Statistical presentation in international scientific publications 6. Reporting more complicated findings Malcolm Campbell Lecturer in Statistics, School of Nursing, Midwifery & Social Work, The University of Manchester Statistical Editor, Health & Social Care in the Community

26 March 2008Statistical presentation - 6. Reporting more complicated findings 2 6. Report more complicated findings Contents 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

26 March 2008Statistical presentation - 6. Reporting more complicated findings Introduction Reporting 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

26 March 2008Statistical presentation - 6. Reporting more complicated findings Reporting factor analysis Suggestions – see Tabachnick & Fidell (2001, pp ) 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

26 March 2008Statistical presentation - 6. Reporting more complicated findings 5 The Good 1 An 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, –methods used

26 March 2008Statistical presentation - 6. Reporting more complicated findings 6 The Good 2 The excellent methodological paper on factor analysis Matthews et al (2006) –sample size

26 March 2008Statistical presentation - 6. Reporting more complicated findings 7 The Good 3 That excellent methodological paper on factor analysis again Matthews et al (2006) –pattern matrix showing interpretation, % variance explained, factor loadings and internal reliability of factors

26 March 2008Statistical presentation - 6. Reporting more complicated findings Reporting analysis of variance Suggestions – simplify Lang and Secic (1997, pp ) 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

26 March 2008Statistical presentation - 6. Reporting more complicated findings 9 The Good 1 A 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

26 March 2008Statistical presentation - 6. Reporting more complicated findings 10 The Good 2 The paper using repeated measures ANOVA Salmon et al (2006): descriptive statistics in table

26 March 2008Statistical presentation - 6. Reporting more complicated findings 11 The Good 3 That 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, p0.001” or “F 2,23 =54.615, p0.001”

26 March 2008Statistical presentation - 6. Reporting more complicated findings Reporting multiple regression Suggestions – simplify Lang and Secic (1997, pp ) 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 R 2 % 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

26 March 2008Statistical presentation - 6. Reporting more complicated findings 13 The Good 1 A 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), –methods used

26 March 2008Statistical presentation - 6. Reporting more complicated findings 14 The Good 2 That paper using stepwise regression Perry and McLaren (2004) –stepwise selection and final model

26 March 2008Statistical presentation - 6. Reporting more complicated findings 15 The Good 3 That paper using stepwise regression again Perry and McLaren (2004) –the final model explained

26 March 2008Statistical presentation - 6. Reporting more complicated findings 16 The Good 4 An 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), –final models presented another way

26 March 2008Statistical presentation - 6. Reporting more complicated findings Reporting logistic regression Suggestions – simplify Lang and Secic (1997, pp ) 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 R 2 measure of variation in dependent explained by model

26 March 2008Statistical presentation - 6. Reporting more complicated findings 18 The Good 1 A 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), –methods used

26 March 2008Statistical presentation - 6. Reporting more complicated findings 19 The Good 2 The paper with a pragmatic way of selecting variables Peters et al (2004) –final results

26 March 2008Statistical presentation - 6. Reporting more complicated findings 20 The Good 3 Another 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), –more detailed results

26 March 2008Statistical presentation - 6. Reporting more complicated findings Reporting survival analysis 1 Suggestions – simplify Lang and Secic (1997, pp ) 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

26 March 2008Statistical presentation - 6. Reporting more complicated findings Reporting survival analysis 2 Suggestions – simplify Lang and Secic (1997, pp ) 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

26 March 2008Statistical presentation - 6. Reporting more complicated findings 23 The Good 1 A 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), –methods used

26 March 2008Statistical presentation - 6. Reporting more complicated findings 24 The Good 2 Survival analysis Trappes-Lomax et al (2006) –sample size

26 March 2008Statistical presentation - 6. Reporting more complicated findings 25 The Good 3 Survival analysis Trappes-Lomax et al (2006) –Kaplan-Meier survival plot

26 March 2008Statistical presentation - 6. Reporting more complicated findings 26 The Good 4 Survival analysis Trappes-Lomax et al (2006) –Cox regression results