Adaptive designs for diagnostic accuracy studies - S5... · 07/11/2017 Adaptive designs for DTA...

Adaptive designs fordiagnostic accuracy studies

07/11/2017 Adaptive designs for DTA studies 2

Although adaptive designs cannot ‘change theanswer’ regarding the accuracy of a particulardiagnostic test, they can increase the efficiency infinding an answer.

[adopted from Kairalla et al., 2012]


Adaptive study designs

Diagnosticaccuracy studies

Blindedinterim analysis

Adaptationsregarding:‐ prevalence‐ % discordant results‐ % missing values‐ reference standard‐ external: noninferiority margins,cutoff value

Unblindedinterim analysis

Adaptationsregarding:‐ estimated accuracy‐ target population‐ comparator‐ hypotheses

Randomizeddiagnostic studies


Adaptationsregarding:diagnosticaccuracy orbenefit‐riskratio(external)


Adaptationsregarding:‐ proportion of TP, TN,FP, FN or benefit‐riskratio (internal)‐ study design

Adaptiveseamless designs


[FDA, 2016]

Structure

• Blinded interim analyses

• Unblinded interim analysis

• To look ahead


Structure



• To look ahead



Adaptations regarding:‐ prevalence‐ % discordant results‐ % missing values‐ reference standard‐ disease spectrum‐ accuracy of the comparator?

[FDA, 2016]

Modifications based entirely on information from a source completely external to the study [e.g. cutoff value, non‐inferiority margins] are not adaptive designs...

• The lower the conditional dependence between the tests,

the larger the sample size

→ largest sample size for maximum negative dependence

• Sensitivity:

• Specificity:

/

= prevalence

Sample size re‐estimation in pairedcomparative diagnostic accuracy studies I


[McCray et al., 2017; Alonzo et al., 2002]

• Null hypotheses: , : 1 , : 1

→ extendable to non‐inferiority

• Worst case scenario:

1 1 1 1

• Interim analysis based on , , and π using maximum

likelihood estimation under a multinomial model

Sample size re‐estimation in pairedcomparative diagnostic accuracy studies II


[McCray et al., 2017]

Sample size re‐estimation in pairedcomparative diagnostic accuracy studies III



Sample size re‐estimation in pairedcomparative diagnostic accuracy studies IV



Application

• Comparison of CT and PET/CT for the diagnosis of pancreatic cancer

• Assumptions: π = 0.47

• Minimum sample size for TPPR = 0.71, TNNR = 0.46 → N = 186

• Interim analysis after 187 patients: TPPR = 0.79, TNNR = 0.64, π = 0.44

• Sample size re‐estimation: N = 275


diagnostic test se sp

pre‐PET 81 % 66 %

post‐PET 90 % 80 %

Further approaches

• Blinded sample size recalculation in clinical trials with binary

composite endpoints (internal pilot study design) [Sander et al., 2017]

‐ two endpoints: composite endpoint and main component

‐ not co‐primary → at least one rejected null hypothesis required

• Blinded sample size re‐estimation in superiority and noninferiority

trials (internal pilot study design) [Friede et al., 2013]

‐ continuous outcome

‐ blinded estimation of the variance→ not possible for binary data


Structure



• To look ahead



[FDA, 2016]

Group sequential designs with / withoutsample size re‐assessment (e.g. based on corrected accuracy assumptions)

Adaptations regarding:‐ target population‐ comparator‐ hypotheses

Approaches

• Interim evaluation of futility in clinical trials with co‐primary endpoints[Asakura et al., 2017]

‐ interim monitoring with predicted interval

‐ for normal distributed data → transferable to other scales

• Sequential designs for clinical trials with simultaneous bivariate response

(efficacy and safety) for normal distributed data[Todd, 2003; Jennison and Turnbull, 1993; Cook and Farewell, 1994]


Structure



• To look ahead



Adaptive study designs

Diagnosticaccuracy studies


Adaptationsregarding:‐ prevalence‐ % discordant results‐ % missing values‐ reference standard‐ external: noninferiority margins,cutoff value


Adaptationsregarding:‐ estimated accuracy‐ target population‐ comparator‐ hypotheses

Randomizeddiagnostic studies


Adaptationsregarding:diagnosticaccuracy orbenefit‐riskratio(external)


Adaptationsregarding:‐ proportion of TP, TN,FP, FN or benefit‐riskratio (internal)‐ study design

Adaptiveseamless designs

Approaches

• Adaptive clinical trial designs for simultaneous testing of matched

diagnostics and therapeutics [Scher et al., 2011]

• A conditional error function approach for subgroup selection in

adaptive clinical trials [Friede et al., 2012]

• Biomarker adaptive designs in clinical trials [Chen et al., 2014]

• A Bayesian adaptive design for biomarker trials with linked

treatments [Wason et al., 2015]


Further topics

• Adaptive seamless designs

• Development of R packages and SAS macros

• Next workshop in spring 2019?

• Webpage of the DFG‐project: http://www.ams.med.uni‐

goettingen.de/p‐mgmt/Flexh.html


Questions for discussion


• Sample size reassessment based on interim estimates for the

comparator? Blinded?

• Re‐estimation of the cutoff value?

→ training and validation dataset

• Modification of the reference standard?

• Modification of the target population – seamless design?

References I• Alonzo et al. (2002). Sample size calculations for comparative studies of medical tests for

detecting presence of disease. Stat Med, 21:835–52.• Asakura et al. (2017). Interim evaluation of futility in clinical trials with co‐primary endpoints.

Talk on the CEN‐ISBS, Vienna. • Chen et al. (2014). Biomarker adaptive designs in clinical trials. Transl Cancer Res, 3(3):279‐

292.• Cook et al. (1994). Guidelines for monitoring efficacy and toxicity responses in clinical trials.

Biometrics 50:1146–1152.• FDA (2016). Adaptive designs for medical device clinical studies. Guidance for Industry and

Food and Drug Administration Staff. https://www.fda.gov/downloads/ medicaldevices/deviceregulationandguidance/guidancedocuments/ucm446729.pdf (last access 5/11/17)

• Friede et al. (2013). Blinded sample size re‐estimation in superiority and noninferiority trials: bias versus variance in variance estimation. Pharmaceutical Statistics, 12(3):141–146.

• Friede et al. (2012). A conditional error function approach for subgroup selection in adaptive clinical trials. Statistics in Medicine, 31(30):4309‐20.

• Jennison et al. (1993). Group sequential tests for bivariate response: interim analyses of clinical trials with both efficacy and safety endpoints. Biometrics 49:741–752.

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References II

• Kairalla et al. (2012). Adaptive trial designs: a review of barriers and opportunities. Trials, 13:145.

• McCray et al. (2017). Sample size re‐estimation in paired comparative diagnostticaccuracy studies with a binary response. BMC Med Res Metthodol, 17:102.

• Rana et al. (2012). Clinical evaluation of an autofluorescence diagnostic device for oral cancer detection: a prospective randomized diagnostic study. Eur J Cancer Prev, 21(5):460‐466.

• Sander et al. (2017). Blinded sample size recalculation in clinical trials with binary composite endpoints. J Biopharm Stat. 2017;27(4):705‐715.

• Scher et al. (2011). Adaptive clinical trial designs for simultaneous testing of matched diagnostics and therapeutics. Clinical Cancer Research, 17(21):6634‐6640.

• Todd (2003). An adaptive approach to implementing bivariate group sequential clinicaltrial designs.

• Wason et al. (2015). A Bayesian adaptive design for biomarker trials with linked treatments. Br J Cancer, 113:699‐705.

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