24: Academic Emergency Medicine

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Section 24 Academic Emergency Medicine

Edited by George Jelinek

24.1 Research methodology 724

24.2 Writing for publication 733

24.3 Principles of medical education 735

24.4 Undergraduate teaching in emergency medicine 741

24.5 Postgraduate emergency medicine teaching and simulation 746

24.1 Research methodology

David McD. Taylor

ESSENTIALS

1 Research projects should be designed and undertaken in a structured, predetermined fashion.

2 During the study design phase, assistance from a statistician is highly recommended.

3 The study protocol should be written well in advance of data collection and adhered to throughout the project.

4 Most research mistakes relate to inadequate sample size calculations and selection bias in subject recruitment.

5 Ethical issues related to research are becoming more important, especially since the introduction of new privacy legislation. Ethics committee authorization should be sought prior to study commencement.

Introduction

The basic strategy of clinical research is to compare groups of people. These might be different groups or the same group pre- and post-intervention. The methods used are mainly non-experimental, that is observational. They are based on what we can observe and compare in groups of people within populations. By comparing the characteristics (such as behaviours and exposures) and the health experiences of these groups of people, it is possible to identify associations that might be responsible for the cause of a disease.

Initiating the research project

The research question

The research question forms the basis of every research study and is the reason that it is undertaken. It is the scientific, clinical, practical or hypothetical question that, when answered, will allow the researcher to apply newly found knowledge for some useful purpose. The researcher should always aim to undertake studies that will impact upon clinical practice.

The research question may be generated from many sources, including questions raised by clinical observations, the published medical literature, scientific conferences, seminars and discussions, or the effectiveness of currently used or new treatment.

For example:

• Is drug A better than drug B?

The study hypothesis

A hypothesis is a bold statement of what we think the answer to the research question is. Essentially, it is our best guess of what the underlying reality is. As such, it has a pivotal role in any study. The purpose of a research study is to weigh the evidence for and against the study hypothesis. Accordingly, the hypothesis is directly related to the research question.

For example:

• directional hypothesis: drug A is better than drug B

• null hypothesis: drug A is as good as drug B.

In expressing a hypothesis, the researcher needs to be very specific about who or what is to be observed and under what conditions. A failure to define clearly the study groups and the study endpoints often leads to sloppy research.

The study aims

The aims of a study are a description of what the researcher hopes to do in order to weigh up the evidence regarding the study hypothesis.

For example:

• We aim to determine which is the better drug, drug A or drug B.

Just as the research question begs the hypothesis, the hypothesis begs the study aims. The examples above demonstrate clearly the natural progression from research question through to the study aims. This is a simple, yet important, process and time spent defining these components will greatly assist in clarifying the study’s objectives. These concepts are discussed more fully elsewhere.^1,²

Assembling the research team

All but the smallest of research projects are undertaken as collaborative efforts with the co-investigators each contributing in their area of expertise. Co-investigators should meet the criteria for co-authorship of the publication reporting the study’s findings.³

Usually, the person who has developed and wishes to answer the research question takes the role of principal investigator (team leader) for the project. Among the first tasks is to assemble the research team. Ideally, the principal investigator determines the areas of expertise required for successful completion of the project (e.g. biostatistics) and invites appropriately skilled personnel to join the team.¹ It is advisable to keep the numbers within the team to a minimum. In most cases, three or four people are adequate to provide a range of expertise without the team becoming cumbersome. It is recommended that nursing staff be invited to join the team, if this is appropriate. This may foster research interest among these staff, improve departmental morale and may greatly assist data collection and patient enrolment.

All co-investigators are expected to contribute time and effort to the project, although the extent of this contribution will vary. The temptation to include very senior staff or department heads simply to bolster the profile of the project should be avoided if possible. It is recommended that personality and track record for ‘pulling one’s weight’ be considered when assembling the team. There is little more frustrating than having poor contributors impede the progress of a study. Assigning specific responsibilities, in writing, to each member of the team is a useful tactic in preventing this potential problem. However, care should be taken to ensure that the timelines for assignment completion are reasonable.

The importance of good communication within the research team cannot be overemphasized. This is usually the responsibility of the principal investigator and may involve regular meetings or reports. At the risk of flooding each co-investigator with excessive or trivial communications (e.g. e-mail), selected important communications should be forwarded as they appear, for instance notification of ethics committee approval and updates on enrolment.

Development of the study protocol

The protocol is the blue print or recipe of a research study. It is a document drawn up prior to commencement of data collection that is a complete description of study to be undertaken.⁴ Every member of the study team should be in possession of an up-to-date copy. Furthermore, an outside researcher should be able to pick up the protocol and successfully undertake the study without additional instruction.

Purpose of the study protocol

Research protocols are required:

• for the ethics committee application

• for applications for research funding

• to facilitate the smooth and efficient running of the study through the provision of well-researched and documented information

• for the basis of the Introduction and Methods sections of the final research report.

Protocol structure

The protocol should be structured largely in the style of a journal article’s Introduction and Methods sections.⁴ Hence, the general structure is as follows:

Introduction

• Background, including a brief summary of the literature

• Research question

• Hypothesis

• Aims

• Need for the proposed research, that is the purpose of the study.

Methods

• Study design – a simple description of the design of the proposed study, e.g. randomized clinical trial, cohort study, cross-sectional survey

• Study setting and period – a description of where and when the study will take place

• Study subjects – inclusion and exclusion criteria and a description of how participants are to be recruited

• Procedures and interventions – the nature of any interventions to be used, including information on safety, necessary precautions and rationale for the choice of dose(s)

• Study endpoints (outcome variables) – variables that are impacted upon by the factors under investigation e.g. those that are affected by a study intervention

• Data-collection instruments, e.g. questionnaires, proformas, equipment

• Data-collection procedures – including quality-control procedures to ensure integrity of data

• Data management – including a description of how data will be handled, how privacy concerns will be addressed and how storage and back-up of data will be undertaken

• Bias and confounding control – sources of bias and variability, and measures to be taken to address them

• Ethical issues – subject confidentiality, safety, security and access to data

• Statistical analysis

(a) sample size: a description of calculations used to determine sample size and assumptions included in this process should be included. This should include calculations, where appropriate, to ensure that it is clear that the study can recruit a sufficient number of patients to achieve a significant result

(b) data analysis: this should include a description of the primary variables to be analysed, a specification of any a priori subgroup analyses and the statistical methods to be used. Few researchers are adequately trained in research statistical methods to undertake their own data analysis. It is highly recommended that a statistician be consulted during protocol development and data analysis.

This general plan should be followed in the preparation of any study protocol. However, the final protocol will vary from study to study.

Study design

Study design, in its broadest sense, is the method used to obtain data to prove or disprove the study hypothesis. Many factors influence the decision to use a particular study design and each design has important advantages and disadvantages. For a more extensive discussion on study design the reader is referred elsewhere.^1,⁵

Observational studies

In general, research studies examine the relationship between an exposure or risk factor (e.g. smoking, obesity, vaccination) and an outcome of interest (e.g. lung cancer, cardiac disease, protection from infection).

In observational (non-experimental) studies, the principal challenge is to find a naturally occurring experiment, i.e. a comparison of two or more populations that enables the investigator to address a hypothesis about the outcome of interest.

Experimental studies

In an experimental study, the researcher is more than a mere observer, and actively manipulates the exposure of study subjects to an exposure of interest (risk) and measures the effects (outcomes) of this manipulation.

The preferred form of experimental study is currently the randomized, controlled trial, in which the intervention is randomly assigned at the level of the individual study subject. Although this is the most scientifically rigorous design, other study designs must often be used for a number of reasons including:

• the state of knowledge about a disease process

• real-world opportunities

• logistics and costs

• ethical considerations.

For ethical reasons, we cannot easily use experimental studies to study factors that are thought to increase the risk of disease in humans. For example, you could not do a study where you ask half of the group to smoke for 10 years and half of the group to remain non-smokers.

Types of clinical trials

• Parallel group trials – these are the most common type of clinical trial and involve two or more groups of patients treated separately, but concurrently.

• Two-period crossover trial – patients are treated for two periods using a different treatment in each period. Patients are randomly allocated to the two possible orders of treatment so that half the patients receive the treatments in the sequence AB and the other half in the sequence BA.

• Other types – factorial trials, N-of-one trials and sequential trials– are used much less frequently.

Key features of clinical trials

Randomization

Randomization is a process by which patients are allocated to one of two or more study groups, purely by chance. The overwhelming advantage of randomization is that it prevents any manipulation by the investigators or treating doctors in the creation of the treatment groups. This prevents a situation whereby a doctor can, for example, allocate the sicker (or not so sick) patients to a new treatment. Randomization also has the benefit of producing study groups comparable to one another with respect to known, as well as unknown, risk factors and guarantees that statistical tests will have valid significance levels. The most convenient methods of randomizing patients are random number tables in statistical textbooks or computerized random-number-generating programs.

A fundamental aspect of randomization is that it must only take place after the commitment to participate has been made (enrolment has taken place). Another important principle is that randomized patients are irrevocably committed to follow-up and must not be excluded from, or lost to, follow-up, regardless of their subsequent compliance or progress (‘intention to treat analysis’).

Blinding

Blinding is the most effective method of minimizing systematic error (bias) in clinical trials. In single-blinded studies, patients participating in the trial are unaware which treatment they are receiving but the investigators do know. In double-blind studies, neither the subjects nor the investigators know which patient is receiving which treatment. This type of study is usually only feasible with drug studies where it is possible to provide identically appearing medication. This is often achieved using the double dummy approach in which patients receive two medications, one active and the other placebo. The alternative treatment involves a swap-over of the active and placebo medications. Even in apparently blinded studies, there may be various indicators that allow the patient or investigator to determine which treatment they are receiving. In this circumstance, additional methods of bias control may be needed.

Concepts of methodology

Validity and repeatability of the study methods

It is essential that the study uses valid and repeatable methods, that is measurements that measure what they purport to measure. Ideally, the validity of each of the measurements used in any study should be tested, during the design stage of the study, against another method of measuring the same thing that is known to be valid.

Two types of validity are described:

• Internal validity means that, within the confines of the study, the results appear to be accurate, the methods and analysis used bear scrutiny, and the interpretation of the investigators appears supported.

• External validity is the extent to which the results of a study can be generalized to other samples or situations.

Again, for all types of study, it is important that repeatable methods are used, for example measurements that are closely similar when repeated under the same circumstances. Thus, if someone is asked the same question twice about a characteristic that has not changed in the meantime (such as their height), it would be said to be repeatable if they always (or almost always) answered in the same way. Repeatability of the question should be tested during the design phase, though it is also useful to monitor it during the main study. A good example would be a haemoglobinometer that consistently measured the haemoglobin level 2 g/dL too low. Although the haemoglobin measurements would be repeatable, they would be wrong (invalid).

Response rate

Non-response is a problem for many types of observational study. Almost invariably, people who participate in a study (responders) have different characteristics from those who do not (non-responders). This can introduce substantial selection bias into the prevalence estimates of a cross-sectional study. In order to minimize this bias, as large a sample as possible is required. To this end, investigators undertaking cross-sectional surveys aim for at least 70% of invited participants to actually respond. Unfortunately, a target response rate of 70% is often not met and low response rates are likely to impact significantly upon bias and validity of the study.

Study variables

A variable is a property or parameter that may vary from patient to patient. The framework for the study hypothesis is the independent variable. This variable is often the factor that is thought to affect the measurable endpoints, or dependent variables, in the study. For example, cigarette smoking causes lung cancer. In this example, cigarette smoking is the independent variable and lung cancer is the dependent variable, as its incidence and nature depends upon cigarette smoking.

Study endpoints

Study endpoints are variables that are impacted upon by the factors under investigation. It is the extent to which the endpoints are affected, as measured statistically, that will allow us to accept or reject the hypothesis. For example, a researcher wishes to examine the effects of a new anti-hypertensive drug. It is known that this drug has minor side effects of impotence and nightmares. A study of this new drug would have a primary endpoint of blood pressure drop and secondary endpoints of the incidence of the known side effects.

A good hypothesis predicts what we would find if we were to measure certain endpoints. The testing of the hypothesis involves seeing whether or not the endpoint measurements confirm these predictions. Accordingly, the careful selection of appropriate endpoints is of vital importance, as is the accurate measurements of those endpoints.

Essentially, all forms of investigation involve counting or measuring to quantify the study endpoints. In doing so, there is always the opportunity for error, either in the measurement itself or in the observer who makes the measurement. Such errors (measurement bias) can invalidate the study findings and render the conclusions worthless.

Sampling study subjects

There are several important principles in sampling study subjects:

• The sample must be representative of the study population. If the study population comprises all people living in a certain area, the study sample should include a representative sample of all members of the population. Certain groups are frequently left out (for instance the homeless, squatters, people in institutions or people with no telephone). Such groups must be thought of in advance, and steps taken to ensure their inclusion. Otherwise, selection bias may be introduced.

• The sample must be derived from the population randomly. The way in which the sample is drawn from the study population is critical to how well the sample represents that population. This determines how ‘generalizable’ the results will be. Although there are many alternative ways to maximize sample representativeness, as a general rule, a random sample is preferred. A random sample is one in which each member of the population has an equal likelihood or probability of being selected.

• Loss to follow-up. The researcher must avoid loss of members from the sample once it has been taken, for two reasons. First, loss of subjects will effectively decrease the study sample size and may impact adversely on the power of the study to generate statistically significant results. Second, if subjects lost to follow-up differ in important ways from subjects who remain in the study, then the study results may be affected by selection bias.

Sampling frame

This is a list of all members (for instance persons, households, businesses) of the target population that can be used as a basis for selecting a sample. For example, a sampling frame might be the electoral roll, the membership list of a club, or a register of schools. It is important to ensure that the sampling frame is complete, that all known deficiencies are identified and that flaws have been considered (omissions, duplications, incorrect entries).

Sampling methodology

Probability sampling

When every member of the population has some known probability of inclusion in the sample, we have probability sampling. There are several varieties:

Simple random sampling: in this type of sampling, every element has an equal chance of being selected and every possible sample has an equal chance of being selected. This technique is simple and easy to apply when small numbers are involved, but requires a complete list of members of the target population and it is very cumbersome to use with large populations.

Systematic sampling: this employs a fixed interval to select members from a sampling frame. For example, every twentieth member can be chosen from the sampling frame. It is often used as an alternative to simple random sampling as it is easier to apply and less likely to make mistakes. Furthermore, the cost is less, its process can be easily checked and it can increase the accuracy and decrease the standard errors of the estimate.

Stratified sampling

A stratified sample is obtained by separating population elements into non-overlapping groups (strata) and by selecting a single random (or systematic) sample from each stratum. This may be done to:

• gain precision – this is possible by dividing a heterogeneous population into strata in such a way that each stratum is internally homogeneous

• accommodate administrative convenience – field work is organized by strata, which usually results in cost savings

• obtain separate estimates for each stratum

• accommodate different sampling plans in different strata, e.g. over-sampling.

However, the strata should be designed so that they collectively include all members of the target population, each member must appear in only one stratum and the definitions or boundaries of the strata should be precise and unambiguous.

Non-probability sampling

Convenience sampling is an example of non-probability sampling. This technique is used when patients are sampled during periods convenient for the investigators. For example, patients presenting to an emergency department after midnight are much less likely to be sampled if research staff are not present. This technique is less preferred than probability sampling, as there is less confidence that a non-probability sample will be representative of the population of interest or can be generalized to it. However, it does have its uses, such as in in-depth interviews for groups difficult to find, and for pilot studies.

Data-collection instruments

Surveys

Surveys are one of the most commonly used means of obtaining research data. While seemingly simple in concept, the execution of a well-designed, questionnaire-based survey can be difficult.

Designing a survey

From a practical point of view, the following points are suggested:

Before a survey

• Define the research question(s) to be answered.

• Determine the sampling strategy.

• Design, test and revise the questionnaire (validation).

• Train the data collectors.

• Determine the technique for cross-validation.

• Define the methods of data analysis.

During the survey

• Verify and cross-validate the questionnaire.

• Check timetables and budget.

After the survey

• Cross-check all the data again.

• Perform the main data analysis.

• Perform any other exploratory data analysis.

• Write the report.

If possible, incorporate commonly asked questions into your questionnaire. One good source of such questions is standard surveys (such as Australian Bureau of Statistics). There are many other sources of pre-validated questions (for instance measures on quality of life, functional ability and disease-specific symptoms). The scientific literature, accessible through MEDLINE and other databases is a good start. This is particularly important if you want to compare the sample with other surveys or, in general, if you want to be able to compare the sample’s responses to previously completed work.

Also, previously used questionnaires for similar topics are very helpful and often can be used directly. The advantage to doing this is that these questionnaires’ reliability and validity are established.

The wording of a question can affect its interpretation. Attitude questions with slightly different wordings can elicit differing responses, so several questions on the same topic may be helpful to be certain that the ‘true attitude’ of the respondent is obtained. This technique can enhance internal validity and consistency.

Pre-testing of a questionnaire is most important. Consider the following points:

• Assess face validity of all questions.

• Is the wording clear?

• Do different people have similar interpretations of questions?

• Do closed questions have appropriate possible answers?

• Does the questionnaire give a positive impression?

• Is there any bias in the questions?

It is always worth checking with your colleagues to determine whether the questionnaire will answer the study question. Also, test the questionnaire on a cross-section of potential respondents of differing reading levels and background. There can be a few surprises, and several revisions may be required before the final questionnaire is determined.

Data-collection proformas

These documents are generally used to record individual case data that are later transferred to electronic databases. These data may be obtained from the patient directly (e.g. vital sign measurements) or extracted from the medical records or similar source.

While simple in concept, careful design of a data-collection proforma should be undertaken. First, a list of the data required should be drafted and translated into data fields on the proforma. These fields should be clearly laid out and well separated. Prior to data collection, the proforma should be trialled on a small selection of subjects. In such an exercise, it is commonly found that the data fields are not adequate for the collection of the required data. Hence, revision of the proforma is often required.

Consideration should be given to the ease of data entry and extraction from the proforma. Data entry should progress logically from the top to the bottom of the document without interruption. This is particularly important for data extraction from medical records. Data extracted from the front of the record should be entered at the top of the proforma and so on. Consideration should also be given to later translation of the data to an electronic database. This should follow the same principles as described above. If possible, design a proforma that will allow data to be scanned directly into an electronic database.

Bias and confounding

Study design errors

In any study design, errors may occur. This is particularly so for observational studies. When interpreting findings from an observational study, it is essential to consider how much of the association between the exposure (risk factor) and the outcome may have resulted from errors in the design, conduct or analysis of the study.⁵ The following questions should be addressed when considering the association between an exposure and outcome:

• Could the observed association be due to bias (systematic errors) in the way subjects were selected for the study or in the way information was obtained from them?

• Can the result be explained by confounding factors?

• Could the result be due to chance?

Systematic error (bias)

Bias in the way a study is designed or carried out can result in an incorrect conclusion about the relationship between an exposure (risk factor) and an outcome (such as a disease) of interest.⁵ Small degrees of systematic error may result in high degrees of inaccuracy. It is important to note that systematic error is not a function of sample size. Many types of bias can be identified:

• Selection bias occurs when there is a difference between the people selected for a study (study sample) and those who are not, for instance employed versus unemployed. Only proportional representation of all groups can, in a way, indicate the absence of selection bias.

• Non-response bias is a function of two components: the non-response rate and the extent to which non-respondents systematically differ from respondents. We may need to ask why the question was not answered. Is it not clear? Is it too personal? Is there a negative interaction with the interviewer? Is the subject afraid of answering ‘yes’? Non-response bias may be a type of selection bias.

• Measurement bias may result from faulty methods to measure study endpoints. These may include poorly calibrated machines or stretched measuring tapes, for example. Strictly speaking, the following examples of bias are all types of measurement bias.

• Prevarication bias relates to subjects purposely giving incorrect answers and may result from threatening or insensitive questions.

• Interviewer bias results from the incorrect interpretations by the interviewer of the responses made by the interviewee. This is often an unconscious process, but may result if the interviewer expects, or would like, certain responses.

• Interpretation bias may result from questions that are not clear enough or that the subject does not understand. Some subjects may ‘interpret’ the question differently from others; for instance, does ‘teeth’ include ‘dentures’?

• Recall bias may result when asking about events that happened a long time ago. For example, ‘Were you ever vaccinated against tetanus?’ Every effort to avoid historical questions should be made.

Confounding

This is not the same as bias. A confounding factor can be described as one that is associated with the exposure under study and independently affects the risk of developing the outcome.⁵ Thus, it may offer an alternative explanation for an association that is found and, as such, must be taken into account when collecting and analysing the study results.

Confounding may be a very important problem in all study designs. Confounding factors themselves affect the risk of disease and if they are unequally distributed between the groups of people being compared, a wrong conclusion about an association between a risk factor and a disease may be made. A lot of the effort put into designing non-experimental studies is in addressing potential bias and confounding. For example, in an often-cited case-control study on the relationship between coffee drinking and pancreatic cancer, the association between exposure and disease was found to be confounded by smoking. Smoking is a risk factor for pancreatic cancer; it is also known that coffee drinkers are more likely to smoke than non-coffee drinkers. These two points create a situation in which the proportion of smokers will be higher in those who drink coffee than in those who do not. The uneven distribution of smokers then creates the impression that coffee drinking is associated with an increased rate of pancreatic cancer when it is smoking (related to those who drink coffee and to pancreatic cancer) that underlies the apparent association.

Common confounders

Common confounders that need to be considered in almost every study include age, gender, ethnicity and socioeconomic status. Age is associated with increased rates of many diseases. If the age distribution in the exposure groups differs (such as where the exposed group is older than the non-exposed group) then the exposed group will appear to be at increased risk for the disease. However, this relationship would be confounded by age. Age would be the factor that underlies the apparent, observed, association between the exposure and disease. Although age is a common confounder, it is the biological and perhaps social changes that occur with age that may be the true causes that increase the rate of disease.

There are several ways to control for the effect of confounding. To control for confounding during the design of the study, there are several possible alternatives:

• Randomization – random assignment into treatment groups, the cornerstone of a randomized, controlled trial, randomly distributes potential confounding factors between the control and intervention groups.

• Restriction – restricting the participants to one level of a potentially confounding variable is another method used in the design of a study to control for confounding, for instance only enrolling patients aged 60 years or more.

• Matching – matching subjects on potential confounding variables ensures that these variables are evenly distributed between cases and controls, especially in case-control studies.

In the analysis phase of a study, one can use:

• Stratification – during the analysis phase of a study, the effect of potential confounders can be assessed within separate strata of the confounding variable.

• Statistical modelling – regression models offer the benefit of controlling for multiple confounders simultaneously.

Principles of clinical research statistics

Sample size

The sample must be sufficiently large to give adequate precision in the prevalence estimates obtained by the study for the purposes required. On the other hand, any increase in the sample size increases the study’s cost, so an excessive sample size, though a much rarer event, should also be avoided.

The most common mistake made by inexperienced researchers is to under-estimate the sample sizes required for their study. As a result, the sample sizes used may be too small and not representative of the population that the sample is meant to represent. This usually leads to outcome measures that have very wide 95% confidence intervals and, hence, statistically significant differences between study groups may not be found.

To ensure that a study has adequate sample sizes to show statistically significant differences, if they are there, sample sizes should be calculated prior to the study commencement. In reality, sample size is often determined by logistic and financial considerations, that is to say a trade-off between sample size and costs.

Study power

The power of a study is the chance of correctly identifying, as statistically significant, an effect that truly exists. If we increase the sample size, we increase the power. As a general rule, the closer the power of a study is to 1.0, the better. This means that the type II error will be small and there will be only a small chance of not finding a statistical difference when there really is one. Usually, a power of 0.8 or more is sufficient.

Statistical versus clinical significance

To determine statistical significance, we can obtain a P value, relative risk or some other statistical parameter that is indicative of a difference between study groups. However, a statistical difference (e.g. P < 0.05) between groups may be found if the study is highly powered (many subjects), even though the absolute difference between the groups is very small and not a clinically significant or meaningful difference.

This difference is important for two reasons. First, it forms the basis of sample size calculations. These calculations include consideration of what is thought to be a clinically significant difference between study groups. The resulting sample sizes adequately power the study to demonstrate a statistically and clinically significant difference between the study groups, if one exists. Second, when reviewing a research report, the absolute differences between the study groups should be compared. Whether or not these differences are statistically significant is of little importance if the difference is not clinically relevant. For example, a study might find an absolute difference in blood pressure between two groups of 3 mmHg. This difference may be statistically significant, but too small to be clinically relevant.

Databases and principles of data management

The fundamental objective of any research project is to collect information (data) to analyse statistically and, eventually, produce a result or report. Data can come in many forms (laboratory results, personal details) and is the raw material from which information is generated. Therefore, how data are managed is an essential part of any research project.⁴

Defining data to be collected

Many a study has foundered because the wrong data were collected or important data were not collected. Generally, data fall into the following groups with examples:

• Identification data: personal information needed to link to the appropriate patient.

• Research data: provides the information that is analysed to answer the study question, i.e. endpoints.

• Administrative data: initials of the data collector, the study centre if multi-centred trial.

Collect only the research data that are essential to answer the study aims. It is important to avoid collecting data that will not be of use. This is time-consuming, expensive and may detract from the quality of the remaining data. However, there will usually be a minimum of data that must be collected. If these data are not collected, then the remaining data may not be analysed adequately. This relates particularly to data on confounding factors.

Database design

A database is a specific collection of data that is organized in a structured fashion. In other words, database software provides us with a way of organizing the data we collect from a research project in a systematic way.

Good database design will:

• reduce repetitiveness, for instance entering in an address or age for a patient many times

• include validation

• have data in a convenient form for analysis

• be pilot tested.

Data entry

This refers to the entry of data into the electronic database, e.g. Access®, Excel®. Even if the study design and the data collection have been well done, the final data set may contain inaccurate data if the data-entry process is inadequate. This relates particularly to manually entered data where mistakes are bound to happen.

Data entry can be achieved in many ways:

• Manual data entry – this may be single entry undertaken by one person. Alternatively, double entry involves two independent people entering the same data. Any differences between the two are reconciled. This is a form of double-checking but is clearly more time-consuming and, therefore, expensive.

• Direct data entry – this can be achieved by having database forms (proformas) on a computer screen. Direct entry of data via an Internet web page is one form of direct data entry. Alternatively, scannable forms can be fed into a scanner, avoiding the need for manual transcription.

Data validation

Effectively, this is a quality assurance process that confirms the accuracy of the data during its various phases of the study. Data validation can be done in the following ways:

• Visual review: matching data on questionnaires with medical records (source data)

• Value range checks: cholesterol levels should be >0 and <20 mmol/L (i.e. do the numbers in the database make sense?)

• Field type checks: text should not be entered into numerical field

• Logical checks (if, then): if classed as a non-smoker, then cigarettes per day should be zero.

Research ethics

Participation in a clinical trial involves a sacrifice, by the participant, of some of the privileges of normal medical care for the benefit of other individuals with the same illness. The privileges forgone might include:

• the right to have treatment decided entirely on the basis of the treating doctor’s judgement rather than by random allocation

• the right to have concomitant therapy according to requirements, rather than be standardized for all trial participants.

Participation also requires the discomfort and inconvenience associated with additional investigations and the potential incursion on privacy. Without the willingness of some individuals to make the sacrifices associated with participation in clinical trials, progress in clinical medicine would be greatly impaired. Most individuals who now expect to receive safe and effective medical care are benefiting by the sacrifices previously made by other individuals.

Some have argued in contrast, that enrolment into clinical trials ensures the absolute best care currently available, with greater involvement and scrutiny by attending healthcare teams.

If one accepts that clinical trials are morally appropriate, then the ethical challenge is to ensure a proper balance between the degree of individual sacrifice and the extent of the community benefit. However, it is a widely accepted community standard that no individual should be asked to undergo any significant degree of risk regardless of the community benefit involved, that is the balance of risks and benefits must be firmly biased towards an individual participant. According to the Physician’s Oath of the World Medical Association ‘concern for the interests of the subject must always prevail over the interests of science and society’.

Because of the trade-offs required and because of the spectrum of views about the degree of personal sacrifice that might be justified by a given community benefit, it is accepted that all clinical trials should be reviewed by an ethics committee that should have as a minimum:

• sufficient technical expertise to quantify the risks and benefits involved

• adequate community representation so that any decisions are in keeping with community standards.

Scientific value

It is unethical to request individuals to undergo the risks, inconvenience and expense of a study that is unlikely to provide a scientifically worthwhile answer. It is also unethical to request sacrifices from volunteers that are out of keeping with the value of the research being undertaken. In keeping with this principle, studies that suffer from substantial design errors or are susceptible to serious bias in event recording or measurement should not be approved until these deficiencies are remedied.

It is unethical to allow scientifically invalid studies to proceed. Sample-size calculations should be scrutinized because of the ethical undesirability of including too few subjects to provide an answer or many more than is needed to provide a convincing answer. Another safeguard to ensure that the research will be valuable is that the investigator should be qualified, experienced and competent, with a good knowledge of the area of study, and have adequate resources to ensure its completion.

Benefits forgone

It is unethical to require any patient to forgo proven effective treatment during the course of a trial. It follows that clinical trials should only be undertaken when each of the treatments being compared is equally likely to have the more favourable outcome.

Very commonly, however, there is an expectation that one or other treatment is the more beneficial before a trial is commenced. This may be based on results of uncontrolled studies or even on biochemical or physiological expectations. The large number of times such expectations have been proven wrong can still provide strong justification for a trial.

If such an expectation of benefits is held strongly by an individual, it is probably not ethical for that individual to participate in a study. Furthermore, it is the responsibility of an ethics committee to assess the strength of the presumptive evidence facing one or other treatment, and consider whether any substantial imbalance in likely outcome exists. This must be considered in relation to the importance of the question being addressed.

Informed consent

Participants in clinical trials have a fundamental right to be fully informed about the nature of a clinical trial and to be free to choose whether or not to take part. Ethical principles also dictate that prospective participants be:

• told they are taking place in a clinical trial and have an unambiguous right to decline to participate or to withdraw at any time

• provided with a full explanation about the discomforts and inconvenience associated with the study, and a description of all risks that may reasonably be considered likely to influence the decision whether or not to participate.⁴

It is usual practice to provide prospective participants with a Plain Language Statement that provides a simple, easy to understand account of the purposes, risks and benefits associated with participation in the study. Ethics committees are required to review these statements and confirm that they provide a reasonable account.

In practice the procedures involved in obtaining informed consent are often problematic. Considering the dependence of sick patients on the health system, their anxiety and their desire to cooperate with their physicians, it is doubtful whether informed consent is ever freely given. When ethics committees identify situations where this scenario is likely to be a particular problem, the involvement of an independent uninvolved person to explain the study may be useful.

Controversies and future directions

1 The issue of consent of patients requiring resuscitation raises serious ethical issues. In this circumstance, the patient is clearly unable to give consent and some argue that this automatically precludes their enrolment. Others disagree and note that such a position would terminate much research in this difficult area.

2 In response to perceived difficulties in the passage of research through the ethical approval process, ethics committee streamlining is occurring in some jurisdictions and standardization of application forms has begun at state level.

3 Substantial clinical research requires skilled personnel, time, funding and a supportive infrastructure. Emergency medicine in Australasia has established a respected clinical practice. However, among its present challenges is the establishment of a culture of research with the resources to support and promote it.

References

1 Taylor DMcD. Practical issues in the design and execution of an emergency medicine research study. Emergency Medicine. 1999;11:167-174.

2 Hall GM, editor. How to write a paper, 2nd edn, London: British Medical Journal Publishing, 1999.

3 Uniform requirements for manuscripts submitted to biomedical journals. Available http://www.jama.ama-assn.org/info/auinst_req.html (accessed March 2002)

4 Good Research Practice Committee. A guide to good research practice. Melbourne: Department of Epidemiology and Preventive Medicine, Monash University, 2001.

5 Jekel JF, Katz DL, Elmore JG, editors. Epidemiology, biostatistics, and preventive medicine, 2nd edn, Philadelphia: WB Saunders, 2001.

24.2 Writing for publication

Anne-Maree Kelly

ESSENTIALS

1 Check and follow the journal’s suggested format and length. Pay particular attention to format of abstract, text and references.

2 Make sure that the objectives, methods, results and conclusions are logically consistent.

3 Be clear and concise.

Introduction

Sharing of knowledge and experience through publication is an important way of improving clinical practice. Communication may be by way of an original research publication, brief report, case report or letter to the editor. Each of these has different requirements in terms of content, format and length, and these requirements may vary between journals. It is useful to choose the intended journal for publication early on. While impact factor may be a consideration in this choice, most authors are more concerned with publishing in a journal that has the appropriate target audience for the subject matter of the paper. It is important to check the Instructions for Authors for the chosen journal to ensure that your submission matches that journal’s requirements. Failure to do so reduces the chances of acceptance considerably.

Although journals may have differences in format and style, all prefer clear and concise communications. In particular, it is important for the material to be arranged logically so that clear relationships can be seen between the objective of the study or communication, the evidence and any conclusions drawn.

Manuscript preparation

Original research manuscripts

Original research manuscripts are usually divided into five sections: Abstract, Introduction (or Background), Methods, Results and Discussion. In addition, some journals prefer a separate concise Conclusion, although many prefer this as the last paragraph of the Discussion. A few journals have additional section headings such as Theoretical Concept and Limitations, although this is uncommon. It is very important to check the journal’s preferred format for each section and ensure the manuscript complies. Most manuscripts also require a key word list of up to five words or phrases to assist with indexing.

Abstract

This is a summary of the paper, usually under the headings ‘Objectives’, ‘Methods’, ‘Results’ and ‘Conclusions’. The usual word limit is 250 words. It is wise not to exceed this word count, not only to comply with the journal’s requirements, but also because indexing services such as PubMed truncate abstracts at 250 words and information may be lost to readers who search electronically. There is considerable variation between journals about how the abstract section is set out. Some prefer a single paragraph without sub-headings but many of the major journals are moving to structured abstracts requiring particular sub-headings usually detailed under Instructions for Authors.

With the easy availability of electronic searching, the abstract is the most commonly accessed part of a manuscript so must contain all the key data. In particular, the specific aims, methods, outcomes of interest, main results (with numbers) and conclusions should be clear enough to be understood without the support of the text. It is important that no data or conclusions appear in the abstract that have not been presented in the main body of the paper.

Introduction

Shorter introductions are often more effective. The aim is to convince the reader why the area of study is important, what this study adds to the body of knowledge and the specific aims of the study. A lengthy review of the literature should be avoided unless it is imperative to put the study in context. A concise review of the literature is more appropriately reported in the Discussion. The last paragraph should explicitly state the aims of the study.

Methods

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