title: Comparing like with like: some historical milestones in the evolution of methods to create unbiased comparison groups in therapeutic experiments.
abstract: Histories of clinical trials have recorded and analysed the development of quantification in therapeutic evaluation, the emergence of probabilistic thinking, the application of statistical methods and theory, and the sociology, ethics and politics of clinical trials; but it is surprising that they only rarely identify as a distinct theme the development of efforts to control biases. An exception is Kaptchuk's recent account of the history of blinding and placebos for reducing observer biases. In this complementary paper I introduce and discuss some milestones between 1662 and 1948 in the development of methods to control selection biases when assembling therapeutic comparison groups, to ensure, as far as possible, that 'like is compared with like'. In the paper I note (i) that treatment allocation based on strict alternation abolishes selection bias as effectively as treatment allocation based on strict random allocation; (ii) that use of schedules based on random numbers is more likely to prevent foreknowledge of allocation schedules, and thus the risk of introducing selection bias at the point of recruitment to trials; (iii) that a concern to conceal allocation schedules was the rationale for using schedules based on random numbers in the Medical Research Council trials of vaccination for whooping cough and streptomycin for pulmonary tuberculosis; and (iv) that the introduction of allocation concealment more than half a century ago remains the most recent substantive milestone in the history of efforts to control selection biases in therapeutic experiments.
authors.affiliation: UK Cochrane Centre, NHS Research and Development Programme, Summertown Pavilion, Middle Way, Oxford OX2 7LG, UK. ichalmers@cochrane.co.uk
authors.forename: I
authors.initials: I
authors.lastname: Chalmers
mesh_terms: D004812:Epidemiologic Methods; D006801:Humans; D008091:Literature; D015983:Selection Bias