Bland-Altman Agreement Analysis in Laboratory Research

Bland-Altman Agreement Analysis in Laboratory Research Use of Bland-Altman agreement analysis in laboratory research: A survey of current reporting standards. Introduction- Advances in technology have led to development of new instruments and measurement devices in field of medicine. The clinicians and researchers often need to compare a newer method of measurement with an established one, to check for interchangeability. While assessing for interchangeability the emphasis should be on testing how well two methods agree with each other. Earlier Pearson’s product-moment correlation coefficient was used as a measure of agreement[R]. However the approach was inappropriate as this coefficient merely indicated association rather than agreement [R]. Hence Bland and Altman in their series of publications[R] stressed on quantification of bias. They provided a simpler and visually attractive plot for agreement analysis of continuous variables measured on the same scale.[R] After its introduction to medical literature in 1983, the Bland-Altman’s (B-A) method [R] is one of most commonly used statistical method for agreement analysis. The method is extensively used in evaluating the agreement of laboratory analytes, physiological variables, newer instruments and other devices. B-A method[R] advocates the construction of a scatter plot, where the absolute difference between the paired measurements is plotted on y-axis against the mean of two methods on x-axis. The SD of differences between paired measurements is then used to construct 95% limits of agreement (as  ± 1.96 SD). The 95% of differences between paired measurements are expected to lie between these upper and lower LOA. The conclusions on agreement and interchangeability of two methods are then made based upon the width of these LOA in comparison to a priori defined clinical criteria[R]. The plot also enables the researcher to visually assess the bias, data scatter and the relationship between magnitude of difference and size of measurement. Often in biologic systems data scatter and the magnitude of differences increases proportionally to the size of the measurement (hetero-scedastic distribution). Bland and Altman recommended the logarithmic or percentage transformation of data in case of heter o-scedastic distribution and then constructing B-A plot with transformed data[R] instead of classical absolute difference plot. Contrary to conventional statistical hypothesis testing, the output of B-A analysis consists of bias and LOA, both of which are estimates[R]. The estimates have inherent risk of sampling error and hence the authors suggested calculation of confidence interval (CI) of bias and LOA. The method also advocated the collection of data in replicates. Replicates are defined as two or more measurements on the same individual by the same method, taken in identical conditions. Replicates enable the comparison of the agreement between the two methods with the agreement each method has to itself (repeatability) [R] B-A also advocated for sample size calculations on in method comparison studies[R]. Despite its simplicity and frequent use in clinical laboratory research, the B-A method is not properly interpreted and reported in medical literature. Studies [R]conducted a decade ago highlighted poor reporting standards of B-A method, however there is paucity of current information on the same. Furthermore, uniform statistical reporting of results not only increases the generalizability of results, but also facilitates the inclusion of studies in systemic reviews and meta-analysis. Hence the aim of study was to review the current reporting standards of B-A method in laboratory research in medical literature. Material and methods- Three researchers (VC, RB, and SK) participated in this study. All researchers were qualified health professionals. VC and SK had previous experience of publishing laboratory research [R] with use of B- A agreement analysis. Eligibility criteria- Studies which tested agreement of laboratory analytes with continuous measurements, as per B-A methodology were included. Literature search- A thorough search of PUBMED, MEDLINE and GOOGLE SCHOLAR was conducted for studies published in years 2012 and 2013. The search strings used to search potential studies were â€Å"Agreement analysis† AND/OR â€Å"Bland Altman analysis† (MeSH) and â€Å"Laboratory analytes† and â€Å"clinical biochemistry† (MeSH). Included studies were evaluated according to Bland and Altman methodology on a predesigned checklist. The studies were evaluated for following 8 items: (1.) Measures of repeatability (2.) Representation and correct definition of LOA (3.) Correct representation of x-axis on BA plot (4.) Reporting of CI of LOA (5.) Comparison of limits of agreement with a priori defined clinical criteria (6.) Evaluation of pattern of relationship between difference (y-axis) and average (x-axis) (7.) Use of logarithmic or percentage conversion of data in case of heteroscedastic relationship between the difference and average (8.) Sample size calcula tions. Each item on the checklist was rated as ‘Yes’or ‘No’. We also recorded the data on use of other statistical methods for testing of agreement. However, we did not perform detailed evaluation of included studies for other statistical methods of agreement. To ensure accurate data retrieval, each included study was evaluated twice by one author (VC) and data recorded on predesigned checklist. Opinion was taken from second author (SKK) in case of confusion arising during data extraction. We compared the results of our study with 3 similar surveys done earlier. Results- A total of 156 studies were screened for potential inclusion in the study. A total of 50 studies, were retrieved and included in the final study. The 38% of included studies were published in journals of various streams of internal medicine, while 30%, 26% and 6% were published in journals of laboratory medicine, emergency medicine, anaesthesia respectively. Results of survey and its comparison to three previous studies are as shown in Table-1.The other statistical methods used in addition to B-A plot in included studies were correlation coefficient (70%), Deming Regression(14%), Passing Bablok regression (14%), linear regression (24%), Lin’s Concordance (8%), Sensitivity specificity analysis (16%), Interclass correlation coefficient (6%), Grid error plot (10%), Critchley polar plots (2%). Discussion- Use of B-A for method comparison has increased in recent years with most of authors using it for analysing agreement. The original paper on agreement analysis by B-A[R] is among one of most cited statistical publication, with more than 34000 citations. Although claimed as a method which is simpler to perform and interpret, the method is often used and interpreted without proper understanding. Review by Berthelsen et al[R] in 2006 and earlier studies[R] demonstrated unsatisfactory reporting of B-A analyses, in anaesthesiology literature. Williamson et al[R] proposed a method of meta-analysis of method comparison studies, however authors also highlighted the problem of non-uniform reporting of studies. [R] Twomey et al [R] suggested use of method hierarchy for selection of x-axis and advocated use of gold standard method as x-axis in B-A plot. However Bland and Altman statistically proved that use of any single method instead of average of two methods as x-axis is misguided and leads to misinterpretation[R]. Results of our study suggest that 94% of studies reported x-axis correctly, which is almost similar to results of earlier studies conducted by Mantha et al (94%) [R] and Dewitt et al (87%) [R]. although most method comparison computer softwares (analyse it, Graphpad Prism, EP evaluator) automatically select x-axis as mean of two methods, errors in selection of x-axis are still noticed. The 95% LOA were correctly defined and drawn in 94 %( 47) of included studies. Further among 47 studies with correct definition of LOA, the 3 studies interpreted LOA wrongly concluding good agreement because 95 % of differences were present in-between upper and lower LOA. The 95 % LOA are in-fact drawn so as to contain 95% of differences between them. It is not LOA per se, but width of these LOA in comparison to a priori defined clinical criteria that conclusions regarding agreements can be made. The decision on acceptable differences between two methods is primarily clinical rather than statistical. Earlier studies by Dewitte et al [R]and Mantha et al[R] had shown that comparison with pre-defined clinical criteria was missing in >90% of studies. Total 74 % of authors in our study commented on agreement on basis of predefined clinical criteria which represents a significant improvement in reporting standards. The specifications for clinical acceptance criteria of laboratory analytes have been provided as by Ricos et al[R], CLSI[R], and West guard QC[R]. Alternatively a Delphi survey (expert opinion) can be done to determine acceptable limits before instituting study. The CI limits of LOA were reported in only 6% of included studies in our study. The LOA are estimates and reporting LOA without CI is equivalent to reporting a sample mean without its CI. The CI limits [Ludbrook et al] represent the range within which a single, new, observation taken from the same population would be expected to lie. Although strongly recommended by B-A[R], and subsequently proved by a simulation study conducted by Hamilton et al[R], the statistical reporting of CI of LOA has remained poor (Mantha et al-2.6%) [R]. Although recommended by B-A method, the pattern of relationship between difference and wider concentration range is rarely evaluated[R]. Drawing difference plot with parallel LOA in datasets with heteroscedastic scatter makes LOA wider in lower concentration range and narrower in higher concentration range thus affecting validity of interpretation. [R] Bland and Altman [R]proposed logarithmic transformation of data with heteroscedasticy and then constructing difference plot against average of two methods using log transformed data. For meaningful understanding of LOA, they suggested back-transformation (antilog) of the log transformed data. Alternatively[R] plot of ratios of two methods or percent difference can be plotted against average of two methods for simpler interpretation. Transformation of data usually renders the scatter of differences as uniform (Homoscedastic). Twomey et al[R] recommended the drawing up of funnel shaped or V shaped LOA instead of classical parallel LOA in data sets with heteroscedastic scatter. Another option is breaking the data into smaller subsets and then analysing these subsets with absolute difference plot to make conclusions. [Twomey et al] We observed that only 28 % of studies made an attempt at evaluation of pattern of scatter. Rest of authors did not comment on pattern thus affecting the validity of results. Another important problem noticed was lack of assessment of repeatability (38%), a practice that has not shown any substantial improvement Table-1. Conclusions drawn from studies without repeatability assessment are likely to be uncertain. Assessment of errors of the two methods (repeatability) enables the construction of the worst-case acceptable LOA. [R] With poor repeatability of one or both methods, the agreement between the two methods is bound to be unacceptable. [R] Sample size calculations were done in only 15 studies. Lack of power and sample size analysis reduces validity of results. Different researchers have proposed sample size calculation for method comparison studies using Bayesian[R], regression[R], or concordance [R] approach. However Stockl et al[R] proposed an approach incorporating CI of LOA and predefined error limits in B-A plot. The approach is simple and allows for visual interpretation of appropriate sample size, as the classical B-A plot provides for agreement. Despite a lot of research on B-A method in field of statistics, the uptake of the method in medical research has been slow. While efforts are on in statistical community for use of modifications of B-A plot in special situations like repeated measure studies[R] or using bar charts in B-A plots with limited value ranges[R], unfortunately reporting standards of classical B-A method among medical community are unacceptable. Guidelines â€Å"Reporting reliability and Agreement Studies (GRRAS)† were published as a guide to appropriate reporting of reliability and agreement studies. We found unsatisfactory reporting of B-A analysis in our study.

Concept map

Take for example, peer pressure from bad associates. They eight pressure us to ditch classes, cheat, drink alcohols, smoke, try taking drugs and influence us to disobey our parents. Another is peer pressure from our own wrong desires. Its given since in our teenage stage, we youth are adventurous. We wanted to try something new we haven't experience yet and we crave on something we don't have. Thus, being attentive can help us to caution ourselves from dangers.If we see troubles coming in our way, don't walk right in to it find another way. Secondly, think about the consequences. Why not ask yourself? For instance: How lives feel later if I give in? What may be the outcome? How can it affect the others, specially, to your love ones? Remember, bible says at Proverbs 14: 1 5, smart people watch their steps. Doing right decisions requires a lot of time to meditate. For this, we just don't jump in to any conclusions. Use your imaginations.Third step, we must plan ahead. Proverbs 24: 5, à ¢â‚¬Ëœwith knowledge a man increases his power'. If we know why we don't do those something and the reasons why it's bad, we can have the strength to say No. There are two ways to do that, strengthen your convictions and seek good explanations. Know how to distinguish right from wrong and give them the right reason for them to to bother you again. Finally the fourth step, take action. Now, we don't have to lecture them. Just say ‘No', clearly and confident.Stand firm in our beliefs for they are not theories but facts. Granted that, those beliefs are all according from the scriptures. Each time we show determinants to resist pressure, we grow strong and stronger. It'll be easier for us to resist peer pressure the next time. Therefore, reacting to peer's pressure depends on us. If we remain weak and unable to stand firm, well never get rid of it. Yes, they are inevitable but by following those steps, you'll see things differently.

7 Extreme Activities Every Student Must Try

You live only once – this phrase may sound a bit clichà ©d, but it doesn’t make it any less true. When you are at college it may seem that your whole life is ahead of you, with innumerable exciting activities to try out, things to do and people to meet. However, after you graduate you are not very likely to have enough free time for yourself and pursuing your dreams for a very, very long time. Looking for a job, climbing up a career ladder, earning money to support yourself and, later, your family – all this is going to take its toll, first of all – in terms of time. This means that right now, when you are still at college and have relatively few responsibilities may be the best time for you to try some extreme activities – for if you put them off, you may never get around to them. 1.  Zip Lining Zip lining is gliding down a zip cord stretched between two points at a considerable height. Great speed, whistling of air in your ears and excitement from passing over the scenery – that’s what it is all about. Another reason why it is perfectly suited for students is that it requires you neither to have and maintain your equipment nor to spend a great deal of money. Zip lines are easy to find and are rather cheap to try out. 2.  Parachuting Jumping off a plane at a great height combines two important aspects: it is incredibly exciting while being relatively safe. While falling down several thousands of meters may not be your idea of safety, it is actually far less dangerous than many activities that don’t require you to leave ground. 3.  Hang Gliding Hang gliding is probably the closest a human being is ever going to get to feeling what it is like to be a bird. You simply glide through air and leave all your problems down on the ground. 4.  Base Jumping Base jumping is known as one of the most extreme of the extreme sports. And for a good reason – jumping of tall buildings of cliffs with a special rapidly deploying parachute requires excellent reflexes and perfectly maintained equipment. But the adrenaline rush you feel in the process is unsurpassed by anything as well. 5.  White-Water Rafting Another choice not for the faint of heart, white-water rafting. It requires good physical condition and stamina, ability to work in a team and ability to make quick decisions. If you meet all these requirements you are not going to forget this ride in a hurry. 6.  Mount Biking Although also rather dangerous and demanding, mount biking is one of the most accessible extreme sports out there. All you need to have is a bike capable of passing difficult terrain, a good trail and, preferably, a company that will look out for each other. 7.  Cliff Jumping Cliff jumping is for those who are tired of using jumping boards. Just make sure you know what you are doing – people sometimes kill themselves by unwittingly jumping down in too shallow places. So, if you’ve been putting off something from this list – why not try it out right now? Who knows when you are going to have another opportunity?

Hamlet Analytical Essay About Style - 834 Words

Hamlet has style. Hamlet as in the play, not the character, and style not in terms of fashion and knowing how to dress, but as in the combination of different literary elements in order to capture that certain elegance in one’s writing. Shakespeare implements these various literary devices for the purpose of enhancing his writing, typically to emphasize a specific point or to set a certain mood for a scene. In passages that are important in the play there are usually an abundance of these devices. Ones that are more easily detectable and obvious, then there are also ones that are more subtle and sophisticated. Soliloquies are often places where emphasis is needed and thus a majority of the time they are full of literary devices. A†¦show more content†¦But sure that sense /Is apoplexed, for madness would not err, /Nor sense to ecstasy was neer so thralled, /But it reserved some quantity of choice /To serve in such a difference† (3.4.73-8). Hamlet is saying that he knows his mother has sense, but her sense must be disabled right now because even a crazy person would be able to see the difference between Hamlet and Claudius, which is basically saying that Gertrude has less judgment than someone who is insane. â€Å"Eyes without feeling, feeling without sight, /Ears without hands or eyes, smelling sans all, /Or but a sickly part of one true sense /Could not so mope.† Hamlet is exaggerating again and saying that even a person who only had one of her five senses could have made a better decision than she did. In addition to the repetition and use of hyperbole Shakespeare also alludes to direct us to see certain people in a good or bad way and uses metaphors and similes to describe specific people and to create an image for us. 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