The effect of a GnRH vaccine, GonaConTM on the growth of juvenile tammar wallabies

1Bob Forrester, 2 Melissa Snape and 2Lyn A. Hinds

1Statistical Consulting Unit, ANU, Canberra, ACT2 Invasive Animals CRC, Canberra, ACT

Outline of talk

• Introduction• Data• Questions of interest• Modelling growth responses• Concluding remarks

Introduction

Vaccination against gonadotrophin releasing hormone disrupts hormonal regulation of reproduction

GonaConTM is effective in eutherian mammals (eg deer, horses, bison)

Tested here on marsupials (Tammar wallabies – relatively small and easy to handle)

Introduction – why vaccinate animals?

Control of overabundant populations

eg Possums in New Zealand, kangaroos in Australia

More humane than poison baits

Avoids emotional responses

More politically acceptable

Data

35 juvenile male Tammar wallabies

3 treatments – sham control, Vac1 (week 0), Vac2 (week 0 and week4)

12 variables measured

Key variables associated with testes size

20 measurement times, up to 131 weeks after treatment

Data

Repeated measurements

Unequally spaced intervals

All animals measured at the same time

Two animals have incomplete records (both animals died)

All analyses carried out using GenStat

Tammar wallabies

110

90

1201008060

120

40

80

20

100

0

Week

Arm

length (m

m)

Tammar wallabies

25

15

5

1201008060

20

40

0

20

10

0

Week

Teste

s volum

e

Tammar wallabies

2

0

1201008060

3

40

-1

20

1

0

Week

ln(T

estes vo

lum

e)

Data

Week 0 7 11 15 19 24 28Interval 7 4 4 4 5 4

Week 34 40 48 54 62 71Interval 6 6 8 6 8 9

Week 79 91 101 108 115 123 131Interval 8 12 10 7 7 6 8

Questions of interest

Is the vaccine effective?

Is Vac2 more effective than Vac1?

Does the effectiveness wear off over time?

Are body measurements other than testes affected?

Summary statistics approach

Method effective for measurements such as arm length

Answers overall question of treatment effect

Does not explore treatment interaction with time

Cannot handle the complex responses observed in testes measurements

Fitting problems with short response runs

Arm length – exponential model

Means

Parameter Control Vac1 Vac2 SEDA 130.4 110.2 105.5 3.13B -48.7 -27.6 -23.2 2.91R 0.98891 0.986 0.98538 0.001315

Arm length = A + B*R**Weeks , R = exp(-K)

Model variance structure

Measurements unequally spaced, but all animals measured at the same time

Antedependence or power models

Fixed effects of Treatment, Week, Treatment.Week

Suitable for arm length and also testes measurements

Arm length

Antedependence model order 1 (change of deviance)

Additional animal variance component (9.219, SE=2.331)

Fixed term Wald statistic d.f. Wald/d.f. chi prweek 3389.51 19 178.4 <0.001treatment 174.73 2 87.37 <0.001week.treatment 359.66 38 9.46 <0.001

Arm length

week Control Vac1 Vac2 Av. SED0 85.67 84.4 83.92 1.2047 85.02 85.67 83.59

101 114.78 102.42 100.22108 115.43 102.33 99.68115 116.01 103 100.58123 116.42 103.55 100.83131 117.28 104.17 101.41

Greater insight into treatment effects over time

ln(Testes volume)

Log transform needed to stabilize the variance

Antedependence order 1 model

When variance component included for animal, estimation problems with week 115

Antedependence order 2 model better, but effect on the predicted means is slight (smaller SED)

ln(Testes volume)

week Control Vac1 Vac2 Av. SED0 0.258 0.791 0.566 0.22457 0.957 0.536 0.197

11 1.366 0.314 0.08415 1.485 -0.038 -0.29819 1.907 0.032 -0.19224 2.096 0.013 -0.265

115 2.953 0.789 0.503123 3.025 0.995 0.684131 3.078 1.015 0.777

ln(Testes volume)

Significant interaction due to lack of differences until week 11

Large differences between treated and untreated animals thereafter

No significant differences between Vac 1 and Vac 2 at any stage

One Vac 1 animal effect perhaps wearing off

Tammar wallabies

2

0

1201008060

3

40

-1

20

1

0

Week

ln(T

estes vo

lum

e)

Concluding remarks

Two methods for analysing repeated measures data used on Tammar wallaby data

Summary statistics work well with smooth data

More complex method required to explore interactions over time and with data that is not smooth