Clinical Study Design Henrik Ekberg, MD, PhD Malmö, Sweden Associate Editor: American Journal of Transplantation Editorial Board Member: Transplantation Transplant International Clinical Transplantation Journal of Transplantation Guangzhou October 9, 2010

Rejection of submitted manuscript - various reasons Rejected on priority grounds: Maybe a good study –but not a topic of interest, or done before Rejected, not allowed resubmission: –a bad study; design problems, cannot be re- written in a good way Rejected but allowed resubmission: –no serious design problems, interesting topic, but needs to be rewritten for language, discussion, figures, tables, etc.

Rejection of submitted manuscript - various reasons Rejected on priority grounds: Maybe a good study –but not a topic of interest, or done before Rejected, not allowed resubmission: –a bad study; design problems, cannot be re- written in a good way Rejected but allowed resubmission: –no serious design problems, interesting topic, but needs to be rewritten for language, discussion, figures, tables, etc.

Study design alternatives Retrospective studies = Using medical charts of existing data Uncontrolled Case-controlled Hypothesis generating Prospective studies = Protocol directives for Rx and F/u Uncontrolled, one-arm, pilot Randomized Controlled Trial (RCT) Hypothesis testing

Clinical study design phases Phase 1 Drug action, metabolism, PK, PD, safety Phase 2 Limited (un)controlled study for efficacy and safety Phase 3 Large randomized multicenter study Determine efficacy and safety for FDA and EMEA Phase 4 After drug release: new uses of the drug Marketing

 Hypothesis  Appropriate population  Clinically relevant achievement  Adequately-powered  End points  Comparison group (placebo)  Randomized  Double-blind  Intent-to-treat analysis  Protocol  Analysis plan Key Elements of Trial Quality

Experimental Hypothesis May be based on a pilot or retrospective study or on hopes for a new drug Drug A > drug B (or placebo) with regards to … Null hypothesis (H 0 ): A B (no difference) A < B (non-inferiority) Key Elements of Trial Quality

Appropriate population Include: Normal risk kidney transplant recipients from living or deceased donors Exclude: High risk patients, such as PRA > 20% (50%?) Retransplants (all?) High donor age ? Expanded donor criteria? Cold ischemia time ? HLA- DR mismatch ? Key Elements of Trial Quality

 Hypothesis  Appropriate population  Clinically relevant achievement  Adequately-powered  End points  Comparison group (placebo)  Randomized  Double-blind  Intent-to-treat analysis  Protocol  Analysis plan Key Elements of Trial Quality

With one-year graft survival > 90% and acute rejection rates < 20% The Success

With one-year graft survival > 90% and acute rejection rates < 20% we have a high level of success and further improvement is difficult to achieve and demonstrate we need very large studies! The Problem

Primary end point The parameter on which 1. the hypothesis is based, to be verified or rejected 2. the sample size is calculated Secondary end points Additional parameters which may 1. describe the patients, events and results 2. be used for formulations of new hypotheses End Points and Sample Size

1. Select the primary end point 2. Clinically relevant achievement regarding end point = Difference between control and experimental groups e.g.: GFR increased by 10 ml/min AR rate reduced by 10% 3. Determine the number of patients in each group needed to verify that the difference between the groups most likely is true (<5% risk of mistake). 4. With a certain power and p-value. End Points and Sample Size

“We chose to study 313 patients in each group in order to have 80% power of detecting a 33% reduction in AR rate from a baseline rate of 30% with a significance level of 0.05”. End point: Acute Rejection Clinically relevant achievement: 33% reduction (from 30% to 20%) Power: 80% Significance level: 5% Therefore: Number of patients in each group: 313 End Points and Sample Size

“We chose to study 313 patients in each group in order to have 80% power of detecting a 33% reduction in AR rate from a baseline rate of 30% with a significance level of 0.05”.  = p-value = 0.05; means a 5% risk of obtaining a group difference by chance (although there is no true difference).  = 0.20 means a Power of 80%; that is 80% chance of obtaining a group difference and 20% risk of missing it (when there is a true difference). P 1 = 0.30 and ∆ = 0.10 (33% of p 1 ) End Points and Sample Size

“We chose to study 313 patients in each group in order to have 80% power of detecting a 33% reduction in AR rate from a baseline rate of 30% with a significance level of 0.05”.  = p-value = 0.05; means a 5% risk of obtaining a group difference by chance (although there is no true difference).  = 0.20 means a Power of 80%; that is 80% chance of obtaining a group difference and 20% risk of missing it (when there is a true difference). P 1 = 0.30 and ∆ = 0.10 (33% of p 1 ) End Points and Sample Size Question: If there is a true difference between the groups and we do 100 studies with 313 patients in each group How many studies will result in a group difference, that is at least a 33% reduction of AR? 1.5 studies 2.20 studies 3.80 studies 4.95 studies

“We chose to study 313 patients in each group in order to have 80% power of detecting a 33% reduction in AR rate from a baseline rate of 30% with a significance level of 0.05”.  = p-value = 0.05; means a 5% risk of obtaining a group difference by chance (although there is no true difference).  = 0.20 means a Power of 80%; that is 80% chance of obtaining a group difference and 20% risk of missing it (when there is a true difference). P 1 = 0.30 and ∆ = 0.10 (33% of p 1 ) End Points and Sample Size Question: If there is a true difference between the groups and we do 100 studies with 313 patients in each group How many studies will result in a group difference, that is at least a 33% reduction of AR? 1.5 studies 2.20 studies 3.80 studies 4.95 studies

“We chose to study 313 patients in each group in order to have 80% power of detecting a 33% reduction in AR rate from a baseline rate of 30% with a significance level of 0.05”.  = p-value = 0.05; means a 5% risk of obtaining a group difference by chance (although there is no true difference).  = 0.20 means a Power of 80%; that is 80% chance of obtaining a group difference and 20% risk of missing it (when there is a true difference). P 1 = 0.30 and ∆ = 0.10 (33% of p 1 ) End Points and Sample Size 80 studies will show a significant difference and 20 studies will not. Comment: 20% risk of not seeing a true difference is quite high

“We chose to study 313 patients in each group in order to have 80% power of detecting a 33% reduction in AR rate from a baseline rate of 30% with a significance level of 0.05”.  = p-value = 0.05; means a 5% risk of obtaining a group difference by chance (although there is no true difference).  = 0.20 means a Power of 80%; that is 80% chance of obtaining a group difference and 20% risk of missing it (when there is a true difference). P 1 = 0.30 and ∆ = 0.10 (33% of p 1 ) End Points and Sample Size Question: If there is not a true difference between the groups and we do 100 studies with 313 patients in each group. How many studies will result in a group difference? 1. 5 studies studies studies studies

“We chose to study 313 patients in each group in order to have 80% power of detecting a 33% reduction in AR rate from a baseline rate of 30% with a significance level of 0.05”.  = p-value = 0.05; means a 5% risk of obtaining a group difference by chance (although there is no true difference).  = 0.20 means a Power of 80%; that is 80% chance of obtaining a group difference and 20% risk of missing it (when there is a true difference). P 1 = 0.30 and ∆ = 0.10 (33% of p 1 ) End Points and Sample Size Question: If there is not a true difference between the groups and we do 100 studies with 313 patients in each group. How many studies will result in a group difference? 1. 5 studies studies studies studies

“We chose to study 313 patients in each group in order to have 80% power of detecting a 33% reduction in AR rate from a baseline rate of 30% with a significance level of 0.05”.  = p-value = 0.05; means a 5% risk of obtaining a group difference by chance (although there is no true difference).  = 0.20 means a Power of 80%; that is 80% chance of obtaining a group difference and 20% risk of missing it (when there is a true difference). P 1 = 0.30 and ∆ = 0.10 (33% of p 1 ) End Points and Sample Size Question: If there is not a true difference between the groups and we do 100 studies with 313 patients in each group. How many studies will result in a group difference? 5 studies will show a group difference although this is not true

“We chose to study 313 patients in each group in order to have 80% power of detecting a 33% reduction in AR rate from a baseline rate of 30% with a significance level of 0.05”. P-value = 5%; The risk of seeing a difference which is not true Power = 80%; The chance of seeing a difference which is true P 1 = 0.30 and ∆ = 0.10 (33% of p 1 ) End Points and Sample Size

p=0.05 Sample Size for Acute Rejection AR Treatment AR Control (P 1 ) PowerSample size %313

p=0.05 Sample Size for Acute Rejection AR Treatment AR Control (P 1 ) PowerSample size % %700

p=0.05 Sample Size for Acute Rejection AR Treatment AR Control (P 1 ) PowerSample size % % %954 We need to do large multicenter studies !!!

Question: The primary end point (PEP) and 10 secondary end points (SEP) were analysed; SEP in two ways each. The PEP was NS, one of the SEP was stat sign (P<0.05). Why is the analysis more reliable for PEP than SEP? Is this significant result of the SEP reliable? 10 x 2 = 20 tests What is the probability of a “significant finding” by chance? End Points and Sample Size

The trap of multiple tests No. of independent tests Probability of one or more p < 0.05 by chance 10%23%40%64%92% To keep  = 0.05 accept as significant only p less than

The trap of multiple tests No. of independent tests Probability of one or more p < 0.05 by chance 10%23%40%64%92% To keep  = 0.05 accept as significant only p less than Use p = 0.05 / no. of tests

 Hypothesis  Appropriate population  Clinically relevant achievement  Adequately-powered  End points  Comparison group (placebo)  Randomized  Double-blind  Intent-to-treat analysis  Protocol  Analysis plan Key Elements of Trial Quality

Clinical End Points We want to achieve improvement in patient survival and graft survival These are the Clinical end points

Five cadaver kidney transplant recipients received azathioprine One patient survived 365 days, becoming the first successful cadaveric transplant Uncontrolled Trial: Patient Survival (n=5) Murray, et al. New Engl J Med 1963; 268:1315

1-year graft survival  CsA %  Aza % 1-year patient survival  CsA %  Aza % European Multicentre Trial Group. Lancet 1983; 2:986 p=0.001 RCT: Graft & Patient Survival (n=232) NS

Acute rejection is associated with graft survival Acute rejection became the surrogate end point for graft survival Where Did We Go From Here?

 Acute rejection at 6 mo.  MMF 2g %  MMF 3g %  Pla/Aza %  1-year graft survival  MMF 2g %  MMF 3g %  Pla/Aza % Halloran, et al. Transplantation 1996; 63:39 p<0.01 RCT: Acute Rejection (n=1493) NS

Conclusion of MMF trials: “Acute rejection was reduced but graft survival was not improved” Was this true - or a question of insufficient power of the study? What difference in graft survival should have been expected? Where Did We Go From Here?

Sample size and power to verify true differences in graft survival of 4% or 5%. Graft survival in treatment groups Difference in Graft survival Sample size at 80% power Power at sample size %90 %4 % % 75 %80 %5 % % Ekberg H. Transpl Rev 2003; 17: 187

Surrogate Endpoint Definitions Clinical endpoint: A characteristic or variable that reflects how a patient feels, functions or survives. Surrogate endpoint: A biomarker that is intended to substitute for a clinical endpoint, and predict clinical benefit … Biomarkers Definitions Working Group. Clin Pharmacol Ther 2001; 69:89

Risk factors after transplantation Acute rejection Graft function New onset of diabetes mellitus Cholesterol levels Treatment failure (drug toxicity) Malignancy Do they predict graft or patient survival? Risk factors and potential End points

Possible Surrogate Endpoints  Acute rejection  Acute rejection + 1/Cr return to baseline  1-year graft function  Composite end point  Association or Prediciton ?

Acute Rejection with 1/Cr return to baseline Transplants 1995–2002 Log-rank P value for equality of strata ≤ Meier-Kriesche et al. ATC Time Posttransplantation (mo) 1.0 Graft Survival (%) AR-1/SCr worse than 5% from baseline 49.4% n = 55,092 n = 4,061 n = 2,782 n = 22,212 n = 2,669 n = 1,455 n = 2,891 n = 414 n = 221 AR-1/SCr within 5% from baseline 73.4% 73.1% No acute rejection

Predictive Quality for Graft Loss: AR vs. AR Without Return to Baseline 6 years 2 years Follow-up Positive Predictive Value Acute Rejection No Return to Baseline Acute Rejection Meier-Kriesche et al. ATC Conclusion: AR and AR with return to baseline are associated but not predictive of graft survival

“Post-transplant Renal Function at 1 Year Predicts Long-Term Kidney Transplant Survival” Months Posttransplantation < >3 N = 61,157 Graft Survival (%) Hariharan S et al. Kidney Int. 2002; 62: 311.

ROC Plot for 7-Year Overall Graft Loss From 1-Year Creatinine Baseline Level AUC = Sensitivity 1 - Specificity ROC = receiver operator curve. H-U Meier-Kriesche

ROC Plot for 7-Year Overall Graft Loss From 1-Year Creatinine Baseline Level AUC = Sensitivity 1 - Specificity ROC = receiver operator curve. H-U Meier-Kriesche

Prediction Diagnostics for Seven Year Overall Graft Loss from One Year Creatinine Level Patient population: Adult first transplant recipients from USRDS database after 1988 with minimum seven years follow up Prediction Diagnostics SensitivitySpecificityPPVNPV Creatinine Cutoff Level %55%53%64% %71%58%62% %82%63%61% H-U Meier-Kriesche

Possible Surrogate Endpoints Acute rejection Acute rejection + 1/Cr return to baseline 1-year graft function Composite end point

Composite end point (CEP) 1,389 KTx at Univ of Minnesota Creat at 1 year (Cr 12 ) Cr > 10 yr GS from 75% to 25% Suggested Composite End Point: Graft loss 2.0 Reduction of CEP incidence by 33% 626 patients in total needed in such study Paraskevas et al Transplantation 2003; 75: 1256

Composite end point (CEP) CEP definition: Occurrence of at least one Acute rejection, Graft loss, Death or S-Creat > 1.5 UNOS data base : 59,000 patients 61.2% met the CEP - Margin for improvement - Less number of patients needed Siddiqi et al ATC 2003; #1160 Hariharan et al AJT 2003; 3: 933

Composite end point (CEP) CEP: Not a surrogate end point – no prediction Not a clinical end point – incl ‘surrogate’ factors Weighted score: Death1.0 x proportion Graft loss0.5 x proportion Acute rej0.25 x proportion S-crea> x proportion Hariharan et al AJT 2003; 3: 933

Clinical end point (short term only) Alternatively; Clinical end point (“how the patient functions …”) without prediction of long-term patient or graft survival e.g. GFR (Cockcroft-Gault formula) at 12 mo. Symphony study e.g. New Onset of Diabetes After Transplantation (NODAT) according to American Diabetes Association (ADA) definitions

Conclusions on End Points  What are the best end points?  Acute rejection  Acute rejection + 1/Cr return to baseline  1-year graft function  Composite end point  NODAT

 Hypothesis  Appropriate population  Clinically relevant achievement  Adequately-powered  End points  Comparison group  Randomized  Placebo-controlled  Double-blind  Intent-to-treat analysis  Protocol  Analysis plan Key Elements of Trial Quality

Question: We are designing a study on CNI nephrotoxicity and are discussing the treatment of the control group. It was decided to give them CsA with trough levels ng/ml first 2 months and then ng/ml months OK? The Comparison Group

The benefits, risks, burdens and effectiveness of a new method should be tested against those of the best current prophylactic, diagnostic, and therapeutic methods. World Medical Association Declaration of Helsinki The Comparison Group

The new drug or method should hypothetically and potentially be better than the best known current treatment (= standard of care) - but not yet proven to be so The Study Group

Placebo vs Study Drug Study drug in addition to the best current regimen e.g. placebo vs daclizumab Old Drug vs New Drug Either drug in addition to the best current regimen e.g. Aza vs MMF Controlled trial

 Hypothesis  Appropriate population  Clinically relevant achievement  Adequately-powered  End points  Comparison group (placebo)  Randomized  Double-blind  Intent-to-treat analysis  Protocol  Analysis plan Key Elements of Trial Quality

Random Assignment of Treatment Parameters associated with outcome should be similarly distributed between study and comparison groups Methods for example: computerized and via telephone 1:1 or 2:1 Stratification (per center or LD/DD) Randomized Controlled Trial

Double Blind Physician not knowing which treatment Patient not knowing Problems:drug administration drug monitoring Labs and visits the same in both groups Sometimes extra blood sampling in controls (ethics?) The Blind Treating The Blind

 Hypothesis  Appropriate population  Clinically relevant achievement  Adequately-powered  End points  Comparison group (placebo)  Randomized  Double-blind  Intent-to-treat analysis  Protocol  Analysis plan Key Elements of Trial Quality

ITT analysis – the Standard method = All participating patients are included Does not exclude treatment failures Conclusion: “With this intention, we had the results...” Limitation of the ITT Analysis In a long-term study (e.g. 3 yrs), many patients would have switched therapy or been withdrawn Physicians regard the fate of the patient more important than the study -> Reduced differences between treatment groups Intention-to-Treat Analysis

Per Protocol (PP) Analysis = On-treatment analysis Emphasis on the positive results of treatment Excludes premature withdrawals (“failures”) Limitation of the PP analysis Conclusion: “Only in successful cases, we had these results...” “Only patients who could follow this protocol, …” -> Seriously biased results when excluding failures Per Protocol Analysis

 Hypothesis  Appropriate population  Clinically relevant achievement  Adequately-powered  End points  Comparison group (placebo)  Randomized  Double-blind  Intent-to-treat analysis  Protocol  Analysis plan Key Elements of Trial Quality

Synopsis and Protocol Synopsis A short summary of the study protocol Used to invite investigators to participate Protocol A detailed description of all relevant aspects of the study Used to make sure all centers perform the study correctly Used for approval of Ethical Committee and Health Authorities Patient Information and Consent

 Hypothesis  Appropriate population  Clinically relevant achievement  Adequately-powered  End points  Comparison group (placebo)  Randomized  Double-blind  Intent-to-treat analysis  Protocol  Analysis plan Key Elements of Trial Quality

Analysis Plan A detailed description of all analyses that are planned; statistical methods, outlines of tables and graphs Including: Primary end point to verify or reject the null hypothesis Secondary end points to further describe the data and formulate new hypotheses Secondary analyses (ad hoc, made after viewing the results and not part of the analysis plan) should be avoided Interim analyses - confidentially for Data Safety Monitoring Board (DSMB). To report in public interim results during the study should not be done!

Further Reading A Uniform Clinical Trial Registration Policy for Journals of Kidney Disease, Dialysis and transplantation Couser WG, AJT 2005; 5: Design and Analysis of Clinical Trials in Transplantation Schold JD, AJT 2008; 8: 1779