# 11: Randomization, blinding, and coding

$$\newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} }$$

$$\newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}}$$

$$\newcommand{\id}{\mathrm{id}}$$ $$\newcommand{\Span}{\mathrm{span}}$$

( \newcommand{\kernel}{\mathrm{null}\,}\) $$\newcommand{\range}{\mathrm{range}\,}$$

$$\newcommand{\RealPart}{\mathrm{Re}}$$ $$\newcommand{\ImaginaryPart}{\mathrm{Im}}$$

$$\newcommand{\Argument}{\mathrm{Arg}}$$ $$\newcommand{\norm}[1]{\| #1 \|}$$

$$\newcommand{\inner}[2]{\langle #1, #2 \rangle}$$

$$\newcommand{\Span}{\mathrm{span}}$$

$$\newcommand{\id}{\mathrm{id}}$$

$$\newcommand{\Span}{\mathrm{span}}$$

$$\newcommand{\kernel}{\mathrm{null}\,}$$

$$\newcommand{\range}{\mathrm{range}\,}$$

$$\newcommand{\RealPart}{\mathrm{Re}}$$

$$\newcommand{\ImaginaryPart}{\mathrm{Im}}$$

$$\newcommand{\Argument}{\mathrm{Arg}}$$

$$\newcommand{\norm}[1]{\| #1 \|}$$

$$\newcommand{\inner}[2]{\langle #1, #2 \rangle}$$

$$\newcommand{\Span}{\mathrm{span}}$$ $$\newcommand{\AA}{\unicode[.8,0]{x212B}}$$

$$\newcommand{\vectorA}[1]{\vec{#1}} % arrow$$

$$\newcommand{\vectorAt}[1]{\vec{\text{#1}}} % arrow$$

$$\newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} }$$

$$\newcommand{\vectorC}[1]{\textbf{#1}}$$

$$\newcommand{\vectorD}[1]{\overrightarrow{#1}}$$

$$\newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}}$$

$$\newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}}$$

$$\newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} }$$

$$\newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}}$$

• 11.1: Introduction to randomization, blinding, and coding
As discussed in Chapter 4, the random allocation of participants in a trial to the different interventions being compared is of fundamental importance in the design of investigations that are conducted to produce the highest-quality evidence of any differences in the effects of the interventions. Only if the units to which the interventions are applied (for example, individuals, households, or communities) are randomized between the interventions under study and the study is of a sufficient size
• 11.2: Randomization schemes for individual participants
Simple random allocation of individuals between the different intervention groups is carried out most conveniently by using a computer. For example, in Microsoft Excel, the instruction ‘= RANDBETWEEN(1,3)’ will produce a random number between 1 and 3, i.e. each of the numbers 1, 2, or 3 has an equal chance of being generated. The equivalent of tossing a coin is = RANDBETWEEN(1,2). Some calculators also have a key which generates a random number on the display (usually a decimal number between 0
• 11.3: Randomization schemes for community or group-based interventions
As discussed in Chapter 4, trial designs have been increasingly employed in recent years, in which the unit of allocation of the intervention is a community or group, rather than an individual. These cluster randomized trials may involve the randomization of communities that can be quite large; consequently, the number of communities that can be included in a trial is often relatively small and may be of the order of 20 communities or fewer. If a method of simple unrestricted randomization is us
• 11.4: Blinding
Whenever possible, neither the participants nor the investigators should know to which intervention group each participant belongs until after the end of the trial. Such ‘double-blind’ designs (both the investigator and the participants are blind to the knowledge of who have received each intervention) eliminate the possibility that knowing to which intervention an individual is allocated may affect the way the individual behaves, is treated, or is monitored during the trial, or the way an indiv
• 11.5: Coding systems
In some circumstances, it may be necessary to break the intervention code for an individual. This might arise, for example, if a severe adverse event becomes manifest and the treatment for it may be influenced by knowledge of what intervention the individual received.
• 11.6: References
Binka, F. N., Kubaje, A., Adjuik, M., et al. 1996. Impact of permethrin impregnated bednets on child mortality in Kassena-Nankana district, Ghana: a randomized controlled trial. Tropical Medicine and International Health, 1, 147–54.[Cross Ref][PubMed] Brown, J. A. and Stone, M. M. 1966. B.C.G. vaccination of children against leprosy: first results of a trial in Uganda. BMJ, 1, 7–14.[Cross Ref][PubMed] Convit, J., Sampson, C., Zuniga, M., et al. 1992. Immunoprophylactic trial with combined Myco

This page titled 11: Randomization, blinding, and coding is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by Drue H. Barrett, Angus Dawson, Leonard W. Ortmann (Oxford University Press) via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.