Statistical presentation in international scientific publications 3. Statistics by section

Statistical presentation in international scientific publications 3. Statistics by section

Malcolm CampbellLecturer in Statistics, School of Nursing, Midwifery &

Social Work, The University of ManchesterStatistical Editor, Health & Social Care in the Community

26 March 2008 Statistical presentation - 3. Statistics by section 2

3. Statistics by section Contents

• 3.1 The Abstract• 3.2 The Methods• 3.3 The Results• 3.4 The Discussion

• Follow the guidelines for studies– CONSORT for randomised controlled trials– TREND for non-randomised trials– STROBE for observational studies


3.1 The AbstractStatistical reporting starts here (some readers stop here!)

• Quantitative Abstracts need statistical content– aims and type of study design– the location, setting and dates of data collection– the selection and number of participants– descriptions of interventions, instruments & outcomes– a summary of main findings (with p-values)– conclusions and implications

• Should it be structured or unstructured?– please check the requirements of the planned journal!– plan a structured one & remove structure if necessary


3.2 The Methods The sections of Peat et al (2002, p 54-63)

• Ethical approval– and informed consent

• Study design– each has own strengths

and limitations, and determines the statistical approach

• Participants– recruitment, sampling

frame, inclusion/exclusion criteria, logic behind groupings

• Sample size– bivariate analyses

• Questionnaires– details, and how developed,

validated, tested & administered• Interventions

– details and how administered– randomisation, allocation

concealment & blinding • Clinical assessments

– how clinical info collected• Statistical methods

– how the data were analysed (assumptions, tests), and with what software

• [+ Main outcomes?]


The Methods (part 1 of 3)How was the study carried out? (CONSORT/TREND/STROBE)

• Describe the methods used, including (where appropriate)– the specific aims of the study (hypotheses tested)– the type of design used (in common terminology)– clear descriptions of any interventions, main and any

secondary outcome measures– source of participants with eligibility criteria

• description of target population and study population– how access to the participants was arranged– methods used for randomisation, allocation concealment

or probability sampling


The Ugly (Methods)No inferences please, we’re purposive

• Randle (2003)– Changes in self-esteem during a 3-year pre-registration

Diploma in Higher Education (Nursing) programme, J Clinical Nursing 12, 142-143

• “Purposive samples were recruited from all four branches of the nursing programme…”

• “Analyses were performed using SPSS (SPSS Inc., Chicago, IL, USA) to see if there were significant differences or correlations between respondents’ scores and variables: cohort, age of student, nursing branch studied and referrals at any of the academic summative assessment stages.”

• no numerical results reported at all! – no counts, no percentages, no descriptive statistics, no test results…


More Methods (part 2 of 3)How was the study carried out?

• … and report (where appropriate) …– details of ethical approval and participants’ consent– dates, settings and locations for data collection– sample size calculations (see your research proposal?)

• whether the study was powered to detect a clinically important result

• not necessary for pilot studies, time-restricted studies or audit-based analyses

• can be difficult to do if there is no prior information on which to base calculations

• many papers do not justify their sample size (eg Altman, 1998)

– samples usually small, occasionally large


The Ugly (Methods)No sample size calculations

• Gillespie and Melby (2003)– Burnout among nursing staff in accident and emergency

and acute medicine: a comparative study, J Clinical Nursing 12(6), 842-851

• 28 questionnaires sent to nurses in A&E and 28 to nurses in acute medicine in an NHS Trust

– no details of where or when, no justification for 28– 20 replies from A&E, 16 from acute medicine

• “The sample was small in numbers and findings cannot therefore be generalised beyond this sample of participants.”

• [also poor presentation of results]


The GoodSample size calc.

• Young et al (2005)– A prospective

baseline study of frail older people before the introduction of an intermediate care service, HSCC 13(4), 307-312


The (slightly) Bad (Methods)Kim and Ooooh-no - we forgot about the dropout…

• Kim and Oh (2003)– Adherence to diabetes control recommendations: impact of

nurse telephone calls, J Advanced Nursing 44(3), 256-261• “For repeated measures ANOVA, for an effect size of 0.60, at a

power of 0.80, and an alpha level of 0.05, 25 subjects in each group were needed to ensure an adequate trial for 1% reduction of HbA1c levels…”

• “A total of 50 patients… agreed to participate. They were randomised by a toss of a coin… Only 36 subjects completed the entire study… Four moved to another city, 10 refused before completing the post-test.”

• [otherwise nicely reported (intervention group did show a drop in mean HbA1c level from 8.8% to 7.6%)]


Even more Methods (part 3 of 3)How was the study carried out?

• … and finally (usually in a paragraph at end of the section) report– statistical methods used for data analysis

• with references for those that are not well-known– the name and version of software used for data

management and analysis• not in CONSORT or STROBE statements but is in TREND

statement• make sure the software is referenced appropriately

according to the planned journal – some require formal references, some don’t


The Ugly (Methods)Wrong statistical tests probably used!

• Paxton et al (1996)– Evaluating the workload of practice nurses: a study,

Nursing Standard 10(21), 33-38• study comparing workload of same 34 practice-employed

and health board attached nurses before and after introduction of the New General Practitioner Contract

• “Data were coded and analysed using the Statistical Package for the Social Sciences (SPSS) and significance between categorical variables determined by the chi square statistic.” Statistical methods used for other variables (% of time, hours per FTE) not described

• [also no sample size calculations]• no test statistics reported; only p-value ranges (see later)• testing should have taken the paired nature of the data

into consideration


The GoodStatistical methods

• Christensen et al (2005)– Recruitment of religious

organisations into a community-based health promotion programme, HSCC 13(4), 313-322


The (ever-so-slightly) Bad (Methods)SPSS is a four-letter word, not an acronym

• Chan and Yu (2004)– Quality of life of clients with schizophrenia, J Advanced

Nursing 45(1), 72-83• “Data were entered into the Statistical Package for Social

Science (SPSS) version 10.0.”• SPSS stopped being “the Statistical Package for the Social

Sciences” certainly by the mid-1980s when the MSDOS version, SPSS/PC+ was introduced

• it should be referenced as “SPSS™” for first use and “SPSS” subsequently, cited in a form such as

SPSS (2003), SPSS for Windows, Rel. 11.5. SPSS Inc., Chicago ILsee http://www.spss.com/corpinfo/faqs.htm

• [otherwise statistics are well reported!]

http://www.spss.com/corpinfo/faqs.htm

http://www.spss.com/corpinfo/faqs.htm


3.3 The Results The template of Peat et al (2002, p 65)

• Paragraph 1– describe study sample– who did you study?

• Paragraph 2– univariate analyses– how many participants

had what?

• Paragraphs 3 to n-1– bivariate analyses– what is the relation

between the outcome and explanatory variables [taken one at a time]?

• Last paragraph/s– multivariate analyses– what is the result when

the confounders and effect modifiers have been taken into account?


Detailed ResultsWhat was found? – text (eg Altman et al, 2000)

• A description of the statistical findings in simple language, including (where appropriate)– numbers of participants and participation rates– characteristics of/baseline information on participants

• including baseline comparison of any groups– known information on non-participants– the results of preliminary analyses, in lesser detail– results of main analyses, as determined by the aims of the

study, in full detail– results of any secondary analyses, in lesser detail– should be clear, factual and concise (Kelley et al, 2003)

• The most exciting part of the paper!


The Good (Results)Simple recruitment flow chart

• CONSORT/TREND/STROBE requirement

• Peters et al (2004)– Factors associated with

variations in older people’s use of community-based continence services, HSCC 12(1), 53-62


The Good (Results)Another flow chart

• Perry and McLaren (2004)– An exploration of

nutrition and eating disabilities in relation to quality of life at 6 months post-stroke, HSCC 12(4), 288-297


Reporting numbersHow do I report numerical information?

• Unless readability is compromised, report numbers with percentages– eg 123 (45%)

• Report estimate of centre with estimate of spread– eg means with SDs, medians with ranges or IQRs

• Report test results in full with supporting statistics so reader can understand the findings– test results include label for test statistic to indicate test,

value of test statistic (eg t =, M-W Z =, 2 =), degrees of freedom and actual p-value (all where applicable)

– report even results non-significant, assuming the test was important (if not important, why do it?)


Reporting numbers continued Golden rules for reporting numbers – Peat & Barton (2005)

• In text, give numbers– with units (eg cm) as numbers– < 10 as words– ≥ 10 as numbers– at start of sentence as words

• Use a 0 before decimal point for numbers < 1

• No space between number and % sign but space between number and unit

• Use 2 decimal places for most test statistics & correlations*

• Rules for sample size & %:– < 20: use numbers not %s– < 100: % to nearest whole

number– > 100: % to 1 decimal place

• Use one more decimal place than unit of measurement when reporting descriptive statistics

• Report last decimal place if 0*• Report p-values to 3 decimal

places or 2 significant figures, or p < 0.001 if very small*

* My rules!


The Good Results in the text




The GoodMore results in text

• Trappes-Lomax et al (2006)– Buying Time I: a

prospective, controlled trial of a joint health/social care residential rehabilitation unit for older people on discharge from hospital, HSCC 14(1), 49-62


More ResultsWhat was found? - tables and figures

• Summarise findings in tables and figures in a clear, consistent layout, including (where applicable) – flow chart of recruitment (CONSORT/TREND/STROBE)– characteristics of the participants (eg by group)– results of main analyses (maybe secondary analyses)

• Each table or figure should have a specific role• Arrange results clearly and appropriately

– eg tables with groups by column, variables by row for reading left to right

– number of cases should be shown


And more ResultsMore about tables and figures

• Tables and figures should be self-contained– imagine that a reader may want to use one in a talk– titles should be self-explanatory– include an interpretation in the text to guide the reader

through the table• Can a figure be summarised in a table or a

sentence?– tables are more compact than figures– sentences are more compact than tables

• Avoid charts with unnecessary third dimension– perspective can distort interpretation


The GoodA good summary table




The Good (Results)Another good summary table • Armstrong and Earnshaw (2005)

– A comparison of GPs and nurses in their approach to psychological disturbance in primary care consultations, HSCC 13(2), 108-111


The Bad (Results)Tables that require an explanation 1 (see next slide)

• Söderhamn et al (2001)– Attitudes towards older people among nursing students and

registered nurses in Sweden, Nurse Education Today 21, 225-229

• [also no sample size calculation– 86 first year & 65 second year students, 41 registered nurses– insufficient details of sampling: authors call it a convenience sample

with a response rate of 100%, but it may have been interpretable as a representative random sample for inferences]

• tables give p-value ranges but no test statistics, & include abbreviations that need the reader to refer to the text

– you might make an educated guess that they were subscales of the scale mentioned in the table title


The Bad (Results)Tables that require an explanation 2


The Good (Results)A table with descriptive statistics and test results

• Evans et al (2005)– The impact of ‘statutory duties’ on mental health social

workers in the UK, HSCC 13(2), 145-154


The Bad (Results)Could have done it better with tables

• Chang et al (2002)– A continuing educational initiative to develop nurses’

mental health knowledge and skills in rural and remote areas, Nurse Education Today 22, 542-551

• [also does not follow IMRaD structure– vague characteristics of participants in Methods section;

response rate is given in Methods and Results (twice)• no sample size calculation (202 questionnaires returned

from 303 sent)]• nurses’ responses to 7-point Likert statements on mental

health program reported repeatedly in text as % “A/SA” (agree or strongly agree): could have been presented more compactly with more detail in one or more tables


The GoodA double bar chart• Klinkenberg et al

(2005)– The last three

months of life: care, transitions and the place of death of older people, HSCC 13(5), 420-430


The Ugly (Results)Read off the scale – emotional exhaustion 1 (see next slide)

• Gillespie and Melby (2003) [again]– Burnout among nursing staff in accident and emergency

and acute medicine: a comparative study, J Clinical Nursing 12(6), 842-851

• [also small sample size, not justified (see earlier)• software used for analysis not named (probably Excel)]• clustered bar charts used to present frequency counts

instead of tables – have to read number in each bar from vertical scale

• accompanying text quotes percentages only, while bar charts have counts axis

• [also actual p-values but no test statistics; Kruskal-Wallis results not explained]


The Ugly (Results) Read the numbers off the scale – emotional exhaustion 2


The Bad (Results)Chart that requires an apology 1 (see next slide)

• Salanterä and Lauri (2000)– Nursing students’ knowledge of and views about

children in pain, Nurse Education Today 20, 537-547• [also characteristics of the sample given in Methods• method used to divide students into two groups (those who

thought their knowledge was good and those who thought it was poor) not clearly defined

• comparison of groups with no supporting statistics and test results concentrating on significant findings only]

• important results displayed as 3D bar charts, where percentages have to be read against axis – would have been better presented as tables


The Bad (Results)Chart that requires an apology 2


The GoodA good line plot• Roelands et al

(2005)– Knowing the

diagnosis and counselling the relatives of a person with dementia: the perspective of home nurses and home care workers in Belgium, HSCC 13(2), 112-124


The GoodError bars (a confidence interval plot)• Armstrong and

Earnshaw (2005)– A comparison of

GPs and nurses in their approach to psychological disturbance in primary care consultations, HSCC 13(2), 108-111

– better if super-imposed on bars


The GoodA clever box plot• Pollard et al (2004)

– Collaborative learning for collaborative working? Initial findings from a longitudinal study of health and social care students, HSCC 12(4), 346-358


3.4 Discussion The template of Peat et al (2002, p 87)

• Paragraph 1– what did this study show?– address the aims shown in

the Introduction[/Methods]• Paragraph 2

– strengths and weaknesses of methods

• Paragraphs 3 to n-1– discuss how the results

support the current or refute current knowledge literature

• Final paragraph– future directions– “so what?”, “where next?”– impact on current thinking

or practice


The Discussion 2 Statistics up for discussion – Kelley et al (2003)

• Interpret and discuss findings– compare findings with those of other studies– critical reflection on both results and data collection– assess how well the study met the research question– describe problems encountered– honestly judge the limitations of the work

• the authors are the ones best placed to guide the reader• Present conclusions and recommendations

Documents

Statistical presentation in international scientific publications 3. Statistics by section