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1 Chapter 11 Issues in Analysis of Randomized Clinical Trials.

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1 1 Chapter 11 Issues in Analysis of Randomized Clinical Trials

2 2 Issues in Analysis of Randomized Clinical Trials Reference: May, DeMets et al (1981) Circulation 64: Peto et al (1976) British Journal of Cancer

3 3 Sources of Bias 1.Patient selection 2.Treatment assignment 3.Patient Evaluation 4.Data Analysis Methods to Minimize Bias 1.Randomized Controls 2.Double blind (masked) 3.Analyze what is randomized

4 4 What Data Should Be Analyzed? Basic Intention-to-Treat Principle –Analyze what is randomized! –All subjects randomized, all events during follow-up Randomized control trial is the “gold” standard” Definitions Exclusions –Screened but not randomized –Affects generalizability but validity OK Withdrawals from Analysis –Randomized, but not included in data analysis –Possible to introduce bias!

5 5 Patient Closeout ICH E9 Glossary –“Intention-to-treat principle - …It has the consequence that subjects allocated to a treatment group should be followed up, assessed, and analyzed as members of that group irrespective of their compliance with the planned course of treatment.”

6 6 Intention To Treat (ITT) Principle Analyze all subjects randomized & all events Beware of “look alikes” –Modified ITT: Analyze subjects who get some intervention –Per Protocol: Analyze subjects who comply according to the protocol

7 7 Patient Withdrawn in Analysis (1) Common Practice s –Over 3 years, 37/109 trials in New England Journal of Medicine published papers with some patient data not included Typical Reasons Given a.Patient ineligible (in retrospect) b.Noncompliance c.Competing events d.Missing data

8 8 Patient Withdrawn in Analysis (2) A.Patient INELIGIBLE –After randomization, discover some patients did not in fact meet entry criteria –Concern ineligible patients may dilute treatment effect –Temptation to withdraw ineligibles –Withdrawl of ineligible patients, post hoc, may introduce bias

9 9 Betablocker Heart Attack Trial (JAMA, 1982) 3837 post MI patients randomized 341 patients found by Central Review to be ineligible Results % Mortality PropranololPlacebo Eligible Ineligible Best Total  In the ineligible patients, treatment works best

10 10 Acceptable Policies For Ineligible Subjects 1.Delay randomization, confirm eligibility and allow no withdrawals (e.g. AMIS) (Chronic Studies) 2.Accept ineligibles, allow no withdrawals (e.g. BHAT, MILIS) (Acute Studies) 3.Allow withdrawals if: a. Procedures defined in advance b.Decision made early (before event) c.Decision independent and blinded d.Use baseline covariates only (two subgroups) e. Analysis done with and without

11 11 B.WITHDRAWL FOR NON-COMPLIANCE References:Sackett & Gent (1979) NEJM, p Coronary Drug Project (1980) NEJM, p Two Types of Trials 1.Management -"Intent to Treat" Principle -Compare all subjects, regardless of compliance 2.Explanatory -Estimate optimum effect, understand mechanism -Analyze subjects who fully comply WITHDRAWALS FOR NON-COMPLIANCE MAY LEAD TO BIAS!

12 12 Cancer Trial (5-FU & Radiation) Gastric Carcinoma Reference:Moertel et al. (Journal of Clinical Oncology, 1984) 62 patients randomized –No surgical adjuvant therapy vs. –5-FU and radiation 5 year survival results RandomizedPercent (%) Treatment23%P < 0.05 No Treatment4%

13 13 Cancer Trial (5-FU & Radiation) Gastric Carcinoma According to treatment received 5 year survival Received % Survival Treatment20% Refused Treatment30%NS Control4%

14 14 Example: Coronary Drug Project 5-Year Mortality Clofibrate Placebo N% Deaths N% Deaths Total (as reported) By Compliance < 80% > 80% Adjusting for 40 covariates had little impact Compliance is an outcome Compliers do better, regardless of treatment

15 15 Example: Coronary Drug Project 2-Year Mortality Compliance Assessed Estrogen Placebo N% Deaths N% Deaths Total < 80% > 80% Comments Higher % of estrogens patients did not comply Beneficial to be randomized to estrogen & not take it (6.1% vs. 9.9%) Best to be randomized to placebo & comply (4.8%)

16 16 Example: Wilcox et al (1980) Trial, BMJ 6-Week Mortality Propranolol Atenolol Placebo N% Deaths N% Deaths N% Deaths Total Compliers Non-compliers Comments Compliers did better than placebo Treatment non-compliers did worse than placebo Placebo non-compliers only slightly worse than compliers Analysis by compliers overestimates benefit

17 17 Aspirin Myocardial Infarction Study (AMIS) % Mortality ComplianceAspirinPlacebo Good Poor Total

18 18 Summary of Compliance No consistent pattern ExampleNon-compliance Did Worse AMISBoth Treatment & Control CDP EstrogenControl Only Beta-blocker, WilcoxTwo Treatments, Not Control Compliance an outcome, not always independent of treatment Withdrawal of non-compliers can lead to bias Non-compliers dilute treatment Try hard not to randomize non-compliers

19 19 II. Competing Events Subject may be censored from primary event by some other event (e.g. cancer vs. heart disease) Must assume independence If cause specific mortality used, should also look at total death If non-fatal event is primary, should also look at total death and non-fatal event Problem for some response measures

20 20 III. Problem of Definitions ClassificationAnturanePlaceboP-value ART30/81248/ Another Committee 28/81239/ Cause specific definitions hard to apply Example: Anturane Reinfarction Trail (ART) (NEJM, 1980) Sudden Death

21 21 Anturane Reinfarction Trial Sudden Death Category SourcePlaceboAnturaneP-value All patients & all NEJM48/81730/ sudden deaths AC39/81728/ "Eligible" patients & NEJM46/78528/ all sudden deaths AC37/78225/ Problem of cause specific definitions AC = Another review committee

22 22 IV. "Wrong", Inconsistent, Outlying Data "Wrong" or "outlying" data may in fact be real Decisions must be made blind of group assignment All modifications or withdrawals must be documented

23 23 V. Missing Outcome Data Design with zero –missingness may be associated with treatment for analysis, data are not missing at random even if same number missing, missing may be for different reason in each treatment group Implement with minimum possible Analyze exploring different approaches –if all, or most, agree, then more persuasive

24 24 “Best” and “Worst” Case Analyses TreatmentControl Total Events Lost to Follow-up "Best" Case "Worst" Case200220

25 25 VI. Poor Quality Data

26 26 Poor Quality Data (1) 1.Lost to Follow-up (enforced withdrawals)  NO DATA: PROBLEMS: –Not necessarily independent of treatment –Raises questions about study conduct

27 27 Poor Quality Data (2) SOLUTIONS: 1.Keep to a minimum Easiest if vital status is the outcome Hardest if the response variables are time-related measures requiring a hospital or clinic visit 2.Censor at the time lost –Can be done in survival analysis –Assumes independence of treatment

28 28 Poor Quality Data (3) SOLUTIONS: 3.Estimate missing data using previous data or averages 4.“Best” case and “worst” case analyses

29 29 VII. Poor Clinic Performance in a Multicenter Study If randomization was stratified by clinic, then withdrawal of a clinic is theoretically valid Withdrawal must be done independent of the outcome at that clinic

30 30 Mortality in Aspirin Myocardial Infarction Study (AMIS) Aspirin PlaceboP-value All 30 Centers246/ / “Selected” Centers 39 66< 0.01 In “selected” centers, aspirin showed superiority

31 31 Mortality in Beta-Blocker Heart Attack Trial (BHAT) PropranololPlaceboP-value All 32 Centers 138/ /1921< 0.01 Cox adjusted Z = “Selected” Centers4326< 0.05 In “selected” centers, propranolol worse

32 32 VIII. Special Counting Rules Events beyond a specified number of days after treatment stopped not counted "non-analyzable" Examples 1."7 Day Rule"Anturane (1978) NEJM 2."28 Day Rule"Timolol (1981) NEJM If used, must –Specify in advance –Be a long period to insure termination not related to outcome –Analyze results both ways

33 33 IX. Fishing or Dichotomizing Outcomes Common practice to define a response (S,F) from a non-dichotomous variable By changing our definition, we can alter results Thus, definitions stated in advance Definitions should be based on external data

34 34 Dichotomizing Outcomes Heart Rate Trt ATrt B SubjectPrePost  PrePost  Mean Example

35 35 Three Possible Analyses (1) Change  Treatment ATreatment BP-Value 1.F = < S = > 720

36 36 Three Possible Analyses (2) Change  Treatment ATreatment BP-Value 1.F = < S = > F = < S = > 560

37 37 Three Possible Analyses (3) Change  Treatment ATreatment BP-Value 1.F = < S = > F = < S = > F = < S = > 387

38 38 X. Time Dependent Covariate Adjustment Classic covariate adjustment uses baseline prognostic factors only –Adjust for Imbalance –Gain Efficiency Adjustment by time dependent variates not recommended in clinical trials (despite Cox time dependent regression model) Habit from epidemiology studies

39 39 Coronary Drug Project 5-Year Mortality Baseline Cholesterol % Deaths Cholesterol ChangeClofibratePlacebo < 250mg%*Fall < 250Rise > 250 mg%Fall > 250 **Rise Little change in placebo group Best to have a.Low cholesterol getting lower * b.High cholesterol getting higher ** Example

40 40 Example: Cancer Trials A common practice to compare survival on patients with a tumor response Problem is that patient must first survive to be a responder length - bias sampling

41 41 Cancer Trials (1) Advanced Breast Cancer: Surgery vs. Medicine Santen et al. (1981) NEJM (Letter to editor, Paul Meier, U of Chicago) A randomized clinical trial comparing surgical adrenalectomy vs. drug therapy in women with advanced breast cancer 17 pts withdrawn from surgery group 10 pts withdrawn from medical group

42 42 Cancer Trials (2) Reasons –Medical group (10 pts) 2 stopped taking their drugs 5 drug toxicity –Surgical group (17 pts) 7 later refused surgery 8 rapid progression precluding surgery No follow-up data on these 27 pts presented

43 43 XI. Subgroup Analyses

44 44 False Positive Rates The greater the number of subgroups analyzed separately, the larger the probability of making false positive conclusions. No. of SubgroupsFalse Positive Rate

45 45 Subgroup Analyses Focusing on a particular “significant” subgroup can be risky –Due to chance –Results not consistent Estimates not precise due to small sample size

46 46 MERIT Total Mortality

47 47 MERIT

48 48 MERIT MERIT (AHJ, 2001)

49 49 Praise I Praise I Ref: NEJM, 1996 Amlodipine vs. placebo NYHA class II-III Randomized double-blind Mortality/hospitalization outcomes Stratified by etiology (ischemic/non-ischemic) 1153 patients

50 50 PRAISE I

51 51 PRAISE I - Interaction Overall P = 0.07 Etiology by Trt Interaction P = Ischemic P = NS Non-Ischemic P < 0.001

52 52 PRAISE I - Ischemic

53 53 PRAISE I – Non- Ischemic

54 54 PRAISE II Repeated non-ischemic strata Amlodipine vs. placebo Randomized double-blind 1653 patients Mortality outcome RR  1.0

55 55 Three Views Ignore subgroups and analyze only by treatment groups. Plan for subgroup analyses in advance. Do not “mine” data. Do subgroup analyses However view all results with caution.

56 56 Analysis Issues Summary Important not to introduce bias into the analysis ITT principle is critical Important to have “complete” follow- up Off treatment is not off study


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