Bayesian Hierarchical Models for Detecting Safety Signals in
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Bayesian Hierarchical Models for Detecting Safety Signals in Clinical Trials H. Amy Xia and Haijun Ma Amgen, Inc. MBSW 2009, Muncie, IN March 20, 2009 Disclaimer: The views expressed in this presentation represent personal views and do not necessarily represent the views or practices of Amgen. Outline • Introduction • A motivating example • Bayesian Hierarchical Models – Meta analysis of Adverse Events data from multiple studies incorporating MedDRA structure – Incorporate patient level data – Effective graphics • Closing Remarks Three-Tier System for Analyzing Adverse Events in Clinical Trials • Tier 1: Pre-specified Detailed Analysis and Hypothesis Testing – Tier 1 AEs are events for which a hypothesis has been defined • Tier 2: Signal Detection among Common Events – Tier 2 AEs are those that are not pre-specified and “common” • Tier 3: Descriptive Analysis of Infrequent AEs – Tier 3 AEs are those that are not pre-specified and infrequent Gould 2002 & Mehrotra 2004 SPERT White Paper 2008 Multiplicity Issue in Detecting Signals Is Challenging • Detection of safety signals from routinely collected, not pre-specified AE data in clinical trials is a critical task in drug development • Multiplicity issue in such a setting is a challenging statistical problem – Without multiplicity considerations, there is a potential for an excess of false positive signals – Traditional ways of adjusting for multiplicity such as Bonferroni may lead to an excessive rate of false negatives – The challenge is to develop a procedure for flagging safety signals which provides a proper balance between ‘no adjustment’ versus ‘too much adjustment’ Considerations Regarding Whether Flagging an Event • • • • Actual significance levels Total number of types of AEs Rates for those AEs not considered for flagging Biologic relationships among various AEs 1st two are standard considerations in the frequentist approach. The 2nd two are not, but relevant in the Bayesian approach -- Berry and Berry, 2004 Bayesian Work in Signal Detection • Spontaneous adverse drug reaction reports – Gamma Poisson Shrinker (GPS) on FDA AERS database (DuMouchel,1999) – Bayesian Confidence Propagation Neural Network (BCPNN) on WHO database (Bate, et al. 1998) • Clinical trial safety (AE) data – Bayesian hierarchical mixture modeling (Berry and Berry, 2004) Meta Analysis • Glass (1976) Meta-analysis refers to a statistical analysis that combines the results of some collection of related studies to arrive a single conclusion to the question at hand • Meta-analysis based on – aggregate patient data (APD meta-analysis) – Individual patient data (IPD) meta-analysis • Bayesian modeling is a natural choice to incorporate the complex hierarchical structure of the data George Chi, H.M. James Hung, Robert O’Neill (FDA CDER) “Safety assessment is one area where frequentist strategies have been less applicable. Perhaps Bayesian approaches in this area have more promise.” (Pharmaceutical Report, 2002) An Example • Data from four double-blind placebo-controlled studies on drug X. Study populations are similar. • Sample sizes: Study Drug X N Drug X Subj-yr Placebo N Placebo Subj-yr Study A 57 28.25 55 19.02 Study B 486 104.75 166 34.93 Study C 390 85.44 193 40.97 Study D 312 68.78 306 65.91 • After converting all AEs into same MedDRA version, reported AEs are coded to 464 PTs under 23 SOCs and 233 HLTs SYSTEM ORGAN CLASS INFECTIONS AND INFESTATIONS HIGH LEVEL TERM HERPES VIRAL INFECTIONS INFECTIONS AND INFESTATIONS PREFERRED TERM STUDY n_0 n_1 N_0 N_1 rt_0 rt_1 HERPES SIMPLEX 1 2 3 4 0 0 1 2 3 5 8 2 55 166 193 306 57 486 390 312 0.00 0.00 0.52 0.65 5.26 1.03 2.05 0.64 UPPER RESPIRATORY TRACT INFECTIONS SINUSITIS 1 2 3 4 2 6 0 4 8 19 8 11 55 166 193 306 57 486 390 312 3.64 3.61 0.00 1.31 14.04 3.91 2.05 3.53 INFECTIONS AND INFESTATIONS UPPER RESPIRATORY TRACT INFECTIONS RHINITIS 1 2 3 4 0 0 1 0 0 0 4 0 55 166 193 306 57 486 390 312 0.00 0.00 0.52 0.00 0.00 0.00 1.03 0.00 SKIN AND SUBCUTANEOUS TISSUE DISORDERS URTICARIAS URTICARIA 1 2 3 4 0 1 0 1 1 1 4 4 55 166 193 306 57 486 390 312 0.00 0.60 0.00 0.33 1.75 0.21 1.03 1.28 SKIN AND SUBCUTANEOUS TISSUE DISORDERS PURPURA AND RELATED CONDITIONS ECCHYMOSIS 1 2 3 4 0 0 0 0 0 4 5 3 55 166 193 306 57 486 390 312 0.00 0.00 0.00 0.00 0.00 0.82 1.28 0.96 INJURY, POISONING AND PROCEDURAL COMPLICATIONS NON-SITE SPECIFIC INJURIES NEC EXCORIATION 1 2 3 4 0 0 0 0 0 7 0 1 55 166 193 306 57 486 390 312 0.00 0.00 0.00 0.00 0.00 1.44 0.00 0.32 INJURY, POISONING AND PROCEDURAL COMPLICATIONS NON-SITE SPECIFIC PROCEDURAL COMPLICATIONS PROCEDURAL PAIN 1 2 3 4 0 0 0 2 0 0 1 1 55 166 193 306 57 486 390 312 0.00 0.00 0.00 0.65 0.00 0.00 0.26 0.32 N_0: sample size in placebo arm; N_1: sample size in treatment arm n_0: # subject with AE in placebo arm; n_1: # subject with AE in treatment arm rt_0: subject incidence in placebo arm; rt_1: subject incidence in treatment arm Proposed Bayesian Approach • Hierarchical mixture models for aggregated binary responses was constructed based on the work by Berry & Berry(2004) – Explore impact of using different MedDRA hierarchy – Inclusion of study effects – Further extended to a hierarchical Poisson mixture model, to account for different exposure/follow-up times between patients • Individual patient level models are discussed • Implemented the above models with available software – WinBUGS for model implementation – S-Plus graphics for inference MedDRA • MedDRA (the Medical Dictionary for Regulatory Activities Terminology) is a controlled vocabulary widely used as a medical coding scheme. • MedDRA Definition (MSSO): – MedDRA is a clinically-validated international medical terminology used by regulatory authorities and the regulated biopharmaceutical industry. The terminology is used through the entire regulatory process, from pre-marketing to post-marketing, and for data entry, retrieval, evaluation, and presentation. https://eudract.emea.europa.eu/docs/medDRA/Introduction_To_MedDRA.ppt MSSO: Introduction to MedDRA http://www.fda.gov/cder/present/dia-699/dia628/index.htm MedDRA and Pharmacovigilance - The Way Forward, 7/8/99 http://www.fda.gov/cder/present/dia-699/dia628/index.htm MedDRA and Pharmacovigilance - The Way Forward, 7/8/99 Example of MedDRA Hierarchy SOC = Respiratory, thoracic and mediastinal disorders HLGT = Respiratory tract infections SOC = Infections and infestations HLGT = Viral infectious disorders HLT =Viral upper respiratory tract infections HLT = Influenza viral infections PT = Influenza https://eudract.emea.europa.eu/docs/medDRA/Introduction_To_MedDRA.ppt MSSO: Introduction to MedDRA Hierarchical Structure of MedDRA • Bayesian hierarchical models allow for explicitly modeling AEs with the existing coding structure – AEs in the same SOC more likely to be similar within than across SOCs. – Allow for this possibility, but does not impose it, depending on the actual data – SOC tends to be too broad. HLT is more closely related to medical concepts. • In fact, clinical and safety people would (informally) consider the similarity of the AEs, say, within SOCs when they review AE tables – For example, if differences in several CV events were observed, then each would be more likely to be causal than if differences came from medically unrelated areas (eg, skin, neurological, thrombosis, cancer) • Bayesian hierarchical modeling allows a scientific, explicit, and more formal way to take it into consideration Notations • Study i=1,…I, SOC b=1,…B and PT j=1,…kb • Data: For AEibj – Treatment group: Yibj incident events observed in Nit patients with Tibj subjects’ exposure – Control group: Xibj incident events observed in Nic patients with Cibj subjects’ exposure Bayesian Meta Analysis Hierarchical Logistic Regression Yibj ~ Binomial (N it , tibj ), X ibj ~ Binomial (N ic , cibj ), where tibjand cibj are event rates for AE ibj in the treatment and control groups, respective ly. •Common treatment effect for same PT across studies logit(c ) , logit(t ) ibj ibj ibj ibj bj ibj ~ N( bj , 2,bj ) , bj ~ N( b , 2b ) Mixture Prior for treatm ent effect log - OR : Stage 1 prior : bj~ b (0) (1 b ) N ( b , 2b ); Stage 2 prior : b ~ Beta ( p , p ); p ~ exp( ) I ( p 1); Stage 3 prior : p ~ exp( ) I ( p 1) Bayesian Meta Analysis Hierarchical Logistic Regression (Cont.) • Treatment effect with additive study effects: logit(c ibj ) ibj , logit(t ibj ) ibj bj i Studyi ibj ~ N( bj , 2,bj ) , bj ~ N( b , 2b ) bj~ b (0) (1 b ) N ( b , 2b ) i ~ N (0,0.01) • A random treatment effect/multiplicative model: logit(c ibj ) ibj , logit(t ibj ) ibj ibj ibj ~ N( bj , 2,bj ) , bj ~ N( b , 2b ) ibj~ N(bj , 2bj ) , bj~ b (0) (1 b ) N ( b , 2b ) Bayesian Meta Analysis Hierarchical Logistic Regression (Cont.) • Other priors – Stage 1 λbj ~N(μλb , σ2λb ); – Stage 2 μλb~N(μλ0, σ2λ0 ); σ2λb ~IG(αλ, βλ); μθb ~N(μθ0, σ2θ0 ); σ2θb ~IG(αθ, βθ) - Stage 3 μλ0~N(μλ00 , σ2λ00 ); σ2λ0 ~IG(αλ00, βλ00) μθ0~N(μθ00 , σ2θ00 ); σ2θ0 ~IG(αθ00, βθ00) Hyperparameters μλ00, σ2λ00, αλ00, βλ00, μθ00, σ2θ00, αθ00, βθ00, αλ, βλ , αθ, βθ are fixed constants Inference • AEbj is flagged if – Pr( θbj > d*| Data) > p, where θbj is log-OR in Binomial models and log-RR in Poisson models. – d* and p are all prespecified constants. • Graphs are useful tools in deciphering data and presenting results Model Selection • Deviance Information Criteria (DIC) was used to compare models with same data • Limited sensitivity analyses were done to check the robustness of the models • Different levels of MedDRA structures were used – SOC/PT, HLT/PT and SOC/HLT/PT • Treatment effect with additive study effects model using SOC/PT structure was chosen Bayesian Meta Analysis Hierarchical Log-linear Regression • Poisson models – Adjust for different exposures in treatment and control – Assume constant hazard over time – Unless AEs are fairly common or follow up of studies are quite unbalanced between treatment arms, usually are not very different from Binomial models Yibj~Pois(tibj Tibj ); Xbj~Pois(cibj Cibj) where tibj and cibj are event rates, and Tibj and Cibj for AEibj are total exposure times in the treatment and control groups, respectively log(cibj )=λibj; log(tibj )=λibj + θbj , Note that θbj =log(RRbj) 2.5 Fisher's Exact Test 2-sided P-values 2.0 SINUSITIS 1.5 FATIGUE EXCORIATION 0.5 1.0 HERPES SIMPLEX 0.0 - log10 (Fisher exact p-value) ECCHYMOSIS GASTROINTESTINAL DISORDER... INFECTIONS AND INFESTATIO... SKIN AND SUBCUTANEOUS TIS... NEOPLASMS BENIGN, MALIGNA... GENERAL DISORDERS AND ADM... PSYCHIATRIC DISORDERS INJURY, POISONING AND PRO... RESPIRATORY, THORACIC AND... CARDIAC DISORDERS MUSCULOSKELETAL AND CONNE... REPRODUCTIVE SYSTEM AND B... RENAL AND URINARY DISORDE... EYE DISORDERS INVESTIGATIONS NERVOUS SYSTEM DISORDERS METABOLISM AND NUTRITION ... IMMUNE SYSTEM DISORDERS EAR AND LABYRINTH DISORDE... SURGICAL AND MEDICAL PROC... VASCULAR DISORDERS ENDOCRINE DISORDERS HEPATOBILIARY DISORDERS BLOOD AND LYMPHATIC SYSTE... -0.01 0.0 0.01 Raw Risk Difference(%), Treatment - Placebo 0.02 Peto's Method M-H TA CC Method ECCHYMOSIS FATIGUE SINUSITIS SINUSITIS FATIGUE HERPES SIMPLEX HERPES SIMPLEX ECCHYMOSIS DYSPEPSIA HEADACHE INJECTION SITE BRUISING INJECTION SITE BRUISING HEADACHE DYSPEPSIA EXCORIATION INFLUENZA URTICARIA NASOPHARYNGITIS BASAL CELL CARCINOMA DIZZINESS TENSION HEADACHE BRONCHITIS DIZZINESS URTICARIA BRONCHITIS PHARYNGITIS INFLUENZA UPPER RESPIRATORY TRACT INFECTION GASTROENTERITIS VIRAL TENSION HEADACHE 0 1 2 log Odds Ratio 3 0 1 2 log Odds Ratio 3 ECCHYMOSIS 0.4 0.6 BLOOD AND LYMPHATIC SYSTE... CARDIAC DISORDERS EAR AND LABYRINTH DISORDE... ENDOCRINE DISORDERS EYE DISORDERS GASTROINTESTINAL DISORDER... GENERAL DISORDERS AND ADM... HEPATOBILIARY DISORDERS IMMUNE SYSTEM DISORDERS INFECTIONS AND INFESTATIO... INJURY, POISONING AND PRO... INVESTIGATIONS METABOLISM AND NUTRITION ... MUSCULOSKELETAL AND CONNE... NEOPLASMS BENIGN, MALIGNA... NERVOUS SYSTEM DISORDERS PSYCHIATRIC DISORDERS RENAL AND URINARY DISORDE... REPRODUCTIVE SYSTEM AND B... RESPIRATORY, THORACIC AND... SKIN AND SUBCUTANEOUS TIS... SURGICAL AND MEDICAL PROC... VASCULAR DISORDERS URTICARIA 0.2 SINUSITIS HERPES SIMPLEX BASAL CELL CARCINOMA FATIGUEBLOOD PRESSURE INCREASED TENSION HEADACHE DIZZINESS FOLLICULITIS DYSPEPSIA ANXIETY PHARYNGITIS MIGRAINE HYPERSENSITIVITY DERMAL CYST NERVE COMPRESSION CAROTID ARTERY STENOSIS BLOOD CHOLESTEROL INCREASED FLUSHING LIVER DISORDER MEMORY IMPAIRMENT GASTROENTERITIS VIRAL HYPERTONIA DIZZINESS POSTURAL INSUFFICIENCY DISTURBANCE IN SYNDROME ATTENTION HEADACHE SENSORY DISTURBANCE NEURALGIA SYNCOPE CEREBROVASCULAR FACIAL FURUNCLE PALSY ACCIDENT SOMNOLENCE HYPERSOMNIA CYSTITIS VAGINAL INFECTION DYSGEUSIA CARPAL TUNNEL BRONCHITIS RESTLESS LEGS ENDODONTIC PROCEDURE ACNE THROAT IRRITATION SKIN INFECTION PARAESTHESIA VIRAL UPPER RESPIRATORY TRACT INFECTION LETHARGY SCIATICA SEBORRHOEIC KERATOSIS ECZEMA BURNING SENSATION EAR WEIGHT INFECTION DECREASED INFECTED INSECT BITE BRONCHITIS VERTIGO BLOOD GLUCOSE CHRONIC INCREASED INFLUENZA NIGHT SWEATS SYNCOPE LABYRINTHITIS VASOVAGAL ERECTILE DYSFUNCTION ONYCHOMYCOSIS WEIGHT INCREASED BODY TEMPERATURE INCREASED DRY EYE HERPES RHINORRHOEA VIRUS INFECTION MALAISE CELLULITIS CONJUNCTIVITIS STAPHYLOCOCCAL HYPOAESTHESIA FUNGAL EXCORIATION INFECTION VIRAL NASAL INFECTION CONGESTION LARYNGITIS VERTIGO POSITIONAL ONYCHOMADESIS TOOTH SKIN SKIN LESION PAPILLOMA ABSCESS ABDOMINAL INFECTION GINGIVAL INFECTION DIVERTICULITIS TINEA VERSICOLOUR STRESS SINUS HAEMATURIA HEADACHE GUTTATE SIALOADENITIS SKIN HAEMORRHAGE PSORIASIS BIPOLAR TINNITUS IINCONTINENCE DISORDER ALCOHOLISM INFECTED URINARY PREMATURE CRURIS INCONTINENCE SEBACEOUS EJACULATION CYST RHINITIS MASTITIS PULPITIS IRRITABILITY DENTAL CAT ACUTE POSTOPERATIVE HYPERCHOLESTEROLAEMIA STRESS SCRATCH SINUSITIS DISEASE WOUND INFECTION ARTERIOSCLEROSIS EPISTAXIS CORONARY VAGINAL APPENDICITIS INFECTIOUS DYSPHONIA CANDIDIASIS MONONUCLEOSIS PUNCTURE AGITATION ATTENTION SITE DEFICIT/HYPERACTIVITY INFECTION DISORDER GENITAL EYELID INFECTION INFECTION FEMALE TONSILLITIS CANDIDIASIS ERYSIPELAS PNEUMONIA ARRHYTHMIA NERVOUSNESS TENDONITIS TINEA ACTINIC INFECTION KERATOSIS RASH PUSTULAR INJECTION SITE BRUISING INGROWING NAIL DYSPHORIA BUNDLE BRANCH BLOCK LEFT SUBCUTANEOUS ABSCESS ORAL TRIGONITIS FALL PUSTULE CERVICITIS CONDYLOMA OTITIS FIBROUS EXTERNA HISTIOCYTOMA ACUMINATUM URINARY CERVICAL RETENTION DYSPLASIA LIPOMA CALCULUS DERMATITIS BLADDER CONTACT EAR SQUAMOUS SLEEP DISCOMFORT APNOEA CELL SYNDROME CARCINOMA OFARTERY SKIN LYMPHOEDEMA SINUS CONGESTION PROSTATE CANCER LOWER RESPIRATORY TRACT INFECTION INJECTION POLLAKIURIA ALLERGIC COUGH SITE HAEMATOMA INTERTRIGO LIBIDO DECREASED INTERVERTEBRAL SNEEZING DYSPNOEA DISC DEGENERATION SNORING NASOPHARYNGITIS TOOTH DYSPNOEA CONJUNCTIVAL INFECTION EXERTIONAL HAEMORRHAGE ABDOMINAL FOOT FRACTURE PAIN UPPER GASTROINTESTINAL INFLAMMATION SHOULDER PAIN NEPHROLITHIASIS HYPERVENTILATION VAGINAL RHINITIS ALLERGIC LACERATION VULVOVAGINAL RHONCHI DRYNESS HAND OTITIS MEDIA JOINT DRY EYELID POST-TRAUMATIC MOUTH EFFUSION OEDEMA PAIN PRURITUS SEASONAL FLATULENCE ALLERGY BENIGN ABDOMINAL PROSTATIC DISTENSION HYPERPLASIA EYE INFLAMMATION OVARIAN CATARACT HOT FOOD FLUSH POISONING CYST RUPTURED DIPLOPIA GASTROOESOPHAGEAL REFLUX DISEASE STOMACH DISCOMFORT HYPERTENSION GASTROINTESTINAL HYPERMOTILITY PAIN GINGIVAL THERMAL BLEEDING BURN DEPRESSION INSOMNIA DISORDER ABDOMINAL TENDERNESS ROAD SCAPULA TRAFFIC FRACTURE ACCIDENT TOOTH FRACTURE HEAT STROKE MYALGIA TOOTHACHE INJURY ANIMAL BITE WOUND BACK PAIN TENDON INJURY CONTUSION DIARRHOEA NAUSEA 0.0 P(OR > 1|Data) 0.8 1.0 Posterior Summary of Bayesian Hierarchical Meta-Analysis Model -3 -2 -1 0 log-OR posterior means 1 2 Inferences of Binomial Hierarchical Model with Mixture Prior SOC GENERAL DISORDERS AND ADMINISTRATION SITE INFECTIONS AND INFESTATIONS PT FATIGUE HERPES SIMPLEX INFECTIONS AND INFESTATIONS SINUSITIS SKIN AND SUBCUTANEOUS TISSUE DISORDERS ECCHYMOSIS Two sided Post Post Post Post Post Fisher's Prob Prob Prob Prob Prob Exact OR=1 OR>1 OR>1.1 OR>1.2 OR>2 0.019 0.82 0.18 0.18 0.18 0.10 0.039 0.75 0.25 0.25 0.25 0.21 0.012 0.72 0.27 0.27 0.27 0.19 0.005 0.11 0.89 0.89 0.89 0.89 Bayesian Patient Level Models • IPD models to include within patient correlation and patient level factors while incorporating MedDRA coding hierarchy • Data from one study is used:618 subject, 207 unique AEs, N = 127926 Ykbj ~ Bernoulli (tkbj ), X kbj ~ Bernoulli (ckbj ), logit(c kbj ) bj Zk k , logit(t kbj ) bj bj Zk k where Zk is the set of covariates for subject k 2 ~ MVN(0, b I), k ~ Normal (0, 2 ) Simulation Study • Simulation scheme: – Randomly assign subjects to treatment or placebo to create a “null” scenario – Adverse events within subject remain unchanged to maintain the SOC/PT hierarchy – 1000 simulated datasets • Family-wise error rates (also FDR in this case) of Fisher’s exact text unadjusted for multiplicity and Poisson regression with mixture prior are compared • Percentage of simulated datasets yielding Y=0, 1, 2 or ≥3 incorrectly flagged adverse events out of 464 PTs are also compared Distribution of Y (%) FWER/ FDR 1 2 >=3 Non-adjusted 2sided Fisher’s exact test 2-sided test, p-value <=0.05 99.1 2.7 5.4 91 Non-adjusted 1sided Fisher’s exact test p-value<=0.05 90.1 22.7 26.9 40.5 Bayes Hierarchical Poisson Model * c=1, p=0.7 10.2 7.5 2.0 0.7 c=1, p=0.8 6.1 4.6 1.3 0.2 c=1, p=0.9 2.8 2.7 0.1 0.0 c=1.2, p=0.7 10.2 7.6 1.9 0.7 c=1.2, p=0.8 6.0 4.6 1.2 0.2 c=1.2, p=0.9 2.7 2.6 0.1 0.0 c=2, p=0.7 7.0 5.3 1.5 0.2 c=2, p=0.8 3.7 3.3 0.4 0.0 c=2, p=0.9 1.3 1.2 0.1 0.0 RR≠1, p=0.7 13.8 10.3 2.5 1.0 RR≠1, p=0.8 7.9 6.3 1.4 0.2 RR≠1, p=0.9 3.6 3.5 0.1 0.0 * Pr( RR bj c | Data ) p Simulation Study • 464 independent tests with alpha=0.05 would yield in average about 23 signals and have FDR=1 if no multiplicity is adjusted for. • Correlation of the AE data reduced the error rate in our simulation study • But the FDR is still as high as 99.1%. For 91% cases there are at least 3 falsely identified signals. • The FWERs/FDRs for all Bayes model results are much lower and acceptable. Closing Remarks • Current traditional approach of flagging routinely collected AEs based on unadjusted p-values or CIs can result in excessive false positive signals – As a result, it can cause undue concern for approval/labeling/post marketing commitment • Commonly used meta-analysis methods for aggregated binary outcome (OR) – Peto’s method is not recommended for severely unbalanced studies or common events unless treatment effects are small – MH method: needs continuity correction Closing Remarks (Cont.) • Bayesian meta-analysis hierarchical mixture modeling provides a useful tool to analyze data from multiple studies and address multiplicity – Allows for explicitly modeling AEs with the existing MedDRA coding structure – Use a mixture prior by allowing a point mass on equality of the treatment and control rates – Study differences can be accounted for – No need to add continuity correction. Double zero studies are included. – For less common AEs and studies without a great amount of follow-up variation between treatment groups, inferences from Poisson regression and logistic regression models are very similar • Computation for signal detection using IPD is challenging • Graphics are effective in displaying flagged signals Future Work • More sensitivity analysis on the performance of the models • Further simulation study on type II error and operating characteristics of Bayesian models • Zero-inflated Poisson model might be a good approach for relatively healthy population • Incorporating severity information of AEs • Multi-axial structure of MedDRA coding system • The field of clinical trial signal detection is still in its infancy – More research and practice are needed – Statisticians need to work with clinicians/safety scientists closely to further advance this field References • • • • • • • • Bate A, Lindquist M, Edwards, IR, Olsson S, Orre R, Lansner A, and De Freitas RM (1998). A Bayesian neural network method for adverse drug reaction signal detection. Eur J Clin Pharmacol 54:315-321 Berry S and Berry D (2004) Accounting for multiplicities in assessing drug safety: a three-level hierarchical mixture model. Biometrics, 60: 418-426 Chi G, Hung HMJ, and O’Neill R (2002). Some comments on “Adaptive Trials and Bayesian Statistics in Drug Development” by Don Berry. In Pharmaceutical Report, Vol 9, 1-11 Crowe B, Xia A, Watson D, Shi H, Lin S, Kuebler J, Berlin J, et al. (2008). Recommendations for Safety Planning, Data Collection, Evaluation and Reporting During Drug, Biologic and Vaccine Development: A Report of the PhRMA Safety Planning, Evaluation and Reporting Team (SPERT). Manuscript in preparation. DuMouchel W (1999). Bayesian data mining in large frequency tables, with an application to the FDA Spontaneous Reporting System (with discussion). The American Statistician 53:177-202 Gould AL. Drug safety evaluation in and after clinical trials. Deming Conference, Atlantic City, 3 December 2002 Mehrotra, DV and Heyse, JF (2004). Multiplicity considerations in clinical safety analysis. Statistical Methods in Medical Research 13, 227-238 Spiegelhalter DJ, Best NG, Carlin BP and van der Linde A (2002) Bayesian measures of model complexity and fit (with discussion). J. Roy. Statist. Soc. B. 64, 583-640. Thank You! Back-up Slides 50 0 10 20 30 40 Min. 0 1st Qu. 0 Median 0 Mean 0.003 3rd Qu. 0.003 Max. 0.333 0.00 0.10 0.20 0.30 80 Incidence Rate of Treatment Arm 0 20 40 60 Min. 0 1st Qu. 0 Median 0 Mean 0.003 3rd Qu. 0 Max. 0.164 0.00 0.05 0.10 0.15 Incidence Rate of Placebo Arm Bayesian Hierarchical Model for AE (Berry & Berry 2004) • PT level assumptions: ij ~ N ( i , 2 ) ij ~ i I [0] (1 i ) N ( i , 2 ) • SOC level assumptions: i i i ~ N ( 0 , T2 ) ~ N ( 0 , T2 ) ~ Beta ( , ) • Global assumptions: 0 0 ~ N ( 00 , T200 ) ~ N ( 00 , T200 ) T2 ~ IG ( T , T ) T2 ~ IG ( T , T ) 2 2 ~ IG ( , ) ~ IG ( , ) ~ Exp( ) I ( 1) ~ Exp( ) I ( 1) SOC: Injury, Poisoning and Procedural Complications Preferred Term 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Alcohol Poisoning Animal Bite Arthropod Bite Back Injury Cartilage Injury Chemical Eye Injury Closed Head Injury Concussion Contrast Media Reaction Contusion Epicondylitis Excoriation Eye Injury Fall Foot Fracture Foreign Body in Eye Hand Fracture Heat Exhaustion Heat Stroke Hip Fracture Injury Corneal Jaw Fracture Joint Injury Joint Sprain Ligament Injury Ctrl r(%) 0.00 0.14 0.56 0.69 0.14 0.14 0.00 0.28 0.14 0.83 0.14 0.00 0.14 0.00 0.00 0.14 0.00 0.14 0.00 0.00 0.14 0.14 0.00 0.28 0.14 Trt r(%) 0.08 0.24 0.24 0.24 0.00 0.00 0.08 0.00 0.00 0.80 0.08 0.64 0.08 0.32 0.32 0.00 0.24 0.00 0.08 0.08 0.00 0.08 0.08 0.16 0.08 diff (Trt-Ctrl ) 0.080 0.102 -0.315 -0.453 -0.139 -0.139 0.080 -0.278 -0.139 -0.030 -0.059 0.643 -0.059 0.321 0.321 -0.139 0.241 -0.139 0.080 0.080 -0.139 -0.059 0.080 -0.117 -0.059 Preferred Term 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Limb Injury Mouth Injury Muscle Injury Muscle Strain Neck Injury Pain Trauma Activated Pneumothorax Traumatic Post Procedural Complication Post Procedural Pain Procedural Pain Rib Fracture Road Traffic Accident Scapula Fracture Skeletal Injury Skin Laceration Splinter Sunburn Tendon Injury Thermal Burn Tooth Fracture Tooth Injury Traumatic Haematoma Wound Wrist Fracture Ctrl r(%) 0.14 0.00 0.00 0.69 0.14 0.14 0.00 0.14 0.00 0.28 0.14 0.00 0.00 0.42 1.11 0.14 0.28 0.00 0.00 0.00 0.00 0.28 0.14 0.14 Trt r(%) 0.16 0.08 0.08 0.72 0.08 0.40 0.08 0.00 0.08 0.08 0.08 0.16 0.08 0.00 0.72 0.08 0.24 0.08 0.16 0.08 0.16 0.08 0.24 0.00 diff (Trt-Ctrl ) 0.022 0.080 0.080 0.028 -0.059 0.263 0.080 -0.139 0.080 -0.197 -0.059 0.161 0.080 -0.417 -0.388 -0.059 -0.037 0.080 0.161 0.080 0.161 -0.197 0.102 -0.139
