Interpreting Translational Research Findings

Incredible Years Conference, Cardiff March 9th, 2011

Christopher Whitaker, Senior Statistician, NWORTHTracey Bywater, Research Fellow, School of

Psychology

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

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: www.cancer.gov/researchandfunding/trwg/TRWG-definition-and-TR-continuum

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

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 ineffectivenessNumber and variability of outcomes - a single primary outcome may not be most appropriate, a range of measures may be required

Levels of evidence

1. Expert opinion The developer says

2. Case series Observe IY recipients

3. RCT Randomly assign to IY or TAU

Randomisation 1:1 ratio

Welsh Sure Start StudyHutchings 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, 36-252, cut off 127– Problem scale – yes/no answers, 0-36, cut off 11

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)

ECBI-I Follow up

TAU 144.0

(n = 49)

IY 122.3

(n = 104)

ECBI mean at 6-month (1st) follow up

ECBI-I Baseline Follow up

TAU 141.3 144.0

(n = 49)

IY 146.8 122.3

(n = 104)

ECBI mean at 6-month (1st) follow up and baseline

IY mean = 122.3, TAU mean = 144.0

ECBI-I Baseline Follow up

TAU 141.3 144.0

(n = 49) (26.8) (33.0)

IY 146.8 122.3

(n = 104) (27.0) (35.1)

ECBI mean and SD at 6-month (1st) follow up and baseline

IY mean = 122.3, TAU mean = 144.0

ECBI-I Baseline Follow up BL - FU

TAU 141.3 144.0 +2.7

(n = 49) (26.8) (33.0)

IY 146.8 122.3 -24.5

(n = 104) (27.0) (35.1)

-27.2

ECBI mean and SD at 6-month (1st) follow up, baseline and change

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 value2. Sure start area

• Statistical analysis finds IY lowers ECBI by 25.05 points on the scale

Better summary

• IY lowers ECBI by 25.05 points on the scale• 95% Confidence Interval (CI) for this mean is

14.92 to 35.18

• Based on this sample of data we are 95% confident that the effect of IY is to reduce ECBI between 14.92 and 35.18 points on the scale

95% CI for other measures

mean Lo CI Hi CI Significance

ECBI-I 25.05 14.92 35.18 p < .001

ECBI-P 4.42 2.00 6.85 p < .001

Conners 3.39 1.47 5.31 p < .001

Kendall SRCS 8.16 0.68 15.61 p = .033

SDQ total 1.52 -0.24 3.28 p = .091

Normal distribution plots of data

Normal distribution plots of artificial data (SD = 5)

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

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

Which measure has IY had biggest effect on

Effect size Lo CI Hi CI Significant

ECBI-I 0.89 0.54 1.24 p < .001

ECBI-P 0.63 0.28 0.98 p < .001

Conners 0.61 0.27 0.96 p < .001

Kendall SCRS 0.38 0.03 0.73 p = .033

SDQ total 0.30 -0.05 0.65 p = .091

At baseline all children have ECBI-I >= 127 OR ECBI-P >= 11

At (1st) Follow up

Number Below both cut-offs

Benefit (%)

TAU 49 9 18%

IY 104 38 37%

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

Number Needed to Treat (NNT)Calculation

• 38/104 benefit with IY• 9/49 benefit with TAU• Difference is 38/104 – 9/49 = 0.1817

• NNT = 1 / 0.1817 = 5.5• NNT is the number of families that need to be

treated with IY rather than TAU for one additional family to benefit

Attrib

utab

le R

isk R

educ

tion

(%) 10

20

30

40

0

-10

2.5

3.3

5

10

10

NNT

NNT 5.5 (72.5, 3.1)

NUMBER NEEDED to TREAT

Benefit

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

ReferencesConners, 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), 581-586.

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, 678-682. Accessible at: http://www.bmj.com/content/334/7595/678.full

Kendall, P. & Wilcox, L. (1979). Self-control in children: Development of a rating scale. Journal of Consulting and Clinical Psychology, 47, 1020-1029.

Medical Research Council (2009). Developing and Evaluating Complex Interventions: New guidance. Accessible at: www.mrc.ac.uk/complexinterventionsguidance

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:– http://www.bmj.com/content/319/7223/1492.full

• Confidence Intervals for the difference between 2 proportions:– http://faculty.vassar.edu/lowry/prop2_ind.html

Diolch yn Fawr

Questions???????

c.j.whitaker@bangor.ac.uk 01248 383218 t.bywater@bangor.ac.uk 01248 383845