Citation counts are often regarded as a measure of the utilization and contribution of published articles. The objective of this study is to assess whether statistical reporting and statistical errors in the analysis of the primary outcome are associated with the number of citations received.We evaluated all original research articles published in 1996 in four psychiatric journals. The statistical and reporting quality of each paper was assessed and the number of citations received up to 2005 was obtained from the Web of Science database. We then examined whether the number of citations was associated with the quality of the statistical analysis and reporting.A total of 448 research papers were included in the citation analysis. Unclear or inadequate reporting of the research question and primary outcome were not statistically significantly associated with the citation counts. After adjusting for journal, extended description of statistical procedures had a positive effect on the number of citations received. Inappropriate statistical analysis did not affect the number of citations received. Adequate reporting of the primary research question, statistical methods and primary findings were all associated with the journal visibility and prestige.In this cohort of published research, measures of reporting quality and appropriate statistical analysis were not associated with the number of citations. The journal in which a study is published appears to be as important as the statistical reporting quality in ensuring dissemination of published medical science. Citation by other authors is important in the dissemination of published study findings. The attention that scientific articles get can be assessed using citation analysis. In this context, Egghe & Rousseau  claim thStatistical methods play an important role in medical research. This is reflected in the high proportion of articles which are essentially statistical in their presentation ,4. The mThe association between statistical reporting and the number of citations received is presently unclear . Our aimAmerican Journal of Psychiatry (AJP), Archives of General Psychiatry (AGP), the British Journal of Psychiatry (BJP) and the Nordic Journal of Psychiatry (NJP). AJP and AGP are the two leading medical journals covering psychiatric research and have consistently been the top two as ranked by Garfield's impact factor (IF), while BJP is the most cited psychiatric journal outside the United States and NJP represents the large group of journals having a markedly lower IF than the other three studied here. The four journals had the following impact factors in 2004: AGP 11.207, AJP 7.614, BJP 4.175 and NJP 0.887.For our investigation we selected four general English-language psychiatric journals: The All the articles published in these four journals in 1996 were supplied by the Medical Libraries in the authors' institutes. Papers were included for examination if they had been published as original research articles in 1996, reported research findings based on the systematic collection of data, and used statistical methods for data analysis. The total number of articles reviewed was 448, representing about 47% of all the articles in the four journals (N = 951). Those excluded were mostly letters (n = 287), brief reports , reviews (n = 22) or editorials. Further details of the sample and the statistical methodology used in the articles have been published in an earlier paper [Each article's citations, over 9 years till April 2005, were obtained from the Web of Science databases  in April 2005. Self-citation was deemed to have occurred whenever the set of co-authors of the citing article shared at least one author with that of the cited one, a definition used in various recent citation studies . The numOne reviewer (P.N.) assessed all papers to determine the primary outcome and main response variable(s) together with possible explanatory or grouping factors. The primary outcome was that which was stated in the research objectives (in the abstract or introduction) or labelled as "primary" in the methods. When no outcome met these criteria, the reviewer used his own judgment to select the outcome that was presented in the abstract, and/or the first outcome presented in the results, that appeared crucial to the final conclusions. The psychiatric sub-field and study design were also assessed. Papers that were difficult to assess were additionally reviewed by GR and MS, then jointly assessed.To ensure consistency of evaluation (assessments), the assessor used the same detailed  classification scheme for each paper, and was blind to the number of citations received.The reliability of the evaluation was investigated by comparing the ratings of two reviewers (P.N. and Jouko Miettunen). They independently reviewed all the 448 articles in separate research projects with different research questions; however, their review protocols shared two items. For the first, 'whether data analysis procedures were completely described in the methods part of the research report', the overall agreement between raters was 90.7% and kappa coefficient for inter-rater reliability was 0.75 (95% CI 0.68 – 0.82). For the second, 'whether the statistical software used in the study was named in the report', the overall agreement was 96.9% and kappa coefficient 0.93 (95% CI 0.89 – 0.96).To evaluate the quality of reporting, the following information was obtained: (i) whether the primary research question or hypothesis was clearly stated in the report's introduction or methods section; (ii) whether sample size and data analysis procedures were described in the report's methods section, and (iii) whether the article was difficult to read due to lack of clarity about the primary response or outcome variable.Each article was also assessed for inappropriate use of statistical methods. Specifically, each article was checked for the specific analysis errors defined by Altman  as 'defiStudies were categorised as including insufficient or incomplete analysis if the article drew conclusions not supported by the study data, reported significant findings without a statistical test or CI, or explicitly or implicitly made comparisons between p-values. The overuse of statistical tests, defined to be present if there was no clear main hypothesis, or several sub-group analyses using the primary outcome, was also assessed.As it is plausible that studies with larger sample sizes include more evidence, we categorised each study's sample size as small (< 50), medium (50 – 360) or large (>360).Box plots showed that the distribution of the number of citations received is highly positively skewed, so we use the median as a measure of location. Mann-Whitney tests, Kruskal-Wallis ANOVA and negative binomial regression were used to investigate possible associations between the number of citations and reporting quality. We adjusted for journal to control for the effect of journal visibility. The statistical significance of differences in statistical reporting and errors between the four journals was evaluated using chi-square test.The statistical software used were the SPSS for Windows version 12.0 (SPSS Inc.) and SAS Release 9.1 (SAS Institute Inc.).The articles in our sample came from a variety of specialties: epidemiology, clinical topics, psychopharmacology, biological topics and others. The distribution of published articles by journal and topic is shown in table Figure Table Table Table Table An estimated multivariate negative binomial regression model for the effects of quality of statistical reporting and analysis on the number of received citations, adjusted for the publication forum, is shown in Table Further, in an additional analysis (suggested by a reviewer) we investigated whether there is a difference in the number of citations received by papers with (i) statistical errors that potentially affect the study results and (ii) papers with reporting failures. To this end, a combined variable "Presence of errors potentially affecting the study results" was defined using the last four variables given in Table Only 16 out of the 448 psychiatric articles published in the four journals in 1996 included sample size calculations, power analysis or any other justification for the sample size, contrary to the CONSORT  and STROFigure This study investigated the association between the quality of an article's statistical reporting and analysis and the number of citations it received. In this set of articles, failing to state essential information, such as the primary research question or the primary outcome variable did not affect the number of citations the article received. However, a sufficient description of the methods used was an important factor in increasing the number of citations received in two of the four journals. Statistical errors and sample size were not associated with number of citations received. Reporting quality was associated with the journal visibility and prestige.West and McIlwaine  have anaThe importance of stating the purpose and a priori hypotheses of a research project in the report is obvious, but such a statement was often (in 34.6% of papers) missing. In these cases, the results cannot be interpreted in light of a priori hypotheses. Further, unless the research question is clearly stated, the appropriateness of the study design, data collection methods and statistical procedures cannot be judged. For other researchers to cite the paper, however, it does not appear to matter whether the initial purpose of the cited study was clear, or whether the analyses are exploratory and speculative.We found that 25% of the articles were difficult to read due to an unclear definition of the primary response or outcome variable. Although it is valuable for medical studies to evaluate several aspects of patients' responses, it is important to identify a small set of primary outcome or response variables in advance . It is aArticles with clearly documented research methods did receive more citations. This association was more marked in papers published in AJP and BJP. In our sample, documentation of statistical methods used was generally sufficient in AGP (92.2%), consistent with the editorial policy of the journal which requires an extended methods section in submitted manuscripts.We included in our review four general psychiatric journals with different prestige and visibility. By involving several journals we were able to control for the effect of journal visibility on the number of citations received and compare the prestige of a journal with the quality of statistical presentation. The reporting of statistical information was more detailed, comprehensive and useful for the reader in the two leading journals (AGP and AJP). Again, this is consistent with their detailed guidelines for presenting statistical results, and also a more rigorous review process, including extensive statistical reviewing . In low-Several findings have demonstrated that a non-negligible percentage of articles – even those published in 'high -prestige' journals –, are not statistically faultless ,8,19,20.It has been claimed that researchers prefer to cite large studies rather than small studies . Our datIn this cohort of published research, measures of reporting quality and appropriate statistical analysis were not associated with the number of citations. The journal in which a study is published appears to be as important as the statistical reporting quality in ensuring dissemination of published medical science ,24. A hiMost of the errors and shortcomings in the application and reporting of statistical information in the journal articles reviewed here are related to topics included in most introductory medical statistics books. Some of these errors are serious enough to call the author's conclusions into question. It seems strange that a problem seemingly so important, so wide spread and so long-standing should continue ,9. PossiThe author(s) declare that they have no competing interests.PN and MS had the idea for the article. PN collected data, did the statistical analysis and wrote the paper. GR contributed to the data collection, the statistical analysis and writing of the paper. JC contributed to the statistical analysis and writing of the paper. MS initiated the study project, coordinated the collection of material and contributed to the writing of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Brand's four reasons for clinical tests and his analysis of the characteristics of valid biomechanical tests for use in orthopaedics are taken as a basis for determining what methodologies are required for gait analysis in a clinical rehabilitation context.The state of the art of optical systems capable of measuring the positions of retro-reflective markers placed on the skin is sufficiently advanced that they are probably no longer a significant source of error in clinical gait analysis. Determining the anthropometry of the subject and compensating for soft tissue movement in relation to the under-lying bones are now the principal problems. Techniques for using functional tests to determine joint centres and axes of rotation are starting to be used successfully. Probably the last great challenge for optical systems is in using computational techniques to compensate for soft tissue measurements. In the long term future it is possible that direct imaging of bones and joints in three dimensions (using MRI or fluoroscopy) may replace marker based systems.There is still not an accepted general theory of why we walk the way we do. In the absence of this, many explanations of walking address the mechanisms by which specific movements are achieved by particular muscles. A whole new methodology is developing to determine the functions of individual muscles. This needs further development and validation. A particular requirement is for subject specific models incorporating 3-dimensional imaging data of the musculo-skeletal anatomy with kinematic and kinetic data.Clinical gait analysis is extremely limited if it does not allow clinicians to choose between alternative possible interventions or to predict outcomes. This can be achieved either by rigorously planned clinical trials or using theoretical models. The evidence base is generally poor partly because of the limited number of prospective clinical trials that have been completed and more such studies are essential. Very recent work has started to show the potential of using models of the mechanisms by which people with pathology walk in order to simulate different potential interventions. The development of these models offers considerable promise for new clinical applications of gait analysis. The core of most contemporary gait analysis is the measurement of joint kinematics and kinetics. Other measurements regularly made are electromyography (EMG), oxygen consumption and foot pressures. A systematic physical examination of the patient is usually conducted as part of a gait analysis.For the purposes of this paper clinical gait analysis. Richard Brand [clinical i.e. a clinical test is one conducted in order to select from among different management options for a patient (including the possibility of not intervening).Rehabilitation is a clinical discipline and this paper will thus concentrate on rd Brand ,2 proposclinical research. This differs from clinical testing in that the reason is not to make clinical decisions for the individual patient, but to learn about a condition affecting a group of patients or the effect of an intervention. It is important to remember that the criteria for valid clinical research may not be the same as those for valid clinical testing. For example if a measurement made on a patient cannot be relied upon because of random errors then that measurement will not be useful for clinical purposes. By increasing the number of patients in a sample however, even measurements with quite large random errors can result in meaningful conclusions in clinical research. This paper will focus on gait analysis for clinical use. It will also focus on methodology rather than areas of clinical application.Much contemporary gait analysis is done for the purpose of Brand's ,2 other Brand went on to propose a number of criteria for assessing the usefulness of biomechanical measurements in general which, with some modification, can be used as criteria for the usefulness of all clinical gait analysis. These are listed in Table In order to perform a diagnostic function it is necessary for measurements to be able to distinguish normal from abnormal patterns of movement and also between the characteristics of one disease entity and another. There are two aspects to this. The first is having measurement systems capable of working to adequate precision. The second is a knowledge of what characterises normal walking or a particular disease entity.The requirement for patient assessment pre-supposes that a diagnosis does not give sufficient information to determine the most appropriate management for a patient and that measuring the precise characteristics of a patient's condition are essential for this. Measurements thus have to be sufficiently precise to reveal clinically important differences between patients with the same diagnosis. For monitoring purposes measurements need to be sufficiently precise to be able to determine whether a patient's condition is stable, improving or deteriorating.Brand suggested that the measurement technique should not affect the function it is measuring. The walking performed in a gait analysis laboratory however, with the patient concentrating on what they are doing in an idealised environment, is not necessarily representative of their normal walking. At the very least this must be taken into account when interpreting results.Gait analysis should reveal information that is useful to the clinician and this will generally require that results are reported in terms analogous to accepted clinical concepts. It must be cost-effective, that is the benefit of performing the test must be worth the cost. This balance need not necessarily be determined in purely financial terms but the financial cost of gait analysis is a significant factor. Finally there is no point doing any clinical test if the results could be obtained sufficiently well by simply observing the patientThe information obtained by assessing the patient is that used for selecting management options. This process does not, therefore, make further demands on the measurement systems but does require an understanding of how the patient's condition is likely to be affected by an intervention (or none) to a level sufficient to determine which options are preferable. Prediction of outcomes takes this one stage further to being able to determine not only which management option is best but also how the patient will be after that intervention.This sequential analysis of the four potential purposes of clinical tests reveals a progression from just requiring reliable and precise measurements to the additional requirement of having an understanding of how such information is incorporated into clinical practice. The state of the art is that the measurement component of gait analysis can reasonably be described as an objective process whereas the interpretation component is predominantly subjective.Making the interpretive component more objective can be achieved in two ways. The first is to develop a general theory of how people walk whether they have recognised pathology or not. As long ago as 1982 Cappozzo lamented, "The approaches to clinical gait analysis and evaluation are not supported by general theories"  and despModern clinical gait analysis traces its origins back to the early 1980s with the opening of the laboratory developed by the United Technologies Corporation at Newington, Connecticut and those provided with equipment by Oxford Dynamics (later to become Oxford Metrics) in Boston, Glasgow and Dundee. Retro-reflective markers were placed on the skin in relation to bony landmarks. These were illuminated stroboscopically and detected by modified video cameras. If two or more cameras detect a marker and the position and orientation of these cameras are known then it is possible to detect the three-dimensional position of that marker .Whilst the basic principles remain the same as the earliest systems, the speed, accuracy and reliability has advanced beyond all recognition. It is not uncommon now to find clinical systems using 8, 10 or more cameras functioning at over 100 Hz and capable of detecting reliably the presence of many tens of markers of between 9 and 25 mm diameter. Calibration of the systems  can generally be accomplished in less than a minute. Marker positions from clinical trials can be reconstructed and markers labelled automatically in real time . The determination of the accuracy of such systems is now generally limited by the accuracy of any alternative means to determine marker position and can be taken to be of the order of 1 mm. This is probably an order of magnitude smaller than other sources of error in determining joint kinematics and kinetics. This particular measurement technology has thus reached a mature state of development that, whilst advances will almost certainly continue, already probably delivers all that is required by conventional gait analysis [The same cannot be said of the computer models used to derive joint kinematics and kinetics from the marker position data supplied by the measurement hardware. Almost all commercially available clinical systems use some variant of the Conventional Gait Model  which haThe primary problem of current measurement technology is that of reliability in routine clinical use. Several studies have now been reported in which a single subject has been analysed in a number of different laboratories -14. ThesAt least one recent study has shown that it is possible to get levels of reliability sufficient to justify the continued clinical use of gait analysis within a single centre . Too fewWhilst not the most exciting field of research, a very real need of clinical gait analysis is for the development of techniques for establishing the reliability of measurement techniques and of methods of quality assurance that will ensure that the very highest standards of reliability are achieved in routine clinical practice.model calibration). This has two aspects, placing markers accurately with respect to specific anatomical landmarks and determining the location of the joint centres  in relation to these markers. Failure to place markers accurately is probably the single greatest contributor to measurement variability in contemporary clinical gait analysis. This is partly a matter of appropriate staff training and quality assurance but at least as important, and more fundamental, is the problem that many of the landmarks used to guide marker placement are not themselves particularly well defined in patients with certain conditions [There are two principal sources of error. The first is the difficulty determining the anthropometry of the individual subject . Once such technology is available however it is quite possible that this will supersede the presently available systems. The cost of any such new systems however is likely to prohibit ready clinical availability in the foreseeable future.There has been some work done on markerless optical methods. By placing a number of video cameras around a subject and tracing the silhouette of the walking subject on each it is possible to generate a 3-dimentional silhouette of that subject. This has already been achieved but the next step of using such a silhouette to determine the co-ordinate systems associated with the moving body segments has not yet been satisfactorily achieved.It is possible that the problem of skin movement can only be satisfactorily addressed by making direct measurements of bone position. It is now possible to take 3-dimensional images of bones (and muscles) using MRI but only within a very restricted capture volume -35. The Using 3-dimensional imaging techniques to directly determine bone movements during walking either as a technology with potential clinical applicability or for use as a gold reference standard from which to improve the implementation of conventional marker based technologies is one of the greatest challenges in this area.The second element of clinical gait analysis is the interpretation of data. Conventions for describing 3-dimensional joint kinematics and kinetics are well formulated. Many laboratories are augmenting conventional kinematics and kinetics with muscle length and, less commonly, moment arm graphs. Normal patterns of movement as represented by these data are now generally fairly well understood by clinical specialists although there is actually very little normative data published in the peer-reviewed literature. Similarly, many abnormal patterns of movement are quite widely recognised by clinicians but there few published attempts at formal classification of these -46. Manymoment arm in three dimensions [Despite the widespread acceptance of many of these conventions there are still problems. Baker  demonstrmensions  and it iA consistent, comprehensive and clear method for describing joint kinematics and kinetics in three dimensions would be of immense benefit for the clinical gait analysis community.what happens rather than why it happens. Many in the clinical gait analysis community regard kinematics as descriptive but contend that kinetics explain movement patterns. This is almost certainly misguided. Kinetics are simply another set of measurements and can thus only be descriptive.Perhaps the most important limitation of our present understanding of human walking, however, is that it is primarily descriptive. We know There have been various attempts at establishing a theory of walking but none is particularly convincing. Saunders, Inman and Eberhart's determinants of normal walking  are perhPerhaps the closest we have come so far to understanding why we walk the way we do has come from the work of Pandy and Anderson ,75. TheyAn obvious challenge in the emerging field of computational biomechanics is to apply similar techniques to model walking with particular forms of pathology.Conceptually, modifying such models to incorporate a specific abnormality of the musculo-skeletal anatomy such as a leg length discrepancy or contracture of a particular muscle is reasonably straightforward. It is much less certain whether such techniques can be applied at all to patients with neuromuscular pathology who are most frequently seen by clinical gait analysis services. Optimisation techniques assume that movements are controlled in such a way that a specific control function is minimised. In many neuromuscular conditions  the problem is one of a loss of central control and this would appear to invalidate any techniques modelling human movement as an optimised process.If such models are developed it will be interesting to see whether they give any insights into the clinical management of patients. Further it will be interesting to see whether their use leads to an understanding of why we walk the way we do which can be formulated as theories that are applicable without the use of such complex models.Perhaps the greatest challenge in clinical gait analysis is still to answer the question. "Why do we walk the way we do and why don't our patients?".Whilst the answer to this question still seems as far away as ever, significant advances have been made over recent years in understanding the mechanisms by which we walk particularly in the way that muscles act. For many years it was assumed that a muscle's anatomical position determines how it acts. It was assumed for example that the action of the hamstrings, passing behind the knee, was always to flex the knee. It is only comparatively recently that biomechanists have come to appreciate that any individual muscle has an effect on all the segments of the body and that in some circumstances this may result in a muscle having an action different to its anatomical function -81. It iSuch work depends on knowing the joint kinematics and kinetics and inertial properties of the body segments. These can be used to estimate the forces in individual muscles -83. Thispatient-specific models of walking.A further area of challenge is in using 3-dimensional imaging techniques to model musculo-skeletal deformities to allow the generation of There is also considerable debate at present about the validity of these techniques . Whilst the general principles are sound the techniques are known to be extremely sensitive to certain aspects of their implementation (and may be sensitive to many more). For example the forward modelling in particular is sensitive to how the interaction between the foot and the floor is modelled with there being no clear consensus as to the most appropriate method for this ,77,81.Implementation of these models must be based on robust techniques being developed to validate models, the first step of this is in rigorous analysis of the sensitivity of models to the assumptions on which they are based.Understanding how to interpret clinical gait analysis data is not itself sufficient to allow selection from amongst treatment options Table . For thiThere are then two methods for understanding the effects of intervention in these patients; clinical research to establish what the actual effect of a given intervention is or using knowledge of the mechanisms of walking to predict the effect of modifying the characteristics of the musculo-skeletal anatomy.By far the most common approach to date has been the use of clinical research – the comparison of gait patterns before and after a particular intervention -55,86-88Perhaps the most challenging field of research for clinical gait analysis is in the design and conduct of prospective clinical trials to ascertain the effects of specific treatments on specific patient groups.An alternative to the use of clinical trials is to use knowledge of the mechanisms of walking as a basis for modelling the effect of changing that mechanism. Reports of such studies are now starting to emerge. For example Arnold et al.  have repApplication of such techniques to a wider range of clinical problems represents another exciting sphere of research in clinical gait analysis. It may well be that such techniques are limited to the fairly narrow range of interventions that are based on correction of the mechanisms for very specific aspects of walking but identifying the range of potential applications will be an important part of this process.The author has received research funding from Oxford Metrics Plc


Geographic Information Systems (GIS) have been used in a wide variety of applications to integrate data and explore the spatial relationship of geographic features. Traditionally this has referred to features on the surface of the earth. However, it is possible to apply GIS in medicine, at the scale of the human body, to visualize and analyze anatomic and clinical features.    In the present study we used GIS to examine the findings of transanal endoscopic microsurgery (TEM), a minimally-invasive procedure to locate and remove both benign and cancerous lesions of the rectum.  Our purpose was to determine whether anatomic features of the human rectum and clinical findings at the time of surgery could be rendered in a GIS and spatially analyzed for their relationship to clinical outcomes.   Maps of rectal topology were developed in two and three dimensions. These maps highlight anatomic features of the rectum and the location of lesions found on TEM.  Spatial analysis demonstrated a significant relationship between anatomic location of the lesion and procedural failure.This study demonstrates the feasibility of rendering anatomical locations and clinical events in a GIS and its value in clinical research.  This allows the visualization and spatial analysis of clinical and pathologic features, increasing our awareness of the relationship between anatomic features and clinical outcomes as well as enhancing our understanding and management of this disease process.  This paper addresses technical considerations in rendering data on transanal endoscopic microsurgical outcomes in a GIS. It is a companion to the clinical paper published in the Journal of Gastrointestinal Surgery a. set base height expression = [Z] - [Base]b. set extrusion expression = adding to each feature's base heightc. Apply extrusion by: D. Plotting lesions: 3DArcMap, using Table 1. In a. x1 = 2cosθb. y1 = 2sinθc. z = - distance fieldFile-Add Data).2. Add data table to map  and export data to save coordinates (right-click event layer- Export data).3. Plot xArcScene4. Display in a. set base heights to [z] – points will float in spaceb. Overlay on Regions layerAnimated 3-D rendering of anatomic regions with lesions. Animated 3-dimensional visualization of features allows exploration from all perspectives.Click here for fileAnimated transparent 3-D anatomic regions. Transparent rendering in ArcScene allows features to be highlighted more clearly. The peritoneal reflection appears as an oblique plane in cross-section.Click here for file


Preterm labor affects up to 20% of pregnancies, is considered a main cause of associated neonatal morbidity and mortality and is responsible for neonatal care costs of multimillion euros. In spite of that, the commercial market for this clinical indication is rather limited, which may be also related to high liability. Consequently, with only a few exceptions, preterm labor is not in the orbit of great interest of the pharmaceutical industry. Coordinated effort of research community may bring the change and help required to reduce the influence of this multifactorial syndrome on society. Between the novel techniques that are being explored in a SAFE  group, there are new research models of preterm labor as well as novel methodology of analysis of biological signals. In this article, we briefly describe new clinical and nonclinical human models of preterm labor as well as summarize some novel methods of data processing and analysis that may be used in the context of preterm labor. In spite of decades of ongoing research, its pathophysiology still remains not fully discovered ,2, and cEthical issues in clinical studies significantly limit the possibility of using placebo-controlled protocols. On the other hand, the application of alternative protocols drastically reduces the chances of drug candidates in the process of registration. The effect of combination of above-mentioned factors creates the situation where very few medicines are labeled for use in pregnancy, and especially, preterm labor. Considering the whole situation surrounding preterm labor, it appears that only a coordinated effort of research networks, such as SAFE  and those involved in preterm labour, is capable of bringing the new data that could be of potential use in discerning its pathophysiology and point to new pharmacological targets.Therefore, in this setting there is much to be done towards solving this important problem. Here, we put forward new clinical and non-clinical models of uterine contractile activity together with new methods towards the detection and processing of biological signals that can be used as significant examples to build upon between collaborating partners.1a vasopressin receptor [in vivo and in vitro models that could closely reflect the reactivity of human myometrium/uterus, serving in pathophysiologic/pharmacologic studies.One of the major concerns in studies on preterm labor, especially on uterine contractile activity is that practically none of the models is adequately sufficient and easy to use – mice uteri lack Vreceptor , rats prreceptor  and laboreceptor . Studiesreceptor , howeverin vitro studies on uterine strips isolated from human pregnant/non-pregnant women as well as in vivo human or animal models [in vitro model of contractility of human myometrium presented in the literature [et al. [in vitro studies.Valuable information on the biology and pharmacology of uterine contractile activity may be provided by l models -11. One terature ,13, may  [et al.  we confiIn vitro studies are usually preceding clinical application of drug candidates. As ethical specificity practically excludes testing on pregnant volunteers, there is a hectic need for optimal in vivo model. Consideration should, therefore, be given to the uterus of non-pregnant women – where, aparting from the lack of significant rise in oxytocin receptors as seen in late pregnancy [regnancy , most ofregnancy -18. Moreregnancy , showingregnancy  or magneregnancy  activityregnancy . SpecialOur advanced methods of signal processing and analysis reported here give added support to the potential benefit of new preclinical and clinical models of preterm labor. Such an attitude could provide new insights into "well known" phenomena such as uterine contractile activity. Classical modalities of signal analysis, such as time domain and frequency domain-based analysis are frequently insufficient in providing significant biologic data on uterine contractile activity. In the classical, so called "time domain" analysis, studied parameters including area under curve recording (AUC), maximal amplitude of contractions and various statistical quantities . Although easily computed even for short time recordings , these aContrary to the Fourier decomposition, the continuous and discrete wavelet transforms allow extracting local and global variations of the recorded contractions. Graphical presentation of such data  shows the distribution of energy in the time-frequency plane. For normal contractions, scalograms of the fundal and cervical signals are very similar. In the case of dysmenorrheic contractions, patterns are quite different . StudiesIn the case of bivariate sets of signals (i.e. recorded in two distant locations of the uterus), which contain two simultaneously recorded time series, it appears clinically important to understand how the variables interact. Cross-correlation function can be employed to analyze the association between two time series ,19,26. TTime shifts may be an important indicator of propagation of uterine contractions, which could be altered in women in preterm labor. Figure Fractal analysis serves another example for advanced data analysis to be utilized in the studies on uterine contractile activity. Fractals, a relatively new analytical concept of the last few decades, have been successfully applied in many areas of science and technology. We performed a comparative study of ten methods of fractal analysis of signals of intrauterine pressure. We found significant differences between uterine contractions in healthy volunteers and women with primary dysmenorrhoea . There wNonlinear dynamics is one more example of advanced data analysis that could be implemented for uterine contractility signals. In recent years the physiological signals obtained from the complex systems like the brain or the heart have been investigated for possible deterministic chaotic behavior. The human uterus is undoubtedly a complex system. Smooth muscles comprising the myometrium interact in a complex manner. We used the techniques of surrogate data analysis to testing for nonlinearity in the uterine contraction signals. The results showed that the spontaneous uterine contractions are considered to contain nonlinear features  which inAfter decades of continuous research, especially as no breakthrough in the interest of pharmaceutical industry can be seen, only well-planned and coordinated international programs may provide additional data of different aspects of preterm labor. Possible directions for future research may be related to development of new biophysical methods of evaluation of uterine activity  together with implementation of novel methodology of analysis of signals and diagnosis of molecular genetic malfunctions.The authors declare that they have no competing interests.All the authors  have made substantive and equal intellectual contributions to a published study and have been involved in drafting the manuscript or revising it critically for important intellectual content.


The randomized study by Auvert et al.  on male Some might argue that the intent-to-treat analysis is more scientific, and reduces the impact of some selection or behavioral bias in those who opted for circumcision notwithstanding their allocation to the control arm. For example, it could be that men allocated to the noncircumcision group who were predisposed to less risky behavior but wanted to be extra safe might have chosen to be circumcised. In the opposite direction, those who were indulging most in risky behavior might have chosen to be circumcised to reduce their risk.In my view, however, if we are interested in the true biologic effect, it is not very scientific to bias in favor of the analysis with a 10% contamination of that biological effect. In this study, we are fortunate enough to have quite richly reported behavioral data. More analysis of the per protocol analysis should have been presented, including the behavior of the crossovers. It is reasonably likely that any difference in the behavior of the crossovers would have little impact since, in the intent-to-treat analysis, adjustment for the increase in riskier behavior in the treatment group had little effect on the overall result.The paper should have had prominent presentation of both analyses. In either case, the protection is quite substantial. But from an epidemiologic and personal perspective, what amounts to a failure rate of 40% versus 24% could be quite important.


The analysis of large-scale gene expression data is a fundamental approach to functional genomics and the identification of potential drug targets. Results derived from such studies cannot be trusted unless they are adequately designed and reported. The purpose of this study is to assess current practices on the reporting of experimental design and statistical analyses in gene expression-based studies.We reviewed hundreds of MEDLINE-indexed papers involving gene expression data analysis, which were published between 2003 and 2005. These papers were examined on the basis of their reporting of several factors, such as sample size, statistical power and software availability.t-test and ANOVA represent the most widely applied techniques in microarray data analysis. But remarkably, only 5 (3.5%) of the application papers included statements or references to assumption about variance homogeneity for the application of the t-test and ANOVA. There is still a need to promote the reporting of software packages applied or their availability.Among the examined papers, we concentrated on 293 papers consisting of applications and new methodologies. These papers did not report approaches to sample size and statistical power estimation. Explicit statements on data transformation and descriptions of the normalisation techniques applied prior to data analyses (e.g. classification) were not reported in 57 (37.5%) and 104 (68.4%) of the methodology papers respectively. With regard to papers presenting biomedical-relevant applications, 41(29.1 %) of these papers did not report on data normalisation and 83 (58.9%) did not describe the normalisation technique applied. Clustering-based analysis, the Recently-published gene expression data analysis studies may lack key information required for properly assessing their design quality and potential impact. There is a need for more rigorous reporting of important experimental factors such as statistical power and sample size, as well as the correct description and justification of statistical methods applied. This paper highlights the importance of defining a minimum set of information required for reporting on statistical design and analysis of expression data. By improving practices of statistical analysis reporting, the scientific community can facilitate quality assurance and peer-review processes, as well as the reproducibility of results. The analysis of large-scale gene expression has become a fundamental approach to functional genomics, the identification of clinical diagnostic factors and potential drug targets. DNA microarray technologies provide exciting opportunities for analysing the expression levels of thousands of genes simultaneously . A fundat-test, ANOVA) has improved in these disciplines, some errors regarding their sound application and reporting can still be found. For instance, the t-test and ANOVA are fairly robust to moderate departures from its underlying assumptions of normally-distributed data and equality of variance (homogeneity) except in the presence of very small or unequal sample sizes, which can considerably decrease the statistical power of the analyses [Consolidated Standards of Reporting Trials (CONSORT) have been adopted. CONSORT has significantly assisted researchers in improving the design, analysis and reporting of clinical trials [Over the past few decades the medical research disciplines, especially the area of clinical trials, have widely emphasised the importance of rigorous experimental design, statistical analysis implementation and the correct use of statistics in peer-reviewed publications -6. Althoanalyses -10. In ol trials . This isl trials ,13 that t-test and ANOVA. Our research also examined how papers present fundamental statistical justifications or assumptions for the correct application of the t-test and ANOVA, which are widely applied to gene expression data analysis.The main objective of this investigation is to assess the reporting of experimental design and statistical methodologies in recently published microarray data analysis studies. Among the experimental design factors under study are sample size estimation, statistical power and normalisation. This paper also provides insights into the design of studies based on well-known statistical approaches, such as PubMed [methodologies and biomedical-relevant applications); b) reporting of methods of sample size calculation and statistical power; c) reporting of data standardisation  and method of normalisation applied; d) description of data analysis techniques applied; e) discussion about missing values; f) explicit statement of directionality (i.e. one-sided or two-sided test); g) explicit statement of hypothesis and alternative; and h) reference to software tools applied for implementing data analyses. In this study application papers refer to any paper whose main contribution is the generation or testing of biological or biomedical hypotheses, including potential diagnostic, prognostic and therapy design applications, as well as biologically-relevant discoveries. Methodology articles emphasize the presentation of a novel, problem-specific  method or procedure, which may drive a biologically-relevant advancement or discovery.PubMed  was usedPubMed . The repIn connection to the description of data analysis techniques applied, we concentrated on the assessment of techniques or models that were fundamental to obtain key findings in the application and methodology papers. With regard to the discussion of missing data estimation methods, we targeted the application of previously-published imputation or estimation methods.We examined the two main categories of papers on the basis of the factors defined above. For all the factors, except for factor d), we asked whether or not a factor was reported in each paper. In relation to factor d), we reviewed the techniques applied and then organised the papers into groups according to major data analysis paradigms or approaches. Table We reviewed papers published in Medline-indexed journals. Among these papers 152 (51.9%) concentrated on the presentation of new methodologies for gene expression data analysis, and 141 (48.1%) papers mainly contributed application studies, e.g. discoveries directly relevant to molecular biology and clinical studies. The definition of these paper categories was provided above.Our results show that none of the 293 applications and methodology papers reported approaches to sample size calculation. Moreover, none of these papers reported information relating to the statistical power of their analyses. Only 23 (7.8%) of the papers (9 application and 14 methodology papers) presented discussions about the limitations of small sample sizes in their analyses of real data. Among the methodology papers, only 9 (5.9%) manuscripts provided evidence that their analyses techniques were adequate  for small sample sizes. Only 1 of the application papers discussed statistical power and sample size factors. Among the methodology papers, 94 (61.8%) used real data for assessing the data analysis methodologies or techniques proposed. Three of the methodology papers (2%) only used simulated data to support their evaluations; and 55 (36.2%) papers analysed both real and simulated data for evaluating the methodologies proposed. Table t-test and ANOVA represent the most widely applied techniques in microarray data analysis studies (Table t-test (21 papers) or ANOVA (47 papers). However, our review showed that only 5 (3.5%) of the application papers discussed variance homogeneity assumptions in their analyses. Moreover, only 7 (4.6%) of the methodology papers presented statistical justifications for the application of either ANOVA or the t-test.Among the 141 application papers, 11 papers (7.8%) did not report the statistical methods used in their data analyses. Clustering-based analysis, the es Table . Table 3Our results showed that among the methodology and application papers, 133 (87.5%) and 115 (82%) did not report the directionality of the tests (one-sided or two-sided test) respectively. Also among the methodology and application papers, only 19 (12.5%) and 26 (18%) included discussions about missing values  respectively. Explicit statements of hypothesis and alternative hypothesis were reported in only 43 (28%) and 29 (20.6%) methodology and application papers respectively. In addition, of the 141 application papers, only 52 (36.9%) included sections or sub-sections to describe data analysis methods applied.As shown in Table t-test) then spurious predictions and type II errors (β) can be seriously misleading. In fact, undetected significant differences may be explained by a lack of statistical power for detecting true differences between genes or as a result of inadequate sample sizes (subjects or arrays). Our study showed that very few research studies (i.e. either methodology or application papers) discuss power and sample size requirements in microarray experiments, which are fundamental factors to accomplish the validation of the statistical analyses [Our assessment suggests that published papers lack relevant information regarding the determination of sample sizes and statistical power in microarray data analysis studies. These studies often involve hundreds or thousands of genes and only a fraction of genes are expected to be differentially expressed. Therefore, genes that do not show clear patterns of differential expression are filtered out, by performing statistical group comparisons. However, if the subjects or arrays (sample size) have not been properly estimated before the statistical comparisons  methodology and application papers explicitly discussed issues relating missing values, e.g. sources and estimation methods. Gene expression data often contain missing expression values, which may require the application of missing data estimation or imputation techniques to obtain a complete matrix of expression values. Like in the case of data normalisation, authors not only should report on missing values, but also on their approaches to dealing with such a problem. Again this is a crucial factor because different estimation methods may have different effects on the same dataset -42,54. AFinally, our review suggests that the above reporting practices may be improved by encouraging authors to provide separate sections or sub-sections focusing on data analysis. Only 36.9% of the application papers, for example, included a section dedicated to these aspects, i.e. detailed discussion of methods, tools, assumptions. A section (or sub-section) on statistical methods should clearly state, for instance, how the sample size was estimated and how the data were analysed in relation to each of the objectives and underlying biological and statistical assumptions made. Such a section should also include information about statistical software or tools applied for data analysis (e.g. origin and availability) and the directionality of the statistical tests applied.Even when this study did not aim to analyse the possible causes of such relative lack of statistical information reporting standards, it is necessary to stress the importance of ensuring the participation of statisticians in both the design and analysis phases of gene expression studies. However, in some cases this may be accomplished only if adequate provisions and decisions are made during the project formulation and funding assessment phases (i.e. adequate budget considerations should me made to achieve such participation). An interpretation of the results on the reporting of test directionality should also take into account that for many authors it may be common practice not to report test directionality as they may assume that two-sided directionality is the default setting. However, this assumption should not be used to justify the lack of more rigorous reporting practices, which are commonly adopted in other statistics-driven areas, such as medical sciences, epidemiology and clinical trials.It is also necessary to recognise that the lack of more rigorous reporting standards may be understood in the light of the technical complexities and constraints presented by the area of gene expression data analysis. For example, there is a need for more comprehensive theoretical and empirical studies about the statistical nature of gene expression data in order to help researchers to present deeper discussions on sample size and power analysis. In relation to these factors, one may also argue that, unlike the clinical sciences domain, there is a lack of accepted, comprehensively-validated methods tailored to gene expression data. Therefore, it is fundamental to promote deeper investigations and the generation of robust, user-friendly tools to assist researchers in their approaches to the discussion of these factors.Gene Expression Omnibus Database using this procedure [More investigations on the application and reporting of other important experimental procedures, such as sample pooling prior to hybridization, are required. It has been shown that pooling may significantly affect the quality of data analysis . Our revrocedure .Another fundamental analysis factor that continues deserving additional investigations is the application and reporting of P-values adjustments. Our review revealed that only 15 (10.7%) and 28 (18.4%) of the application and methodology papers respectively explicitly reported the P-value adjustment method applied. For instance, among the 141 application papers, 8 (5.7%) and 7 (5%) papers reported the use of Bonferroni and Benjamini-Hochberg adjustment methods respectively. With regard to the methodology papers : 14 (9.2%), 12 (7.9%) and 2 (1.3%) papers reported the application of Bonferroni, Benjamini-Hochberg and Hochberg adjustment methods respectively. The selection of a suitable adjustment method depends on the error rate that one wants to control . For exaOur study may be complemented by other reviews on the correct application of evaluation strategies, such as data sampling and significance interpretation . AdditioFuture work may involve an analysis of potentially interesting, significant time-dependent trends relating to statistical information reporting. This may allow the scientific community to assess emergent practices and patterns of knowledge generation and reporting in gene expression data analysis.Medical research disciplines, especially the area of clinical trials, have placed relatively more emphasis on the reporting of experimental design, statistical analysis implementation and the correct use of statistics in peer-reviewed publications -6 in comThe present survey indicates that the quality and coverage of information regarding experimental design and statistical analysis in gene expression data-driven studies deserve to be improved. The reporting of statistical power, sample size, normalisation and missing data estimation techniques requires a more rigorous treatment. Poor or incomplete reports may significantly affect our capacity to interpret results and assess the relevance and validity of research studies. Moreover, inadequate reporting of statistical analysis information may increase the likelihood of publishing spurious associations or predictions. By paying more attention to these factors authors will be facilitating quality assurance and peer-review processes, as well as the reproducibility of results, which are fundamental factors for the advancement of scientific and technological development, policy and decision making.Community-driven efforts such as the MIAME (Minimum Information About a Microrray Experiment) protocol  may be uThe author(s) declare that they have no competing interests.PJ and FA co-designed the study. PJ implemented it by retrieving and reviewing papers and constructing quantitative descriptions of the review. FA selected relevant papers and resources for the analysis. PJ and FA co-wrote the manuscript.The pre-publication history for this paper can be accessed here:Origin of papers- List of journalsClick here for file


Statistical analysis methods and applications have traditionally placed relatively little attention to the graphical visualisation of data and information. Such techniques are fundamental not only for summarising the outcomes originating from statistical analyses, but also for facilitating the detection and assessment of relevant findings. Data-intensive application domains such as medical informatics, biomedical engineering and bioinformatics rely on the application of several statistical analysis and information visualisation techniques to support the generation, validation and integration of hypotheses. This requires an understanding of fundamental principles of data sampling, comparison and classification.S-Plus, R and SAS. Executable functions and macros are also provided for several graphical display methods.Heiberger and Holland have produced a comprehensive, well-presented review of essential statistical topics. Furthermore, they emphasise the application of graphical display techniques to summarise data and assess analysis results. This book explains why, how and when to apply particular techniques and tools. Another important feature is the incorporation of examples and exercises directly relevant to three leading statistical languages: After discussing basic statistical concepts, such as probability distributions and hypothesis testing, graphical display techniques are overviewed with an emphasis on scatterplot-based methods. An introductory chapter on inference illustrates how to compare two populations, determine sample sizes and measure goodness of data fit. Chapters dedicated to one-way analysis and multiple comparisons are followed by several chapters on linear, multiple and logistic regression. It also discusses two-way analysis of variance, design of experiments, bivariate and nonparametric statistics. The last section focuses on time series analysis.The authors, collaborators and publishers made a serious effort to achieve a high quality presentation of text, tables and figures (only black and white). Although the book concentrates on the application of S-Plus, R and SAS, it is a relevant resource to users of other software platforms.Overall, this is an excellent book for supporting advanced undergraduate courses on statistics or data analysis. Postgraduate students of machine learning and data mining topics may also be greatly benefited from reading most of its chapters. This is a valuable reference source to researchers from these and related areas including medical decision support, clinical engineering and bioinformatics.


For scientific, ethical and economic reasons, experiments involving animals should be appropriately designed, correctly analysed and transparently reported. This increases the scientific validity of the results, and maximises the knowledge gained from each experiment. A minimum amount of relevant information must be included in scientific publications to ensure that the methods and results of a study can be reviewed, analysed and repeated. Omitting essential information can raise scientific and ethical concerns. We report the findings of a systematic survey of reporting, experimental design and statistical analysis in published biomedical research using laboratory animals. Medline and EMBASE were searched for studies reporting research on live rats, mice and non-human primates carried out in UK and US publicly funded research establishments. Detailed information was collected from 271 publications, about the objective or hypothesis of the study, the number, sex, age and/or weight of animals used, and experimental and statistical methods. Only 59% of the studies stated the hypothesis or objective of the study and the number and characteristics of the animals used. Appropriate and efficient experimental design is a critical component of high-quality science. Most of the papers surveyed did not use randomisation (87%) or blinding (86%), to reduce bias in animal selection and outcome assessment. Only 70% of the publications that used statistical methods described their methods and presented the results with a measure of error or variability. This survey has identified a number of issues that need to be addressed in order to improve experimental design and reporting in publications describing research using animals. Scientific publication is a powerful and important source of information; the authors of scientific publications therefore have a responsibility to describe their methods and results comprehensively, accurately and transparently, and peer reviewers and journal editors share the responsibility to ensure that published studies fulfil these criteria. Scientific progress is driven by developing and testing novel hypotheses. Investigating these new ideas using appropriately and robustly designed experiments is fundamental to this process. The entire scientific community is also equally reliant on published research being transparently and accurately reported. Critical appraisal of scientific publications, for instance by peer review, is only possible if the methods and results of the studies are comprehensively reported. Accurate and transparent reporting is therefore vital to allow the reader to assess the methods of the study, and the reliability and importance of the scientific findings. This is particularly necessary for scientific research using animals, as poorly designed experiments and reporting omissions can raise both ethical and scientific concerns.The National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), established by the UK government in 2004, is an independent scientific organisation dedicated to finding innovative solutions to replace animals in research with non-animal alternatives, reduce the number of animals used in experiments, and minimise suffering and improve animal welfare by refining husbandry and procedures (the 3Rs). It is widely accepted that applying the 3Rs to experiments using animals is consonant with good scientific practice In order to assess the scope for improved experimental design, statistical analysis and reporting, and to further the implementation of the 3Rs, the NC3Rs has carried out a systematic survey of the quality of reporting, experimental design and statistical analysis of recently published biomedical research using laboratory animals. This paper reports the main findings and conclusions of this survey.A systematic search of the Medline and EMBASE databases was carried out to identify potentially relevant scientific papers published between January 1999 and March 2005 reporting original research on live rats, mice and non-human primates (referred to hereafter as ‘primates’) see . RodentsThe search identified studies covering a wide variety of experimental fields, including behavioural and diet studies, drug and chemical testing, and immunological experiments see , publishThe main experiment reported in each publication was identified and detailed information was collected on objective measures such as the numbers and characteristics of the animals used including the species, strain, age, sex, and weight. Details of the experimental design such as the size and number of experimental groups, how animals were assigned to experimental groups, how experimental outcomes were assessed, what statistical and analytical methods were used, were also recorded. This information was collected in two distinct stages. In phase 1, data were collected from all 271 papers, and in phase 2, a random sub-sample of 48 papers  was chosen from the 271 papers evaluated in phase 1, and assessed in more detail see . The majThe survey's first question addressed the fundamental premise of each scientific publication. A clear statement of the objective of the study, or the main hypothesis being tested, was described in the introduction by 95% of the 271 publications; the remaining 5% of the studies either did not describe the purpose of the study at all, or it was not clear to the assessors see . In 6% oIn 4% of the 271 included publications, the number of animals used in the main experiment assessed was not reported anywhere in the In order to investigate the proportion of papers that had multiple reporting omissions and to provide an overall assessment of the quality of reporting, we identified those papers which clearly stated the study hypothesis, reported three animal characteristics , and also reported the number of animals used; 59% of all 271 papers reported all this information see .Next we assessed the quality of experimental design, and in particular, how many papers had incorporated measures to reduce bias such as randomisation and blinding. Formal randomisation is a process used to allocate animals to experimental groups and is carried out to avoid any bias in assigning the animals to the treatment groups, making it more likely that the groups are comparable Qualitative scoring of an experimental observation or result by a researcher often involves a subjective assessment or judgement, and as such is more susceptible to bias than quantitative (numeric) measures (e.g. weight). Blinding, where the researcher does not know which treatment the animal has received when judging an experimental result, is an effective way of minimising this bias A design factor is an experimental variable (e.g. treatment) to which an animal is allocated by a researcher, ideally at random. Other variables that may influence the effect(s) of treatment(s) but that cannot be randomly assigned to the animals (such as sex or strain) can also be chosen by the researcher. Factorial and stratified experimental designs allow combinations of two or more design factors to be evaluated in one experiment at the same time in the same animals. These types of experimental design are efficient ways of maximising the information gained from each experiment, and can reduce the overall number of animals used whilst increasing the strength of the scientific findings Statistical methods were used to analyse the data in 91% of all 271 studies; ANOVA and t-tests were the methods used most frequently. However in 4% (10/247) of the studies that used statistics, it was unclear what statistical method had been used, i.e. a p-value or statistical significance was indicated, but no other methodological details were reported see . FurtherAlthough almost all (99%) of the 271 included studies presented numerical data see , only abThe NC3Rs survey has provided a detailed analysis of both the quality of the reporting and the quality of the experimental design and statistical analysis of experimental research using laboratory animals. The survey has identified a number of issues – particularly reporting omissions.Every study faces a trade-off between maximising power within the study itself  by minimising sample heterogeneity and maximising the generalisability of the findings . The number of papers assessed in this survey is approximately twice the number of studies included in similar surveys published to date (133 and 149 papers respectively) Nature and Science. Whilst it would be useful to know if there is a relationship between the quality of the papers surveyed and the impact factors of the journals they were published in, this analysis was not in the remit of this survey.Our study was carefully designed to ensure that the sample was representative of the target literature. The search strategy, which included species names, will have selected for a subset of papers, i.e. those that at least reported the species of animal used. Whilst our findings apply to this sample of papers, our results may in fact underestimate the extent of the reporting omissions. It is highly unlikely that our search terms or inclusion/exclusion criteria would have biased the sample to include a disproportionate number of poor quality publications from lower ranking journals. In fact, the search retrieved papers from a range of publication years (1999 – 2005), covering a wide variety of research areas, and an extensive range of journals across the impact factor spectrum, including Scientific papers should report sufficient relevant information about the experimental objectives, animal characteristics, experimental methods used and results obtained, in order to critically assess the findings and both the scientific and ethical implications of a study, or to allow the work to be repeated. Surprisingly, some of the studies surveyed either did not describe the purpose of the study at all or it was unclear to the assessors, and thus presumably also to any non-specialist reader. In addition, in some of the studies surveyed it was unclear whether one or more experiments were being described, and the experimental unit  was not clearly identified.Many of the studies surveyed omitted details about the strain, sex, age and weight of the animals used. These are all factors that can potentially influence experimental results and are therefore scientifically important Many journals offer supplementary online space  not only for methodological information but also for additional results and tables. This information resource was considered, where it was available, for the papers surveyed. The availability of this resource negates the argument that the lack of detail in published papers is primarily due to a lack of space. Studies have found that some experimental details  are extensively discussed in the body of the paper, whilst information about animal characteristics, sample sizes etc are scantily provided or are absent In some of the included publications, the number of animals used was not reported anywhere in the Power analysis or other very simple calculations, which are widely used in human clinical trials and are often expected by regulatory authorities in some animal studies, can help to determine an appropriate number of animals to use in an experiment in order to detect a biologically important effect if there is one The assessment of experimental design found that random allocation of animals to treatment groups was reported in only a very small proportion of all the studies surveyed. Randomisation reduces selection bias, increases the validity of the findings and, in principle, is always an appropriate and desirable aspect of good experimental design when two or more treatments are compared We cannot rule out that some of the studies surveyed may have used randomisation where appropriate, but did not report using it. If this was the case, then this kind of reporting omission can easily be rectified. But if not, incomplete reporting masks potentially flawed experimental methods.When humans have to make observations there is always the possibility of bias” “Reviews of animal research in the field of emergency medicine found that studies which did not use randomisation and blinding to reduce bias when comparing two or more experimental groups, were significantly more likely to find a difference between the treatment groups Statistical methods are important for calculating the degree of confidence in, for example, the reproducibility and general validity of experimental results, and were used and reported by the majority of studies. The majority of the studies that used and described a statistical method were judged to have used a correct statistical method. Whilst the majority of papers that used a statistical method described it and reported the numerical results with an error measure, many papers did not. Reporting the statistical method used together with an indication of the measure of variation or uncertainty is essential for interpreting any results, and has implications for the reliability and generalisability of the findings to other species and systems  Our findings indicate that there are problems both with the transparency of reporting and the robustness of the statistical analysis of almost 60% of the publications surveyed. In many papers, due to the lack of information detailing the statistical methods it was difficult to judge whether or not the statistical analysis were appropriate, or if data had been efficiently extracted and analysed.These issues are not new, as previous surveys of publications describing animal research and assessing specific aspects of experimental design, statistical analysis and reporting, have shown The problems with experimental design and reporting that we have identified are also in line with similar reviews of the literature in various other scientific and clinical research areas Standards developed for reporting clinical research have improved the quality and transparency of reporting of clinical trials and have been adopted by many leading medical journals as part of their instructions to authors This is the largest and most comprehensive survey of this kind carried out to date. We provide evidence that many peer-reviewed, animal research publications fail to report important information regarding experimental and statistical methods. Whilst our findings are limited to experimental studies using rodents and primates carried out in UK and US laboratories, this is the statistical population that dominates the biomedical research literature, so our results are important and indeed, indicate cause for concern.Scientific publication is the method by which research has traditionally been described and the results communicated and it remains a powerful and important source of information. The authors of scientific publications therefore have a responsibility to describe their experimental and statistical methods and results comprehensively, accurately and transparently, and journal editors share the responsibility to ensure that published studies fulfil these criteria. This is particularly pertinent for research involving animals, as poorly designed and reported experiments raise ethical as well as scientific concerns. Whilst we recognise that in some studies, not all of the details we assessed  will necessarily have an important impact on the overall findings, there are principles at stake – namely the transparency, reproducibility, and reliability of scientific publications. We are simply arguing for the inclusion of all relevant information that will allow a suitably skilled reader to assess, analyse, and repeat the study's findings.There are many opportunities for the scientific community to improve both the experimental design and the quality of reporting of biomedical research using animals. Serious efforts are needed to improve both the quality of experimental design and the quality of reporting in order to make research articles better suited to the needs of readership. The NC3Rs has identified a number of ways of helping to make these improvements. Raising awareness that these problems exist will be the first step in tackling these fundamental issues. In addition, working with researchers, journal editors and funding bodies, the NC3Rs is building on the results of this survey by developing a set of reporting guidelines to assist researchers, journal editors and research funding bodies to take appropriate steps to improve the quality and transparency of reporting in the scientific publications with which they are associated.An information specialist searched the Medline and EMBASE databases for all potentially relevant English language scientific papers published between 1 January 1999 and 31 March 2005, reporting original research on live rats, mice and non-human primates (referred to hereafter as ‘primates’) carried out in publicly funded research establishments in the UK and the USA. (See supplementary online information for search terms).Databases were searched using the following search terms:1. exp MICE 14. Hominidae2. usa.in. 15. 12 or 133. 1 and 2 16. 15 not 144. exp great britain17. Pan troglodytes5. england.in.18. 16 or 176. uk.in.19. exp CEBIDAE7. 4 or 5 or 620. exp MACACA8. 1 and 721. exp Papio9. exp RATS22. 18 or 19 or 20 or 2110. 9 and 223. 22 and 711. 9 and 724. 22 and 212. PRIMATES25. 3 or 8 or 10 or 11 or 23 or 2413. HaplorhiniAn upper limit on the number of papers that would be included in the survey was set at 300 – made up of approximately 50 papers for each of three species and two countries. This limit was based on pragmatic considerations that included the time taken to assess and extract information from each publication. The sample size for surveys such as this is not normally based on formal statistical considerations, as there are no primary hypotheses being tested. There was therefore no need to formally power this study.A sample of the most recently indexed abstracts was selected from the total number of potentially relevant publications identified in the database search. We chose the most recently indexed papers from all the papers identified in the search as an unbiased way of selecting the publications. When a journal is added to a database and becomes indexed, all previous issues are also indexed, enabling us to have a spread of publication years in the sample. The abstracts were appraised and publications were selected or rejected based on the exclusion criteria listed below see . The fulAll relevant English language studies published between January 1999 and March 2005, reporting original scientific research carried out in UK or USA publicly funded research establishments and whose source(s) of funding were UK- or USA- publicly funded bodies such as universities, charities or other non-industry funding, such as the NIH, USPHS, MRC, BBSRC, etc., were included. Studies that had any commercial/industry funding were included only if the majority of the other funding sources were UK or USA public funding sources and the work was carried out in a UK or USA publicly funded research establishment. Studies that had any non-UK or non-USA public funding were included only if the majority of the other funding sources were from UK or USA public funding sources and the work was carried out in a UK or USA publicly funded research establishment. Studies whose funding source was not stated were included only if the research was carried out at a UK or USA publicly funded institution. Note was made that the funding source information was not reported.We chose to limit our investigation to publicly funded research in the USA and UK because the funding for this study came from both US and UK publicly funded bodies, the two countries are highly influential in setting the scientific agenda, and because there should theoretically be no constraints on reporting publicly funded research for reasons of confidentiality or commercial sensitivity.The survey was restricted to original scientific research studies using mice, rats, and primates. The experiments had to use live animals  and state that they had used UK Animals [Scientific Procedures] Act 1986 (ASPA) licensed interventions, or equivalent USA institutional guidelines for animal care and use. Rodents are the most widely used animals and primates are the most high profile and ‘ethically sensitive’ group (for convenience primates are designated a species here). Other species or groups such as fish, birds, rabbits and guinea-pigs are either used in small numbers or in more specialised areas of research. The sample sizes for these species would have been too small to draw any strong inferences about the reporting standards in these research areas. In addition, every such study that was included would reduce the statistical power of the study for drawing inferences about reporting and experimental design standards studies involving more widely used species.In vitro studies, studies using tissue from animals killed before use, or that did not involve experimental procedures/testing, technical or methodological papers not involving actual experiments using animals, review articles, genetics papers reporting linkages of genes, studies with no abstract, and brief communications with no methods, were also excluded. No more than two papers were included from any single laboratory to ensure that the survey results were not unduly influenced by the bad – or good – practice of one particularly productive laboratory.Publications were excluded if industry/commercial funding was the sole source of funding, or if the research was solely funded by an organisation not based in the USA or UK. The unit of analysis was the ‘main experiment’ reported in the paper. Many papers report the results of more than one experiment; accordingly, the number of experiments per paper was noted. For those studies that reported more than one experiment, the experiment that used the most animals was considered the ‘main experiment’. Details and results from the main experiment were used to complete the data collection sheets. Although the specific details described in this report relate to a single experiment assessed in each publication, the whole paper was searched for information relevant to that experiment, and to the way the experimental work was conducted and analysed in general.The survey was carried out in two steps identified as phases 1 and 2.In phase 1, the full texts of the 271 included studies were divided equally between two assessors who were experienced statisticians (one from the UK and one from the USA). Assessor 1 analysed the even numbered papers, assessor 2 analysed the odd numbered papers extracting the relevant information to complete the Quality of Reporting checklist see . Any supIn phase 2, a random sub-sample of 48 papers chosen from the 271 papers evaluated in phase 1, stratified by animal and by country (i.e. 8 papers×3 species×2 countries), was assessed. This number was selected as an appropriately sized sub-sample of the papers assessed in phase 1 based, as was the case for phase 1, on the time necessary to complete the very detailed reports. The statistical methods and analysis of the papers were assessed to determine whether the experimental design and the statistical analysis were appropriate. This involved the expert judgement of two statisticians, both of whom assessed all 48 papers using the Quality of Experimental Design and Analysis checklist see . The maiAny disagreements or differences in interpretation of the checklists were resolved by consultation and discussion with a third assessor and, where necessary, the relevant studies were re-analysed. To allow for possible discrepancies between the two assessments, in phase 2 the mean of the results from the two statisticians are reported in all data summary tables. Overall agreement between the assessors was assessed once during each phase of the survey – In phase 1 both assessors applied the relevant checklist to the same sub-set of 30 (of 271) papers and their analyses compared, and in phase 2, all 48 papers were used to assess agreement see .Supporting Information S1Survey Checklists(0.17 MB PDF)Click here for additional data file.


Pseudoreplication occurs when observations are not statistically independent, but treated as if they are. This can occur when there are multiple observations on the same subjects, when samples are nested or hierarchically organised, or when measurements are correlated in time or space. Analysis of such data without taking these dependencies into account can lead to meaningless results, and examples can easily be found in the neuroscience literature.A single issue of Nature Neuroscience provided a number of examples and is used as a case study to highlight how pseudoreplication arises in neuroscientific studies, why the analyses in these papers are incorrect, and appropriate analytical methods are provided. 12% of papers had pseudoreplication and a further 36% were suspected of having pseudoreplication, but it was not possible to determine for certain because insufficient information was provided.p-values are reported. This information should be a requirement for all publications.Pseudoreplication can undermine the conclusions of a statistical analysis, and it would be easier to detect if the sample size, degrees of freedom, the test statistic, and precise  With a sample size of ten rats, there should only be eight degrees of freedom  associated with this statistical test. The concept of degrees of freedom is perhaps not the most intuitive statistical idea, but it can be thought of as the number of independent data points that can be used to estimate population parameters , and whenever something is estimated from the data, a degree of freedom is lost. Therefore the total df is equal to the sample size only if all the samples are independent: measuring the height of ten unrelated individuals provides ten independent pieces of information about the average height of the population from which they were drawn; measuring the height of one person ten times provides only one independent piece of information about the population. In the above rat example, the three observations from each rat (from the three days of testing) were treated as independent samples, and hence the 28 degrees of freedom arose from thinking that the fifteen data points in each group contain independent information about the effect of the treatment . Incidentally, the correct analysis with a t-statistic of 2.1 on 8 df has a corresponding p-value of 0.069. In addition to the incorrect degrees of freedom, there is also the problem of false precision, which is discussed at greater length below  are independent, once the effects of all the other explanatory variables have been taken into account. In addition, other variables that are not included in the analysis (e.g. the order in which the samples were obtained) must not influence the outcome or be correlated with the residuals. The remainder of the introduction will define some commonly used terms, illustrate why pseudoreplication is problematic, and finally, discuss the four situations in which it can arise.sample, replicate, observation, experimental unit, n, and experiment have overlapping meanings, are often used interchangeably, and can have different meanings based on the context. An experimental unit is defined as the smallest entity that can be randomly assigned to a different treatment condition 66].F-statistic so that the actual α-level is closer to the nominal α-level . Modern statistical methods can model different types of variance-covariance relationships directly, making ad hoc adjustments to degrees of freedom unnecessary. Kristensen and Hansen provide an excellent introduction and a comparison of different analyses on rats [One important drawback of the repeated measures ANOVA is that the assumptions of compound symmetry and sphericity are rarely met. These terms refer to the correlation structure of the data. Returning to the rat rotarod example, the correlation of the outcome variable at each combination of time points can be calculated . If these three correlations are all similar, and in addition the variances at each day are similar (homogeneity assumption), then the data are said to be compound symmetrical. If differences rather than correlations between each combination of time points are calculated, then the data are said to be spherical if these difference scores all have the same variance , Crawle on rats ,70 and F on rats , and a m on rats .Mixed models and their extensions  are the preferred methods for analysing the type of data discussed in this paper and are already being used to model litter effects  and hiert-test; Table df = 5 + 5 - 2 = 8, and not the 28 that the authors reported. Even if degrees of freedom are not reported, it is still possible to determine whether pseudoreplication was handled correctly using standard software such as OpenOffice Calc or MicroSoft Excel. Both of these programmes have a TDIST function which can calculate a p-value given a t-statistic, degrees of freedom, and whether the test is one or two-sided. If the dfs were not reported for the rotarod example, we could check the results with =TDIST, where 2.1 is the reported t-statistic , 8 is the correct number of degrees of freedom, and 2 indicates that it is a two-tailed test. The result is p = 0.07, which does not correspond to the reported value of p = 0.045. Substituting 28 df for 8 in the above command does give p = 0.045, and allows us to conclude that the reported analysis is incorrect. The same procedure can be carried out in R using the command pt*2 .When the necessary information is provided, it is easy to check whether pseudoreplication has been handled correctly, for this one needs to know the degrees of freedom associated with common statistical tests, and these are provided in Table F-tests using the =FDIST function in OpenOffice/Excel and pf in R/S-Plus. This analysis may not be of much use if the authors only report whether the p-value was greater or less than 0.05, but it does provide a method for checking partially reported results.A similar procedure can be carried out for ANOVA The problem of pseudoreplication has been recognised for many years in ecology and related areas , as welln), degrees of freedom, the test statistic, and precise p-values to be reported, allowing many of these errors to be detected at a glance. This information also allows for a more detailed analysis if required, either by editors, reviewers, or other readers.Statistical competence will not happen overnight, but stricter reporting requirements will make it easier to detect pseudoreplication, and this requires the sample size  and research background are primarily in experimental neuroscience. SEL also holds a masters in Computational Biology and currently works as a Senior Research Statistician in the pharmaceutical industry.by Christopher D. Fiorillofiorillo@kaist.ac.krEmail: Address: Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, KoreaLazic makes many useful points about the misuse of statistics. However, his article on pseudoreplication does not demonstrate a clear understanding of the goals and issues at stake in primate neurophysiology, nor does it clarify the importance of independence in statistical tests.Lazic's criticisms of my paper  generaliThe statistic comparison that the author has questioned (in figures three c and four c) was designed to test whether there is a difference in neuronal firing rates (across the recorded population of neurons) between responses to juice reward depending on when juice was delivered following a conditioned stimulus. If one knows the subject matter and reads my paper for more that just statistical methods, then one has a strong prior expectation that the statistical comparisons made in figures three c and four c will be highly significant, and thus the statistical significance is not of much interest. The interesting point of the figures, as described in the main text, is that although significant, the differences are surprisingly small relative to the very large difference seen in comparing either of these responses to unpredicted reward. The statistical significance that was quantified in these figures was thus superfluous and could have been omitted entirely. There were no statistical tests performed on the important effect because it was so large as to be obvious (see mean ± sem firing rates in figures three b and four b). To paraphrase a colleague who is an excellent scientist but a reluctant statistician, it passed the "bloody ... obvious test."Lazic also makes the point that "neurons of the same brain are not independent." If this statement does not signify confusion on the part of its author, it may nonetheless confuse readers. There is an important difference between physical or causal independence on the one hand, and logical or statistical independence on the other. Neurons in the same brain may be physically or causally related. However, that fact in itself does not necessarily require that the neurons are or are not statistically or logically independent. Statistical independence is all that matters with respect to statistical tests, and statistical dependencies could be present regardless of whether the neurons are in the same brain or different brains.A second critical error that is often made is to confuse the functioning of a physical system with our knowledge of that system. Statistics and hypotheses are derived from the latter. "Statistical independence" means that we, the people performing the statistical test, do not have knowledge of how two pieces of data (such as the firing rates of two neurons) are related. We believe that neurons within a defined population, clustered together in the same region of the brain, are likely to have direct or indirect physical interactions with one another, and likewise, to display some sort of correlations in their firing rates. But at the outset of a typical study, we do not know what these correlations are, and thus it is rational for us to treat the data from each neuron as independent. By contrast, if we already knew neurons within clusters of known dimensions to be tightly coupled to one another through gap junctions, then we would probably try to avoid recording from neurons within the same cluster, and when we did obtain data from neurons in the same cluster, we might average it together before doing an analysis of responses across clusters. The data from my neurons was "statistically independent" because, at the time that I did the statistical test, I was ignorant of any relevant relationship between the firing rates of discrete neurons. Given a different state of knowledge, statistical independence may not apply and another type of statistical test may be more appropriate.Probability Theory, the Logic of Science, in which E.T. Jaynes discusses these issues and other "pathologies of orthodox statistics other "path " [I strongly recommend  "path " . It will "path " .C.D.F. was supported by a "World Class University" Grant from the Korea Science and Engineering Foundation (R32-2008-000-10218-0).By Takahisa Furukawa, M.D and Ph.D.furukawa@obi.or.jpE-mail: Address: Department of Developmental Biology, Osaka Bioscience Institute, 6-2-4 Furuedai, Suita, Osaka, 565-0874, JapanMany life science studies employ histological analyses, frequently immunostaining of tissue sections, and incorporate this image data into papers. Although it used to be that only the most representative images were displayed in paper figures, quantitative analysis along with statistical analysis of data are often required to obtain an analytical conclusion on immunostaining image data for publication. The author, Lazic, mentioned in his paper that the reason he picked Nature Neuroscience in particular for his case study is that this journal has detailed instructions for statistical analyses. As Lazic mentioned, authors basically examine their results to test whether or not their data is statistically significant, and present their data with statistical analysis results in their papers. This means that this journal requires high quality data and expects researchers conduct their studies according to these requirements. In Lazic's paper, the author picked multiple examples, In including our statistical analysis of our electron microscopy (EM), and indicated that the statistical analysis method used in our paper was inappropriate . Lately,In order to confirm this EM observation, we also performed 3D electron tomography analysis and described the result in the paper. Furthermore, the results from other experiments including electroretinogram and optokinetic responses also support our conclusion that photoreceptor synaptic terminal formation is impaired in the pikachurin null retina. Ultimately, the Lazic paper points out some very important issues in conducting appropriate statistical analysis for biological studies. We understand that we have to be very careful in choosing a suitable statistical method for analyzing our data in the future, however, the Lazic paper does not affect the conclusions in our paper.Classification of manuscripts. Summary of the papers in the August 2008 issue of Nature Neuroscience.Click here for file


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