RANDOMIZED CONTROLLED TRIALS Design, Performance, Analysis ...

RANDOMIZED CONTROLLED TRIALS Design, Performance, Analysis ...

PLANNING AND PERFORMING A RANDOMIZED CONTROLLED CLINICAL TRIAL REPRODUCIBILITY IN RESEARCH Growing alarm about results that cannot be reproduced Nature Supplement, Challenges in Irreproducible Research, October 7, 2015 Reproducibility, rigor, transparency and independent verification are cornerstones of the scientific method NIH-Science-Nature Workshop on Reproducibility and Rigor of Preclinical Research

Nature 2014;515:7 ENHANCING REPRODUCIBILITY AND RIGOR IN CLINICAL RESEARCH Study design of high methodologic quality Minimizes bias: better estimate of truth ENHANCING REPRODUCIBILITY AND RIGOR IN CLINICAL RESEARCH Study design of high methodologic quality Minimizes bias: better estimate of truth

Transparent (full and clear) presentation of methods and analyses Enables assessment of methods and results Allows duplication of study, re-analysis of results ENHANCING REPRODUCIBILITY AND RIGOR IN CLINICAL RESEARCH Study design of high methodologic quality Minimizes bias: better estimate of truth Transparent (full and clear) presentation of methods and analyses Enables assessment of methods and results

Allows duplication of study, re-analysis of results Registration before trial begins Prevents changing design or pre-specified outcomes/analyses without explanation ENHANCING REPRODUCIBILITY AND RIGOR IN CLINICAL RESEARCH Study design of high methodologic quality Minimizes bias: better estimate of truth Transparent (full and clear) presentation of methods and analyses

Enables assessment of methods and results Allows duplication of study, re-analysis of results Registration before trial begins Prevents changing design or pre-specified outcomes/analyses without explanation Trials reported per international standards All appropriate elements included REPRODUCIBILITY ISSUES Preclinical Research Most Susceptible

Clinical trials seem to be less at risk because already governed by regulations that stipulate rigorous design and independent oversight (randomization, blinding, power estimates, pre-registration in standardized, public databases, oversight by IRBs and DSMBs) and adoption of standard reporting elements Collins FS, Tabak LA. Nature 2014;505:612 CLINICAL TRIALS LEVELS OF EVIDENCE FOR CLINICAL

RESEARCH STUDY DESIGNS Schillaci et al. Hypertension. 2013;62:470 OBSERVATIONAL VS. RANDOMIZED TRIALS OBSERVATIONAL VS. RANDOMIZED TRIALS There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say, we know there are some things we do not know. But there are also unknown unknowns--the ones we dont know we dont know.

- Donald Rumsfeld OBSERVATIONAL VS. RANDOMIZED TRIALS Observational Studies Distribution of baseline factors that may impact outcome (e.g., age, meds, comorbidities) vary in study groups Known knowns: known to impact outcome, collected Statistical adjustment, matching Known unknowns: known to impact outcome, cant be collected Unknown unknowns: dont know impact outcome, not collected Randomized Trials

All factors (known and unknown) that may impact outcome equally distributed among study groups RANDOMIZED CONTROLLED TRIAL Only randomized trials of sufficient size can adequately control for known and unknown confounding variables to minimize bias No substantive differences between groups except study intervention (randomly assigned) Difference between groups in predefined outcome can be attributed to the intervention being studied Hennekens & Buring Epidemiology in Medicine. 1987

DO WE ALWAYS NEED AN RCT TO DOCUMENT BENEFIT OF AN INTERVENTION? DO WE ALWAYS NEED AN RCT TO DOCUMENT BENEFIT OF AN INTERVENTION? Perception that parachutes are a successful intervention based largely on anecdotal evidence No RCTs identified in systematic review Under exceptional circumstances apply common sense BMJ 2003;327:1459

RCT MAY NOT BE POSSIBLE OR PRACTICAL Not ethical/possible to assign intervention Cigarette smoking and lung cancer H. pylori infection and ulcers Impractically large sample size Very low-incidence outcome e.g., rare side effect of medication Impractically long duration Outcome requires many years to develop e.g., development of cancer

RANDOMIZED CONTROLLED TRIALS First Steps RANDOMIZED CONTROLLED TRIALS First Steps Clinically relevant question Greatest impact if limited information or high variability in care or outcomes Can be answered by properly designed RCT Feasible to perform at your center(s)

RANDOMIZED CONTROLLED TRIALS First Steps Clinically relevant question Greatest impact if limited information or high variability in care or outcomes Can be answered by properly designed RCT Feasible to perform at your center(s) Systematic review Identify available information Justify importance of question Help design study

RANDOMIZED CONTROLLED TRIALS First Steps Define key elements of study Population Intervention Comparator Outcome State primary hypothesis Expected result for primary outcome in population

e.g., in patients with cirrhosis fewer deaths with new intervention vs. control STUDY DESIGN RANDOMIZATION RANDOMIZATION Generate sequence of allocation Computer generated, random numbers table Randomize in blocks Other features of randomization include

Concealed allocation Non-manipulable allocation schedule Off-site randomization schedule ideal Stratification Most important factor(s) that may impact endpoint CONCEALED ALLOCATION Concealed allocation is an extension of randomization When obtaining informed consent to enroll a patient into a trial, the investigator does not know if the next patient will get new

treatment or control CONCEALED ALLOCATION RCT comparing new therapy vs. placebo for abdominal pain in irritable bowel syndrome Investigator interviews the next eligible patient, who complains of long-term severe, unrelenting symptoms that have never responded to previous medical therapy Next patient to enter trial will get placebo CONCEALED ALLOCATION Investigator thinks that placebo is unlikely

to relieve abdominal pain in this patient Investigator may subconsciously try to convince patient not to enroll in the trial Consequence: patients with severe abdominal pain will NOT be evenly divided between new therapy and placebo groups STRATIFICATION To assure baseline factor(s) that impact study outcome equally distributed in study groups Especially useful in smaller trials

Choose factor(s) that have greatest impact on primary outcome Aspirin use in MI study Separate randomization schedules for patients with and without factor RANDOMIZATION Block Size (e.g., 4, 10, 20; Random) Assures equal number in each study arm for every successive block of patients enrolled Prevents unequal numbers in study arms

Prevents differences in distribution over time e.g., study intervention mostly early, comparator mostly later Disadvantage: if block size figured out, next allocation may be predictable (unconcealed) selection bias Larger block sizes; random sequences of block sizes BLINDING BLINDING Not known if subject getting new therapy or control Subjects

Healthcare providers making management decisions Investigators collecting/analyzing data Prevents bias in management decisions and in assessment of outcomes by subject or investigator Knowledge receiving placebo or active drug may influence Administration of another therapy that my impact outcome Assessment of symptoms, signs (endpoints) BLINDING Identical appearing therapies Real vs. sham surgery/procedure Surgical team uninvolved in further care/assessment

Double-dummy Subjects receive identical active and control therapy together Side effect of a therapy may unblind subjects Assess whether unblinded TREATMENT EFFECT OVERESTIMATED WITHOUT RANDOMIZATION AND BLINDING 35

25 20 ase 0 Not Randomized Randomized Treatment

Placebo Treatment 5 Placebo 10 Treatment

15 Placebo Fatality Rates 30 Concealed Allocation; Blinded Chalmers, et al. N Engl J Med 1983; 309: 1358

PATIENT POPULATION Inclusion and exclusion criteria Broad: exclude few, more generalizable Restricted: exclude many, less generalizable Prospectively screen consecutive patients with condition of interest Skipping patients may introduce bias Screening log Subjects screened, but not enrolled Brief characteristics, reason not enrolled ?Differences from those enrolled

Is study generalizable? STUDY INTERVENTIONS Define all aspects of study interventions so uniform in trial, able to be reproduced Control Placebo control Best to define efficacy of study therapy May not be ethical, practical Cant withhold standard care if documented effective Active control (a current standard) Hypothesis: new therapy superior, non-inferior, or

equivalent to active control ENDPOINTS What do you want to achieve with the new intervention Primary endpoint Additional endpoints Surrogate vs. clinical endpoints Surrogate endpoint: measure of treatment effect felt likely to correlate with clinical endpoint e.g., gastric acid inhibition for ulcer prevention CLINICALLY MEANINGFUL

ENDPOINTS PREFERRED Which study endpoint would alter practice? Lab test (CRP) or clinical outcome (death) Studies of intermediate/surrogate endpoints may indicate areas for further research, but generally dont alter patient management Some surrogate endpoints are accepted as true indicators of clinical outcomes e.g., blood pressure, cholesterol, colon polyps SAMPLE SIZE DETERMINATION

SAMPLE SIZE DETERMINATION Assumptions for Superiority Study Primary endpoint result for the intervention Primary endpoint result for the comparator Assumptions based on available data, clinical judgment Hypothesized difference should be clinically meaningful, realistic p = 0.05 probability of finding difference when doesnt exist (type I)

Power (1 ) ) Probability of finding difference when does exist e.g., 80%, 90% ) : probability of not finding a difference when does exist (type II) SAMPLE SIZE DETERMINATION Why Did They Stop the Study When They Did? RCT: Wonderdrug vs. placebo in pancreatic cancer Primary endpoint: 5-yr survival

Wonderdrug: 50% Placebo: 10% P-value ( = 0.05) 90% power to detect 40% difference between Wonderdrug and placebo 52 patients required (if 1:1 randomization) SAMPLE SIZE DETERMINATION Assumptions for Non-Inferiority Study

Determine non-inferiority margin Clinical: maximal difference that would be considered clinically non-inferior Not unacceptably worse than the control Statistical: maintain benefit above placebo Control is 20% more efficacious than placebo Margin of 10% retains half control treatment effect Margin (e.g., control test drug = 3%) less than upper bound of CI of difference observed in study Difference = 0% (95% CI -5% to 5%): Not non-inferior Difference = 0% (95% CI -1% to 1%): Non-inferior

SAMPLE SIZE DETERMINATION Non-Inferiority Study Determine non-inferiority margin Clinical: maximal difference that would be considered clinically non-inferior Potential reasons toworse do non-inferiority Not unacceptably than the control study

Statistical: above placebo New interventionmaintain has some benefit other advantage that would recommend Control is 20% more efficacious than placebo

it if efficacy similar (non-inferior) to current standard therapy Margin of 10% retains half control treatment effect e.g., cheaper, safer, easier to use (pill vs. enema), more readily Margin (e.g., control test drug = 3%) less than

available (oral rehydration vs. IV fluids); commercial upper bound of CI of difference observed in study Difference = 0% (95% CI -5% to 5%): Not non-inferior Difference = 0% (95% CI -1% to 1%): Non-inferior NON-INFERIORITY STUDY: FDA EXAMPLE New thrombolytic (R) vs. approved therapy (S) Outcome: Mortality New thrombolytic must retain 50% benefit of approved therapy to be acceptable alternative Mortality difference S vs. placebo 2.6% (lower bound 95% CI = 2.1%)

Study has to rule out 1.05% increase in mortality with R compared to S 95% CI of difference in mortality for R vs. S < 1.05% Accept 1.05% increase as not unacceptably worse http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM202140.pdf IS THE SAMPLE SIZE FEASIBLE Review medical records at study center(s) to Determine number who meet enrollment criteria Confirm assumptions about outcomes

Preparatory to research review doesnt require IRB approval of the protocol This type of access is limited to a review of data to assist in formulating a hypothesis, determining the feasibility of conducting the study . . . or other similar uses that precede the development of an actual protocol. Submit Request for Access form POPULATIONS FOR ANALYSIS POPULATIONS FOR ANALYSIS Intention to Treat Analysis All randomized patients are included in final

data analysis Per Protocol Analysis Only patients who complete the trial according to protocol are analyzed 45 POPULATIONS FOR ANALYSIS Intention-to-treat population All patients randomized regardless of follow-up or receipt of study intervention Per-protocol population excludes those who

Did not receive sufficient study intervention Did not return for adequate follow-up Had major violations of inclusion criteria e.g., did not have the disease being studied Had major violations during the study Took non-study PPI during PPI vs. placebo study INTENTION-TO-TREAT ANALYSIS Example Comparison of radiology procedure (TIPS) vs. drug () -blocker) for prevention of

recurrent variceal bleeding with death as the primary endpoint If a patient is randomized to get TIPS and dies from bleeding before the procedure can be done, should the patient be included in the final data analysis? POPULATIONS FOR ANALYSIS Choose the most conservative analysis Less likely to favor intervention, be overly optimistic Superiority study Per-protocol assesses intervention under optimal

circumstances (not real world, ignores study quality) e.g., excluded if non-adherence, protocol violations, drop-out ITT avoids bias to treatment difference and superiority Non-inferiority study ITT can bias to no treatment difference (non-inferiority) e.g., non-adherence, drop-outs, misclassified subjects/endpoints Per protocol analysis should be included COMPLETE FOLLOW-UP OF PATIENTS Another Requirement for High Methodologic Quality

If numerous patients are lost to follow-up, results of the trial may not be accurate Predefine method to deal with such patients Last observation carried forward Imputation methods Re-calculate results assuming that patients lost to follow-up in new treatment group had bad outcome and patients lost to follow-up in control group had good outcome RECRUITMENT AND RETENTION Engagement, communication with participants

before and throughout trial Brochures, ads, social media, phone/text/email/websites Reminders for study personnel and participants Benefits of participation for subjects Societal, personal, financial reimbursement for time Benefits of participation for research personnel Academic (e.g., authorship), financial Identify and minimize barriers to participation Easy access to study personnel and activities Participation as non-onerous as possible

STUDY ANALYSIS STUDY ANALYSIS Predefine presentation of data Proportions vs. time-to-event curves Mean vs. median Predefine statistical analyses Comparisons for primary, additional outcomes Subgroup analyses Other analyses e.g., multivariable analyses, sensitivity analyses

COMPARING THE STUDY GROUPS P-VALUE: DID DIFFERENCE BETWEEN TREATMENT AND CONTROL OCCUR DUE TO CHANCE? Null hypothesis True proportion of success with treatment equals true proportion of success with control If the null hypothesis correct and treatments are equally effective, p-value indicates Probability of observing a difference between

treatment and control at least this large Probability that difference at least this large is due to chance P-VALUE: DID DIFFERENCE BETWEEN TREATMENT AND CONTROL OCCUR DUE TO CHANCE? A small p-value (< 0.05) means finding a difference at least this large is unlikely if the null hypothesis (treatments equally effective) is true Reject the null hypothesis

P-VALUE WITH MULTIPLE COMPARISONS Increased chance comparisons significant Type 1 error: 2 endpoints: ~10%; 10 endpoints: ~40% Process to control rate of false positives Single primary outcome, limited additional outcomes Correct for multiplicity Bonferroni (p/# analyses: 0.05/10 = 0.005) Hierarchical testing (predefined sequence, stop when p>0.05) Other techniques Provide results without claims of significance Dont perform many analyses stating significance is p<0.05

for all analyses http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM536750.pdf 95% CONFIDENCE INTERVALS A More Precise Tool to Assess the Results New therapy vs. control Absolute difference = 9%, 95% CI 4 14% If trial repeated 100 times, difference would be between 4% and 14% in 95 of 100 trials

95% CONFIDENCE INTERVALS A More Precise Tool to Assess the Results Tighter the interval around the observed result, the more precise the estimate Difference = 0%: -30% to 30% vs. -1% to 1% 95% CONFIDENCE INTERVALS A More Precise Tool to Assess the Results Tighter the interval around the observed result, the more precise the estimate Difference = 0%: -30% to 30% vs. -1% to 1%

If bounds of 95% CI Both >0 (for absolute difference) or >1 (for RR) of new therapy vs. control Consistent with new treatment more efficacious Difference=20%, 10-20%; RR=2.5, 1.5-3.5 95% CONFIDENCE INTERVALS A More Precise Tool to Assess the Results Tighter the interval around the observed result, the more precise the estimate Difference = 0%: -30% to 30% vs. -1% to 1%

If bounds of 95% CI Both >0 (for absolute difference) or >1 (for RR) of new therapy vs. control Consistent with new treatment more efficacious Difference=20%, 10-20%; RR=2.5, 1.5-3.5 Cross 0 (for absolute difference) or 1 (for RR) Consistent with new treatment better or worse Difference=5%, -5% to 15%; RR=1.2, 0.6 to 2.0 95% CONFIDENCE INTERVALS A More Precise Tool to Assess the Results

Treatment Control p-value Difference 95% CI Interpretation Success (Small Study) 13/26 (50%) 10/26 (38%) 0.40

12% -15% to 38% Large effect size; high uncertainty Success (Large Study) 1000/2000 (50%) 973/2000 (48.7%) 0.40 1.3% -1.8% to 4.4% Small effect size;

low uncertainty Connor. AJG 2004;99:1638 SUBGROUP ANALYSES Does intervention behave differently in subset of population e.g., men vs. women, old vs. young Should be pre-defined and justified Post hoc more risk of bias since results known Less power than overall analysis

More analyses increases chance of significance occurring by chance Smaller number in each subgroup SUBGROUP ANALYSES Dont assess if treatment effect is significant in each subgroup Dont compare outcome for new therapy vs. control in each subgroup If baseline assumptions about results for outcomes in sample size calculation were correct, wouldnt expect smaller subgroups to

show significant difference even if treatment effect exactly the same as in overall group SUBGROUP ANALYSES Dont assess if treatment effect is significant in each subgroup Dont Study of New Therapy vs. Control compare outcome

for new therapy vs. control in each subgroup 200 subjects to show 20% difference (50% vs. 30%) If baseline about results Overall:assumptions

50/100 (50%) vs. 30/100 (30%) for p=0.01 outcomes size vs. calculation were Men:in sample 20/40 (50%) 12/40 (30%) p=0.11 correct,

wouldnt expect to Women: 30/60 (50%) smaller vs. 18/60 subgroups (30%) p=0.04 show significant difference even if treatment effect exactly the same as in overall group SUBGROUP ANALYSES

Compare Treatment Effects in Subgroups Is treatment effect (e.g., RR of death for new therapy vs. control) different for old vs. young? Is there a treatment by subgroup interaction? Qualitative interaction Treatment effects in opposite direction e.g., RR < 1 for old; RR > 1 for young Quantitative interaction Treatment effects of different magnitude--statistically heterogeneous (interaction test p < 0.05) e.g., old: RR = 0.4, 0.1-1.0; young: RR = 0.9, 0.7-1.2

DATA SAFETY MONITORING BOARD DATA SAFETY MONITORING BOARD Independent, external experts No involvement, conflicts related to study Periodic assessments

Data quality, timeliness; trial site performance Recruitment, accrual and retention Safety and efficacy outcomes (risk-benefit) Factors external to the study New data may impact safety or ethics of study Make recommendations to continue, modify, or terminate study DATA SAFETY MONITORING BOARD Independent, external experts No involvement, conflicts related to study

Periodic assessments Ensures that a clinical trial is stopped if the Data quality, timeliness; trial site performance Recruitment, benefit-risk balance or the accrual for andparticipants retention Safety and efficacy outcomes (risk-benefit) expected

value to society no longer justifies Factors external to the study New data may impact safety or ethicsLewis of study continuing et al. JAMA 2016;316:2359

Make recommendations to continue, modify, or terminate study INTERIM ANALYSIS Predefined Formal Data Analysis Partway Through Trial Efficacy: outcome assumptions used in sample size determination may be inexact/uncertain Modify study (adaptive design) Stop study (may have predefined stopping rules) Futility: no possibility can document benefit Efficacy: unequivocal benefit in clinically important outcome

Very conservative p-value (e.g., 0.001) Prevents incorrect conclusion (vs. p-value closer to 0.05) Minimal spending with multiple comparisons Final significant p-value only slightly below 0.05 INTERIM ANALYSIS Predefined Formal Data Analysis Partway Through Trial Efficacy: outcome assumptions used in sample size determination may be inexact/uncertain Modify study (adaptive design) Stop study (may have predefined stopping rules)

Futility: no possibility can document benefit Efficacy: unequivocal benefit in clinically important outcome Very conservative p-value (e.g., 0.001) Prevents incorrect conclusion (vs. p-value closer to 0.05) Minimal spending with multiple comparisons Final significant p-value only slightly below 0.05 Safety: Concern/uncertainty about therapys safety Stop study if unacceptable risk-benefit balance Add additional monitoring, therapies, safeguards TRIAL REGISTRATION

PURPOSE OF TRIAL REGISTRATION International Committee of Medical Journal Editors (ICMJE) Prevent selective publication and selective reporting of research outcomes Prevent unnecessary duplication of research Help public know of planned or ongoing trials into which they might want to enroll Give ethics review boards considering approval of new studies a view of similar work and data relevant to the research http://icmje.org/recommendations/browse/publishing-and-editorial-issues/clinical-trial-registration.html

CLINICAL TRIAL DEFINITION ClinicalTrials.gov Participants receive intervention per protocol Drug, device, procedure, diet, brochure, video Outcomes measured to determine safety, efficacy ICMJE (Medical Journal Editors) Prospectively assigns people to an intervention, with or without concurrent comparison, to study the cause-andeffect relationship between a health-related intervention and a health outcome NIH

Human subject(s) prospectively assigned to intervention to evaluate the effects on health-related biomedical or behavioral outcomes https://clinicaltrials.gov/ct2/about-studies/learn; http://icmje.org/recommendations/browse/publishing-and-editorial-issues/clinical-trial-registration.html https://oir.nih.gov/sourcebook/intramural-program-oversight/intramural-data-sharing/guide-fdaaa-reporting-research-results/nih-definition-clinical-trial CLINICAL TRIAL DEFINITION ClinicalTrials.gov Participants receive intervention per protocol Drug, device, procedure, diet, brochure, video Outcomes measured to determine safety, efficacy

Clinical Trial ICMJE (Medical Journal Editors) Prospectively assigns people to an intervention, with or Prospective without concurrent comparison, to study the cause-and Assignment effect relationship between a health-related intervention of intervention

and a health outcome Measurement of health outcomes after intervention NIH Human subject(s) prospectively assigned to intervention to evaluate the effects on health-related biomedical or behavioral outcomes https://clinicaltrials.gov/ct2/about-studies/learn; http://icmje.org/recommendations/browse/publishing-and-editorial-issues/clinical-trial-registration.html https://oir.nih.gov/sourcebook/intramural-program-oversight/intramural-data-sharing/guide-fdaaa-reporting-research-results/nih-definition-clinical-trial

WHY REGISTER A CLINICAL TRIAL Required by law (U.S. FDA) Controlled clinical investigations of FDA-regulated drug, biologic, or device other than Phase 1 (drugs/biologics) or small feasibility studies Enable publication (ICMJE) All interventional studies, including Phase 1 Ethical (e.g., WHO, Declaration of Helsinki) The registration of all interventional trials is a scientific, ethical and moral responsibility (WHO) https://clinicaltrials.gov/ct2/manage-recs/background

REPORTING TRIALS GUIDELINES FOR REPORTING TRIALS Ensures all important elements of study are included and reported appropriately CONSORT guidelines Framework for reporting RCTs Required by many journals Checklist for the content of the title, abstract, introduction, methods, results, and discussion CONSORT flow diagram

CONSORT=Consolidated Standards of Reporting Trials RANDOMIZED CONTROLLED TRIALS Best design to minimize bias in assessment of an intervention Factors to provide high methodologic quality e.g., concealed allocation, blinding, complete f/u Predefined outcomes, sample size, analyses Pre-trial registration ensures no change from original design without explanation Reporting guidelines ensure all important elements provided in publication

ASSESSMENT OF RCT BACKGROUND AND HYPOTHESIS Background Relevance, importance, novelty of topic Rationale clear Hypothesis Primary hypothesis stated BACKGROUND AND HYPOTHESIS Background

Colonoscopy with sedation extremely common Guidelines suggest diphenhydramine if difficult to sedate with standard benzodiazepine/opioid No studies assessed this practice Hypothesis Introduction Goal is to determine if diphenhydramine superior to continued midazolam in difficult-to-sedate patients Statistics Hypothesis: diphenhydramine is superior in achieving adequate moderate sedation in difficult-to-sedate patients

TRIAL REGISTRATION AND REPORTING Trial Registered e.g., Clinicaltrials.gov Trial reported per guidelines CONSORT for RCTs TRIAL REGISTRATION AND REPORTING Trial Registered Methods Registered with ClinicalTrials.gov (NCT01769586)

Trial reported per guidelines CONSORT flow diagram (Figure 1) No other mention of CONSORT Journal requires CONSORT checklist with submission STUDY DESIGN Randomization Method of randomization stated

Allocation concealed Block size Stratification Blinding Patients, caregivers, investigators Method of blinding stated (e.g., double-dummy) STUDY DESIGN Randomization

Computer generated by uninvolved individual Allocation concealed (opaque covering) Block size: not stated Stratification: Not stated (presume not done) Blinding Patients, caregivers, investigators blinded Person giving drug unblinded (not otherwise involved) Better if coded syringes from central location Method of blinding: identical syringes

STUDY DESIGN Population Inclusion, exclusion criteria clearly stated Broad vs. restrictive Consecutive patients enrolled Intervention and Control Characterized fully Clinically appropriate Simulate standard practice Control is an acceptable standard of care

STUDY DESIGN Population Inclusion, exclusion criteria clear Broad in that virtually anyone undergoing colonoscopy who was difficult to sedate Consecutive patients enrolled Yes, but only days when 2 investigators available Intervention and Control Characterized fully: Yes Clinically appropriate

Simulate standard practice and control acceptable standard of care: Yes STUDY DESIGN Primary and additional outcomes defined Clinically relevant Appropriately measured Sample size assumptions, calculation Superiority vs. non-inferiority study Assumptions reasonable STUDY DESIGN

Primary and additional outcomes defined Adequate sedation is clinically relevant Patient and endoscopist assessment most relevant? All patients could receive more meds if not adequately sedated MOAA/S valid instrument to measure sedation ?difference in MOAA/S performance with different drugs Sample size assumptions, calculation Superiority study Outcome assumptions not well justified Based on clinical experience 20% difference reasonable--clinically meaningful

ANALYSES Population ITT vs. per-protocol Full accounting of subjects Primary and additional analyses Predefined analyses presented

Appropriate statistical comparisons Magnitudes and precision of effect Subgroup analyses (choice of subgroups justified) Other analyses: e.g., sensitivity, multivariable Post hoc analyses ANALYSES Population ITT Full accounting of subjects: Yes (no drop-outs) Primary and additional analyses

Predefined analyses presented Appropriate statistical comparisons Magnitudes and precision of effect 95% CIs of difference for all; p-value for primary outcome Subgroup analyses (choice of subgroups justified) Reasons given; ORs of subgroups compared (interaction test >0.05) Multivariable analysis post hoc due to somewhat unequal distribution of potential confounders CONCLUSIONS Do results support conclusions

Limitations discussed Potential sources of bias Magnitude and precision of results Generalizability Results placed in context of current knowledge and practice Consider outcomes not studied e.g., cost, availability, ease of use, other risks Consider other relevant evidence outside study CONCLUSIONS

Do results support conclusions: yes Limitations discussed Potential sources of bias: probably not Magnitude and precision of results: Not precision Generalizability: Yes Results placed in context of current knowledge and practice Consider outcomes not studied No (safety, time to d/c, willingness to repeat studied) Other relevant evidence outside study Prior study; information on the drugs

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