Guidelines in the Preparation Phase: Data Collection - EMGO

Guidelines in the Preparation Phase: Data CollectionPilot study........................................................................................................................................... 2Information Analysis ........................................................................................................................... 5Codebook ........................................................................................................................................... 7Data Entry Methods...........................................................................................................................12Recruiting and Training Data Collectors.............................................................................................14Recruiting and Training Data Entry Clerks .........................................................................................16Dealing with Study Participants .........................................................................................................17Authorship .........................................................................................................................................19Updated: 28 January 20131

Title of the document:Pilot studyPage. 2 of 21Rev. Nr.: Effective date:1.1 1 Jan 2010HB Nr. : 1.1C-01 rdv1. AimThe aim of pilot studies is to explore certain issues before undertaking a large-scale study2. Definitions3. KeywordsPilot study, feasibility,4. DescriptionIntroductionThe aim of pilot studies is to explore certain issues before undertaking a large-scale study (1). Pilotstudies are rarely just a small version of a larger study; they have other objectives than the largescalestudy.Aims of a pilot studyCertain aspects of the large-scale study can be tested out in a pilot study. This may involve testingfeasibility in practice or improving the methodological quality of parts of the study. Examples will beprovided for both of these. Occasionally people undertake pilot studies in order to obtain an estimateof the effect size, with the notion of whether it is worth the effort to conduct a large-scale study in theback of their minds. Pilot studies are not suitable for this purpose, as will be explained below in detail.Examples of questions about feasibility• What is the anticipated number of patients/participants that can be included? Patient inclusionusually appears to be much lower than the total number of eligible patients, and also much lowerthan the numbers doctors (and other recruiters) promise to deliver. For instance, in some recenttrials it appeared that approx. 60% of company doctors (2) and physiotherapists (3) who hadpromised to provide patients, had not registered a single patient, and approx. 20% of “recruiters”had only recruited a single patient.• How many of the potential patients satisfy the inclusion and exclusion criteria? You may considerto relax some of the criteria in the large-scale study.• Will the eligible participants agree to be randomised for comparison of the interventions? This isespecially important for certain populations, for instance, the elderly or (parents of) small childrenor groups where experience from other studies is lacking.• Why do some participants not want to take part or what are the reasons for drop out? You mightbe able to interview these people to understand the reasons for non-participation (these mayperhaps be reasons that could be easily overcome in the large-scale study).• Are all medical files or information from other data sources retrievable?• Have all the necessary details been registered in statuses (doctors are sometimes overly positiveabout this issue), and is the quality of the data adequate?• Can all measurements be easily implemented and are they not too bothersome for the researchparticipants?• What is the best recruitment method? For instance, is it beneficial to involve the GP, or would thishave the opposite effect?• Is the intervention appropriate for the intended target group (e.g. language use, type ofintervention)?• Is it possible to carry out the intervention in the target group with respect to acceptability andcompliance?• Is it possible to the implement the intervention and/or study in the organisation?• How much time does the measurement procedure take, and how many times can this beundertaken per day?• What is the best way to approach the target group?2

Examples of questions about methodological quality• Is it possible to blind the patients for the type of intervention?• Are the measurement instruments valid?• Are the measurements reproducible: Are your research assistants performing the measurementsin exactly the same way?• Are the questionnaires being completed properly? Is the wording of the questions clear andcomprehensible?• What is the most efficient measurement method for reproducible measurements (see for instanceObservations: Who, when and how often?)• Is exposure (for instance in a cohort study) being properly measured?Pilot studies are less suitable for estimates of effect sizeThis applies to both efficacy of an intervention, as well as to the strength of association in anobservational study. The reason for this is that pilot studies are too small to achieve a reliableestimate of the effect. In other words, the confidence interval around the observed effect size will bevery large, and all values lying within the confidence interval may be potential values of the actualeffect size. The effect size determined in a pilot study is often used to calculate, on the basis of theconfidence interval, the number of participants required in a large trial in order for the effect to bestatistically significant. This is not justified. The erroneous assumption here is that the effect sizefound represents the true effect. Beurskens et al. (1) has presented an illustrative example of howmisleading a pilot study can be. The correct interpretation is that all values in the confidence intervalcould represent the actual effect, meaning that when the confidence interval includes the neutralvalue (0 for differences and 1 for ratios), the effect could actually be both positive and negative.However, pilot studies can be used to provide an indication of the variance in measurement, that is,the standard deviation which can be used for the power calculation for the main study.Organisation: Internal or external pilot studiesExternal pilot:Pilot studies may precede larger studies and may include a clear go/no go decision. If the researchaim/question of the pilot study is formulated in concrete, measurable terms, then a clear decision canbe taken on the basis of the results. This decision may be for instance, that clear modifications instudy design are required or that the larger study is not feasible.Internal pilot:If the pilot study exactly replicates the large study, e.g. a trial, then it may turn out that nothing needsto be altered based on the pilot study Therefore, why shouldn’t patients in the pilot study be includedas the first participants in the larger study? There is nothing to stop you from doing this. However, youdo need to make sure that the decision not to change the procedures is made, because one wants toadd the patients of the pilot study to the larger study. It helps if clear agreements have been made inadvance about this.5. DetailsExampleObservations: Who, when and how often?You want to measure employees’ physical exertion using a cohort study on “musculoskeletalcomplaints”. The decision is made to create video recordings, which are reviewed at a later stage byresearch assistants scoring certain parameters. A feasibility study (4) can be carried out in order tojudge whether these recordings need to be made and/or scored for all employees (with the samerole), or whether whole working days (8 hours per day) need to be scored, and for how many days inthe week, and whether the research assistants are scoring in the same way. A study such as this isaimed at finding the most efficient and reliable way of collecting the data.All potential variations can be included in the pilot study (e.g. different research assistants, differentdurations, different days, different employees with the same role). Subsequently, the resultingvariance components can be used to determine the most efficient and preferable method to be usedin the larger study. This example can, of course, also be translated to other situations that includeobservations: E.g. observations of patients or their carers, or certain types of child behaviour.3

6. Appendices/references/linksReferences:Beurskens AJHM, de Vet HCW, IJ Kant. Dwalingen in de methodolologie (Methodological errors).VIII. Pilot onderzoeken: zin en onzin. (Pilot studies: Sense and nonsense). Ned T Geneesk 1998;142: 2142-2145.Steenstra I. Back pain management in Dutch occupational health care. Thesis VU Amsterdam 2004(in general discussion).Pool J. Neck pain, a pain in the neck? A study on therapeutic modalities and clinimetrics. Thesis VUAmsterdam 2007 (in general discussion).Streiner DL, Norman GR. Health measurements scales. Chapter 9 Generalisability theory. 3rd ed,Oxford University Press, 2003.7. AmendmentsV1.1 1 Jan 2010: Translated into English.V1.0 14 Feb 2007.4

A standard Access database is available for tracking the data flow during a project. Once modified,this database is suitable for many projects and can be used for sending questionnaires, reminders,etc. The Data Management department will configure this database for use in your project.ResultsThe most important conclusions following the information analysis are:Is an administrative system required for monitoring and managing the data flow?What is the best way to import data?Who does what? Which activities fall within the Data Management basic supportpackage, and which chargeable activities can be included in the project?The information analysis outcomes will be described in the information plan. The information planconsists on the one hand of an inventory of the data management aspects of the research, and onthe other of advice regarding the best approach. This information plan is drawn up and archived bythe D&S department. A copy of the plan will be provided to the researcher and project leader.Different studies place different demands on data usage: For large cohort studies, such as LASA,AGGO and the Hoorn study, there is often a great deal of infrastructure and additional datamanagement support already present for carrying out the research.5. DetailsAudit questions:Has an early appointment been made with the Data Management department or your own datamanager associated with your research group? If not: What are reasons for this? Has the advicedescribed in the information plan been followed?6. Appendices/references/links7. AmendmentsV 1.3: 1 Jan 2010: English translation.V1.2: 27 May 2009: Support from the Data Management department developed further and realisedand need for a paperless office emphasised.V1.1: 19 Okt 2006: Support from D&S described more extensively and updated.6

Title of the document:CodebookPage. 7 of 21Rev. Nr.: Effective date:1.2 1 Jan 2010HB Nr. : 1.1C-03 wk1. AimTo clarify what constitutes a good codebook.2. DefinitionsCodebook: Description of the data as stored on the computer.3. KeywordsCodebook, coding4. DescriptionThe codebook contains the names and descriptions of the variables, the range of correct values andincorrect values and coding for correct and incorrect missing values.A codebook is required:To create a data entry systemFor data entryFor statistical analysisWhen archiving data files once the study has been completed to, for instance, enable follow-upstudies.A good codebook consists of columns containing the item or the description of the item along with theitem number, the allocated variable name (consisting of a maximum of 8 characters), the variabletype, normally (numeric(N), alphanumeric/string (A) or a date) and the number of characters, thepotential values the variable can take and coding for the values, including coding for(correct/incorrect) missing values. A codebook can also be potentially created by typing variablenames, codes, etc. into an empty list of questions. It is also possible to derive a codebookretrospectively in SPSS from an SPSS file by selecting the Utilities option in the main menu, and thenselecting File Info.For any data entry method it is necessary for the researcher to create a codebook in advance. Thisalso applies if the data entry and/or creation of the entry screen or questionnaire scanning have beenoutsourced. When creating a codebook, a transcription takes places from the questionnaire to thevariables. The questions are coded and this includes the allocation of values for missing observations(missing values). The aim is to create a separate codebook for each questionnaire. An entry screencan be created in BLAISE, MS-Access, Netquestionaires / Elisten or Teleform using the codebook.The codebook allows the individuals entering the data to understand how encoded items should beentered. Furthermore, a codebook is a necessity for statistical analysis using, for instance, SPSS.Variables are referred to by a variable name of a maximum 8 characters in virtually all statisticalprogrammes. These variable names often obscure the actual meaning of the variable. Refer to thedetails for advice on coding variables.7

5. DetailsNaming variablesTry to provide meaningful names for the variables that say something about the nature of the item.Avoid cryptic descriptions. One suggestion is to always use the first letter(s) of the relevant form forthe start of the variable name. Use capitals for the variable names for clarity. Use specific symbolswith care. For instance, variable names cannot start with a number in SPSS. Make sure that avariable name is never longer than 8 characters, even though variable names may be longer than 8characters within the associated database programme. Avoid using spaces in variable names. Themajority of statistical packages are only able to handle variable names that are no longer than 8characters. When converting a 10-character name from Access, for instance, there may be a risk thatthe variable name is no longer unique in SPSS.For instance:Access variable namePYNSCORE10PYNSCORE20Resulting SPSS variable namePYNSCORPYNSCOREExampleVAR. NAMECINCLUDEIORIGINFREGSYMP1SOCIALQUESTION ON FORMFormulate a “card index search”, in which a specificinclusion criterion is completedFormulate “inclusion consultation”, in which ethnic origin iscompletedFormulate “follow-up consultation”, in which occurrence ofregular symptoms is documentedPatient questionnaire time point 1, in which effect of illnesson social life is exploredIf the same questions occur at different time points, this needs to be indicated clearly in the variablenames by including the relevant time point in the name. For instance: P1PAIN, P2PAIN, P3PAIN, (theparticipant is asked about pain at 3 time points). This does not, of course, apply to variables withunique values allowing files to be linked together, such as patient numbers, etc. These variablesshould always have the same name for each time point and form, for instance, PATNO.The find and replace function in Word can be used to reduce the amount of time needed to createcodebooks for different time points.Questions allowing more than one answerMultiple choice questions allowing multiple answers need to be split up in the codebook into as manyvariables as there are response categories. The values for these variables may be either 1 or 0, ifselected or not selected, respectively. Missing values are not possible for this type of question, as it isimpossible to distinguish between “response categories missed” and “response category notendorsed". If the item contains a conditional question, that is, where the response is dependent onthe answer to a previous question, then the "not applicable value" is also potentially possible.8

Two types of missing valuesA missing value resulting from an erroneously missing detail (omission or refusal to answer thequestion, etc) is usually coded with highest possible out-of-range value. For instance, for a multiplechoice question with response categories 1 to 5, this would be value 9.For a date variable it is best to use a date from the distant past as a value for the incorrect missingvalue (usually the date 14-12-1985 is used for this – the Blaise input programme does thisautomatically).If the missing value results from a correct missing detail (i.e. the question is not applicable), then thevariable is left blank. In SPSS this will be automatically coded as system missing (a dot). Thesevalues are usually filled in automatically in the data entry programme where the not applicablequestion has been omitted. Particularly in older files a value other than may be entered for acorrect missing value, for instance, 8 or 88, etc.As alphanumeric variables are not handled by statistical programmes, no distinction is made betweencorrect or erroneous missing alphanumeric values. In both instances the cell is simply left empty.Audit questions: Is there a codebook? If not, why not?If yes, would another researcher not associated with the project be able to understand it and is thecodebook up-to-date?9

QuestionVariablenameType Value CodeCompulsory1. Patient numberPATNO N 3 001-998 001-998 Yes &unique2. GP code GPCODE N 1 1-9missing3. Sec SEX N 1MaleFemaleMissing4. Date of birth DOB datum 01/01/1940-01/01/1980missing1-99912901/01/1940-01/01/198014/12/1985yesyesno5. Number of pregnancies PREGNANT N 1 0-8missingna (sex=1)6. Illness in family FAMILL N 1 yesnomissing0-89empty129noyes7. Which family illnesses- Diabetes DIABETES N 1 selectednot selected- Respiratory RESP N 1 selectednot selected10 no10 no- Cardiovascular CARDIOV N 1 selectednot selected- Other illnesses OTHILL N 1 selectednot selected7a. Which other illnesses WHICHILL A 30missingna (othill=0)8. Weight in Kg WEIGHT N 3 45-140missing9. Height in metres HEIGHT N 1.2 1,20-2,25missing10 nee10 noleegleegno45-140999 yes1,20-2,259,99 yes10. Last job JOB A 50 noAn example can be found in the quality assurance handbook with this guideline.6. Appendices/references/linksStandard codebookThe Introductory Meeting Data Management part1, which is compulsory for doctoral students,discusses codebooks in more detail.7. AmendmentsV1.2: 1 Jan 2010: English translation.V1.1: 19 October 2006: Updated and minor textual amendments.11

During the information analysis there will be an extensive evaluation to select the most efficient inputmethod and associated programme. Please contact the Data Management department for moreinformation and advice about the best choice for entering your data.5. DetailsAudit questions1. Has the most efficient method been selected, given the research population and nature of thedata?2. Has a standard EMGO programme been used for the data input? If not, why not?3. Have efficient input screens been used for the data input? That is, have the data been storedin the underlying database in line with the specifications in the code book?6. Appendices/references/linksIntroductory Meeting Data Management part 1. This document explains in detail the criteria to beemployed when deciding to use a particular system, and the criteria on which you decide to do thisyourself or to outsource the process. The principles of a good data entry screen are also explained inthis document.7. AmendmentsV2.1: 1 Jan 2010: English translation.V2.0: 27 May 2009: Updated based on current standards and processes.V1.1: 19 Oct 2006: Update.13

Title of the document:Page. 14 of 21Rev. Nr.: Effective date:Recruiting and Training Data1.3 1 Jan 2010Collectors HB Nr. : 1.1C-05 mvh1. Aim- Recruiting well-qualified data collectors- To standardise data collection as best as possible2. Definitions3. KeywordsQualification, standardisation, training, protocol, data collector, assistant, student4. DescriptionA good data collector for scientific research needs to have a number of specific qualities that can beused as selection criteria when recruiting. The qualities required may, of course, differ from study tostudy. It is good practice to create a list of qualifications in advance detailing the aspects the datacollector(s) need(s) to be able to fulfil for your study (see examples in details).The data collectors need to be given sufficient background knowledge about the study. They need toknow how and why certain details are being collected (the ultimate aim or the specific information thetest/questionnaire will provide). This knowledge can be acquired through a protocol and training(where required).The necessary background information needs to be established in a comprehensive data collectionprotocol, including a description of all the measurement/interview components. The data collectorsneed to be aware of this protocol.For many studies it is important that the data are collected in an as standardised way as possible.This means that the interviews or test schedules need to be standardised. The data collector needs toconduct the interview or present the test in exactly the same way each time (as little as possible intraobservervariation). Comprehensive instructions and interview or test training will be required for this.Where there are multiple data collectors, mutual differences will need to be measured andminimalised (as little as possible inter-observer variation). Comprehensive training will also berequired for this.To train the data collectors properly, it is important to create a training protocol before the start of thestudy. This protocol will include: the instructions to be provided, who is conducting the training andwhether the training will be repeated during the data collection. It is also important to indicate at whatstage the data collectors will have received sufficient training. Fixed guidelines can be drawn up forthis by setting a specific threshold (for instance Cohen’s Kappa > 0.6 for inter-observer variation), orthe requirement that the data collector meets the same level of measurements/observations as a goldstandard or highly experienced data collector. If there are multiple data collectors active within thestudy, the inter-observer variation needs to be measured and minimalised through training.New data collectors, starting during the data collection process, should be trained by the sameindividuals who provided the original training, and not by other data collectors.N.b.: In some cases it may be necessary for measurements to be generalisable to daily practice (e.g.effects of usual care). In this event comprehensive training will be undesirable.14

5. DetailsA number of examples of qualification requirements for the data collectors are summarised below.These can be used when recruiting staff:• Trainable for conducting interviews/tests;• Experience or affinity with participant;• Knowledge of, or experience with the research topic;• Experience working with GP’s, doctors, specialists, etc.;• Experience using data files;• Experience using specific computer programmes;• Able to undertake simple administrative tasks;• Able to work with high levels of accuracy;• Able to work independently;• Have good communication skills;• Presentable;• Flexible;• Available for the whole study period;• Own car.Experience in data collection might sometimes be helpful, and sometimes be unhelpful. Frommethodological literature about surveys it is known that data collectors’ experience is negativelyassociated with data quality. An explanation for this is the fact that people develop their own style inorder to obtain an answer quickly and efficiently (they may prompt a response, or make suggestions,or just jot something down as they know that item non-response is not preferred, etc., etc.). They alsoappear to be less trainable for new studies. In large American surveys interviewers are sometimesreplaced after 50 interviews in order to randomise background noise.Some of practical tips about recruiting research assistants can be found here.5. Audit questions1) Has a qualification list been created for recruiting the data collectors?Have the data collectors been informed of the aim of the study and the tests/questionnaires?2) Have all the measurement/interview components been established in a protocol?3) Has a training protocol been established for the study?4) Have the data collectors been trained? If so, how and by whom?5) Are multiple data collectors involved in the study? If so, which measures have been taken tomeasure and reduce mutual variation?6) Have new data collectors been recruited during the data collection process? If so, who trainedthese data collectors? Which measures have been taken to measure and reduce mutual variationbetween the new data collectors?6. Appendices/references/links7. AmendmentsV1.3 1 Jan 2010: Translation into English.V1.2: July 8, 2008: Minor textual modifications.V1.1: October 19, 2006: Reference to the quality handbook.15

Title of the document:Recruiting and Training Data EntryPage. 16 of 21Rev. Nr.: Effective date:1.1 1 Jan 2010Clerks HB Nr. : 1.1C-06 mvh1. Aim Promoting accurate data entry2. Definitions3. Keywords4. DescriptionDespite the fact that the fields to be entered can be defined within modern data entry programmes, itis important that the data are entered in as standardised a way as possible. Conscious data entry canrapidly switch over onto automatic pilot. Therefore it is important that the data entry clerk(s)continue(s) to work in the same way. To motivate the clerks and let them understand the importanceof accurate data entry, it is necessary to provide them with extensive background information aboutthe data entry screens and the questionnaires to be entered.If there are multiple data entry clerks, the mutual differences need to be minimalised. This can beachieved through training and mutual co-ordination between tasks.Repeated measurements over time should be processed by the same person as much as possible.When new data entry clerks are recruited during the data collection process, it is preferable for theseindividuals to be trained by the same person who provided the original training. New data entry clerkscan also be trained by a good data entry clerk, provided that the original trainer is present duringthese sessions. This to allow the trainer to monitor whether the input process still corresponds withthe original, approved practice.5. DetailsA good data entry clerk for scientific research needs to have a number of specific qualities. These canbe used as selection criteria when recruiting. The summary below can be viewed as a starting point:• Experience using data files;• Experience using specific computer programmes;• Experience of data collection in scientific research;• Able to undertake simple administrative tasks;• Able to work accurately;• Asks for help on time when things aren‘t clear or when problems occur;• Works independently;• Has knowledge of, or experience with the research topic;Audit questions• Have all data entry clerks received extensive information? (If so, how and by whom? If not, whynot?)• Are multiple data entry clerks involved in the study? (If so, which measures have been taken toreduce mutual variation?)• Are there repeated measurements? (If so, will these measurements be processed by the samedata entry clerk?)• Have new data entry clerks been recruited during the project? (If so, who trained these clerks?)6. Appendices/references/links7. AmendmentsV1.1: 1 Jan 2010: English translation.V1.0: 21 April 2004.16

Title of the document:Dealing with Study ParticipantsPage. 17 of 21Rev. Nr.: Effective date:1.1 1 Jan 2010HB Nr. : 1.1C-07 mp1. AimEnsuring study participants are treated properly2. Definitions3. KeywordsTrial participants, patients, behaviour, treating, code of conduct, physical examination4. DescriptionGeneralRespect for individuals taking part in studies is not just limited to observing regulations or procedures.Researchers and research assistants should be open towards, and feel responsible for the needs ofstudy participants. The researcher should approach participants with general courtesy and respect.Try to put yourself in the participant’s shoes and try to think what the participant would findunpleasant. Certain measurements can be uncomfortable or burdensome, such as physical orcognitive tests, or an interview about a sensitive topic.The researcher needs to ensure there is a relaxed atmosphere, in which the participants are alwaysaware they can stop participating should they wish to do so.InformationThe researcher should provide concrete information prior to/during recruitment, including what exactlyis expected from participants, what they have to do, whether they can leave immediately after a testor whether they need to recover with a cup of coffee or tea, etc. These issues should be addressed inan information brochure or participants' letter. This information should also be provided just prior tothe measurements.The information provided may include timelines for responses, for instance that you will contact themafter x weeks, or send a newsletter or (interim) report. The participant should be contacted if thesetimelines aren’t going to be achieved.Physical examinationThe level of undressing needs to be explicitly described in the information brochure. The researcherneeds to ensure that the study participant’s privacy is optimally safeguarded, by ensuring, amongstother things, adequate study rooms with screens (if required). It is preferable for the physicalexamination to be carried out by staff that is of the same sex as the research participant. If this cannotbe guaranteed in advance, it will need to be clearly stated.Researchers may only conduct physical examinations if they are sufficiently skilled and authorised todo so.You also need to carefully consider the order in which the physical measurements are carried out. Donot leave people standing in their underwear for long periods of time; take the measurements andallow them to get dressed again.If a participant indicates (verbally or non-verbally) that he/she havs a problem with a specific part ofthe study, a researcher should respect this, and look for a solution along with the participant.Cognitive testsCognitive tests can be taxing/tiring, particularly for old(er) participants. Often there are both short andfull versions of tests. Present participants with the short version first; if they score well on this, thencontinuing with a more burdensome test will probably not be necessary. A pilot study can be veryuseful to test what the best approach to this is.17

In-depth interviewsIt is not always possible to properly estimate in advance how taxing an interview will be for aparticipant. An interview may, for instance, be taxing for the elderly or for sick participants, or if theinterview covers a sensitive topic, such as euthanasia or screening for genetic disorders. Indicatebefore the interview starts that the participant does not have to answer the question if he/she doesn’twant to, and also indicate clearly in advance precisely what will happen to the interview and howconfidentiality will be maintained. If the interview appears to be too intensive for someone, you cansuggest that the interview is broken up across multiple time points.ComplaintsIf a study participant has a complaint about the study, the researcher should pass on the complaintimmediately to the project leader. The project leader should inform the relevant professor and worktogether with the researcher towards solving the complaint. If a solution cannot be found through thisprocess then the Complaints Committee of the VUmc should be contacted. The Medical Affairs Office(bureau medische zaken) should be contacted for any insurance claims. Also look at the Law andRegulations guideline (1.1A-01).5. Details6. Appendices/references/links7. AmendmentsV1.1: 1 Jan 2010: English translation.V1.0: 19 Oct 2006.18

Title of the document:AuthorshipPage. 19 of 21Rev. Nr.: Effective date:1.0 28-1-2013HB Nr. : 1.1C-08 mp1. Aim Stimulate good authorship practice2. Definitions. See page 23. Keywords4. DescriptionIn order to avoid discussions and problems with regard to authorship issues, it is wise to starta discussion about authorship when planning the research and to continue to discuss thisissue as the project evolves and to keep a written record on your decisions with regard toauthorship. Preventing a problem is much better than solving it.Although in theory authorship seems rather straightforward, in practice it might often createtensions and discussions. Some common problems relate to naming people in the byline thatcontributed only little or not to the research (i.e. gift authorship) or removing individuals thatcontributed to the research (i.e. ghost authorship). Discussions might arise about whethersomeone’s contribution was substantial or not. Consultation of authorship guidelines andevidence (such as laboratory logbooks or emails) as well as discussion with junior and seniorcolleagues might help elucidate the discussion. Unethical authorship, however, constitutesscientific misconduct. (See Scientific misconduct and Calamities).The International Committee of Medical Journal Editors has recommended the followingcriteria for authorship:• “Authorship credit should be based on 1) substantial contributions to conception anddesign, acquisition of data, or analysis and interpretation of data; 2) drafting the article orrevising it critically for important intellectual content; and 3) final approval of the version tobe published. Authors should meet conditions 1, 2, and 3.• When a large, multicenter group has conducted the work, the group should identify theindividuals who accept direct responsibility for the manuscript (3). These individuals shouldfully meet the criteria for authorship/contributorship defined above, and editors will askthese individuals to complete journal-specific author and conflict-of-interest disclosureforms. When submitting a manuscript authored by a group, the corresponding authorshould clearly indicate the preferred citation and identify all individual authors as well asthe group name. Journals generally list other members of the group in theAcknowledgments. The NLM indexes the group name and the names of individuals thegroup has identified as being directly responsible for the manuscript; it also lists the namesof collaborators if they are listed in Acknowledgments.• Acquisition of funding, collection of data, or general supervision of the research groupalone does not constitute authorship.• All persons designated as authors should qualify for authorship, and all those who qualifyshould be listed.• Each author should have participated sufficiently in the work to take public responsibilityfor appropriate portions of the content.• All contributors who do not meet the criteria for authorship should be listed in anacknowledgments section. Examples of those who might be acknowledged include aperson who provided purely technical help, writing assistance, or a departmentchairperson who provided only general support. Editors should ask corresponding authorsto declare whether they had assistance with study design, data collection, data analysis, ormanuscript preparation. If such assistance was available, the authors should disclose theidentity of the individuals who provided this assistance and the entity that supported it inthe published article. Financial and material support should also be acknowledged.”19

The consent of each author with the final version of the article is an other important issue. Inthe light of the fraud cases in 2012 it is clear that all co-authors can be hold responsible for thearticle Each author must have seen and agreed with the final version before submitting. Sodon’t forget to add this step in your planning.5. DetailsPublications are first of all a way to communicate communicating scientific information tocolleagues. However, they have also become a proof of academic competence. As such theyhave become a measurement in academic performance and way to compare researchers andresearch institutes. Consequently, they are used as criteria in evaluation for academic promotion,project proposals and internal and external allocation of funds. ‘Publish or perish’ has become amantra in the academic world.Publication patters have changes over time. While single-author articles were the norm a fewdecades ago, the average number of authors on scientific manuscripts has drastically increasedin all fields of science. (1;2) This can be partially explained by the fact that modern scientificresearch has become increasingly complex and expensive, and requires a multidisciplinary andmulti-centred research team. As the number of persons involved in a research project increases, itis normal that the number of persons wanting acknowledgement and credit for their work inpublications grows.The dark side of this author proliferation is that numerous articles name authors who do not meetappropriate authorship criteria. (3;4) Various extreme examples also raised discussions about thecriteria for authorship. For example, an article listed 972 authors in a two pages appendix. (5) Astudy was able to identify 20 researchers worldwide who have published an article every 11,3days during a period of 10 years. (6) A case was also describe where researchers initiated ahunger strike against their laboratory director because he wanted to be a coauthor on all thepublications that were issues in his department. (7) The Committee on Publication Ethics(http://publicationethics.org/category/keywords/authorship) describes various cases in whichauthorship is being discussed.These key concepts are copied from:Albert T, Wager E. How to handle authorship disputes: a guide for new researchers.http://publicationethics.org/files/2003pdf12.pdf Accessed 8 March 2012Acknowledgements: Most journals permit (or even encourage) acknowledgement of contributions to aresearch project that do not merit authorship.The ICMJE guidelines state: ‘All others who contributed to thework who are not authors should be named in the Acknowledgments, and what they did should bedescribed’. All those who are listed in this way should be aware of it. Some journals (mainly in the US) willrequire signatures of those acknowledged.Appeals: You may ask a journal to withdraw your name from a paper if it has been included against yourwishes. However most editors are reluctant to get involved in disputes about omitted authors since they donot have enough information to judge such cases. Some journals have an ombudsman, but they deal withcases of alleged misconduct by the journal. Similarly, COPE only hears cases submitted by journal editorsand is not an appeal body for cases of disputed authorship.Contributorship: The ICMJE guidelines now recommend that authors should state their contribution to theproject: ‘authors should provide a description of what each contributed, and editors should publish thatinformation’. Some journals publish this information but in most cases it is for the benefit of the editor, whowants reassurance that the criteria have been fulfilled. (See Instructions to Authors.)Corresponding author: The person who receives the reviewers’ comments, the proofs, etc. and whosecontact details are printed on the article so that readers can request reprints or contact the research group.Journal editors view this as a purely administrative role, but some authors equate it with seniority. Take theviews of your co-authors at an early stage, and decide in advance who will be the corresponding author.Ideally, choose somebody whose contact details are not likely to change in the near future.First and last authors: Generally speaking, the most sought-after position is the first, which is notsurprising given the convention of referring to studies by the first-named author, e.g.‘Smith et al. haveshown that’.The first named author is therefore generally held to have made the greatest contribution to theresearch. Sometimes significance is attached to being the last named author. However, views about this doseem to vary, so don’t assume that everybody feels the same way about it. Authors have often given thelast place to a senior team member who contributed expertise and guidance. This can be consistent withthe ICMJE criteria if this person was involved in study design, the interpretation of the data, and critically20

eviewed the publication. However, cynics may suspect that the final author is often a guest or honoraryauthor. (See Order of authors.)Ghost authors: This phrase is used in two ways. It usually refers to professional writers (often paid bycommercial sponsors) whose role is not acknowledged. Although such writers rarely meet ICMJE criteria,since they are not involved in the design of studies, or the collection or interpretation of data, it is importantto acknowledge their contribution, since their involvement may represent a potential conflict of interest. Theterm can also be used to describe people who made a significant contribution to a research project (andfulfilthe ICMJE criteria) but are not listed as authors. The ICMJE guidelines clearly condemn this practiceand state that ‘All persons designated as authors should qualify for authorship, and all those who qualifyshould be listed.’Gift authors: People who are listed as authors but who did not make a significant contribution to theresearch and therefore do not fulfil the ICMJE criteria. These are often senior figures (e.g. heads ofdepartment) whose names are added to curry favour (or because it is expected). Another type of gift authoris a colleague whose name is added on the understanding that s/he will do the same for you, regardless ofyour contribution to his/her research, but simply to swell your publication lists.Group authorship: Some journals permit the use of group names (e.g. The XYZ Study Group) but manyrequire contributors to be listed (often alphabetically) and/or the writing group to be named as well. Oneproblem with group names is that they are often miscoded on databases such as Medline. The first personin an alphabetical list of contributors sometimes becomes the first author by default, which rather defeatsthe object.Guarantor: Should we expect a radiographer to explain the statistical methods or the statistician to interpretthe x-rays? To take increasing specialisation into account, the latest version of the ICMJE guidelinesacknowledges that it may be unreasonable to ask individuals to take responsibility for every aspect of theresearch. However, the editors felt that it was important that one person should guarantee the integrity ofthe entire project. ‘All persons designated as authors should qualify for authorship, and all those who qualifyshould be listed. Each author should have participated sufficiently in the work to take public responsibilityfor appropriate portions of the content. One or more authors should take responsibility for the integrity of thework as a whole, from inception to published article.’Instructions to authors: While there is a great deal of agreement among journal editors on authorshipmatters, there are also some differences in detailed requirements and the ways in which by-lines arepresented.You should carefully read the Instructions to Authors for your target journal.Number of authors: There are no rules about this. In the past, databases such as Medline limited thenumber of authors they listed. This was shown to influence the number of authors (most groups tried to staybelow the limit) and, in larger groups, probably increased jostling for position. Now, however, mostdatabases list all authors. Rather than decide how many authors there should be, it is probably best toagree who will qualify as an author, and then simply include all those who do. However, remember thatincluding large numbers of authors usually increases the time it takes to prepare, review and finalise apaper.Order of authors: The ICMJE guidelines state that the order of authorship, should be ‘a joint decision ofthe coauthors. Authors should be prepared to explain the order in which authors are listed’. They ratherunhelpfully do not give guidance about the order in which authors are listed. Wherever possible, makethese decisions before starting to write up the project. Some groups list authors alphabetically, sometimeswith a note to explain that all authors made equal contributions to the study and the publication. If you doso, make sure it is clear to the editor.6. Appendices/references/links(1) Rennie D, Yank V, Emanuel L. When authorship fails. A proposal to make contributorsaccountable. JAMA 1997 Aug;20;278(7):579-85.(2) Regalado A. Multiauthor papers on the rise. Science 1995 Apr 7;268(5207):25.(3) Weijer C, Akabayashi A. Unethical author attribution. Camb Q Healthc Ethics 2003;12(1):124-30.(4) Flanagin A, Carey LA, Fontanarosa PB, Phillips SG, Pace BP, Lundberg GD, et al. Prevalence ofarticles with honorary authors and ghost authors in peer-reviewed medical journals. JAMA 1998 Jul15;280(3):222-4.(5) The GUSTO Investigators. An international randomized trial comparing four thrombolytic strategies foracute myocardial Infarction. N Engl J Med 1993 Sep 2;329(10):673-82.(6) Anderson C. Authorship. Writer's cramp. Nature 1992 Jan 9;355(6356):101.(7) Anderson C. Soviet Science - Hunger Strike at Volcanology Institute. Nature 1991 Nov ;354(6348):3.International Committee of Medical Journal Editors. http://www.icmje.orgCommittee on Publication Ethics www.publicationethics.orgAlbert T, Wager E. How to handle authorship disputes: a guide for new researchers.http://publicationethics.org/files/2003pdf12.pdf Accessed 8 March 20127. Amendments V 1.0 28-1-2013 First publication of this guideline.21