1. Scienti c Settingcsph.ucdenver.edu/sites/kittelson/Bios6649-2013/Lctnotes/2011/lct1.2ab.pdf6 Specifying the Treatment Indication Treatment: I The way in which a treatment is delivered

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1. Scientific Setting

1.1 Introduction and motivation(a) Motivation(b) Review of statistical foundations(c) The scientific method (scientist game)

1.2 The study question(a) What is the treatment?(b) Phase I-IV clinical trials(c) Nature of the clinical question (superiority, equivalence,

non-inferiority,...)(d) 1- versus 2-sided questions

1.3 Case study

1. Scientific Setting () 1.2 The Study question 31 Jan 2011 1 / 43

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1.2 The Study Question

(a) What is the treatment?(b) Phase I-IV clinical trials:

I Overview and definition of phasesI Efficiency of phased approach

(c) Nature of the clinical question:I The conceptual structure of parameter spaceI Which decisions are relevant:

F Superiority, non-superiorityF non-inferiority, inferiorityF bio-equivalenceF approximate equivalence

I Non-inferiority trials require careful considerationI Examples

(d) 1- versus 2-sided questions

1.2 The Study Question () (a) Treatment Indication 31 Jan 2011 2 / 43

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1.2(a) What is a treatment?In addition to the actual compound that will be tested, atreatment includes:

I Dose and delivery methodI Schedule and duration of treatmentI Other ancillary or supportive treatments

A treatment is also targeting a disease:I Bacterial versus viral infectionsI Type of cancer; cancer stage

The treatment is also given to particular types of patients:I Newly diagnosed versus recurrent patientsI Patients who have failed other treatmentsI Patients with combinations of diseases (HIV and KS)

Its targeted impact; for example:I Relief of headacheI Reduction of risk for myocardial infarctI Prolongation of life

All of the above elements can be viewed as the elements of an“indication”


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Specifying the Treatment Indication

New therapies are approved for a particular indication.I Most often this is viewed as a class of conditions (diseases) and a

type of patient.The treatment indication should really be viewed as the formaldefinition of the treatment. As such it should include:

I A diseaseI A population of patientsI The treatmentI The desired outcome

View:http://rctdesign.org/cberOctober2009/default.html(Phases of Clinical Trials)


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Notes (see also chapter 1):Disease:

I Diseases can be defined by symptoms (COPD), causal agents(meningococcal meningitis), or treatment (MDR tuberculosis).

I The definition of a disease often changes with factors unrelated tothe disease (e.g., new treatments or a new categorization ofsymptoms)

Population of patients:I The target patient population is similarly dynamic.I Example: better diagnostic tools may detect cancer at earlier stages

or make it easier to detect later-stage disease.


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Treatment:I The way in which a treatment is delivered may change.I New formulations may allow oral instead of IV delivery, which might

extend the use to other populations or other forms of the disease.I Ancillary treatments (standard of care) is always changing.

Desired outcomeI Primary clinical outcomes versus surrogate outcomes (Vioxx;

mammography; colon cancer screening)I Unanticipated or anticipated beneficial (sildenafil citrate) or

harmful (rosiglitazone) effects.


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1.2(b) Phase I-IV clinical trials

My goals in this presentation:(i) Definition and objectives of phase I-IV clinical trials.

F Define/describe the phases.F Motivate the use of a phased approach from practical and ethical

considerations.

(ii) Efficiency of 2-stage evaluationF Show that clinical trials can be viewed as tests to “diagnose” good

treatments.F Show how the use of screening (phase II) trials improves efficiency

of discovery.

1.2 The Study Question () (b) Phase I-IV Trials 31 Jan 2011 7 / 43

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1.2(b) Phase I-IV clinical trials:(i) Definition and objectives

New therapies are usually evaluated in a phased sequence of studies.The phases provide a conceptual framework for organizing the scientificobjectives of the study.

Epidemiologic studies (non-experimental)Pre-clinical studies (not with human subjects)“Pilot” studies

I Not appropriate for estimating or comparing either biological orclinical effects.

I May be used to develop or test procedures:F Testing randomization procedures.F Testing dataflow and data captureF Learning how to run an assay.F Testing whether or not a survey is easily understood.


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(b) Phase I-IV clinical trials

Phase I: preliminary safety and dose-finding studiesI Goals:

F Pharmacokinetics.F Ruling out major adverse events.F Dose determination.F Device configuration.

I Methods:F Dose escalation or device alteration.F Subjects not necessarily from target population.F Subjects might have failed all standard therapies.F Uncommon to have a concurrent control group.


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Phase II: Preliminary efficacy and safety evaluationI Goal:

F Show toxicity is acceptably low.F Screen for evidence of efficacy prior to larger trials.

I Methods:F Study population more closely reflects target population.F Dose (treatment) is the same as the dose that will be used in

subsequent trials.F Endpoint may be a surrogate (biological marker) for the true

clinical endpoint (e.g., CD4 cell count vs survival).F Historical or concurrent controls may be used.F Typically small (< 100 patients).


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Phase III: Full-scale efficacy trialI Goal:

F Evaluate clinical efficacy.F Confirm suitably low toxicity.

I Methods:F Study population closely reflects target population.F Concurrent (randomized) control group.F Primary outcome: clinical efficacy.F Sufficiently large to answer the clinical/scientific question.

I Common applications:F Establish efficacy of new treatment; i.e.,:

Superiority over no treatment.Superiority over an existing treatment.

F Establish equivalence with current treatment; i.e.,:Two-sided equivalence: bioequivalence.One-sided equivalence: non-inferiority

(perhaps superior on a secondary endpoint).F Establish harm of existing treatment.


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Phase IV: Effectiveness trials(post-marketing surveillance)I Goal:

F Determine if estimated effects are actually observed in routineclinical use.E.g., AIDS vaccine which is less than fully protective might causean increase in AIDS because vaccinated individuals resume high-riskbehavior.

F Detect rare adverse events.F Assess long-term outcome.

I Methods:F Study population is the target population.F Rarely with a randomized control group; usually monitoring studies.F Can be very large with long-term follow-up.


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Example: Irinotecan for Lung Cancer

Small-cell lung cancer:I Worse prognosis than non-small-cell lung cancer.I Usually treated with cisplatin chemotherapy.

Irinotecan (new chemotherapy drug):I Good biological rationaleI Has shown strong anti-tumor properties in experimental tumor

models.I Has been active against leukemia, lymphoma, and several common

solid tumors.I Dose-limiting toxicities include luekopenia and diarrhea.


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Example: Irinotecan for Lung Cancer (con’t)

Phase I study:

Phase II study:

Phase III study:


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Example (con’t): Phase I

Reference: Phase I and pharmacologic study of irinotecan incombination with cisplatin for advanced lung cancer (Br. J. Cancer1993 68:777-782).

Study Design: Dose escalation 3 patients per group with 5 additionalpatients at maximum tolerated dose.

Outcome variables:Toxicity (leukopenia, anemia, nausea/vomiting, diarrhea): gradedI-IVPharmacokinetics (plasma concentration versus time)Tumor response (CR, PR, NR)

Primary explanatory variable:Drug dose?


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Phase I (con’t)

Results:Maximum tolerated dose: 90 mg/m2

Dose limiting toxicity: DiarrheaPharmacokinetic profiles


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Example (con’t): Phase II

Reference: Phase II study of irinotecan combined with cisplatin inpatients with previously untreated small-cell lung cancer (J. ClinicalOncology 1998 16:1068-1074).

Study Design: 75 patients all receive the same treatment.Dose reduced to 80mg/m2.

Outcome variables:Tumor response (CR, PR, NR) and response duration.Patient survival.Toxicity (neutropenia, leukopenia, anemia, diarrhea): graded I-IV

Primary explanatory variable:Local versus extended disease.


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Phase II (con’t)

Results:Response rates:

I Overall: 84% (CR + PR); 29% (CR)I Extended disease: 83% (CR + PR); 30% (CR)I Local disease: 86% (CR + PR); 29% (CR)I Toxicity: Major (grade 3 or 4) toxicities in most patients; 2 deaths.


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Example (con’t): Phase III

Reference: Irinotecan plus cisplatin compared with etoposide pluscisplatin for extensive small-cell lung cancer (NEJM, 346:85-91).

Study Design: Randomized controlled trial including 154 patients.

Outcome variables:SurvivalToxicity: (severe or life threatening myleosuppression)

Explanatory variable:Drug (irinotecan versus etoposide)


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Phase III (con’t)

Results:Significant improvement in survival with irinotecan whencompared with etoposideToxicity was more frequent with etoposide


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Summary of published trials

Phase I: Br. J. Cancer (1993) 68:777-782I Design: Dose escalation (14 patients)I Outcome: Grade 3-4 toxicities

(leukopenia, diarrhea)I Primary explanatory: Dose levelI Covariate: None

Phase II: J. Clinical Oncology (1998) 16:1068-1074I Design: Single arm (75 patients)I Outcome: Tumor response; grade 3-4 toxicities.I Primary explanatory: Extended vs local disease.I Covariate: None

Phase III: NEJM (2002) 346:85-91I Design: Randomized controled trial (154 patients)

(irinotecan vs etoposide)I Outcome: SurvivalI Primary explanatory: Treatment groupI Covariate: None


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Trial design from Phase I-III

Phase I Phase II Phase III

Endpoints Tolerance Safety Efficacybioactivity safety

Follow-up Short Moderate Longer

Population Non-target Target Target

Treatment Dose Study Studyfinding dose dose

Comparison Across Historical Randomizeddoses or randomized

Sample Size Very Moderate Largesmall


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Example: Iloprost for prevention of lung cancer

Pre-clinical studies:

Phase I study:

Phase II study:

Phase III study:

Phase IV study:


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Phases in other settings

SurgeryI Phase I: Refining procedures/technique, safety.I Phase II: Safety; preliminary efficacy data (short term and/or

biological outcomes).I Phase III: Definitive efficacy trial, confirm safety.I Phase IV: ‘Post-market’ effectiveness; long-term safety.

Device evaluation:I Phase I: Device refining, safety.I Phase II: Safety; preliminary efficacy data.I Phase III: Definitive efficacy trial, confirm safety.I Phase IV: Post-market effectiveness; long-term safety.

Chemoprevention:I Phase I: Dose-finding; short-term tolerability.I Phase II: Tolerability and biological efficacy.I Phase III: Cause-specific mortality; all-cause mortality.I (Phase IV: Extend the target population.)


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Phases in other settings

Diagnostic tests (biomarker development; Pepe, M, JNCI, 2001)I Phase I: Preclinical explorationI Phase II: Clinical assay and validation (prevalent case-control study)I Phase III: Retrospective longitudinal (incident case-control study)I Phase IV: Prospective screening (extend and type of disease

detected; false referral rate estimated)I Phase V: Disease control (screening with the biomarker reduces

disease mortality).


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1.2(b) Phase I-IV clinical trials

Recall the organization:(i) Definition and objectives of phase I-IV clinical trials.

F Define/describe the phases.F Motivate the use of a phased approach from practical and ethical

considerations.

(ii) Efficiency of 2-stage evaluationF Show that clinical trials can be viewed as tests to “diagnose” good

treatments.F Show how the use of screening (phase II) trials improves efficiency

of discovery.


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1.2(b) Phase I-IV clinical trials:(ii) Efficiency of phased approach

Role of statistical reasoning in medical studies.I Observations are subject to error; that is, in the real world few

patterns are deterministic:F Inherent randomnessF Hidden (unmeasured) variables can create variabilityF Measurement ‘error’

I A lab test classifies individuals with and without disease.I A statistical test on a sample can be viewed as a diagnostic test

that is used to classify (diagnose) beneficial and non-beneficialtreatments.


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Efficiency of phased approachReview of diagnostic tests

Test accuracy: The accuracy of a diagnostic tests is measured byI Sensitivity: Probability that the test is positive in the diseased

population with the disease: P (T+|D+).I Specificity: Probability that the test is negative in the healthy

population: P (T−|D−).Test predictive value: Recall that the predictive value of adiagnostic test is determined by disease prevalence:

I Positive predictive value (PPV): Prevalence of disease individuals inthe test-positive population: P (D+|T+).

I Negative predictive value (NPV): Prevalence of healthy individualsin the test-negative population: P (D−|T−).


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Calculating PPV and NPV(Bayes Rule):

PPV =P (T+|D+)P (D+)

P (T+)

=Sens× Prev

[Sens× Prev] + [(1− Spec)× (1− Prev)]

NPV =P (T−|D−)P (T−)

P (T−)

=Spec× (1− Prev)

[Spec× (1− Prev)] + [(1− Sens)× Prev]


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Example: Quantitative liver function testing

SettingI Standard liver function tests include:

F Albumin: A protein made by the liver that is decreased withchronic liver disease.

F ALT: enzyme in hepatocytes. Liver cell damage leads to high bloodlevels.

F AST: enzyme in hepatocytes; also in red blood cells, cardiac andskeletal muscles. AST/ALT ratio identifies liver-specific damage.

F Alkaline phosphatase: enzyme in cells in bile ducts. Blood levelsrise with bile duct damage.

F bilirubin: breakdown product of heme; liver clears bilirubin.I Uses of tests:

F Tracking disease progression and severityF Diagnosis of chirrhosis (better targeting of liver biopsy)F Diagnosis of varices (better targeting of endoscopy)

I Can a direct measure of liver function provide better diagnoses?


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Efficiency of phased approachReview of diagnostic tests (Example)

Example: Quantitative liver function testing (con’t)

Study of various methods for quantifying liver function(Everson, 2008. Ailment Pharmacol Ther 27, 798-809)

I Chronic HCV patients with liver fibrosis, but not on anti-viraltherapy (HALT-C trial).

I Subjects underwent baseline clinical, laboratory, ultrasonographic,and histological studies.

I 285 consented to more detailed quantitative studies.Cholate shunt test in chronic HCV patients:

I Labelled cholate given orally and IV.I Blood cholate levels sampled over time are used to quantify

portal-systemic shunting.


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Cholate shunt for diagnosing cirrhosis:

Cirrhosis No CirrhosisTest positive 99 96 195Test negative 14 73 87

113 169

I Sensitivity: P (T+|D+) = 99/113 = 0.88I Specificity: P (T−|D−) = 73/169 = 0.43


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Predictive value (depends on prevalence):Using observed prevalence (113/282 = 0.40)

PPV =0.88× 0.4

[0.88× 0.4] + [(1− 0.43)× (1− 0.4)]

= 99/195 = 0.51

NPV =0.43× 0.6

[0.43× 0.6] + [(1− 0.88)× (1− 0.6)]

= 73/87 = 0.84


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Using lower prevalence (10%)

PPV =0.88× 0.1

[0.88× 0.1] + [(1− 0.43)× (1− 0.1)]

= 0.15

NPV =0.43× 0.9

[0.43× 0.9] + [(1− 0.88)× (1− 0.9)]

= 0.97


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Efficiency of phased approachClinical trials as diagnostic tests:

A Statistical hypothesis tests can be viewed as a test for beneficialtreatments.

I P-value: probability of observing a positive (statistically significant)test in absence of a true treatment effect:

F Level of significance is 1 - specificity.F Choosing α = 0.05 gives 95% specificity.

I Statistical power: Probability of observing a positive (statisticallysignificant) test when there is a true treatment effect:

F Power is sensitivity.F Common choice of 80% sensitivity (not usually recommended by

me).

I Prevalence: the percentage of effective treatments among all testedtreatments.

I Positive predictive value: probability that a statistically significanttrial indicates a truly effective treatment.


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Efficiency of phased approachPhase II studies as screening tests:

Less than 5% of experimental cancer treatments are adopted:I NCI drug development program 1970-1985:

F 350,000 unique chemical structures studied.F 83 pass preclinical and phase I testing.F 24 pass phase II tests for biological activity.

I < 10% of new drugs entering clinical trials from 1970-1990 wereapproved for marketing by the FDA (Chabner and Roberts, NatureReviews Cancer, 2005)

I Phase III trials conducted by NCI from 1955-2006 (Djulbegovic,Arch Intern Med, 2008):

F 624 phase III trialsF 781 randomized comparisonsF 216,451 patientsF 30% (221/743) were statistically significant.


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Consider the following approaches to evaluating new treatments:1. Study every treatment in a large definitive experiment.2, Perform small screening tests, and perform large definitive

experiments only in those treatments that pass the screening tests.Suppose that we want to evaluate the efficiency of these strategies.Assume:

I 10% of all treatments actually work.I Level of significance = 0.05 (specificity = 0.95).I 1,000,000 subjects are available for clinical trials.I Power for a clinically important difference:

1000 subjects → 97.5% power500 subjects → 80% power50 subjects → 15% power


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Scenario 1 (only large trials):I Suppose we evaluate 1000 new treatments (100 effective and 900

ineffective).I On average we have positive tests for:

F 98 of the 100 effective treatments (0.975× 100 ≈ 98).F 45 of the 900 ineffective treatments (0.05× 900 = 45).

I PPV: 98/(45 + 98) = 0.63; that is, only 63% of the 143 treatmentsidentified actually work.


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Scenario 2 (preliminary screening trials):(a) Suppose we first screen 12,500 new treatments (1,250 effective and

11,250 ineffective).I Using 50 subjects in the screening trials (625,000 total) with 15%

power.I On average the screening trials give positive tests for:

F 187 of the 1,250 effective treatments (0.15× 1250 ≈ 187).F 563 of the 11,250 ineffective treatments (0.05× 11250 ≈ 563).

I PV+ for the screening phase: 187/(187 + 562) = 0.25.


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(b) Now evaluate the 749 treatments (187 effective and 562 ineffective)from the screening trials.

I Using 500 subjects per trial (374,500 total) with 80% power.I On average these confirmatory trials give positive tests for:

F 150 of the 187 effective treatments (0.8× 187 ≈ 150).F 28 of the 562 ineffective treatments (0.05× 562 ≈ 28).

I PV+ for confirmatory trials: 150/178 = 0.84.


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Comparison of scenarios:I Scenario 1 (large trials only):

F Use 1,000,000 subjectsF Screen 1,000 new treatmentsF Adopt 98 effective treatmentsF Adopt 45 ineffective treatments

I Scenario 2 (screening studies followed by large trials):F Use 999,500 subjectsF Screen 12,500 new treatmentsF Adopt 150 effective treatmentsF Adopt 28 ineffective treatments


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Bottom line:I Using the same number of subjects, phase II studies increase the

predictive value of a positive study. A greater number of effectivetreatments are identified due in part to the greater number oftreatments screened.

I (Different choices of statistical power in screening and confirmatorytrials can be used to optimize the strategy for a particular setting.)


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1.1 (b) Phase I-IV clinical trials:Bottom Line

A wide range of situations/therapies are studied in clinical trials.Globally, clinical trials need to assure:

I Scientific credibilityI Ethical experimentsI Efficient experiments:

F Minimize timeF Minimal number of extra subjectsF Minimize cost

I A high prevalence of truly beneficial therapies among all therapiesused in routine care.


Documents

1. Scienti c Settingcsph.ucdenver.edu/sites/kittelson/Bios6649-2013/Lctnotes/2011/lct1.2ab.pdf6 Specifying the Treatment Indication Treatment: I The way in which a treatment is delivered