Assessment

1. Was the allocation sequence adequately generated?*

Examples of low risk of bias:

• Referring to a random number table
• Using a computer random number generator
• Coin tossing
• Shuffling cards or envelopes
• Throwing dice
• Drawing of lots
• Minimization with or without a random element

Examples of high risk of bias:

• Sequence generated by odd or even date of birth
• Sequence generated by some rule based on date (or day) of admission
• Sequence generated by some rule based on hospital or clinic record number
• Allocation by judgement of the clinician
• Allocation by preference of the participant
• Allocation based on the results of a series laboratory test or series of tests
• Allocation by availability of the intervention

2. Was the allocation adequately concealed?

Examples of low risk of bias allocation concealment techniques:

• Central allocation (including telephone, web-based, and pharmacy-controlled, randomization)

Examples of possible low risk of bias:

• Sequentially numbered drug containers of identical appearance
• Sequentially numbered, opaque, sealed envelopes

Examples of high risk of bias allocation concealment techniques:

• Using an open random allocation schedule (e.g. a list of random numbers)
• Assignment envelopes were used without appropriate safeguards (e.g. if envelopes were unsealed or non-opaque or not sequentially numbered)
• Alternation or rotation
• Date of birth
• Case record number
• Any other explicitly unconcealed procedure

3. Blinding: Was knowledge of the allocated interventions adequately prevented? *

3.a. Were patients blinded?

3.b. Were healthcare providers blinded?

3.c. Were data collectors blinded?

3.d. Were outcome assessors blinded?

3.e. Were data analysts blinded?

Examples of low risk of bias:

• No blinding but the review authors judge that the outcome and the outcome measurement are not likely influenced by lack of blinding
• Blinding of participants and key study personnel ensured, and unlikely that blinding could have been broken
• Either participants or some key study personnel were not blinded, but outcome assessment was blinded and the nonblinding of others unlikely to introduce bias

Examples of high risk of bias:

• No blinding or incomplete blinding, and the outcome or outcome measurement is likely to be influenced by lack of blinding
• Blinding of key study participants and personnel attempted, but likely that the blinding could have been broken
• Either participants or some key study personnel were not blinded, and the nonblinding of others likely to introduce bias

4. Was loss to follow-up (missing outcome data) infrequent?

Examples of low risk of bias:

• No missing outcome data
• Reasons for missing outcome data unlikely to be related to outcome (for survival data, censoring unlikely to be introducing bias)
• Missing outcome data balanced in numbers across intervention groups, with similar reasons for missing data across groups
• For dichotomous outcome data, the proportion of missing outcomes compared with observed event risk not enough to have an important impact on the intervention effect estimate
• For continuous outcome data, plausible effect size (difference in means or standardized difference in means) among missing outcomes not enough to have an important impact on observed effect size
• Missing data have been imputed using appropriate methods

Examples of high risk of bias:

• Reason for missing outcome data likely to be related to true outcome, with either imbalance in numbers or reasons for missing data across intervention groups
• For dichotomous outcome data, the proportion of missing outcomes compared with observed event risk enough to induce important bias in intervention effect estimate
• For continuous outcome data, plausible effect size (difference in means or standardized difference in means) among missing outcomes enough to induce clinically relevant bias in observed effect size
• ‘As-treated’ analysis done with substantial departure of the intervention received from that assigned at randomization
• Potentially inappropriate application of simple imputation

5. Are reports of the study free of selective outcome reporting?*

Examples of low risk of bias:

• The study protocol is available and all of the study’s pre-specified (primary and secondary) outcomes that are of interest in the review have been reported in the pre-specified way
• The study protocol is not available but it is clear that the published reports include all expected outcomes, including those that were pre-specified (convincing text of this nature may be uncommon)

Examples of high risk of bias:

• Not all of the study’s pre-specified primary outcomes have been reported
• One or more primary outcomes is reported using measurements, analysis methods or subsets of the data (e.g. subscales) that were not pre-specified
• One or more reported primary outcomes were not pre-specified (unless clear justification for their reporting is provided, such as an unexpected adverse effect)
• One or more outcomes of interest in the review are reported incompletely so that they cannot be entered in a meta-analysis
• The study report fails to include results for a key outcome that would be expected to have been reported for such a study

6. Was the study apparently free of other problems that could put it at a risk of bias?*

Examples of low risk of bias:

• The study appears to be free of other sources of bias

Examples of high risk of bias:

• Had a potential source of bias related to the specific study design used
• Stopped early due to some data-dependent process (including a formal-stopping rule)
• Had extreme baseline imbalance
• Has been claimed to have been fraudulent

Had some other problem