Bios 6648: Design & conduct of clinical research

Transcription

1 Bis 6648: Design & cnduct f clinical research Sectin 3 - Essential principle 3.1 Masking (blinding) 3.2 Treatment allcatin (randmizatin) 3.3 Study quality cntrl : Interim decisin and grup sequential : fr trial mnitring (b) Overview f grup sequential (c) Example: sepsis trial Bis pg 1

2 fr trial mnitring Mtivatin: Many trials have been stpped early: Physician health study shwed that aspirin reduces the risk f cardivascular death. A phase III study f tamxifen fr preventin f breast cancer amng wmen at risk fr breast cancer shwed a reductin in breast cancer incidence. A phase III study f anti-arrhythmia drugs fr preventin f death in peple with cardiac arrhythmia stpped due t excess deaths with the anti-arrhythmia drugs. A phase III study f flic acid supplements fr preventin f neural tube defects. Wmen s Health Initiative: Hrmnes cause heart disease. Bis pg 2

3 fr trial mnitring What is trial mnitring? Mnitring fr quality cntrl; fr example, Patient accrual. Data quality/cmpleteness. Unanticipated adverse events. Mnitring study endpints(s); fr example, Treatment benefits. Txicity differences. Gd quality cntrl shuld be part f every study t ensure that the study achieves its gals. Mnitring study endpints is nt applicable in every study, and requires special statistical methds t avid increaed statistical errrs. Bis pg 3

4 fr trial mnitring Reasns t mnitr study endpints: T maintain the validity f the infrmed cnsent fr: Subjects currently enrlled in the study. New subjects entering the study. T ensure the ethics f randmizatin. Randmizatin is nly ethical under equipise. If there is nt equipise, then the trial shuld stp. T identify the best treatment as quickly as pssible: Fr the benefit f all patients (i.e., s that the best treatment becmes standard practice). Fr the benefit f study participants (i.e., s that participants are nt given inferir therapies fr any lnger than necessary). Bis pg 4

5 fr trial mnitring If nt dne prperly, mnitring f endpints can lead t biased results: Data driven analyses cause bias: Analyzing study results because they lk gd leads t an verestimate f treatment benefits. Publicatin r presentatin f preliminary results can affect: Ability t accrue subjects. Type f subjects that are referred and accrued. Treatment f patients nt in the study. Failure t design fr interim analyses can lead t hasty decisins. Decisins made in the heat f the mment are subject t: Inadequate cnsideratin f trade-ffs between cmpeting endpints (txicity versus benefit). External pressures frm study investigatrs r spnsrs. Lack f bjectivity by study mnitrs. Bis pg 5

6 fr trial mnitring Thus, Mnitring f study endpints is ften required fr ethical reasns. Mnitring f study endpints must carefully planned as part f study design t: Avid bias Assure careful decisins Maintain desired statistical prperties Bis pg 6

7 fr trial mnitring Key elements f mnitring Hw are trials mnitred? Investigatr knwledge f interim results can lead t biased results: Negative results may lead t lss f enthusias. Psitive interim results may lead t inapprpriate early publicatin. Either result may cause changes in the types f subjects wh are recruited int the trial. Data Safety and Mnitring Bards (DSMB)" are used t avid biased decisins: DSMB members are independent f the study investigatrs The DSMB reviews unblinded data in the midst f a trial t: Assure the trial is safe t cntinue. Make decisins abut early terminatin based n the statistical mnitring plan ( grup-sequential clinical trial design"). DSMB cmpsitin: Subject-matter specialists (2-4) Bistatistician (1-2). Bis pg 7

8 fr trial mnitring Key elements f mnitring The trial mnitring plan is typically pre-specified in tw dcuments: DSMB charter: Defines scpe f trial mnitring Defines DSMB respnsibilities Defines spnsr respnsibilities Pre-specifies mnitring plans and decisins (reasns fr stpping) Interim Statistical Analysis Plan (ISAP): Defines mnitring endpint(s) Pre-specifies analysis timing, decisin criteria, and ratinale Pre-specifies methds fr implementatin (changes t analysis timing) Pre-specifies adjustments t statistical inference abut treatment effects Bis pg 8

9 fr trial mnitring Key elements f mnitring Typical cntent fr DSMB charter: Trial synpsis; fr example: Summary f design Eligibility/exclusins Statistical design and sample size DSMB rganizatin Cmpsitin and selectin f members Respnsibilities f DSMB What will be mnitred (accrual, QC, safety, endpints?) Respnsibilities f spnsr Prviding pen/clsed reprts; data summaries Cmmittee meetings: Open sessin; clsed sessin; executive sessin Cmmunicatin Open reprt; clsed reprt t be prvided t DSMB Respnsibility fr meeting minutes (pen and clsed minutes) Prcess fr DSMB recmmendatins Bis pg 9

10 fr trial mnitring Key elements f mnitring Typical cntent fr ISAP: Safety mnitring plan (if there are frmal safety interim analyses) Decisin rules fr frmal safety analyses Evaluatin f decisin rules (pwer, expected sample size, stpping prbability) Methds fr mdifying rules (changes in timing f analyses) Methds fr inference (bias adjusted inference) Mnitring plan fr primary endpint(s) Decisin rules and reasns fr early terminatin (e.g., efficacy, futility, equivalence, harm) Evaluatin f decisin rules (pwer, expected sample size, stpping prbability) Methds fr mdifying rules (changes in timing f analyses) Methds fr inference (bias adjusted inference) Data handling and respnsibilities fr analysis Bis pg 10

11 (b) Overview f grup sequential Statistical framewrk fr trial mnitring Types f grup sequential Example (next lecture): sepsis trial Bis pg 11

12 b) Overview f grup sequential Statistical framewrk fr trial mnitring: Statistical design f the fixed-sample trial The interim statistical analysis plan is based n the fixed sample design Primary endpint Prbability mdel Functinal Cntrast Statistical hyptheses Statistical standards fr decisins (interval estimate) Bis pg 12

13 b) Overview f grup sequential Statistical framewrk fr trial mnitring: Statistical design f the fixed-sample trial The statistical decisin criteria are referenced t the trial s design hyptheses. Fr example: One-sided superirity test (assume small θ favrs new treatment): Null: θ θ Alternative: θ θ + with θ + < θ, and θ + is chsen t represent the smallest difference that is clinically imprtant. Tw-sided (equivalence) test: Null: Lwer Alternative: θ = θ θ θ Upper Alternative: θ θ + with θ < θ < θ +. θ and θ + dente the smallest imprtant differences. Bis pg 13

14 b) Overview f grup sequential Statistical framewrk fr trial mnitring: Selecting decisin criteria A decisin t stp needs t cnsider what has r has nt been ruled ut. Fr example One-sided superirity test (assume small θ favrs new treatment): Stp fr superirity when any harm (θ θ ) has been ruled ut. Stp fr futility when imprtant benefits (θ θ + ) have been ruled ut. Tw-sided (equivalence) test: Stp fr treatment A better than treatment B when inferirity f A (θ θ ) has been ruled ut. Stp fr treatment B better than treatment A when inferirity f B (θ θ ) has been ruled ut. Stp fr equivalence when imprtant differences (either θ θ + r θ θ ) have been ruled ut. The hyptheses that have been ruled in/ut are given by the interval estimate. Bis pg 14

15 b) Overview f grup sequential Statistical framewrk fr trial mnitring: Grup sequential (superirity trial) Suppse that the trial is planned fr j = 1,..., J interim analyses. Let ˆθ j dente the estimated treatment effect at the jth analysis. Cnsider stpping criteria a j < d j with: ˆθ j a j Decide new treatment is superir ˆθ j d j Decide new treatment is nt superir a j < ˆθ j < d j Cntinue trial Set a J = d J s that the trial stps by the Jth analysis. Hw shuld we chse these critical values? Bis pg 15

16 b) Overview f grup sequential Statistical framewrk fr trial mnitring: Grup sequential (superirity trial) Depictin f the abve decisin rules: O'Brien Fleming 1 sided symmetric stpping rules Mean treatment effect Prprtin f ttal sample size Bis pg 16

17 Interim analyses require special methds Sampling density fr sequentially-mnitred test statistic The filtering due t interim analyses creates nn-standard sampling densities as the basis fr inference. Sampling density depends n the stpping rule. (Illustratin using simulated sample paths) Bis pg 17

18 Interim analyses require special methds Sampling density fr sequentially-mnitred test statistic The filtering due t interim analyses creates nn-standard sampling densities as the basis fr inference. Sampling density depends n the stpping rule. OBF (theta = 1.96) Prbability Density X Bis pg 18

19 Sampling density fr sequentially sampled test statistic Let C j dente the cntinuatin set at the jth interim analysis. Let (M, S) dente the bivariate statistic where M dentes the stpping time (1 M J) and S = S M dentes the value f the partial sum statistic at the stpping time. The sampling density fr the bservatin (M = m, S = s) is: { f (m, s; θ) s C m p(m, s; θ) = 0 else where the (sub)density functin f (j, s; θ) is recursively defined as f (1, s; θ) = f (j, s; θ) = ( ) 1 s n1 V φ n1 θ n1 V ( nj V φ s u n j θ nj V C (j 1) 1 j = 2,..., m ) f (j 1, u; θ) du, Bis pg 19

20 Example: O Brien-Fleming (OBF) 2-sided design bf Fixed 5 mean respnse Sample Size Bis pg 20

21 Sampling density fr OBF bundaries with θ = 0 and θ = 3.92 (crrespnding Nrmal sampling density fr cmparisn): Prbability Density Standard Nrmal (theta = 0) Prbability Density Standard Nrmal (theta = 3.92) X X O'Brien-Fleming (theta = 0) O'Brien-Fleming (theta = 3.92) Prbability Density Prbability Density X X Bis pg 21

22 Example: OBF versus Pcck 1-sided bf pc 8 6 mean respnse Sample Size Bis pg 22

23 Sampling density fr OBF and Pcck 1-sided. OBF (theta = 0) OBF (theta = 1.96) Prbability Density Prbability Density X X Pcck (theta = 0) Pcck (theta = 1.96) Prbability Density Prbability Density X X Bis pg 23

24 Interim analyses require special methds Characteristics f the grup sequential sampling density Density is nt shift invariant Jump discntinuities Requires numerical integratin Sequential testing intrduces bias: E(ˆθ) θ OBF Pcck Bis pg 24

25 Interim analyses require special methds S hw shuld we chse the critical values? Maintain statistical design criteria f the fixed sample trial: Type I errr rate f α = (ne-sided test) r α = 0.05 (tw-sided test). Maintain maximal sample size (with ptential lss f pwer) Maintain pwer (with larger maximal sample size) Other cnsideratins when selecting critical values: Number f interim analyses Timing f interim analyses Degree f early cnservatism Characteristics f the sample size distributin: Expected sample size (Average Sample Number; ASN) Quantiles f the sample size distributin Maximal sample size Stpping prbabilities at each f the interim analyses Bis pg 25

26 Interim analyses require special methds There are many types f grup sequential Fur design categries: One-sided test; One-sided stpping (allw stpping fr efficacy r futility, but nt bth) One-sided test; Tw-sided stpping (allw stpping fr either efficacy r futility) Tw-sided test; One-sided stpping (allw stpping nly fr the alternative(s)) Tw-sided test; Tw-sided stpping (allw stpping fr either the null r the alternative) I will illustrate hw t chse an apprpriate design (sepsis example and yur prjects) Bis pg 26

27 Fur general design categries 1-sided test; stp fr futility 1-sided test; stp fr futility r efficacy Mean Effect Mean Effect Sample Size 2-sided test; stp fr alternative(s) Sample Size 2-sided test; stp fr null r alternative(s) Mean Effect Mean Effect Sample Size Sample Size Bis pg 27