Multiple lines of evidence point to pesticides as stressors affecting invertebrate communities in small streams in five United States regions

Multistressor studies were performed in five regions of the United States to assess the role of pesticides as stressors affecting invertebrate communities in wadable streams. Pesticides and other chemical and physical stressors were measured in 75 to 99 streams per region for 4 weeks, after which invertebrate communities were surveyed (435 total sites). Pesticides were sampled weekly in filtered water, and once in bed sediment. The role of pesticides as a stressor to invertebrate communities was assessed by evaluating multiple lines of evidence: toxicity predictions based on measured pesticide concentrations, multivariate models and other statistical analyses, and previously published mesocosm experiments. Toxicity predictions using benchmarks and species sensitivity distributions and statistical correlations suggested that pesticides were present at high enough concentrations to adversely affect invertebrate communities at the regional scale. Two undirected techniques-boosted regression tree models and distance-based linear models-identified which pesticides were predictors of (respectively) invertebrate metrics and community composition. To put insecticides in context with known, influential covariates of invertebrate response, generalized additive models were used to identify which individual pesticide(s) were important predictors of invertebrate community condition in each region, after accounting for natural covariates. Four insecticides were identified as stressors to invertebrate communities at the regional scale: bifenthrin, chlordane, fipronil and its degradates, and imidacloprid. Fipronil was particularly important in the Southeast region, and imidacloprid, bifenthrin, and chlordane were important in multiple regions. For imidacloprid, bifenthrin, and fipronil, toxicity predictions were supported by mesocosm experiments that demonstrated adverse effects on naïve aquatic communities when dosed under controlled conditions. These multiple lines of evidence do not prove causality-which is challenging in the field under multistressor conditions-but they make a strong case for the role of insecticides as stressors adversely affecting invertebrate communities in streams within the five sampled regions.

Application of smart chemometric models for spectra resolution and determination of challenging multi-action quaternary mixture: statistical comparison with greenness assessment

A multivariate spectrophotometric method is a potential approach that enables discrimination of spectra of components in complex matrices (e.g., pharmaceutical formulation) serving as a substitution method for chromatography. Four green smart multivariate spectrophotometric models were proposed and validated, including principal component regression (PCR), partial least-squares (PLS), multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural networks (ANN). The developed chemometric models were compared to resolve highly overlapping spectra of Paracetamol (PARA), Chlorpheniramine maleate (CPM), Caffeine (CAF), and Ascorbic acid (ASC). The four multivariate calibration models were assessed via recoveries percent, and root mean square error of prediction. Hence, the proposed models were efficiently applied with no need for any preliminary separation step. The models were utilized to analyze the studied components in their combined pharmaceutical formulation (Grippostad® C capsules). Analytical GREEnness Metric Approach (AGREE) and eco-scale tools were applied to assess the greenness of the established models and found to be 0.77 and 85, respectively. Moreover, the proposed models have been compared to official ones showing no considerable variations in accuracy and precision. Therefore, these models can be highly advantageous for conducting standard pharmaceutical analysis of the substances researched within product testing laboratories.

A new pathway for considering trigger factors based on parallel-serial connection models and displaying the relationships of causal factors in low-probability events

BACKGROUND

To determine the effect size of observed factors considering trigger factors based on parallel-serial models and to explore how multiple factors can be related to the result of complex events for low-probability events with binary outcomes.

METHODS

A low-probability event with a true binary outcome can be explained by a trigger factor. The models were based on the parallel-serial connection of switches; causal factors, including trigger factors, were simplified as switches. Effect size values of an observed factor for an outcome were calculated as SAR = (Pe-Pn)/(Pe + Pn), where Pe and Pn represent percentages in the exposed and nonexposed groups, respectively, and SAR represents standardized absolute risk. The influence of trigger factors is eliminated by SAR. Actual data were collected to obtain a deeper understanding of the system.

RESULTS

SAR values of < 0.25, 0.25-0.50, and > 0.50 indicate low, medium, and high effect sizes, respectively. The system of data visualization based on the parallel-serial connection model revealed that at least 7 predictors with SAR > 0.50, including a trigger factor, were needed to predict schizophrenia. The SAR of the HLADQB1*03 gene was 0.22 for schizophrenia.

CONCLUSIONS

It is likely that the trigger factors and observed factors had a cumulative effect, as indicated by the parallel-serial connection model for binary outcomes. SAR may allow better evaluation of the effect size of a factor in complex events by eliminating the influence of trigger factors. The efficiency and efficacy of observational research could be increased if we are able to clarify how multiple factors can be related to a result in a pragmatic manner.

Age and Cohort Trends in Formal Volunteering and Informal Helping in Later Life: Evidence From the Health and Retirement Study

Formal volunteering holds great importance for the recipients of volunteer services, individuals who volunteer, and the wider society. However, how much recent birth cohorts volunteer in middle and late adulthood compared with earlier birth cohorts is not well understood. Even less well-known are the age and cohort trends in informal helping provided to friends and neighbors in later adulthood. Using longitudinal data from the Health and Retirement Study, we estimated age and cohort trends in formal volunteering and informal helping from 1998 to 2018 for a wide range of birth cohorts born between 1909 and 1958. We used multivariate, multilevel models based on Bayesian generalized modeling methods to estimate the probabilities of volunteering and informal helping simultaneously in a single model. Despite having advantages in human and health capital, recent birth cohorts showed volunteering levels in late adulthood that are similar to those of their predecessors. Moreover, more recent birth cohorts were consistently less engaged in informal helping than earlier birth cohorts throughout the observation period. More research is needed to illuminate the sociocultural drivers of changes in helping behaviors and overall prosocial and civic engagement.

Childhood adversities and suicidal thoughts and behaviors among first-year college students: results from the WMH-ICS initiative

PURPOSE

To investigate the associations of childhood adversities (CAs) with lifetime onset and transitions across suicidal thoughts and behaviors (STB) among incoming college students.

METHODS

Web-based self-report surveys administered to 20,842 incoming college students from nine countries (response rate 45.6%) assessed lifetime suicidal ideation, plans and attempts along with seven CAs: parental psychopathology, three types of abuse (emotional, physical, sexual), neglect, bully victimization, and dating violence. Logistic regression estimated individual- and population-level associations using CA operationalizations for type, number, severity, and frequency.

RESULTS

Associations of CAs with lifetime ideation and the transition from ideation to plan were best explained by the exact number of CA types (OR range 1.32-52.30 for exactly two to seven CAs). Associations of CAs with a transition to attempts were best explained by the frequency of specific CA types (scaled 0-4). Attempts among ideators with a plan were significantly associated with all seven CAs (OR range 1.16-1.59) and associations remained significant in adjusted analyses with the frequency of sexual abuse (OR = 1.42), dating violence (OR = 1.29), physical abuse (OR = 1.17) and bully victimization (OR = 1.17). Attempts among ideators without plan were significantly associated with frequency of emotional abuse (OR = 1.29) and bully victimization (OR = 1.36), in both unadjusted and adjusted analyses. Population attributable risk simulations found 63% of ideation and 30-47% of STB transitions associated with CAs.

CONCLUSION

Early-life adversities represent a potentially important driver in explaining lifetime STB among incoming college students. Comprehensive intervention strategies that prevent or reduce the negative effects of CAs may reduce subsequent onset of STB.

Quality control and product differentiation of LMWHs marketed in China using 1H NMR spectroscopy and chemometric tools

Low molecular weight heparins (LMWHs) are heterogeneous mixtures of glycosaminoglycan chains composed of mixture of different lengths and substitution patterns. Structural characterization and quality control of LMWHs have always been challenging. The Chinese drug regulatory authorities have been committed to improve the supervision standards of LMWHs to better regulate the quality and safety of LMWHs in current Chinese market. In the present paper, 80 batches of three types LMWHs (dalteparin, enoxaparin and naldroparin) marketed in China from different manufacturers were studied by ¹H NMR experiments and chemometric analysis. The method can be used not only to monitor impurities and contaminants, but also to check the batch-to-batch consistency of each manufacture. Moreover, for the biosimilar LMWHs from different manufactures, they can be differentiated and clustered according to their slightly different structural compositions originated from production process. By using this method, the quality and safety of LMWHs marketed in China were initially assessed.

Evaluation of different water absorption bands, indices and multivariate models for water-deficit stress monitoring in rice using visible-near infrared spectroscopy

Accurate estimation of plant water status is a major factor in the decision-making process regarding general land use, crop water management and drought assessment. Visible-near infrared (VNIR) spectroscopy can provide an effective means for real-time and non-invasive monitoring of leaf water content (LWC) in crop plants. The current study aims to identify water absorption bands, indices and multivariate models for development of non-destructive water-deficit stress phenotyping protocols using VNIR spectroscopy and LWC estimated from 10 different rice genotypes. Existing spectral indices and band depths at water absorption regions were evaluated for LWC estimation. The developed models were found efficient in predicting LWC of the samples kept in the same environment with the ratio of performance to deviation (RPD) values varying from 1.49 to 3.05 and 1.66 to 2.63 for indices and band depths, respectively during validation. For identification of novel indices, ratio spectral indices (RSI) and normalised difference spectral indices (NDSI) were calculated in every possible band combination and correlated with LWC. The best spectral indices for estimating LWC of rice were RSI (R₁₈₃₀, R₁₈₃₄) and NDSI (R₁₈₃₀, R₁₈₃₄) with R² greater than 0.90 during training and validation, respectively. Among the multivariate models, partial least squares regression (PLSR) provided the best results for prediction of LWC (RPD = 6.33 and 4.06 for training and validation, respectively). The approach developed in this study will also be helpful for high-throughput water-deficit stress phenotyping of other crops.

Multivariate models to investigate the relationship between collision risk and reliability outcomes on horizontal curves

This paper proposes a reliability-based framework to address the risk associated with limitations in the Available Sight Distance (ASD) on curved highway segments considering a three-dimensional (3D) sight distance computation approach. To facilitate this assessment, the ASD on horizontal curves was evaluated and an accurate inventory of curve attribute information was generated using LiDAR (Light Detection and Ranging) data in an automated and efficient manner. These datasets were then used to estimate the risk (probability of noncompliance, P_nc) associated with sight distance insufficiencies. Full Bayes multivariate Poisson log-normal safety performance functions were developed to relate the P_nc to the expected number of collisions. The results show that there was a statistically significant relationship between P_nc and collision frequency. There was also a significant correlation of 0.444 to 0.452 across collision severity levels indicating that curves with high Property-Damage-Only (PDO) collisions could be associated with higher injury and fatal (I + F) collisions. It was also found that P_nc had a greater impact on increasing PDO collisions than I + F collisions, suggesting that collisions associated with insufficient sight distance are likely to be less severe. The results of this analysis are expected to improve our understanding of the risks associated with deviations from design guidelines and quantitatively assess the safety margins due to these variations. The framework presented in this paper can be used to compare different design alternatives and investigate the influence of design deficiencies on collision occurrence across various severity levels.

ANOVA simultaneous component analysis: A tutorial review

When analyzing experimental chemical data, it is often necessary to incorporate the structure of the study design into the chemometric/statistical models to effectively address the research questions of interest. ANOVA-Simultaneous Component Analysis (ASCA) is one of the most prominent methods to include such information in the quantitative analysis of multivariate data, especially when the number of variables is large. This tutorial review intends to explain in a simple way how ASCA works, how it is operated and how to correctly interpret ASCA results, with approachable mathematical and visual descriptions. Two examples are given: the first, a simulated chemical reaction, serves to illustrate the ASCA steps and the second, from a real chemical ecology data set, the interpretation of results. An overview of methods closely related to ASCA is also provided, pointing out their differences and scope, to give a wide-ranging picture of the available options to build multivariate models that take experimental design into account.

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ASCA is a multivariate method for analysis of multi-factor data.

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An overview of the (mathematical) principles of ASCA is presented.

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Key aspects for practical application of ASCA are discussed.

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Detailed explanation of ASCA output in terms of score and loading plots is given.

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Literature review of other multivariate techniques for analysis of multi-factor data.

Low concentrations and low spatial variability of marine microplastics in oysters (Crassostrea virginica) in a rural Georgia estuary

Microplastics are an emerging concern for the health of marine ecosystems. In the southeastern US, the filter-feeding Eastern oyster, Crassostrea virginica, is susceptible to microplastic ingestion. We quantified the distribution of microplastics within adult oysters (harvestable size >7.5 cm) from 28 reefs throughout a rural estuary with limited riverine inputs (St. Catherines Sound, Georgia). To determine which variables best predict microplastic concentration in oysters, we also quantified oyster recruitment, distance to ocean, fetch, and water body width. Oysters averaged 0.72 microplastic particles per individual (0.18 particles per gram wet mass); microfragments and microplastics were equally abundant. Although microplastic concentrations were low, multivariate models identified a positive effect of water body width on the site-level concentration of plastic microfibers; average microfragment length was affected by fetch. Our work informs a growing understanding of microplastic distribution in coastal estuaries, providing an important rural contrast to the urbanized estuaries that have been examined.

Spatio-temporal dynamics of measles outbreaks in Cameroon

PURPOSE

In 2012, Cameroon experienced a large measles outbreak of over 14,000 cases. To determine the spatio-temporal dynamics of measles transmission in Cameroon, we analyzed weekly case data collected by the Ministry of Health.

METHODS

We compared several multivariate time-series models of population movement to characterize the spatial spread of measles in Cameroon. Using the best model, we evaluated the contribution of population mobility to disease transmission at increasing geographic resolutions: region, department, and health district.

RESULTS

Our spatio-temporal analysis showed that the power law model, which accounts for long-distance population movement, best represents the spatial spread of measles in Cameroon. Population movement between health districts within departments contributed to 7.6% (range: 0.4%-13.4%) of cases at the district level, whereas movement between departments within regions contributed to 16.0% (range: 1.3%-23.2%) of cases. Long-distance movement between regions contributed to 16.7% (range: 0.1%-59.0%) of cases at the region level, 20.1% (range: 7.1%-30.0%) at the department level, and 29.7% (range: 15.3%-47.6%) at the health district level.

CONCLUSIONS

Population long-distance mobility is an important driver of measles dynamics in Cameroon. These findings demonstrate the need to improve our understanding of the roles of population mobility and local heterogeneity of vaccination coverage in the spread and control of measles in Cameroon.

Multivariate linear intervention models with random parameters to estimate the effectiveness of safety treatments: Case study of intersection device program

A novel intervention model that analyzes time-series crash data was recently introduced in the road safety statistical field. The model allows the computation of components related to direct and indirect treatment effects using a linearized time-series intervention model. The isolation of a component corresponding to the direct treatment effects, known as the crash modification function (CMFunction), enables the assessment of safety countermeasures over time. To gain new insights into how crash counts are influenced by covariates and to account for the fact that many components affecting crash occurrence are not easily available (unobserved heterogeneity), the linear intervention models with random parameters are implemented to evaluate the safety impacts of a specific treatment. Both matched-pair and full random parameter models were applied. In addition, the analysis was carried out in a multivariate context to account for possible correlation between dependent variables. The safety treatment selected for this study was the Intersection Safety Device (ISD) program implemented in the City of Edmonton (Alberta, Canada). The safety impacts were estimated by assessing the change in crash severity (property-damage-only vs. fatal-plus-injury) over time. Overall, the results showed a lower deviance information criterion (better goodness of fit) of the multivariate linear intervention model with random parameters compared to the univariate form with fixed parameters. The difference of the indexes of treatment effectiveness between the proposed modeling framework and the univariate model with fixed parameters was estimated up to 2.7%, which indicates the importance of accounting for unobserved heterogeneity.

Prediction of clinical outcomes in individuals with chronic low back pain: a protocol for a systematic review with meta-analysis

BACKGROUND

Low back pain (LBP) is one of the most prevalent and recurrent conditions in the general population, with personal, professional, social and economic impact. However, there is a lack of consistent evidence about chronic low back pain (CLBP) prognosis, especially highlighting predictors that influence CLBP outcome. Existing systematic reviews are scarce, outdated and incomplete. The primary aim of this systematic review is to identify multivariable models and/or predictors associated with clinical outcomes in subjects with CLBP (namely pain intensity, disability, return to work, psychological well-being and quality of life).

METHODS

We will systematically search Ovid MEDLINE (PubMed), Scopus and Web of Science databases for longitudinal studies, published until June 2017, including adults with CLBP (defined as persistent pain with ≥ 3 months duration), which studied the association between multivariable models and/or predictors with at least one of the selected clinical outcomes after ≥ 3 months of follow-up. Articles' screening and selection will be conducted by two reviewers, blindly and independently. Disagreements will be resolved by a third reviewer. Models' discriminative ability will be assessed using C-statistic. The link between multivariable models and predictors with the clinical outcome will be analysed through association measures. Qualitative and quantitative synthesis of the available evidence will be performed. Meta-analysis will be conducted to aggregate each type of measure. In the absence or in the presence of only slight to moderate of heterogeneity, we will use the fixed or random effects model, respectively. In case of moderate to severe heterogeneity, an attempt to explain variability in detail will be made through subgroups and sensitivity analyses. Subgroup analysis will be conducted according to clinical outcome, follow-up duration (≤ 6 months versus > 6 months) and type of context (pain management clinics versus other therapeutic settings).

DISCUSSION

We consider that it is urgent to highlight the available evidence about CLBP prognosis. This systematic review will help identify multivariable models and individual predictors that may enhance pain management success. One potential limitation will be the difficulty of aggregating quantitative measures from several prognostic models and predictors, with different clinical outcomes.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42017079233.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R² values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms

Determining amylose content in rice with near infrared (NIR) spectroscopy, associated with a suitable multivariate regression method, is both feasible and relevant for the rice business to enable Process Analytical Technology applications for this critical factor, but it has not been fully exploited. Due to it being time-consuming and prone to experimental errors, it is urgent to develop a low-cost, nondestructive and 'on-line' method able to provide high accuracy and reproducibility. Different rice varieties and specific chemometrics tools, such as partial least squares (PLS), interval-PLS, synergy interval-PLS and moving windows-PLS, were applied to develop an optimal regression model for rice amylose determination. The model performance was evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The high performance of the siPLS method (R=0.94; RMSEP=1.938; 8941-8194cm^-1; 5592-5045cm^-1; and 4683-4335cm^-1) shows the feasibility of NIR technology for determination of the amylose with high accuracy.

Early metabolic markers identify potential targets for the prevention of type 2 diabetes

AIMS/HYPOTHESIS

The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers.

METHODS

We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study.

RESULTS

Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors.

CONCLUSIONS/INTERPRETATION

This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.

Multivariate models from RNA-Seq SNVs yield candidate molecular targets for biomarker discovery: SNV-DA

BACKGROUND

It has recently been shown that significant and accurate single nucleotide variants (SNVs) can be reliably called from RNA-Seq data. These may provide another source of features for multivariate predictive modeling of disease phenotype for the prioritization of candidate biomarkers. The continuous nature of SNV allele fraction features allows the concurrent investigation of several genomic phenomena, including allele specific expression, clonal expansion and/or deletion, and copy number variation.

RESULTS

The proposed software pipeline and package, SNV Discriminant Analysis (SNV-DA), was applied on two RNA-Seq datasets with varying sample sizes sequenced at different depths: a dataset containing primary tumors from twenty patients with different disease outcomes in lung adenocarcinoma and a larger dataset of primary tumors representing two major breast cancer subtypes, estrogen receptor positive and triple negative. Predictive models were generated using the machine learning algorithm, sparse projections to latent structures discriminant analysis. Training sets composed of RNA-Seq SNV features limited to genomic regions of origin (e.g. exonic or intronic) and/or RNA-editing sites were shown to produce models with accurate predictive performances, were discriminant towards true label groupings, and were able to produce SNV rankings significantly different from than univariate tests. Furthermore, the utility of the proposed methodology is supported by its comparable performance to traditional models as well as the enrichment of selected SNVs located in genes previously associated with cancer and genes showing allele-specific expression. As proof of concept, we highlight the discovery of a previously unannotated intergenic locus that is associated with epigenetic regulatory marks in cancer and whose significant allele-specific expression is correlated with ER+ status; hereafter named ER+ associated hotspot (ERPAHS).

CONCLUSION

The use of models from RNA-Seq SNVs to identify and prioritize candidate molecular targets for biomarker discovery is supported by the ability of the proposed method to produce significantly accurate predictive models that are discriminant towards true label groupings. Importantly, the proposed methodology allows investigation of mutations outside of exonic regions and identification of interesting expressed loci not included in traditional gene annotations. An implementation of the proposed methodology is provided that allows the user to specify SNV filtering criteria and cross-validation design during model creation and evaluation.

[Clinical research XXII. From clinical judgment to Cox proportional hazards model]

Survival analyses are commonly used to determine the time of an event (for example, death). However, they can be used also for other clinical outcomes on the condition that these are dichotomous, for example healing time. These analyses only consider the relationship of one variable. However, Cox proportional hazards model is a multivariate analysis of the survival analysis, in which other potentially confounding covariates of the effect of the main maneuver studied, such as age, gender or disease stage, are taken into account. This analysis can include both quantitative and qualitative variables in the model. The measure of association used is called hazard ratio (HR) or relative risk ratio, which is not the same as the relative risk or odds ratio (OR). The difference is that the HR refers to the possibility that one of the groups develops the event before it is compared with the other group. The proportional hazards multivariate model of Cox is the most widely used in medicine when the phenomenon is studied in two dimensions: time and event.