International Committee of Medical Journal Editors (ICMJE) warned against sole reliance on NHST to substantiate study conclusions and suggested supplementary use of confidence intervals (CI). Our objective was to evaluate the extent and quality in the use of NHST and CI, both in English and Spanish language biomedical publications between 1995 and 2006, taking into account the International Committee of Medical Journal Editors recommendations, with particular focus on the accuracy of the interpretation of statistical significance and the validity of conclusions.The null hypothesis significance test (NHST) is the most frequently used statistical method, although its inferential validity has been widely criticized since its introduction. In 1988, the Original articles published in three English and three Spanish biomedical journals in three fields  were considered for this study. Papers published in 1995-1996, 2000-2001, and 2005-2006 were selected through a systematic sampling method. After excluding the purely descriptive and theoretical articles, analytic studies were evaluated for their use of NHST with P-values and/or CI for interpretation of statistical "significance" and "relevance" in study conclusions.Among 1,043 original papers, 874 were selected for detailed review. The exclusive use of P-values was less frequent in English language publications as well as in Public Health journals; overall such use decreased from 41% in 1995-1996 to 21% in 2005-2006. While the use of CI increased over time, the "significance fallacy"  appeared very often, mainly in journals devoted to clinical specialties (81%). In papers originally written in English and Spanish, 15% and 10%, respectively, mentioned statistical significance in their conclusions.Overall, results of our review show some improvements in statistical management of statistical results, but further efforts by scholars and journal editors are clearly required to move the communication toward ICMJE advices, especially in the clinical setting, which seems to be imperative among publications in Spanish. H0 - which, generally speaking, establishes that certain parameters do not differ from each other. He was the inventor of the "P-value" through which it could be assessed [significance testing theory considered the p-value as an index to measure the strength of evidence against the null hypothesis in a single experiment. The father of NHST never endorsed, however, the inflexible application of the ultimately subjective threshold levels almost universally adopted later on .The null hypothesis statistical testing (NHST) has been the most widely used statistical approach in health research over the past 80 years. Its origins dates back to 1279 [hypothesis statistical testing. The Neyman-Pearson approach is based on the notion that one out of two choices has to be taken: accept the null hypothesis taking the information as a reference based on the information provided, or reject it in favor of an alternative one. Thus, one can incur one of two types of errors: a Type I error, if the null hypothesis is rejected when it is actually true, and a Type II error, if the null hypothesis is accepted when it is actually false. They established a rule to optimize the decision process, using the p-value introduced by Fisher, by setting the maximum frequency of errors that would be admissible.A few years later, Jerzy Neyman and Egon Pearson considered the Fisherian approach inefficient, and in 1928 they published an article  that wouH0, is known. With these data the P-value is computed. Finally, the null hypothesis is rejected when the obtained P-value is smaller than a certain comparative threshold  and it is not rejected if P is larger than the threshold.The null hypothesis statistical testing, as applied today, is a hybrid coming from the amalgamation of the two methods . As a maThe first reservations about the validity of the method began to appear around 1940, when some statisticians censured the logical roots and practical convenience of Fisher's P-value . SignifiAlthough there are very comprehensive works on the topic , we list• The P-values are used as a tool to make decisions in favor of or against a hypothesis. What really may be relevant, however, is to get an effect size estimate  rather than rendering dichotomous true/false verdicts -11.• The P-value is a conditional probability of the data, provided that some assumptions are met, but what really interests the investigator is the inverse probability: what degree of validity can be attributed to each of several competing hypotheses, once that certain data have been observed .• The two elements that affect the results, namely the sample size and the magnitude of the effect, are inextricably linked in the value of p and we can always get a lower P-value by increasing the sample size. Thus, the conclusions depend on a factor completely unrelated to the reality studied  ,14.• Those who defend the NHST often assert the objective nature of that test, but the process is actually far from being so. NHST does not ensure objectivity. This is reflected in the fact that we generally operate with thresholds that are ultimately no more than conventions, such as 0.01 or 0.05. What is more, for many years their use has unequivocally demonstrated the inherent subjectivity that goes with the concept of P, regardless of how it will be used later -17.• In practice, the NHST is limited to a binary response sorting hypotheses into "true" and "false" or declaring "rejection" or "no rejection", without demanding a reasonable interpretation of the results, as has been noted time and again for decades. This binary orthodoxy validates categorical thinking, which results in a very simplistic view of scientific activity that induces researchers not to test theories about the magnitude of effect sizes -20.Despite the weakness and shortcomings of the NHST, they are frequently taught as if they were the key inferential statistical method or the most appropriate, or even the sole unquestioned one. The statistical textbooks, with only some exceptions, do not even mention the NHST controversy. Instead, the myth is spread that NHST is the "natural" final action of scientific inference and the only procedure for testing hypotheses. However, relevant specialists and important regulators of the scientific world advocate avoiding them.International Committee of Medical Journal Editors  incorporates the following recommendation to authors of manuscripts submitted to medical journals: "When possible, quantify findings and present them with appropriate indicators of measurement error or uncertainty . Avoid relying solely on statistical hypothesis testing, such as P-values, which fail to convey important information about effect size" [Taking especially into account that NHST does not offer the most important information , many experts recommend the reporting of point estimates of effect sizes with confidence intervals as the appropriate representation of the inherent uncertainty linked to empirical studies -25. Sincct size" .As will be shown, the use of confidence intervals (CI), occasionally accompanied by P-values, is recommended as a more appropriate method for reporting results. Some authors have noted several shortcomings of CI long ago . In spitAlthough twenty years have passed since the ICMJE began to disseminate such recommendations, systematically ignored by the vast majority of textbooks and hardly incorporated in medical publications , it is isignificance fallacy is present, an inertial deficiency that consists of attributing -- explicitly or not -- qualitative importance or practical relevance to the found differences simply because statistical significance was obtained.In addition to assessing the adherence to the above cited statistical recommendation proposed by ICMJE relative to the use of P-values, we consider it of particular interest to estimate the extent to which the Many authors produce misleading statements such as "a significant effect was (or was not) found" when it should be said that "a statistically significant difference was (or was not) found". A detrimental consequence of this equivalence is that some authors believe that finding out whether there is "statistical significance" or not is the aim, so that this term is then mentioned in the conclusions . This meInternational Committee of Medical Journal Editors recommendations, with particular focus on accuracy regarding interpretation of statistical significance and the validity of conclusions.The general objective of the present study is to evaluate the extent and quality of use of NHST and CI, both in English- and in Spanish-language biomedical publications, between 1995 and 2006 taking into account the We reviewed the original articles from six journals, three in English and three in Spanish, over three disjoint periods sufficiently separated from each other  as to properly describe the evolution in prevalence of the target features along the selected periods.Obstetrics & Gynecology and Revista Española de Cardiología); Public Health and Epidemiology  and the area of general and internal medicine . Five of the selected journals formally endorsed ICMJE guidelines; the remaining one (Revista Española de Cardiología) suggests observing ICMJE demands in relation with specific issues. We attempted to capture journal diversity in the sample by selecting general and specialty journals with different degrees of influence, resulting from their impact factors in 2007, which oscillated between 1.337 (MC) and 9.723 (BMJ). No special reasons guided us to choose these specific journals, but we opted for journals with rather large paid circulations. For instance, the Spanish Cardiology Journal is the one with the largest impact factor among the fourteen Spanish Journals devoted to clinical specialties that have impact factor and Obstetrics & Gynecology has an outstanding impact factor among the huge number of journals available for selection.The selection of journals was intended to get representation for each of the following three thematic areas: clinical specialties  analytical papers, or (3) articles that address theoretical, methodological or conceptual issues. An article was regarded as analytical if it seeks to explain the reasons behind a particular occurrence by discovering causal relationships or, even if self-classified as descriptive, it was carried out to assess cause-effect associations among variables. We classify as theoretical or methodological those articles that do not handle empirical data as such, and focus instead on proposing or assessing research methods. We identified 169 papers as purely descriptive or theoretical, which were therefore excluded from the sample. Figure Forty-nine of the 1,092 selected papers were eliminated because, although the section of the article in which they were assigned could suggest they were originals, detailed scrutiny revealed that in some cases they were not. The sample, therefore, consisted of 1,043 papers. Each of them was classified into one of three categories: (1) purely statistically significant. It was deemed that an article uses CI if it explicitly contained at least one confidence interval, but not when it only provides information that could allow its computation . Probability intervals provided in Bayesian analysis were classified as confidence intervals  since what is really of interest here is whether or not the authors quantify the findings and present them with appropriate indicators of the margin of error or uncertainty.To estimate the adherence to ICMJE recommendations, we considered whether the papers used P-values, confidence intervals, and both simultaneously. By "the use of P-values" we mean that the article contains at least one P-value, explicitly mentioned in the text or at the bottom of a table, or that it reports that an effect was considered as substantive, relevant or important. The use of the term "significant effect" when it is only appropriate as a reference to a "statistically significant difference," can be considered a direct expression of the significance fallacy [In addition we determined whether the "Results" section of each article attributed the status of "significant" to an effect on the sole basis of the outcome of a NHST . Similarly, we examined whether the term "significant" (applied to a test) was mistakenly used as synonymous with  fallacy  and, as We also assessed whether the "Conclusions," which sometimes appear as a separate section in the paper or otherwise in the last paragraphs of the "Discussion" section mentioned statistical significance and, if so, whether any of such mentions were no more than an allusion to results.To perform these analyses we considered both the abstract and the body of the article. To assess the handling of the significance issue, however, only the body of the manuscript was taken into account.The information was collected by four trained observers. Every paper was assigned to two reviewers. Disagreements were discussed and, if no agreement was reached, a third reviewer was consulted to break the tie and so moderate the effect of subjectivity in the assessment.Clinical Medicine, Primary Care, and International Journal of Epidemiology) was performed. The results of this pilot study were satisfactory. Our results are reported using percentages together with their corresponding confidence intervals. For sampling errors estimations, used to obtain confidence intervals, we weighted the data using the inverse of the probability of selection of each paper, and we took into account the complex nature of the sample design. These analyses were carried out with EPIDAT [In order to assess the reliability of the criteria used for the evaluation of articles and to effect a convergence of criteria among the reviewers, a pilot study of 20 papers from each of three journals  were found to be analytic, while the remainders were purely descriptive or of a theoretical and methodological nature. Five of them did not employ either P-values or CI. Consequently, the analysis was made using the remaining 869 articles.The percentage of articles that use only P-values, without even mentioning confidence intervals, to report their results has declined steadily throughout the period analyzed Table . The perWhile the percentage of articles referring implicitly or explicitly to significance in an ambiguous or incorrect way - that is, incurring the significance fallacy -- seems to decline steadily, the prevalence of this problem exceeds 69%, even in the most recent period. This percentage was almost the same for articles written in Spanish and in English, but it was notably higher in the Clinical journals (81%) compared to the other journals, where the problem occurs in approximately 7 out of 10 papers Table . The kapThe percentage of papers mentioning a numerical finding as a conclusion is similar in the three periods analyzed Table . ConcernA similar pattern is observed, although with less pronounced differences, in references to the outcome of the NHST (significant or not) in the conclusions Table . The perThere are some previous studies addressing the degree to which researchers have moved beyond the ritualistic use of NHST to assess their hypotheses. This has been examined for areas such as biology , organizall findings as potentially eloquent. This is probably the frequent denunciations of the tendency for those papers presenting a significant positive result to receive more favorable publication decisions than equally well-conducted ones that report a negative or null result, the so-called publication bias [At first glance it is puzzling that, after decades of questioning and technical warnings, and after twenty years since the inception of ICMJE recommendation to avoid NHST, they continue being applied ritualistically and mindlessly as the dominant doctrine. Not long ago, when researchers did not observe statistically significant effects, they were unlikely to write them up and to report "negative" findings, since they knew there was a high probability that the paper would be rejected. This has changed a bit: editors are more prone to judge ion bias -50. ThisThe results did not show a significant difference between groups; however, with a larger sample size, this difference would have probably proved to be significant". The problem with this statement is that it is true; more specifically, it will always be true and it is, therefore, sterile. It is not fortuitous that one never encounters the opposite, and equally tautological, statement: "A significant difference between groups has been detected; however, perhaps with a smaller sample size, this difference would have proved to be not significant". Such a double standard is itself an unequivocal sign of the ritual application of NHST.Consequently, even though it was not an aim of our study, we found many examples in which statistical significance was not obtained. However, many of those negative results were reported with a comment of this type: "significance fallacy is huge.Although the declining rates of NHST usage show that, gradually, ICMJE and similar recommendations are having a positive impact, most of the articles in the clinical setting still considered NHST as the final arbiter of the research process. Moreover, it appears that the improvement in the situation is mostly formal, and the percentage of articles that fall into the The contradiction between what has been conceptually recommended and the common practice is sensibly less acute in the area of Epidemiology and Public Health, but the same pattern was evident everywhere in the mechanical way of applying significance tests. Nevertheless, the clinical journals remain the most unmoved by the recommendations.The ICMJE recommendations are not cosmetic statements but substantial ones, and the vigorous exhortations made by outstanding authorities  are not In some cases, the role of CI is not as clearly suitable , butThe inherent resistance to change old paradigms and practices that have been entrenched for decades is always high. Old habits die hard. The estimates and trends outlined are entirely consistent with Alvan Feinstein's warning 25 years ago: "Because the history of medical research also shows a long tradition of maintaining loyalty to established doctrines long after the doctrines had been discredited, or shown to be valueless, we cannot expect a sudden change in this medical policy merely because it has been denounced by leading connoisseurs of statistics ".It is possible, however, that the nature of the problem has an external explanation: it is likely that some editors prefer to "avoid troubles" with the authors and vice versa, thus resorting to the most conventional procedures. Many junior researchers believe that it is wise to avoid long back-and-forth discussions with reviewers and editors. In general, researchers who want to appear in print and survive in a publish-or-perish environment are motivated by force, fear, and expedience in their use of NHST . FurtherJournal of the American Medical Association published a bibliometric study [British Medical Journal and Annals of Internal Medicine. The data analysis is characterized by methodological orthodoxy. The authors just use chi-square tests without any reference to CI, although the NHST had been repeatedly criticized over the years by two of the authors:For example, ic study  discussiDouglas Altman, an early promoter of confidence intervals as an alternative , and SteThe present effort is certainly partial in at least two ways: it is limited to only six specific journals and to three biennia. It would be therefore highly desirable to improve it by studying the problem in a more detailed way , and continuing the review of prevailing patterns and trends.Overall, results of our review show some improvements in statistical management of statistical results, but further efforts by scholars and journal editors are clearly required to move the communication toward ICMJE advices, especially in the clinical setting, which seems to be imperative among publications in Spanish.The authors declare that they have no competing interests.LCSA designed the study, wrote the paper and supervised the whole process; PSG coordinated the data extraction and carried out statistical analysis, as well as participated in the editing process; AFS extracted the data and participated in the first stage of statistical analysis; all authors contributed to and revised the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/10/44/prepub


This review is atypical by design. It has used a synthesis of the available literature relating to the aetiology of AIS to draw attention to the lack of progress in this area despite intensive research for more than 100 years. The review has argued that if progress is to be made in this area then significant changes in approach to the problem must be made. Such changes have been outlined and major areas of potential focus identified with the intention of creating debate and discussion. There is no doubt that people are working hard in this area of research but this review has deliberately attempted to question its achievements and future directions."It is not enough to be busy, so are the ants. The question is what are you so busy about.'Walden – Henry David Thoreau This paper is not a typical review of the literature that might be expected when considering a review of adolescent idiopathic scoliosis (AIS). In this electronic age, using keywords such as 'scoliosis review' in Google or a medical database quickly reveals an enormous amount of material on this topic which should satisfy those people seeking fundamental, factual material (1478 'hits' by simply typing just 'scoliosis review' into PubMed database) [Scoliosis has been recognized since at least the time of Hippocrates and presents itself as an abnormal lateral curvature of the spine accompanied by vertebral rotation. Interestingly, the plasticity of the elements of the spine allows scoliosis to take many different forms dependent on the multitude of different forces being applied to the various tissues making complex any classification and recognition of underlying causes. Some causes of scoliosis are readily apparent  and such cases have been removed from the general population of scoliosis patients because the underlying mechanism can be explained. Similarly, several diseases have been associated with scoliosis development  and these too have been removed from the general pool of patients even though the underlying mechanism for the development of scoliosis is sometimes not clearly understood. However, even with the removal of these cases from the general pool of patients with scoliosis, there remains a large pool of patients (80%) who are justifiably classified as having idiopathic scoliosis because the cause remains unknown. Consequently, idiopathic scoliosis is a diagnosis by elimination rather than by positive identification which is a significant distinction.Idiopathic scoliosis has three apparent peaks of time for diagnosis: congenital, infantile, and adolescent. AIS manifests itself at the time of puberty. Accordingly this paper focuses on AIS which is the most common type of scoliosis and which has received the most attention.A simple review of the mountain of available literature shows that the aetiology of AIS has been explored extensively for more than 100 years. Seemingly, every conceivable aspect of development and tissue associated with the spine appears to have been placed under close scrutiny using the best techniques available at that time often with repeat evaluations occurring as techniques have improved or become available. Interestingly, the basic conclusions from this research can be synthesized as follows:The development of AIS is associated with:the growth spurt at the time of puberty- greater severity of curve development in females than males- multiple variations of deformity - an inheritance within families- Unfortunately, despite the intensity of the research over such a long period of time, these are the only reliable conclusions that can be made. When you consider the time and money that has been directed towards producing these few conclusions it is disappointing to realize that an observant visitor to a scoliosis clinic could reach the same conclusions probably within 20 minutes.Why is this? Why have more significant advances into understanding the aetiology of AIS not been made? In contrast, there have certainly been significant advances made in the treatment strategies used for AIS and this area forms the bulk of presentations and materials at research meetings. The lack of parallel advancement in understanding the aetiology of AIS is disturbing and disappointing. It is further surprising because a better understanding of the aetiology of AIS would presumably result in the development of better treatment strategies but while there has been much interest, there has been little useful advancement.With further thought, perhaps the four conclusions outlined above are not the only ones that could be drawn from the literature. Maybe another conclusion can be drawn:If major advances in understanding the aetiology of AIS are to be made, then the methods currently being used and the approaches being made must be changed because they have not been very productive so far.It has been said that one sign of madness is to continue doing the same thing repeatedly and expect different results. If the current methods to study the aetiology of AIS continue to be applied, why should different results be expected when such intense, detailed research in the past has revealed so little? What changes should be made if advancement in an understanding of the aetiology of AIS is to be achieved?To study the aetiology of AIS efficiently and effectively, an experimental model needs to be developed to form the foundation of any experiments. The results from the experiments will either support the model as being correct or provide direction for making changes which can then be tested further. Fundamental to any model for the study of AIS is the question:Do all cases of AIS have the same single underlying cause or are the abnormal curvatures the common end-result of several different causes?While this is a very simple question, the answer has enormous consequences especially for experimental design and is an important question to be considered when viewing the available research literature.If the answer selected on which to build an experimental model is that all cases of AIS have the same single underlying cause, then the traditional experimental design would be to obtain samples or measurements from a group of patients who have severe AIS (extreme cases) and compare the results to similar ones collected in the same way from a group of control subjects. The focus of the experiment would be on identifying any significant differences between the mean values of the measurements taken from each group. Experiments with this basic design are common in the literature but the results for any parameter being examined vary widely. It is no surprise that there is confusion and little forward progress being made when results from similar experiments are so diverse. Alternatively, perhaps there is a message in this important and extensive literature that has been overlooked. Perhaps the wide diversity and inconsistency in the results from similar experiments is suggesting strongly that the model being used (a single cause) might be wrong and that AIS actually has several different underlying causes .If the true experimental model for AIS should be built on the premise of there being several different causes then no significant differences for any parameter would be expected using the traditional experimental design. Consider the following situation: if the hypothesis was that there were fewer spindles in the vertebral muscles of patients with AIS  then the traditional experimental design would include samples being collected from patients with AIS and appropriate controls. The samples would be sectioned and stained and the number of muscle spindles counted and compared between the experimental and control groups. Unfortunately, the mean values of the two groups of subjects would probably be very similar and not significantly different even though the hypothesis being examined (e.g. fewer muscle spindles) might well be an underlying cause of AIS. If fewer muscle spindles are the basic cause in only 10% of AIS patients, then their contribution to the mean values of numbers of muscle spindles in the group of AIS patients will be overwhelmed by the normal values from the 90% of AIS patients in the same group who have normal values but whose underlying cause is quite different. Unfortunately, this experimental design has been the used in many experiments performed in search of the aetiology of AIS and, not surprisingly, no reliable evidence has been found to support any conclusions for any parameter studied. In experiments designed in this way, the interesting values would be those of the 'outliers' in the experimental group rather than the mean values of the whole group but these extreme values rarely get reported.If AIS has multiple, different causes then experiments designed with a single-cause being the underlying mechanism (such as described above) will not reveal any meaningful results even if the suspected cause of scoliosis being tested is quite valid. Reviewing the literature from this point of view suggests very strongly that the diverse results obtained so far confirm that AIS is the common end result of several different causes and not just a single cause common to all cases. Contributors to conferences and authors of manuscripts should be required to explain their experimental design to clarify this issue.As groups of patients with associated diseases have been culled from the group of AIS patients, the associated disease has been recognized as the underlying cause. As these groups have been removed, the number of underlying causes within the remaining AIS group presumably has been reduced by one. This raises an interesting question:In the current population of patients diagnosed with AIS, is there just one cause remaining to be identified or are there still several different causes within the population that are simply proving more difficult to identify?For example, currently there is considerable discussion related to identification of syringomyelocoele in boys as being an underlying cause of scoliosis and debate exists over whether or not such cases should be removed from the general AIS pool. On this basis alone, it seems most likely that among patients currently diagnosed with AIS several separate causes have yet to be identified within the population rather than there being a single underlying cause common to all.In contrast, the evidence to support AIS having just a single underlying cause is very limited. Certainly, curve progression seems to have many common characteristics and AIS can usually be recognized from a series of radiographs taken of a patient but there is not much else.When a child develops AIS, they are unaware of its development in the early stages when the curve is small. Usually, it requires observation of sloping shoulders or other physical characteristic by another person before the lateral curve is recognized. By this time, the curve is already quite large for this manifestation to be noticed and has probably been present for several months, if not years, before a specialist is seen. This period of time when the abnormal spinal curves were first developing is critical for research purposes because the underlying cause will be present and yet it is a time of anonymity when no measurements can be made. Extrapolation of later measurements into this time period have proved to be limited because treatment strategies have affected curve development and, consequently, any extrapolation procedures.severe AIS are selected for the experimental group because, while such patients certainly represent the extreme cases, they are usually much older than when their curve first started to develop – which is the most important time for examination to identify the underlying cause. Unfortunately, the time of initial curve development cannot be identified.The biomechanics associated with spinal curve development are complex and not well understood. As it is usually several months (18?) from the time the curve started to when it is first recognized in a patient with AIS, it is possible that the underlying problem causing the development of the curve has been corrected without any treatment. The continued curve progression seen in AIS patients might then be due entirely to the biomechanical aspects of curve development rather than the continued effects of the underlying problem. In these cases, the patient with AIS might be completely normal when being examined! In particular, the experimental group in any experiment might well be perfectly normal in regards to the hypothesis being tested even though the hypothesis was in fact true – but only at the early stages of development. Presumably this would be particularly important when considering possible causes such as differences in hormonal production or timing of hormonal changes, parameters that are very evident during puberty and which might well have harmonized by the time of measurement. It is certainly a problem when patients with At present, patients with AIS cannot be identified prior to initial development of the spinal curve. There is no marker available which can predict future scoliosis development. Consequently, it is almost impossible to separate cause from effect of the scoliosis from any measurement taken from patients with AIS because there are no earlier measurements available for comparison. This makes interpretation of results from experiments involving AIS patients very difficult to interpret as a genuine cause of scoliosis with any degree of confidence when considering the experimental parameter because any significant changes found could be the result of the scoliosis.A bipedal stance appears to be necessary for development of scoliosis and Man is the only true bipedal animal. Bipedalism arose in Man because the increased lumbosacral angle allowed Man to hold his head and torso high while retaining quadrupedal characteristics in the pelvis and lower limbs, necessary for locomotion. No other animal appears to have adopted the same evolutionary pattern. Consequently, the selection of an animal on which to develop a research model is very difficult. Avians, with their pseudo-bipedal stance, have been used with some success but there is a large phylogenetic gulf between birds and mammals and significant differences in physiology and biochemistry make progress in understanding any mechanism very difficult. For example, pinealectomy in newly-hatched chickens often results in scoliosis development but similar surgery in young mammals (quadrupeds) has no effect UNLESS they are also made bipedal by removal of their upper limbs and tail. This model certainly has potential but questions remain concerning the effects associated with the extent of deviation from normal morphology as well as actual time spent in a bipedal stance when compared to normal, quadrupedal mammals.A strong case has been made in this review to suggest that among the population of patients with AIS there are several, different underlying causes that remain to be identified. Consequently, the cause of AIS is described as being multifactorial. Unfortunately, the problem is probably much more complex than simply being a series of different underlying causes because each separate cause might consist of several contributing elements themselves. For any single underlying cause, these constituent elements might also not be equal in importance with some elements being essential for future curve development while others may or may not need to be present either alone or in combination with the other elements for any spinal curve to develop. This interweaving of the threads associated with scoliosis development creates a complex knot and, currently, there is insufficient data to create a theoretical model on which to build.Despite the criticism of the available literature outlined above, there remains a wealth of information contained in the literature which could be gained by re-reading the literature using a different approach. The useful data is mixed in with data from badly designed experiments which have been designed based on a model of AIS having a single underlying cause (as described above). Such experiments need to be identified and their data re-evaluated and either removed or incorporated into a new database of information. In particular, the outliers from experiments designed with a single cause as the model should be identified if possible and their values looked at very carefully.If many underlying causes for AIS remain to be identified, then they need to be evaluated and explored separately. However, this cannot be approached in the traditional way of collecting samples and measurements from a group of patients with AIS and comparing these values with similar measurements from a control group. Instead, a new approach needs to be developed along the lines of:- develop a theory based on the literature and thoughtful ideas- consider if it can be demonstrated in an appropriate animal model- outline the symptoms that would be seen in a specific group of patients with scoliosis if the theory was correct- search for such patients retroactively or among current patientsThis approach is far more laborious (needle in a haystack?) than the traditional approach but any results would be positive and constructive.There are two types of molecular approach that would be productive:It is well recognized that there is a familial relationship in scoliosis. However, this relationship is confusing and not well defined probably because of the complex nature of AIS, especially the multifactorial (more than one cause) and multi-elemental nature of the underlying cause. The environmental influence on scoliosis development is also unknown but the fact that there is only 70% concordance in identical twins diagnosed with AIS illustrates its significance. Similarly, it is interesting to note that the concordance between dizygotic twins  is only 37% and yet the concordance between non-twin siblings is only 6–7%. Such differences raise the question of what influence does prenatal environment have on scoliosis development? The data to explain these differences and the influence of the environment on scoliosis development needs to be expanded so that the focus of research can be more directed.Currently, identifying genes that are different to normal in patients with a specific disease is receiving major attention in many areas other than AIS. In some instances, single specific genes have been identified and make for dramatic headlines. Other diseases have been found to be much more complex with several, if not many, genes being identified as being involved with the disease. AIS falls into the latter group of diseases even though there have been headlines claiming the finding of just single genes being identified as the underlying cause of scoliosis. Again, the problem would probably be reduced if it was possible to identify curves that had developed from different causes and study them separately but, currently, the pool of AIS patients is heterogeneous with a mix of all types of underlying cause in most cases. Certainly, more-refined strategies where members from the same family are being examined will probably reduce the number of suspect genes but if the underlying, single cause has many constituent elements then the situation and model to be developed becomes very complicated and continues to be difficult to solve. Adding to the complexity, the concordance for AIS between monozygotic twins is only 70% which implies that there are some people with identical DNA where one has developed scoliosis while the other has not. Furthermore, how do you identify the contributing genes for scoliosis among family members where the DNA is not identical when scoliosis may or may not develop among people with identical DNA! Similarly, if the complexity of the multifactorial, multi-elemental model proposed for the aetiology of AIS in this review is accepted, then it is quite possible that the lack of concert in gene expression for the elements within a single cause means that some members of the same family will possess similar critical genes but because of the differences in timing patterns, some members of the family develop scoliosis while others do not. This will be a long and difficult process .It used to be thought that 'one gene' meant 'one protein (or enzyme)' but recent advances in cell biology have revealed that gene expression is modified at various levels before protein expression occurs with, in at least one case, at least 180 proteins being found to be expressed from one gene. The unexpected, relatively few genes found in the human genome appears to have been a mask to hide the real complexity and volume of proteins being expressed.Nevertheless, given the success of molecular genetic research in making progress in understanding the aetiology of other diseases, a molecular genetic approach to the aetiology of AIS appears to have the best chance for long-term success. Perhaps it is also possible to make even more rapid progress by copying the methods employed in research of a disease with similar characteristics to AIS but it is difficult to identify one which might be appropriate for comparison because of its complexity. Suggestions for a comparative disease might include cancer and autism, both of which appear to have similar end-results among their patients but which have apparently many different underlying causes. However, it must be realized that even though the genetic differences from normal have been recognized for many years in many diseases, very little progress has been made in introducing corrective genes to the patient.Even if an association is made between scoliosis and a particular characteristic  there is a long path and much explanation required to connect the underlying mechanism with the development of a spinal curve. What is perceived as an association between a particular characteristic and AIS must be connected to a mechanism to produce the spinal curve – and this is often a very difficult and complex path to understand and isolate. Nevertheless, research in this area would be valuable because it would provide insight into the mechanisms of scoliosis development which is currently poorly understood and guide development of successful treatment strategies ,10.There are three prime areas in which a prominent engineering approach would be beneficial and valuable in obtaining a better understanding of the aetiology of AIS:Postero-anterior and lateral radiographs have been used routinely to observe overall curve morphology, especially degree of curvature, as well as curve development between clinic visits. Such images allow a basic 3D image to be created in a rather primitive fashion but it has proved particularly useful to the surgeon in deciding treatment. However, it must be remembered that a radiograph is compression of a 3D structure into 2D and has many inherent problems for interpretation. Scoliosis is a 3D deformity and, as such, should always be thought of in 3D terms. In particular, the compression of a 3D structure into 2D (as in radiographs) does not allow for the exclusion of the same spinal curve being thought of as changing in severity and classification between visits when only simple rotation of the spine has occurred. The ability to observe or image the spine in 3D has improved enormously in recent years and its availability has also increased especially with refined development of MRI technology. If a complete understanding of spinal curve development is to be achieved, then research evaluations have to move away from 2D imaging such as with basic radiographs and observe only 3D images. 2D assessment should be no longer acceptable for research purposes and new techniques of 3D assessment need to be developed to keep pace with the improvements in imaging techniques. The following statement highlights this area:'Measure in 2D, you think in 2D; measure in 3D, you think in 3D' – and scoliosis is a 3D problem.Visits to the scoliosis clinic by patients provide the opportunity to see 'snapshots' along the continuum of spinal curve development. However, spinal curve development is a continuous process and not simply a series of images separated by time. The development of techniques to produce a continuous image (movie?) of curve development from the images collected from patients during visits to the scoliosis clinic would be very valuable. Just as more information would be available by observing curve morphology using 3D imaging, even more information would be available if continuous imaging of curve development was also introduced. There is an opportunity for such techniques to be developed because technology has caught up with the desires of researchers – it is simply a matter of defining the techniques wanting to be created.As a spinal curve develops and increases, it seems reasonable to imagine that the biomechanics associated with column construction affect curve development and associated rotation and that their influence increases with curve progression. It also seems reasonable to suggest that while the abnormal spinal curve remains small, removal of the underlying cause would allow the body possibly to return the curve to normal. An understanding of the biomechanical principles involved with column structure and curve development would be invaluable to understanding the aetiology of AIS but is currently unavailable. Extending this concept, it also appears possible that a point of abnormal curve development might be reached during progression at which correction cannot be achieved by the body even if the underlying cause was removed. Such a point might be related to morphological changes such as those seen with vertebral wedging but the realization that curve development will continue to progress even with the underlying, deforming force removed would be valuable in the development of treatment strategies especially in the area of providing supportive treatments such as those associated with 'bracing' .Perhaps the ultimate research tool would be the development of computer models to simulate spine morphology and normal growth which could include curve development. Such techniques have had outstanding success in other areas of research and there are certainly seeds of development of these techniques in scoliosis. Clearly, the potential for unparalleled success in understanding the mechanics of scoliosis using such techniques suggests strongly that this area of research should receive much more attention in the near future.The lack of any really substantial knowledge relating to the aetiology of AIS as seen in the literature emphasizes the basic areas that should form the focus of all current research because knowledge in these areas is most critical and readily applicable:The development of a marker to identify those children who are going to develop AIS. This would allow treatment strategies to be developed to avoid even initial curve development.The development of a marker which would indicate whether a small, abnormal spinal curve will progress, remain the same, or regress. This would allow aggressive treatment strategies to be applied to those patients whose small curves have been identified as going to progress.The development of techniques to treat small, spinal curves that have already developed to ensure that they do not progress and are corrected instead.The development of new techniques to replace the extensive and invasive techniques currently being used (successfully) to repair large, abnormal spinal curves.cure?' The goal for research into the aetiology of AIS should be to find a cure (other than surgery) as soon as possible, preferably tomorrow – but, unfortunately, a cure remains a dream and does not even seem to be on the distant horizon. Researchers in this field, particularly basic science researchers, should ask themselves if their work has produced results that have affected the treatment of even a single patient with AIS and, if not, then why not?In his recent successful book 'Where have all the leaders gone?' Lee Iacocca, the former CEO of Chrysler, reports coming into contact for the first time with medical researchers (for diabetes) in his retirement years. He commented that medical research was much like government – kind of a self-generating bureaucracy. Cynically, he noted that people do research so that they can write papers to get more funding to do more research to write more papers. He was prompted to ask: 'Hey, isn't anyone trying to find a Finally, a plea for better communication: in today's electronic and computerized environment with relatively easy access to video cameras and the ability to both see and hear each other so readily available, could a world-wide research environment be created that involves all interested researchers in all the different areas with regular intercourse and discussion?


The number of subjects that can be recruited in immunological studies and the number of immunological parameters that can be measured has increased rapidly over the past decade and is likely to continue to expand. Large and complex immunological datasets can now be used to investigate complex scientific questions, but to make the most of the potential in such data and to get the right answers sophisticated statistical approaches are necessary. Such approaches are used in many other scientific disciplines, but immunological studies on the whole still use simple statistical techniques for data analysis.The paper provides an overview of the range of statistical methods that can be used to answer different immunological study questions. We discuss specific aspects of immunological studies and give examples of typical scientific questions related to immunological data. We review classical bivariate and multivariate statistical techniques  and more advanced methods aimed to explore causal relationships  and illustrate their application to immunological data. We show the main features of each method, the type of study question they can answer, the type of data they can be applied to, the assumptions required for each method and the software that can be used.This paper will help the immunologist to choose the correct statistical approach for a particular research question. The understanding of the importance of immunological mechanisms underlying human disease and the identification of associated immunological markers have grown enormously over the past ten years and the number of published immunological studies that investigate the relationships between human disease and cytokines and other immunological parameters has increased rapidly. Technical developments in sample processing and sophisticated immunological techniques permit the analysis of more immunological parameters in larger samples of human subjects, containing information that allows not only the measurement of simple associations between two parameters but also the exploration of the complex relationships between immunity, disease, environmental, social and genetic factors. The potential complexity of the possible relationships between large numbers of immunological parameters poses a special challenge for the applied immunologist: how to select the appropriate statistical techniques to extract the maximum relevant information from complex datasets and avoid spurious findings.Immunologists tend to use simple statistical approaches even when multiple relationships between immunological parameters are expected, ,2 insteaThis paper provides an overview of statistical analysis techniques that may be considered for the analysis of immunological data. We discuss specific aspects of immunological studies, give examples of typical scientific questions related to immunological data and present a statistical framework to help the immunologist to choose the correct statistical approach for a particular research questions. Although we have focused on cytokine data in the examples provided, the methods presented are applicable to most other immunological parameters.In the following section we discuss specific aspects of immunological studies that are relevant for statistical analysis.Before analysing immunological data it is very important to examine the structure of the data because most statistical methods will only give the correct answer if the data has the characteristics required for the use of that method ("satisfy the data assumptions"). For example, common data assumptions are that the observations are approximately normally distributed or that the variances are similar across different subpopulations. Unfortunately, immunological data very frequently do not meet these assumptions and investigators are obliged to either apply data transformations , or to choose an alternative statistical techniques with less stringent data assumptions  may have different effects in different cell populations, at different times and in the presence (or absence) of other immunological parameters. We often aim to explain the complete causal pathway from a non-immunological factor  to an outcome (e.g. atopy or asthma). Clearly simple univariate statistical analysis would not be able to identify such inter-relationships among several study variables and the underlying immunological mechanisms that cannot be measured; multivariate statistical techniques are required that can examine multiple parameters simultaneously. A fundamental step to guide statistical analysis is to make the hypothesis explicit in a conceptual framework . ConceptTwo further important aspects of immunological data that are not the focus of this paper but should be mentioned are:Reproducibility reflects how often we obtain the same result using the same laboratory test and sample. Some variation is expected for any measurement and statistical analysis must take into account the degree of variation. Although reproducibility of immunologic measurements is well defined for some immunological outcomes, particularly those used for diagnostic purposes, such as antibody levels associated with vaccine protection (e.g. levels >10 IU/mL for anti-HBS for vaccine response to hepatitis B vaccine), and for phenotypic characterization , reproducibility is not well defined for most immunological parameters. This is independent of the separate but important issue of repeatability between centres in multicentre studies that measure the same immunological parameters in different laboratories or even the measurement of the same parameter between different studies.multiple testing is becoming increasingly relevant in immunological studies as the number of immunological parameters that can be measured increases and investigators conduct a large number of statistical tests on the same study data [The problem of udy data . A speciudy data .In this section we list typical research questions from immunological studies.Common objectives of immunological studies can be grouped into four overall categories:investigate patterns of associations between several immunological parameters, without assuming any causal relationship (and therefore not classifying study variables as dependent variables (i.e. outcomes) and independent variables (i.e. explanatory variables or covariates).i) Those that For example, typical research questions of such studies are:To assess the magnitude of the correlation between different cytokines or to quantify the balance between levels of cytokine expression. For example, the research question might be to measure the correlation between pro-inflammatory and anti-inflammatory cytokines (e.g. correlation between TNF-α and IL-10) or to quantify the "balance" between pro-inflammatory and anti-inflammatory cytokines .• To identify highly correlated cytokines and to place them into groups which reflect an unobserved underlying mechanism. For example, Th1-related immune responses such as IFN-γ and TNF-α may mediate an inflammatory disease. Depending on the question being investigated, it may be more appropriate to first use a statistical analysis approach to "reduce the data", i.e., to aggregate the correlated Th1 related cytokines to form a "summarising variable" that reflects the underlying immunological mechanism (e.g. "degree of Th1 immune response") and use that summary variable in the analysis rather than using all the variables with the original cytokine levels.• To identify individuals with similar profiles of immunological parameters and to place them into groups . For example, patients with a clinical outcome might be defined as "atopics" or "non-atopics" based on the values of skin prick tests; or subjects with a specific infection may be classified into groups  defined by the overall elevation in antibodies . However, within the same group of patients, clusters with distinct or overlapping profiles might be distinguishable and subsequent analyses might show associations between distinct clusters and disease (or some other outcome).• investigates causal relationships between one or more immunological parameters  and other study variables (e.g. an outcome such as asthma). To guide the investigation of causation it is important to have developed an a priori causal pathway model. This will allow the appropriate definition of variables, i.e. defining which variables are dependent variables (outcomes), intervening variables (mediating the effect) or independent variables  and will determine the choice of statistical approach.ii) The second group of research objectives Possible research objectives for causality include:Identification of determinants of immunological profiles. The objective may be to compare the expression of cytokines between two or more groups defined by an exposure, e.g. people infected or not infected with helminths, or people vaccinated and non-vaccinated in a vaccine trial where the vaccine exposure is assumed to influence the levels of the immunological parameter to be measured. For example, the question may be to determine if BCG vaccination influences the levels of IFN-γ secreted by mononuclear cells stimulated in vitro with a mycobacterial antigen. The immunological parameter is the outcome or dependent variable.• Identification of clinical consequences of immunological profiles,  or, in other words to identify associations between an immunological parameter and clinical (or other) outcomes. For example, immunologists are interested in predicting the probability of a disease occurrence by measurement of cytokine levels. For example, whether elevated TNF-α levels are associated with active disease in rheumatoid arthritis? The immunological parameter is the risk factor  or independent variable.• more complex research questions that may include two or more of the objectives described above. Such questions may examine the role of cytokines in larger causal constructs, including more than one risk factor, intervening variables that mediate and modify an effect, and outcomes; and inter relationships between them. An example will be to investigate the causal inter relationships between early life infections, level of expression of pattern recognition receptors (such as Nods), activity of pro-inflammatory cytokines (IFN-γ and TNF-α) and the development of inflammatory bowel disease.iii) The third group consists of in silico immunology  is a rapidly developing and expanding field and has been used to address several types of study questions, such as:iv) The field of • The prediction of immunogenic sequences from microbial genomes to predict potential vaccine candidates .• The prediction of protein sequences in therapeutic antibodies that may be associated with adverse reactions .• Identification of regulatory molecules in the innate immune system .These approaches are generally high-throughput analyses of large data sets  using available software (e.g. EpiMatrix) to either generate or test hypotheses and have been reviewed in detail elsewhere -15.In silico statistical analyses use many of the multivariate statistical techniques discussed later in this review . Because this is a highly specialised field for which there are many computational tools available [in silico immunology will not be discussed further in this review.vailable , in sili"cytokine$" or terms to identify specific cytokines  and common univariate and multivariate statistical techniques .We conducted a systematic literature search in the database MEDLINE (1980–2005) to review statistical methods that have been previously applied to cytokine data. Because the objective was to get a crude overview rather than to reveal the exact number of papers published in this area we defined quite sensitive search criteria using the following key words: bivariate methods – also called univariate methods when variables are classified as dependent and independent variables). We frequently found standard methods to compare means of immunological parameters between independent groups , bivariate correlation analysis (Pearson's or Spearman's correlation coefficients) and univariate linear regression. By contrast, multivariate techniques  were less frequently applied to cytokine data. Several studies used factor analysis  or cluster analysis  or discrimination techniques such as logistic regression, discriminant analysis . We also found a few examples of advanced modelling techniques  that simultaneously model multiple relationships between the study variables.Table In the following section we provide an overview of statistical methods that can be considered for analysing immunological data that should help the applied immunologists without a detailed knowledge of statistics to select the appropriate statistical technique for each particular research question. The definition of which method is the most appropriate is strongly dependent on the research objective, the type of data collected, whether data assumptions are fulfilled and whether the sample size is sufficient. We begin with a short introduction to these topics.An important first step in analysing immunological data is exploring and describing the data. Whatever the research question investigators should first explore the data in tables showing summary statistics  and apply graphical methods such as bar charts, histograms, Box and Whisker plots, or scatter plots. For multivariate data, scatter plot matrices are a powerful tool to examine associations among several immunological parameters . A good data assumptions. The first assumption is the scale of measurement of the data, i.e. whether the data type is categorical , ordinal  or continuous . This restricts the statistical methods that can be used. When the investigator has to deal with continuous data the second assumption to test is whether the data follow a theoretical distribution . Distributional assumptions can be tested graphically by diagnostic plots or by applying a statistical test that compares the distribution of the data with a theoretical distribution. When the original data do not meet distributional assumptions required for a particular statistical technique  a common approach is transforming the data to meet the data assumptions, for example to use the logarithm of the values [The next thing investigators have to consider when selecting a statistical method is whether their data meet a number of e values . Howevere values . Anothere values ." The coe values . For exaCohen's Power analysis guide is a useful review for calculating sample sizes for common univariate and some multivariate techniques  [A second major issue that immunologist frequently face and that also substantially affects the statistical approach to be used is to estimate the appropriate sample size for a statistical analysis. Whereas for univariate techniques  sample size can be determined by conducting a power analysis , sample ression) . Unforturession) ,22. Authression) . There iression) ,25.Bayesian statistical methods that also might be useful for application to immunological data seeking to combine a priori information with the information captured in the present data; a good overview is provided by Lee [In this section we provide an overview of statistical methods that we consider useful for statistical analysis of immunological data. Our guide for data analysis was written with the "classical statistical approach" in mind, i.e. we provide statistical techniques to extract the maximum information from the present data. We do not discuss d by Lee . Moreoved by Lee . We haveinter-dependence techniques, i.e. statistical methods aimed to explore relationships between study variables without assuming any causal relationship. These techniques are appropriate when the investigator cannot define (or may not wish to define) which variable is the independent variable  and which is the dependent variable . For example, inter-dependence techniques might be very useful in immunological studies to examine relationships between different cytokines measured in the same individual.The first group embraces so called bivariate correlation analysis that aims to assess the magnitude of the linear relationship between two continuous variables  [Factor Analysis [Datasets with several highly correlated immunological parameters can be simplified using "is (PCA) , which iAnalysis . The ideA useful feature of factor analysis and especially PCA is that the weights (the "factor loadings") for each variable within the components can be interpreted as correlation measures between the observed variable and the underlying unobservable component. Data assumptions in factor analysis are more conceptual than statistical. From a statistical point of view, normality is only necessary if a statistical test is applied to measure the significance of the factors, but these tests are rarely used in practice. The more important conceptual assumptions are that some underlying structure does exist in the set of selected variables and that results in some degree of multicollinearity.et al [In immunological studies factor analysis or PCA could be applied to extract information on the Th1- or Th2-related immune response from a set of cytokines or the ratio of a Th1- and Th2-score could be calculated to quantify the degree of "Th1/Th2 bias ,31." Foret al  instead Cluster Analysis (CA) is the appropriate statistical approach when the researcher seeks to group individuals (not variables) according to their values of study variables (e.g. cytokine levels) [Agglomerative algorithms treat each observation as a cluster and group similar individuals into clusters, while divisive algorithms start with the whole study population as a single cluster and divide the population by identifying homogeneous subgroups. The most appropriate clustering approach for a particular dataset depends on the type of data collected and the research question. Agglomerative clustering is preferable when there are extreme values in the data (outliers). levels) . CA grouCA has strong mathematical properties but not statistical foundations. Data assumptions  that are important in other multivariate techniques are of little importance in cluster analysis. However, the researcher is encouraged to examine the degree of multicollinearity in the data because each variable is weighted and variables that are multi-collinear are implicitly more heavily weighted in the clustering algorithm. For example, a cluster solution derived from a dataset with five highly correlated Th1-related cytokines and two correlated Th2-cytokines would substantially overestimate the importance of the Th1-component in the clustering. This can be avoided by first applying a data reduction technique (e.g. PCA) to derive the "principal components" that quantify the magnitude of Th1/Th2-immune response and afterwards clustering the individuals with respect to these immunological components.et al [S. mansoni into clusters defined by levels of parasite specific IgE, IgA, IgM and the IgG subclasses. The authors identified two clusters, a cluster with high levels of IgM and low levels of all other antibodies and a cluster with high IgM and IgG1 and medium IgG4 and low levels of all other antibodies. In further analyses the authors investigated whether epidemiological features of schistosomiasis were associated with cluster membership and whether treatment changed this.In immunological studies cluster analysis may be useful to identify groups of individuals with similar immunological patterns (e.g. cytokine or antibody levels) that reflect an unknown common underlying immunological mechanism. For example, Mutapi et al  sought tCanonical Correlation Analysis (CCA) [Another useful technique to study associations among immunological parameters without assuming any causal relationship is is (CCA)  an approdependence techniques that are appropriate when the study investigates causation, i.e. variables can be classified as independent  and dependent variables , based on concrete a priori hypotheses about the underlying biological mechanisms. For example, an immunological parameter might be considered as an independent variable when it is the proposed cause (e.g. an autoantibody in an autoimmune disease) or the dependent variable when it is considered to be an effect , or an intervening variable or intermediate factor (mediating variable) within a complex causal chain . For example, Black et al [γ response to Mycobacterium tuberculosis (as the outcome variable) before and after receiving BCG vaccination (the independent variable). Cooper et al [The second group of techniques are statistical ck et al  studied er et al  studied The choice of which statistical dependence technique is most appropriate for a particular research question will depend on the study design, the number and "scaling"  of the study variables and other data assumptions . Robust alternatives when data assumptions are violated are non-parametric or non-linear regression techniques. Examples of applications for regression analysis in immunological studies are predicting the levels of expression of a continuous outcome variable (e.g. a cytokine) by a continuous variable  when a causal relationship can be assumed.By contrast, analysis  is the bMultivariate dependence techniques are needed when there are three or more variables involved and at least one variable can be considered the dependent variable.Linear Discriminant Analysis (LDA), a method that derives linear combinations of the independent variables  that best discriminate between the two outcome groups (defined on the basis of the independent variable) [Logistic Regression [These methods are required when there are several independent variables (e.g. cytokines) and one categorical outcome (e.g. atopy). One classical approach is ariable) . LDA reqgression .γ and TNF-α) to discriminate between clinical and asymptomatic forms of visceral leishmaniasis. The authors found that TNF-α, IL-10 and IL-4 were highly correlated with the clinical form, while IL-2, IL-12 and IFN-γ were correlated with the asymptomatic form. In another study, logistic regression was used to explore the role of parasite induced IL10 in decreasing the frequency of atopy: Van Biggelaar et al [In immunological studies classification techniques can be very useful to identify immunological profiles that best discriminate two or more pre-defined groups of interest (e.g. atopy vs. non atopy). For example, Gama et al  used LDAar et al  sought tMultivariate Analysis of Variance (MANOVA) or Multi-way Analysis of Variance (Multi-way ANOVA), are used to compare the distributions of one or more continuous variables between groups defined by one ("one-way") or more ("multi-way") factors of interest  between two groups (asthmatic vs. non asthmatic). By applying MANOVA instead of repeated application of ANOVA the investigator can avoid the problem of type I error inflation for the whole experiment.These techniques such as Multivariate Analysis of Covariance (MANCOVA) is an extension of MANOVA that additionally allows to control for the effect of an other continuous variable to be controlled (e.g. a confounder) [γ, TNF-α, etc.) across groups defined by one or more experimental factors (e.g. vaccinated or control) and adjusted for age.founder) . An applMultiple Regression is appropriate when the research question is to predict a single continuous dependent variable by a set of continuous and/or categorical independent variables [et al [γ, TNF-α, IL-12, IL-10, TGF-β).ariables . The staariables . Regresss [et al  used mulPartial Least Squares (PLS) Regression [gression  is an exmultiple relationships among immunological parameters and other study variables so that a simple multivariate approach might not be sufficient to reflect the complexity of the underlying immunological process. For example, in an immuno-epidemiological study conducted to study risk factors for asthma and allergy, investigators could define a conceptual framework that assumes multiple relationships between risk factors , immunological profiles (e.g. cytokine expression levels) and the occurrence of outcomes   are techniques developed by geneticists ["latent variables" . A structural equation model consists of two components: "a measurement model" that defines how the observed measurements  are related to the unobserved latent variables and a "structural model" that defines the assumed relationships between the observed study variables and one or more latent variables.All the multivariate statistical methods that have been mentioned above have one common limitation: although they may include many variables, they all assume the presence of one single relationship between them. However, in modern immunological studies investigators often assume opy) Fig . To modeeticists ,46 and eeticists  that caneticists . SEM is path diagram of the study in which multiple relationships are assumed between the study variables. In this example we consider that the cytokines IL-12, IFN-γ and TNF-α represent an unobservable latent variable "Th1-related immune response" and the cytokines IL-4, IL-9 and IL-13 represent the latent variable "Th2-related immune response". Further, the structural model assumes relationships between the two latent variables (c1) and the "effects"  of these on an outcome variable . In SEM the concept of the "measurement model" is similar to factor analysis in which a linear relationship between the (observed) indicator variables and the (unobserved) latent variable is assumed. For example, in Figure a priory a path diagram that specifies which variables are the indicators of the underlying latent variables and the correlations of the measurements with the latent variables are derived that best reflect the whole conceptual framework e.g. maximizing the correlation between all latent and observed variables.The concept of latent variables and SEM is likely to be useful in immunological studies because immunologists frequently hypothesise that the measured immunological parameters are the result of unobservable underlying complex immunological processes. For example, imagine a hypothetical immunological study where different cytokines are measured to quantify two important immunological components. Figure et al [α) in a complex causal chain of the metabolic syndrome. There are few applications of conceptual frameworks, path analysis and SEM to immunological data in the literature. However, because of increasing sample size and advancing knowledge about underlying complex immunological mechanisms, these approaches have a potentially important role in the analysis of data from modern immunological studies. The concept of latent variables in SEM will be especially useful because immunological mechanism can rarely be observed directly and must therefore be inferred through the measurement of different immunological indicators. Moreover, the unique feature of SEM that allows the analysis of multiple relationships between the study variables would be appropriate for the inference of causal chains of immunological processes.Application of these techniques to immunological data should allow the inference of complex immunological phenomena. For example, Chan et al  used SEMRandom or Mixed Effects Models [The application of s Models  for stata priori hypotheses that are based on a sound knowledge of the scientific literature.The aim of this paper is to provide a modern overview for applied immunologists to explain and illustrate the statistical methods that can be employed for the analysis of immunological data. Our review should help immunologists without a detailed knowledge of statistics that are faced with the problem of statistical analysis of immunological data to select the appropriate statistical technique that will allow the valid extraction of the maximum information from the data collected. However, the statistical framework presented here should not be used as a substitute for an experienced biostatistician who should be involved from the beginning of the study for advice on study design, calculation of the sample size and planning of the statistical analysis. The systematic literature review illustrates the fact that most immunological studies still employ simple statistical approaches to immunological data even when multiple inter-relationships among several study variables are expected. We think it is important that more sophisticated statistical techniques are used for complex immunological data that will permit a better understanding of complex underlying immunological mechanism. Our focus has been on multivariate techniques that permit the analysis of multiple study variables simultaneously and hope that our examples from both real and hypothetical immunological studies will stimulate immunologists to make more use of these techniques. Moreover, bearing in mind the complexity of research questions addressed by modern immunological studies, we have introduced the idea of conceptual frameworks, latent variables and advanced statistical techniques , providing a toolbox that should help the investigator to analyse multiple relationships among several study variables simultaneously. Finally, it should be pointed out that statistical techniques are a tool for the inference of underlying mechanisms and can never substitute for BG and LCR had the idea for the paper, conducted the literature review, developed the guide for statistical analysis and wrote the manuscript. PJC and MY participated in writing the sections about research objectives of immunological studies and specific aspects of immunological data and evaluated the applicability of the proposed statistical framework for data from modern immunological studies. MLB participated in developing the statistical framework and helped to draft the manuscript. All authors have read and approved the final manuscript.Appendix: Glossary of statistical and epidemiological terms.Click here for file


Short intermolecular distances between the centroids of the five-membered rings [3.5340 (8) Å] indicate the existence of π–π interactions. An interesting feature of the crystal structure is the presence of short intramolecular Cl⋯N interactions [3.0015 (11) Å]. Molecules are linked via pairs of intermolecular N—H⋯O hydrogen bonds, generating R               2               2(8) ring motifs. Furthermore, N—H⋯O hydrogen bonds form R               2               1(7) ring motifs with C—H⋯O contacts, further consolidating the crystal structure. In the crystal, molecules are linked by these intermolecular interactions, forming chains along [001].The title molecule, C Å                           b = 10.4316 (2) Å                           c = 14.4352 (2) Å                           β = 94.599 (1)°V = 1963.21 (6) Å3                                                   Z = 8                           Kα radiationMo −1                        μ = 0.84 mmT = 100 K                           0.49 × 0.22 × 0.08 mm                                 Bruker SMART APEXII CCD area-detector diffractometerSADABS; Bruker, 2005T                           min = 0.683, T                           max = 0.934Absorption correction: multi-scan (16375 measured reflections3742 independent reflectionsI > 2σ(I)3060 reflections with R                           int = 0.029                                                            R[F                           2 > 2σ(F                           2)] = 0.034                           wR(F                           2) = 0.100                           S = 1.13                           3742 reflections140 parametersH atoms treated by a mixture of independent and constrained refinementmax = 0.46 e Å−3                        Δρmin = −0.35 e Å−3                        Δρ                                 APEX2  used to solve structure: SHELXTL (Sheldrick, 2008SHELXTL; molecular graphics: SHELXTL; software used to prepare material for publication: SHELXTL and PLATON (Spek, 2009Data collection: 10.1107/S1600536809026567/tk2498sup1.cif            Crystal structure: contains datablocks global, I. DOI: 10.1107/S1600536809026567/tk2498Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials:


Intramolecular C—H⋯N hydrogen bonds form five-membered rings, generating S(5) ring motifs. Each imino functional group is coplanar with its adjacent benzene ring; the two benzene rings form a dihedral angle of 51.30 (4)°. An interesting feature of the crystal structure is weak intermolecular Cl⋯Cl [3.4752 (4) Å] and Cl⋯N [3.2927 (9) Å] interactions. Intermolecular Cl⋯N interactions link molecules into dimers with 22(22)R ring motifs. The crystal structure is further stabilized by weak π–π [centroid–centroid distances = 3.6970 (6)–3.8560 (6) Å] interactions.The title compound, C Å                           b = 9.3275 (2) Å                           c = 9.7841 (2) Å                           β = 92.213 (1)°V = 1790.96 (6) Å3                                                   Z = 4                           Kα radiationMo −1                        μ = 0.36 mmT = 100 (1) K                           0.51 × 0.35 × 0.10 mm                                 Bruker SMART APEXII CCD area-detector diffractometerSADABS; Bruker, 2005T                           min = 0.836, T                           max = 0.966Absorption correction: multi-scan (21670 measured reflections6419 independent reflectionsI > 2σ(I)5273 reflections with R                           int = 0.028                                                            R[F                           2 > 2σ(F                           2)] = 0.036                           wR(F                           2) = 0.095                           S = 1.03                           6419 reflections208 parametersH-atom parameters constrainedmax = 0.38 e Å−3                        Δρmin = −0.32 e Å−3                        Δρ                                 APEX2  used to solve structure: SHELXTL (Sheldrick, 2008SHELXTL; molecular graphics: SHELXTL; software used to prepare material for publication: SHELXTL and PLATON (Spek, 2003Data collection: 10.1107/S1600536808035307/tk2319sup1.cif            Crystal structure: contains datablocks global, I. DOI: 10.1107/S1600536808035307/tk2319Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials:


Two intermolecular N—H⋯N and C—H⋯N hydrogen bonds to the same acceptor N atom form seven-membered rings, producing R               2               1(7) ring motifs. These interactions link neighbouring molecules into one-dimensional chains extended along the c axis. The crystal structure is further stabilized by weak intermolecular C—H⋯π interactions.In title compound, C Å                           b = 11.5663 (3) Å                           c = 10.0098 (3) Å                           β = 90.154 (1)°V = 709.95 (4) Å3                                                   Z = 4                           Kα radiationMo −1                        μ = 0.37 mmT = 100.0 (1) K                           0.54 × 0.28 × 0.22 mm                                 Bruker SMART APEXII CCD area-detector diffractometerSADABS; Bruker, 2005T                           min = 0.825, T                           max = 0.922Absorption correction: multi-scan (27675 measured reflections3100 independent reflectionsI > 2σ(I)3040 reflections with R                           int = 0.021                                                            R[F                           2 > 2σ(F                           2)] = 0.050                           wR(F                           2) = 0.128                           S = 1.24                           3100 reflections91 parametersH-atom parameters constrainedmax = 0.62 e Å−3                        Δρmin = −0.39 e Å−3                        Δρ                                 APEX2  used to solve structure: SHELXTL (Sheldrick, 2008SHELXTL; molecular graphics: SHELXTL; software used to prepare material for publication: SHELXTL and PLATON (Spek, 2003Data collection: 10.1107/S1600536809001068/at2706sup1.cif            Crystal structure: contains datablocks global, I. DOI: 10.1107/S1600536809001068/at2706Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials:


The nitro group makes a dihedral angle of 71.66 (5)° with the plane of the benzene ring to which it is bound. The two rings are almost coplanar, with a dihedral angle of 4.44 (5)°. Short intermolecular distances between the centroids of the six-membered rings [3.7188 (6)–3.8299 (6) Å] indicate the existence of π–π interactions. The interesting features of the crystal structure are the short intermolecular O⋯O and O⋯N interactions. The crystal packing is stabilized by intramolecular O—H⋯O and intermolecular C—H⋯O (×3) hydrogen bonds, and C—H⋯π interactions.The title compound, C Å                           b = 8.4250 (2) Å                           c = 12.1845 (3) Å                           β = 93.946 (1)°V = 889.02 (4) Å3                                                   Z = 4                           Kα radiationMo −1                        μ = 0.14 mmT = 100.0 (1) K                           0.35 × 0.14 × 0.13 mm                                 Bruker SMART APEXII CCD area-detector diffractometerSADABS; Bruker, 2005T                           min = 0.900, T                           max = 0.982Absorption correction: multi-scan (22792 measured reflections3028 independent reflectionsI > 2σ(I)2493 reflections with R                           int = 0.041                                                            R[F                           2 > 2σ(F                           2)] = 0.041                           wR(F                           2) = 0.119                           S = 1.11                           3028 reflections165 parametersH atoms treated by a mixture of independent and constrained refinementmax = 0.50 e Å−3                        Δρmin = −0.23 e Å−3                        Δρ                                 APEX2 (Bruker, 2005APEX2; data reduction: SAINT (Bruker, 2005SHELXTL (Sheldrick, 2008SHELXTL; molecular graphics: SHELXTL; software used to prepare material for publication: SHELXTL and PLATON (Spek, 2003Data collection: 10.1107/S1600536808021594/at2591sup1.cif            Crystal structure: contains datablocks global, I. DOI: 10.1107/S1600536808021594/at2591Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials:


An intramolecular O—H⋯N hydrogen bond involving the hydroxy and imino groups generates an S(6) ring motif. Intermolecular C—H⋯O and N—H⋯O hydrogen bonds form an R               2               1(7) ring motif involving a methanol O atom and two H atoms of the imidazole and benzene rings, respectively. The latter links neighbouring molecules into one-dimensional extended chains along the a axis. The two benzene rings are inclined towards each other, as indicated by the dihedral angle of 52.13 (10)°. The phenol ring is almost coplanar with the basic quinoxaline unit, making a dihedral angle of 2.43 (6)°. The short distances between the centroids of the five- and six-membered rings prove the existence of π–π interactions [centroid–centroid distances = 3.5234 (9)–3.7885 (10) Å]. The crystal structure is stabilized by intramolecular O—H⋯N, intermolecular O—H⋯O, N—H⋯O and C—H⋯O (× 2) hydrogen bonds and weak intermolecular C—H⋯π and π–π interactions.The title compound, C Å                           b = 11.4574 (2) Å                           c = 11.9983 (2) Å                           α = 116.325 (1)°β = 107.465 (1)°γ = 95.147 (1)°V = 1192.81 (4) Å3                                                   Z = 2                           Kα radiationMo −1                        μ = 0.09 mmT = 100.0 (1) K                           0.39 × 0.29 × 0.12 mm                                 Bruker SMART APEXII CCD area-detector diffractometerSADABS; Bruker, 2005T                           min = 0.876, T                           max = 0.990Absorption correction: multi-scan (23412 measured reflections7076 independent reflectionsI > 2σ(I)5031 reflections with R                           int = 0.043                                                            R[F                           2 > 2σ(F                           2)] = 0.061                           wR(F                           2) = 0.182                           S = 1.07                           7076 reflections335 parametersH atoms treated by a mixture of independent and constrained refinementmax = 0.99 e Å−3                        Δρmin = −0.48 e Å−3                        Δρ                                 APEX2 (Bruker, 2005APEX2; data reduction: SAINT (Bruker, 2005SHELXTL (Sheldrick, 2008SHELXTL; molecular graphics: SHELXTL; software used to prepare material for publication: SHELXTL and PLATON (Spek, 2003Data collection: 10.1107/S1600536808025269/at2610sup1.cif            Crystal structure: contains datablocks global, I. DOI: 10.1107/S1600536808025269/at2610Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials: