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Statistical presentation in international scientific publications 3. Statistics by section

Statistical presentation in international scientific publications 3. Statistics by section. Malcolm Campbell Lecturer in Statistics, School of Nursing, Midwifery & Social Work, The University of Manchester Statistical Editor, Health & Social Care in the Community.

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Statistical presentation in international scientific publications 3. Statistics by section

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  1. Statistical presentation in international scientific publications 3. Statistics by section Malcolm Campbell Lecturer in Statistics, School of Nursing, Midwifery & Social Work, The University of Manchester Statistical Editor, Health & Social Care in the Community

  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 Statistical presentation - 3. Statistics by section

  3. 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 Statistical presentation - 3. Statistics by section

  4. 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?] 3.2 The Methods The sections of Peat et al (2002, p 54-63) Statistical presentation - 3. Statistics by section

  5. 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 Statistical presentation - 3. Statistics by section

  6. 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 Nursing12, 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… Statistical presentation - 3. Statistics by section

  7. 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 Statistical presentation - 3. Statistics by section

  8. 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 Nursing12(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] Statistical presentation - 3. Statistics by section

  9. The GoodSample size calc. • Young et al (2005) • A prospective baseline study of frail older people before the introduction of an intermediate care service, HSCC13(4), 307-312 Statistical presentation - 3. Statistics by section

  10. 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 Nursing44(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%)] Statistical presentation - 3. Statistics by section

  11. 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 Statistical presentation - 3. Statistics by section

  12. The Ugly (Methods)Wrong statistical tests probably used! • Paxton et al (1996) • Evaluating the workload of practice nurses: a study, Nursing Standard10(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 Statistical presentation - 3. Statistics by section

  13. The GoodStatistical methods • Christensen et al (2005) • Recruitment of religious organisations into a community-based health promotion programme, HSCC13(4), 313-322 Statistical presentation - 3. Statistics by section

  14. 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 Nursing45(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 IL see http://www.spss.com/corpinfo/faqs.htm • [otherwise statistics are well reported!] Statistical presentation - 3. Statistics by section

  15. 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? 3.3 The Results The template of Peat et al (2002, p 65) Statistical presentation - 3. Statistics by section

  16. 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! Statistical presentation - 3. Statistics by section

  17. 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, HSCC12(1), 53-62 Statistical presentation - 3. Statistics by section

  18. 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, HSCC12(4), 288-297 Statistical presentation - 3. Statistics by section

  19. 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?) Statistical presentation - 3. Statistics by section

  20. 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* Reporting numbers continued Golden rules for reporting numbers – Peat & Barton (2005) * My rules! Statistical presentation - 3. Statistics by section

  21. The Good Results in the text • Young et al (2005) • A prospective baseline study of frail older people before the introduction of an intermediate care service, HSCC13(4), 307-312 Statistical presentation - 3. Statistics by section

  22. 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, HSCC14(1), 49-62 Statistical presentation - 3. Statistics by section

  23. 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 Statistical presentation - 3. Statistics by section

  24. 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 Statistical presentation - 3. Statistics by section

  25. The GoodA good summary table • Young et al (2005) • A prospective baseline study of frail older people before the introduction of an intermediate care service, HSCC13(4), 307-312 Statistical presentation - 3. Statistics by section

  26. 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, HSCC13(2), 108-111 Statistical presentation - 3. Statistics by section

  27. 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 Today21, 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 Statistical presentation - 3. Statistics by section

  28. The Bad (Results)Tables that require an explanation 2 Statistical presentation - 3. Statistics by section

  29. 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, HSCC13(2), 145-154 Statistical presentation - 3. Statistics by section

  30. 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 Today22, 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 Statistical presentation - 3. Statistics by section

  31. 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, HSCC13(5), 420-430 Statistical presentation - 3. Statistics by section

  32. 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 Nursing12(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] Statistical presentation - 3. Statistics by section

  33. The Ugly (Results) Read the numbers off the scale – emotional exhaustion 2 Statistical presentation - 3. Statistics by section

  34. 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 Today20, 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 Statistical presentation - 3. Statistics by section

  35. The Bad (Results)Chart that requires an apology 2 Statistical presentation - 3. Statistics by section

  36. 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, HSCC13(2), 112-124 Statistical presentation - 3. Statistics by section

  37. 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, HSCC13(2), 108-111 • better if super-imposed on bars Statistical presentation - 3. Statistics by section

  38. 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, HSCC12(4), 346-358 Statistical presentation - 3. Statistics by section

  39. 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 3.4 Discussion The template of Peat et al (2002, p 87) Statistical presentation - 3. Statistics by section

  40. 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 Statistical presentation - 3. Statistics by section

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