for the classical approach, and more importantly, the novel Bayesian-classical hybrid approach that returns more realistic calculations by taking into account local optimality that the classical approach ignores. For users unfamiliar with R, we provide a limited number of RShiny applications based on hybridpower to promote the accessibility of this novel approach to power analysis. We end with a discussion on future developments in hybridpower.

Predicting cancer risk based on family history

A new software package provides more accurate cancer risk prediction profiles and has the ability to integrate more genes and cancer types in the future.

Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO

Identifying individuals who are at high risk of cancer due to inherited germline mutations is critical for effective implementation of personalized prevention strategies. Most existing models focus on a few specific syndromes; however, recent evidence from multi-gene panel testing shows that many syndromes are overlapping, motivating the development of models that incorporate family history on several cancers and predict mutations for a comprehensive panel of genes.

We present PanelPRO, a new, open-source R package providing a fast, flexible back-end for multi-gene, multi-cancer risk modeling with pedigree data. It includes a customizable database with default parameter values estimated from published studies and allows users to select any combinations of genes and cancers for their models, including well-established single syndrome BayesMendel models (BRCAPRO and MMRPRO). This leads to more accurate risk predictions and ultimately has a high impact on prevention strategies for cancer and clinical decision making. The package is available for download for research purposes at https://projects.iq.harvard.edu/bayesmendel/panelpro.

Graphical models for mean and covariance of multivariate longitudinal data

Joint mean-covariance modeling of multivariate longitudinal data helps to understand the relative changes among multiple longitudinally measured and correlated outcomes. A key challenge in the analysis of multivariate longitudinal data is the complex covariance structure. This is due to the contemporaneous and cross-temporal associations between multiple longitudinal outcomes. Graphical and data-driven tools that can aid in visualizing the dependence patterns among multiple longitudinal outcomes are not readily available. In this work, we show the role of graphical techniques: profile plots, and multivariate regressograms, in developing mean and covariance models for multivariate longitudinal data. We introduce an R package MLGM (Multivariate Longitudinal Graphical Models) to facilitate visualization and modeling mean and covariance patterns. Through two real studies, microarray data from the T-cell activation study and Mayo Clinic's primary biliary cirrhosis of the liver study, we show the key features of MLGM. We evaluate the finite sample performance of the proposed mean-covariance estimation approach through simulations.

Evaluating the Effectiveness of Personalized Medicine With Software

We present methodological advances in understanding the effectiveness of personalized medicine models and supply easy-to-use open-source software. Personalized medicine involves the systematic use of individual patient characteristics to determine which treatment option is most likely to result in a better average outcome for the patient. Why is personalized medicine not done more in practice? One of many reasons is because practitioners do not have any easy way to holistically evaluate whether their personalization procedure does better than the standard of care, termed improvement. Our software, "Personalized Treatment Evaluator" (the R package PTE), provides inference for improvement out-of-sample in many clinical scenarios. We also extend current methodology by allowing evaluation of improvement in the case where the endpoint is binary or survival. In the software, the practitioner inputs 1) data from a single-stage randomized trial with one continuous, incidence or survival endpoint and 2) an educated guess of a functional form of a model for the endpoint constructed from domain knowledge. The bootstrap is then employed on data unseen during model fitting to provide confidence intervals for the improvement for the average future patient (assuming future patients are similar to the patients in the trial). One may also test against a null scenario where the hypothesized personalization are not more useful than a standard of care. We demonstrate our method's promise on simulated data as well as on data from a randomized comparative trial investigating two treatments for depression.

Trends in the Usage of Statistical Software and Their Associated Study Designs in Health Sciences Research: A Bibliometric Analysis

Background The development of statistical software in research has transformed the way scientists and researchers conduct their statistical analysis. Despite these advancements, it was not clear which statistical software is mainly used for which research design thereby creating confusion and uncertainty in choosing the right statistical tools. Therefore, this study aimed to review the trend of statistical software usage and their associated study designs in articles published in health sciences research. Methods This bibliometric analysis study reviewed 10,596 articles published in PubMed in three 10-year intervals (1997, 2007, and 2017). The data were collected through Google sheet and were analyzed using SPSS software. This study described the trend and usage of currently available statistical tools and the different study designs that are associated with them. Results Of the statistical software mentioned in the retrieved articles, SPSS was the most common statistical tool used (52.1%) in the three-time periods followed by SAS (12.9%) and Stata (12.6%). WinBugs was the least used statistical software with only 40(0.6%) of the total articles. SPSS was mostly associated with observational (61.1%) and experimental (65.3%) study designs. On the other hand, Review Manager (43.7%) and Stata (38.3%) were the most statistical software associated with systematic reviews and meta-analyses. Conclusion In this study, SPSS was found to be the most widely used statistical software in the selected study periods. Observational studies were the most common health science research design. SPSS was associated with observational and experimental studies while Review Manager and Stata were mostly used for systematic reviews and meta-analysis.

HMP16SData: Efficient Access to the Human Microbiome Project Through Bioconductor

Phase 1 of the Human Microbiome Project (HMP) investigated 18 body subsites of 242 healthy American adults to produce the first comprehensive reference for the composition and variation of the "healthy" human microbiome. Publicly available data sets from amplicon sequencing of two 16S ribosomal RNA variable regions, with extensive controlled-access participant data, provide a reference for ongoing microbiome studies. However, utilization of these data sets can be hindered by the complex bioinformatic steps required to access, import, decrypt, and merge the various components in formats suitable for ecological and statistical analysis. The HMP16SData package provides count data for both 16S ribosomal RNA variable regions, integrated with phylogeny, taxonomy, public participant data, and controlled participant data for authorized researchers, using standard integrative Bioconductor data objects. By removing bioinformatic hurdles of data access and management, HMP16SData enables epidemiologists with only basic R skills to quickly analyze HMP data.

Medical Students' Perception of the Usage of Lecture Recording Software

Background: Lecture recording software is a useful reference tool that allows students to revisit lectures and understand complicated concepts in higher education. It is also a useful tool for students with learning difficulties, allowing them to reference and learn the material at their own pace. A significant advantage of this tool is the accessibility of course material to the students off campus. This study attempted to learn the students’ perception of the purpose, use, and benefit of lecture recording software at a medical school.

Methods: The study was conducted using a structured questionnaire delivered, via an Internet-based survey application in the Fall semester of 2017, to 105 students attending the basic sciences courses. A web link was generated after the 18-point questionnaire was uploaded to an online survey software. The link was communicated electronically to each student along with the date and time of the survey. The survey was anonymous. The results of the survey were summarized using descriptive statistics and graphical methods. Students were asked to submit voluntary, informed consent to participate in the study before attempting to answer the questionnaire. The institutional review board approved the research.

Results: The results showed 77% students used this resource to understand points they missed in the class, 75% of them relearned complex ideas/concepts, and 62% of them used it to rewrite class notes. Reportedly, the software was used by students (78%) who missed a class due to an illness or while attending clinical shadowing. Of the students, 87% agreed that the software is helpful because of its off-campus availability while 84% of the students liked the service, as it allowed them to listen to the lectures at their own pace. Many students (65%) felt that the service helped them score better in the exams, whereas 38% did not think the recordings was helpful to get the desired grade and 50% student felt it was time-consuming.

Conclusion: Despite the time-consuming listening process, students expressed a positive opinion about the usefulness of this software. Recording and archiving class lectures could be a useful academic resource. Students could learn from these archived lectures before the class and engage in the discussion later, enhancing active learning. The result suggests that students should also use other study resources and methods to achieve the desired grades. The induction of this student service into a professional curriculum would enhance the students’ satisfaction, effectiveness, and outcomes.

A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications

Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log–log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata).

2 × 2 Tables: a note on Campbell's recommendation

For 2 × 2 tables, Egon Pearson's N - 1 chi-squared statistic is theoretically more sound than Karl Pearson's chi-squared statistic, and provides more accurate p values. Moreover, Egon Pearson's N - 1 chi-squared statistic is equal to the Mantel-Haenszel chi-squared statistic for a single 2 × 2 table, and as such, is often available in statistical software packages like SPSS, SAS, Stata, or R, which facilitates compliance with Ian Campbell's recommendations.

Joint modelling of repeated measurement and time-to-event data: an introductory tutorial

BACKGOUND

The term 'joint modelling' is used in the statistical literature to refer to methods for simultaneously analysing longitudinal measurement outcomes, also called repeated measurement data, and time-to-event outcomes, also called survival data. A typical example from nephrology is a study in which the data from each participant consist of repeated estimated glomerular filtration rate (eGFR) measurements and time to initiation of renal replacement therapy (RRT). Joint models typically combine linear mixed effects models for repeated measurements and Cox models for censored survival outcomes. Our aim in this paper is to present an introductory tutorial on joint modelling methods, with a case study in nephrology.

METHODS

We describe the development of the joint modelling framework and compare the results with those obtained by the more widely used approaches of conducting separate analyses of the repeated measurements and survival times based on a linear mixed effects model and a Cox model, respectively. Our case study concerns a data set from the Chronic Renal Insufficiency Standards Implementation Study (CRISIS). We also provide details of our open-source software implementation to allow others to replicate and/or modify our analysis.

RESULTS

The results for the conventional linear mixed effects model and the longitudinal component of the joint models were found to be similar. However, there were considerable differences between the results for the Cox model with time-varying covariate and the time-to-event component of the joint model. For example, the relationship between kidney function as measured by eGFR and the hazard for initiation of RRT was significantly underestimated by the Cox model that treats eGFR as a time-varying covariate, because the Cox model does not take measurement error in eGFR into account.

CONCLUSIONS

Joint models should be preferred for simultaneous analyses of repeated measurement and survival data, especially when the former is measured with error and the association between the underlying error-free measurement process and the hazard for survival is of scientific interest.

SAMURAI: Sensitivity analysis of a meta-analysis with unpublished but registered analytical investigations (software)

BACKGROUND

The non-availability of clinical trial results contributes to publication bias, diminishing the validity of systematic reviews and meta-analyses. Although clinical trial registries have been established to reduce non-publication, the results from over half of all trials registered in ClinicalTrials.gov remain unpublished even 30 months after completion. Our goals were i) to utilize information available in registries (specifically, the number and sample sizes of registered unpublished studies) to gauge the sensitivity of a meta-analysis estimate of the effect size and its confidence interval to the non-publication of studies and ii) to develop user-friendly open-source software to perform this quantitative sensitivity analysis.

METHODS

The open-source software, the R package SAMURAI, was developed using R functions available in the R package metafor. The utility of SAMURAI is illustrated with two worked examples.

RESULTS

Our open-source software SAMURAI, can handle meta-analytic datasets of clinical trials with two independent treatment arms. Both binary and continuous outcomes are supported. For each unpublished study, the dataset requires only the sample sizes of each treatment arm and the user predicted 'outlook' for the studies. The user can specify five outlooks ranging from 'very positive' (i.e., very favorable towards intervention) to 'very negative' (i.e., very favorable towards control).SAMURAI assumes that control arms of unpublished studies have effects similar to the effect across control arms of published studies. For each experimental arm of an unpublished study, utilizing the user-provided outlook, SAMURAI randomly generates an effect estimate using a probability distribution, which may be based on a summary effect across published trials. SAMURAI then calculates the estimated summary treatment effect with a random effects model (DerSimonian & Laird method), and outputs the result as a forest plot.

CONCLUSIONS

To our knowledge, SAMURAI is currently the only tool that allows systematic reviewers to incorporate information about sample sizes of treatment groups in registered but unpublished clinical trials in their assessment of the potential impact of publication bias on meta-analyses. SAMURAI produces forest plots for visualizing how inclusion of registered unpublished studies might change the results of a meta-analysis. We hope systematic reviewers will find SAMURAI to be a useful addition to their toolkit.

Release the BEESTS: Bayesian Estimation of Ex-Gaussian STop-Signal reaction time distributions

The stop-signal paradigm is frequently used to study response inhibition. In this paradigm, participants perform a two-choice response time (RT) task where the primary task is occasionally interrupted by a stop-signal that prompts participants to withhold their response. The primary goal is to estimate the latency of the unobservable stop response (stop signal reaction time or SSRT). Recently, Matzke et al. (2013) have developed a Bayesian parametric approach (BPA) that allows for the estimation of the entire distribution of SSRTs. The BPA assumes that SSRTs are ex-Gaussian distributed and uses Markov chain Monte Carlo sampling to estimate the parameters of the SSRT distribution. Here we present an efficient and user-friendly software implementation of the BPA-BEESTS-that can be applied to individual as well as hierarchical stop-signal data. BEESTS comes with an easy-to-use graphical user interface and provides users with summary statistics of the posterior distribution of the parameters as well various diagnostic tools to assess the quality of the parameter estimates. The software is open source and runs on Windows and OS X operating systems. In sum, BEESTS allows experimental and clinical psychologists to estimate entire distributions of SSRTs and hence facilitates the more rigorous analysis of stop-signal data.

Investigation of the freely available easy-to-use software 'EZR' for medical statistics

Although there are many commercially available statistical software packages, only a few implement a competing risk analysis or a proportional hazards regression model with time-dependent covariates, which are necessary in studies on hematopoietic SCT. In addition, most packages are not clinician friendly, as they require that commands be written based on statistical languages. This report describes the statistical software 'EZR' (Easy R), which is based on R and R commander. EZR enables the application of statistical functions that are frequently used in clinical studies, such as survival analyses, including competing risk analyses and the use of time-dependent covariates, receiver operating characteristics analyses, meta-analyses, sample size calculation and so on, by point-and-click access. EZR is freely available on our website (http://www.jichi.ac.jp/saitama-sct/SaitamaHP.files/statmed.html) and runs on both Windows (Microsoft Corporation, USA) and Mac OS X (Apple, USA). This report provides instructions for the installation and operation of EZR.

Multivariate meta-analysis: potential and promise

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.