Statistical presentation in international scientific publications 6. Reporting more complicated...

Statistical presentation in international scientific publications 6. Reporting more complicated findings

Malcolm CampbellLecturer in Statistics, School of Nursing, Midwifery &

Social Work, The University of Manchester

Statistical Editor, Health & Social Care in the Community

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6. Report more complicated findingsContents

• 6.1 Introduction

• 6.2 Reporting factor analysis

• 6.3 Reporting analysis of variance

• 6.4 Reporting multiple regression

• 6.5 Reporting logistic regression

• 6.6 Reporting survival analysis

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6.1 IntroductionReporting multivariate analyses

• It’s important to be consistent and give the reader clear, concise but complete information– more of a problem in more complicated analyses!

– find a compromise between giving too little and too much information

– this compromise may depend on the readership of the journal

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6.2 Reporting factor analysisSuggestions – see Tabachnick & Fidell (2001, pp 647-648)

• You should report

– how variables were initially chosen and types of variables involved

– preliminary assessment of factorability

• correlations, measures of sampling adequacy

– methods used for extracting and rotating factors

• which combinations were compared

– how the number of factors was determined

• whether other factor solutions were explored

– variance explained for each factor

– a table of rotated factor loadings

– an interpretation of the rotated factors

• You could also report

– communalities of variables

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The Good 1An excellent methodological paper on factor analysis

• Matthews et al (2006)– An exploratory study of the conditions important in

facilitating the empowerment of midwives, Midwifery 22, 181-191

– methods used

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The Good 2The excellent methodological paper on factor analysis

• Matthews et al (2006)

– sample size

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The Good 3That excellent methodological paper on factor analysis again

• Matthews et al (2006)

– pattern matrix showing interpretation, % variance explained, factor loadings and internal reliability of factors

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6.3 Reporting analysis of varianceSuggestions – simplify Lang and Secic (1997, pp 127-135)

• Analysis of variance:

– usually one-way ANOVA

– rarely two- or more-way ANOVA

– analysis of covariance

– repeated measures ANOVA

• You should report

– appropriate means and standard deviations

– full F-test results

– post-hoc tests allowing for multiple comparisons if required

• You could also report

– (where applicable) an ANOVA table showing sources of variation, sums of squares, mean squares, F-statistics, degrees of freedom and p-values

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The Good 1A paper using repeated measures ANOVA

• Salmon et al (2006)

– An evaluation of the effectiveness of an educational programme promoting the introduction of routine antenatal enquiry for domestic violence, Midwifery 22, 6-14

– methods used

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The Good 2The paper using repeated measures ANOVA

• Salmon et al (2006): descriptive statistics in table

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The Good 3That paper using repeated measures ANOVA again

• Salmon et al (2006)

– test results in text

– the F-test results were not typeset properly!

– “F(2,23)=54.615, p0.001” or “F2,23=54.615, p0.001”

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6.4 Reporting multiple regressionSuggestions – simplify Lang and Secic (1997, pp 114-119)

• You should report– how variables were initially

chosen and types of variables involved

– how variables were included in the models

• simultaneously, in pre-determined order, stepwise

– whether underlying assumptions were assessed

• linearity; Normality & equality of variance for residuals; no multi-collinearity

– coefficient of multiple determination R2

• % of variation in dependent variable explained by model

– overall test of goodness-of-fit

• analysis of variance F-test results

– a table of estimated coefficients with 95% confidence intervals and p-values of t-test

• give results for every variable in the model, not just those that are significant

• You could also report

– standard errors of coefficients

– estimated regression equation

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The Good 1A paper using stepwise regression

• Perry and McLaren (2004)

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

– methods used

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The Good 2That paper using stepwise regression • Perry and McLaren (2004)

– stepwise selection and final model

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The Good 3That paper using stepwise regression again

• Perry and McLaren (2004)– the final

model explained

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The Good 4An alternative way of presenting results• 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

– final models presented another way

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6.5 Reporting logistic regressionSuggestions – simplify Lang and Secic (1997, pp 122-125)

• You should report

– how variables were initially chosen and types of variables involved

– how variables were included in the models

• simultaneously, in pre-determined order, stepwise

– overall test of goodness-of-fit

• change in -2 log likelihood, Hosmer & Lemeshow test

– a table of estimated odds ratios with 95% confidence intervals and p-values of Wald or t-test

• give results for every variable in the model, not just those that are significant

• You could also report– whether and how

underlying assumptions were assessed

• no multicollinearity

– estimated coefficients and standard errors

– Nagelkerke R2

• measure of variation in dependent explained by model

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The Good 1A paper with a pragmatic way of selecting variables

• Peters et al (2004)

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

– methods used

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The Good 2The paper with a pragmatic way of selecting variables

• Peters et al (2004)

– final results

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The Good 3Another way of presenting results

• Darton (2004) – What types of home are closing? The characteristics of

homes which closed between 1996 and 2001, HSCC 12(3), 254-264

– more detailed results

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6.6 Reporting survival analysis 1Suggestions – simplify Lang and Secic (1997, pp 137-146)

• Kaplan-Meier analysis

• You should report

– nature and extent of the censoring

– survival rates with confidence intervals for each group

• percentage surviving at given time

– median survival time with confidence interval for each group

– Kaplan-Meier curves for each group plotting percentage survival (y) by time (x)

– comparison of survival curves by group

• log-rank (Cox-Mantel) test or Breslow-Wilcoxon test

• You could alternatively report

– life-table analysis

• table of events recorded by time interval

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3.6 Reporting survival analysis 2Suggestions – simplify Lang and Secic (1997, pp 137-146)

• Cox regression

• You should report– how variables were

initially chosen and types of variables involved

– how variables were included in the models

• simultaneously, in pre-determined order, stepwise

– overall test of goodness-of-fit

• likelihood ratio test

– how underlying assumption of proportional hazards was assessed

– a table of estimated hazard or risk ratios with 95% confidence intervals and p-values of Wald test

• give results for every variable in the model, not just those that are significant

• You could also report– estimated coefficients and

standard errors

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The Good 1A rare survival analysis example

• 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

– methods used

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The Good 2Survival analysis

• Trappes-Lomax et al (2006)– sample size

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The Good 3Survival analysis

• Trappes-Lomax et al (2006)

– Kaplan-Meier survival plot

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The Good 4Survival analysis

• Trappes-Lomax et al (2006)

– Cox regression results