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Interpreting Translational Research Findings Incredible Years Conference, Cardiff March 9 th, 2011 Christopher Whitaker, Senior Statistician, NWORTH Tracey.

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Presentation on theme: "Interpreting Translational Research Findings Incredible Years Conference, Cardiff March 9 th, 2011 Christopher Whitaker, Senior Statistician, NWORTH Tracey."— Presentation transcript:

1 Interpreting Translational Research Findings Incredible Years Conference, Cardiff March 9 th, 2011 Christopher Whitaker, Senior Statistician, NWORTH Tracey Bywater, Research Fellow, School of Psychology

2 Overview Translational research & complex interventions How do we/should we report or interpret results Welsh Sure Start RCT of parent programme & outcome measures Methods of assessing change: – Means & Standard deviations – Effect sizes – Numbers needed to treat Summary & conclusions

3 What is translational research? Translational research transforms scientific discoveries arising from laboratory, clinical, or population studies into clinical applications to tackle all sorts of disorders/diseases etc Translational Research Working Group:

4 Complex interventions EVALUATION – “to strengthen or empower”, more recently it is defined as an assessment of value. Should we look at end outcome only or ‘how we got there’? Social policy interventions, delivered in education, public health practice, or family and children services, are complex interventions (Medical Research Council (MRC), 2009). Complex interventions comprise several interacting components

5 Selected dimensions of complexity according to MRC (2009): implications for development and evaluation  Number of components and interactions between them - theoretical understanding is needed of how the intervention causes change, so that weak links in the causal chain can be identified and strengthened  Number and difficulty of behaviour changes required by those delivering or receiving the intervention - a thorough process evaluation is needed to identify implementation problems lack of impact may reflect implementation failure rather than genuine ineffectiveness  Number and variability of outcomes - a single primary outcome may not be most appropriate, a range of measures may be required

6 Levels of evidence 1. Expert opinion  The developer says 2. Case series  Observe IY recipients 3. RCT  Randomly assign to IY or TAU

7 Randomisation 1:1 ratio

8 Welsh Sure Start Study Hutchings et al (2007) Parenting intervention in Sure Start services for children at risk of developing conduct disorder: pragmatic randomised controlled trial Children aged 3-4 years, randomised 2:1 Targeted population – over cut off on Eyberg Child Behaviour Inventory – Intensity 7-point scale, , cut off 127 – Problem scale – yes/no answers, 0-36, cut off 11

9 Measures Measures were administered at baseline, 6, 12, and 18 months post baseline. They included (amongst others): Kendall Self Control Rating Scale (Kendall & Wilcox, 1979) Conners Hyperactivity Questionnaire (Conners, 1994) Strengths & Difficulties Questionnaire (Goodman, 1997)

10 ECBI-IFollow up TAU144.0 (n = 49) IY122.3 (n = 104) ECBI mean at 6-month (1 st ) follow up

11 ECBI-IBaselineFollow up TAU (n = 49) IY (n = 104) ECBI mean at 6-month (1 st ) follow up and baseline

12 IY mean = 122.3, TAU mean = 144.0

13 ECBI-IBaselineFollow up TAU (n = 49)(26.8)(33.0) IY (n = 104)(27.0)(35.1) ECBI mean and SD at 6-month (1 st ) follow up and baseline

14 IY mean = 122.3, TAU mean = 144.0

15 ECBI-IBaselineFollow upBL - FU TAU (n = 49)(26.8)(33.0) IY (n = 104)(27.0)(35.1) ECBI mean and SD at 6-month (1 st ) follow up, baseline and change

16 Conclusion – IY lowers ECBI by 27.2 points on the scale NO – 27.2 is an approximate value Statistical analysis - gives a more precise value Take account of each participants 1.Baseline value 2.Sure start area Statistical analysis finds IY lowers ECBI by points on the scale

17 Better summary IY lowers ECBI by points on the scale 95% Confidence Interval (CI) for this mean is to Based on this sample of data we are 95% confident that the effect of IY is to reduce ECBI between and points on the scale

18 95% CI for other measures meanLo CIHi CISignificance ECBI-I p <.001 ECBI-P p <.001 Conners p <.001 Kendall SRCS p =.033 SDQ total p =.091

19 Normal distribution plots of data

20 Normal distribution plots of artificial data (SD = 5)

21 Conclusion from the plots Differences in the means are the same SD is different Lots of overlap suggests lesser effect Can we measure overlap Difference in means relative to SD

22 Effect size Uses the mean difference Uses the variability of the mean difference (SD) Is comparable between the measures How to calculate effect size – different ways, we use Cohen’s d: d = (IY mean – TAU mean) / SD

23 Which measure has IY had biggest effect on Effect sizeLo CIHi CISignificant ECBI-I p <.001 ECBI-P p <.001 Conners p <.001 Kendall SCRS p =.033 SDQ total p =.091

24 At baseline all children have ECBI-I >= 127 OR ECBI-P >= 11 At (1 st ) Follow up NumberBelow both cut-offs Benefit (%) TAU49918% IY %

25 Idea behind Number Needed to Treat With IY 37% benefit, with TAU 18% benefit In 6 families with IY approximately 2 benefit In 6 families with TAU approximately 1 benefits So in 6 families 1 more benefits with IY than with TAU

26 Number Needed to Treat (NNT) Calculation 38/104 benefit with IY 9/49 benefit with TAU Difference is 38/104 – 9/49 = NNT = 1 / = 5.5 NNT is the number of families that need to be treated with IY rather than TAU for one additional family to benefit

27 Attributable Risk Reduction (%) NNT NNT 5.5 (72.5, 3.1) NUMBER NEEDED to TREAT Benefit

28 Summary & Conclusions Be clear on – What research question is being asked – What service managers/policy makers want to know and why Ensure sensitive validated measures are used Identify most useful method of presenting data for target audience, e.g. in this case – Mean values are sensitive to change but not easy to interpret, SD & other factors should be taken in to account – Effect sizes are derived from means and shows magnitude of change – NNT is not very sensitive but useful to give guidance on numbers required to reduce prevalence rates & therefore costs

29 References Conners, C. K. (1994). The Conners Rating Scales: Use in clinical assessment, treatment planning and research. In M. Maruish (Ed.), Use of Psychological Testing for Treatment Planning and Outcome Assessment. Hillsdale, New Jersey: Erlbaum. Eyberg, S. M. (1980). Eyberg Child Behavior Inventory. Journal of Clinical Child Psychology, 9, 27. Goodman, R. (1997). The Strengths and Difficulties Questionnaire: A research note. Journal of Child Psychology, Psychiatry, and Allied Disciplines, 38 (5), Hutchings, J., Bywater, T., Daley, D., Gardner, F., Whitaker, C., Jones, K., Eames, C. & Edwards, R. T. (2007) Parenting intervention in Sure Start services for children at risk of developing conduct disorder: pragmatic randomised controlled trial. British Medical Journal, 334, Accessible at: Kendall, P. & Wilcox, L. (1979). Self-control in children: Development of a rating scale. Journal of Consulting and Clinical Psychology, 47, Medical Research Council (2009). Developing and Evaluating Complex Interventions: New guidance. Accessible at:

30 Additional reading Effect sizes – Cohen, J. (1988). Statistical Power for the Behavioural Sciences. Erlbaum, Hillsdale, NJ, USA. Calculating the Number Needed to Treat (Altman & Anderson, 1999) Accessible at: – Confidence Intervals for the difference between 2 proportions: –

31 Diolch yn Fawr Questions???????


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