We very much agree with Glaziou et al [1] that in some situations
evidence from case series or non-randomized cohorts render randomised
trials unnecessary. The authors discuss a rate ratio obtained by comparing
the rate of progression between treated and untreated periods for a single
case. This is similar to the relative incidence estimated from case series
using the self-controlled case series approach [2].
The authors recommend their approach when effects are large, but
acknowledge that their approach may yield biased estimates in the presence
of time trends. Indeed such methods have been used inappropriately, as in
investigating a possible association between DTP immunisation and sudden
infant death syndrome (SIDS) (see Example 1 in [3]).
The self-controlled case series method allows for full adjustment for
underlying trends, most flexibly using a semi-parametric approach [4].
Furthermore, it controls implicitly for all fixed confounders and random
individual effects, whether measured or not. Thus, in this respect, the
method achieves a degree of confounder control usually only available in
randomized trials.
Under suitable conditions, the method can be used to evaluate
treatment effects of small and moderate size for both acute and
progressive conditions. It has been applied in a wide range of situations
in pharmaco-epidemiology, including MMR vaccine and autism and
antidepressants and hip fracture (see [5] for details), and in other
contexts such as bacterial resistance to antibiotics [6].
References
1. Glasziou P., Chalmers I., Rawlins M. and McCulloch P. When are
randomized trials necessary? Picking signal from noise. British Medical
Journal 334: 349-351.
2. Farrington, C.P., 1995. Relative incidence estimation from case
series for vaccine safety evaluation. Biometrics, 51 228-235.
3. Farrington, C.P., 2004. Control without separate controls:
Evaluation of vaccine safety using case-only methods. Vaccine, 22 2064-
2070.
4. Farrington, C.P. and Whitaker, H.J., 2006. Semiparametric analysis
of case series data (with Discussion). Journal of Royal Statistical
Society, Series C, 55 553-594.
5. Whitaker, H.J., Farrington, C.P., Spiessens, B. and Musonda, P.,
2006. Tutorial in Biostatistics: The self-controlled case series method.
Statistics in Medicine, 25 1768-1797.
6. Hocine M, Guillemot D, Tubert-Bitter P, Moreau T. 2005. Testing
independence between two Poisson-generated multinomial variables in case-
series and cohort studies. Statistics in Medicine, 24 4035-4044.
Rapid Response:
Evidence from case series
We very much agree with Glaziou et al [1] that in some situations
evidence from case series or non-randomized cohorts render randomised
trials unnecessary. The authors discuss a rate ratio obtained by comparing
the rate of progression between treated and untreated periods for a single
case. This is similar to the relative incidence estimated from case series
using the self-controlled case series approach [2].
The authors recommend their approach when effects are large, but
acknowledge that their approach may yield biased estimates in the presence
of time trends. Indeed such methods have been used inappropriately, as in
investigating a possible association between DTP immunisation and sudden
infant death syndrome (SIDS) (see Example 1 in [3]).
The self-controlled case series method allows for full adjustment for
underlying trends, most flexibly using a semi-parametric approach [4].
Furthermore, it controls implicitly for all fixed confounders and random
individual effects, whether measured or not. Thus, in this respect, the
method achieves a degree of confounder control usually only available in
randomized trials.
Under suitable conditions, the method can be used to evaluate
treatment effects of small and moderate size for both acute and
progressive conditions. It has been applied in a wide range of situations
in pharmaco-epidemiology, including MMR vaccine and autism and
antidepressants and hip fracture (see [5] for details), and in other
contexts such as bacterial resistance to antibiotics [6].
References
1. Glasziou P., Chalmers I., Rawlins M. and McCulloch P. When are
randomized trials necessary? Picking signal from noise. British Medical
Journal 334: 349-351.
2. Farrington, C.P., 1995. Relative incidence estimation from case
series for vaccine safety evaluation. Biometrics, 51 228-235.
3. Farrington, C.P., 2004. Control without separate controls:
Evaluation of vaccine safety using case-only methods. Vaccine, 22 2064-
2070.
4. Farrington, C.P. and Whitaker, H.J., 2006. Semiparametric analysis
of case series data (with Discussion). Journal of Royal Statistical
Society, Series C, 55 553-594.
5. Whitaker, H.J., Farrington, C.P., Spiessens, B. and Musonda, P.,
2006. Tutorial in Biostatistics: The self-controlled case series method.
Statistics in Medicine, 25 1768-1797.
6. Hocine M, Guillemot D, Tubert-Bitter P, Moreau T. 2005. Testing
independence between two Poisson-generated multinomial variables in case-
series and cohort studies. Statistics in Medicine, 24 4035-4044.
Competing interests:
None declared
Competing interests: No competing interests