Friday, May 01, 2015

Study Day in Clinical Study Protocol

Any clinical trial should always have a study protocol. The study protocol will contain a schedule of events table to specify the study visits and the study procedures. For some protocols, study visits will just be the study days (Day 1, Day 7, Day 21,…).

A while ago, I wrote an article “counting the study day” to describe two ways in counting the study day: starting with zero or starting 1. At that time, I said that both ways were acceptable.

It turns out that starting with study day 0 can cause a lot of trouble for programming, especially in SDTM (study data tabulation model).  According to SDTM Implementation Guidelines, there should not be a study day 0 in SDTM data set. If a study protocol uses a study day 0 as the first day of study drug administration, there will be an inconsistency when comparing the SDTM data sets (therefore, the data listings) with the protocol. Here is what it is said about the study day in SDTM implementation guidelines (SDTMIG).






To avoid the inconsistency issue, it is advisable that in study protocols, study day 0 should not be used. In clinical study protocols, the first day of the study drug administration (or randomization day) should always be counted as day 1 (instead of day 0).  With study day 1 being the first day of the study drug administration, the 7 days after treatment will be study day 8, and so on.

The schedule of events table below is advisable. It correctly uses Day 1 as the first day of dosing.



The schedule of events table below is not advisable since it uses Day 0. To avoid the trouble for downstream SDTM programming, it is better to have study visits listed as Day 1, Day 2, Day 8, Day 15,  Day 29.

Additional References:

1. Studyday calculation ( --DY Variable in SDTM)
2. SAS Programming in the Pharmaceutical Industry             

Sunday, April 19, 2015

Hockey stick effect, yo-yo effect, honeymoon effect, halo effect, Hawthorne effect, John Henry effect, and Pygmalion effect

In clinical trials, people need to pay attention to the various effects in designing the clinical trials. A while ago, I mentioned the hockey stick effect that described an initial rebound in a treatment measure followed by the true deterioration of the treatment measures, which occurred in diseases such as Alzheimer’s disease, alpha-1 antitrypsin deficiency, where the purpose of the treatment is to prevent the further deterioration of the disease progression instead of improving.

Other these kinds of effects were described below.

In a clinical trial design discussion, the KOL (key opinion leader) used the yo-yo effect to describe a outcome measure that could fluctuate during the screening period – before the treatment intervention started. 

According to Wikepedia, yo-yo effect, also known as weight cycling, is a term "yo-yo dieting" coined by Kelly D. Brownell at Yale University, in reference to the cyclical loss and gain of weight, resembling the up-down motion of a yo-yo. In this process, the dieter is initially successful in the pursuit of weight loss but is unsuccessful in maintaining the loss long-term and begins to gain the weight back. The dieter then seeks to lose the regained weight, and the cycle begins again.

In a FDA ADCOM meeting minutes, the yo-yo effect is used to describe the hemoglobin level “And hemoglobin levels often plummet to less than 10 before therapy is resumed. This causes a yo-yo effect and is not in the best interests of patients in treating their anemia. There is also no safety net to prevent patients' hemoglobin levels from falling so low that a blood transfusion is required. “


Honeymoon Effect

Honeymoon effect is used to describe the temporary rebound in insulin level right after the initial treatment. After starting treatment with insulin a person's own insulin levels may temporarily improve. This is believed to be due to altered immunity and is known as the "honeymoon phase". The clinical trial for type 1 diabetes must consider honeymoon effect in the study design.

The Honeymoon Phase (or Honeymoon Period) amongst people with type 1 diabetes refers to the period of time shortly following diabetes diagnosis when the pancreas is still able to produce a significant enough amount of insulin to reduce insulin needs and aid blood glucose control.

This does not, unfortunately, indicate that the diabetes is in remission or can be cured. 

There is no hard and fast rule for how long the honeymoon phase lasts amongst people with type 1 diabetes. The diabetes honeymoon phase can last for weeks, months or in some cases years.

In an ADCOM meeting, the honeymoon effect was also used to describe the transient initial effect in epilepsy trial “Beneficial effect of stimulation was lasting. We did not see the familiar and transient honeymoon effect of adding medication after medication to the regimens of these refractory patients. By three years of stimulation, seizures were at a median 68 percent improvement relative to baseline, that is to say a third of their initial level”


The halo effect
is a cognitive bias in which an observer's overall impression of a person, company, brand, or product influences the observer's feelings and thoughts about that entity's character or properties. It was named by psychologist Edward Thorndike in reference to a person being perceived as having a halo. Subsequent researchers have studied it in relation to attractiveness and its bearing on the judicial and educational systems. The halo effect is a specific type of confirmation bias, wherein positive feelings in one area cause ambiguous or neutral traits to be viewed positively. Edward Thorndike originally coined the term referring only to people; however, its use has been greatly expanded especially in the area of brand marketing.

The term "halo" is used in analogy with the religious concept: a glowing circle that can be seen floating above the heads of saints in countless medieval and Renaissance paintings. The saint's face seems bathed in heavenly light from his or her halo. Thus, by seeing that somebody was painted with a halo, the observer can tell that this must have been a good and worthy person. In other words, the observer is transferring their judgment from one easily observed characteristic of the person (painted with a halo) to a judgment of that person's character.

The halo effect works both in both positive and negative directions (the horns effect): If the observer likes one aspect of something, they will have a positive predisposition toward everything about it. If the observer dislikes one aspect of something, they will have a negative predisposition toward everything about it.

In a FDA memo, the halo effect was used to described as a bias in subjects’ assessment of the device effectiveness. “In general, subject data have not been employed as a primary endpoint in device trials because it is difficult to determine whether adequate training of patients has been achieved. Such subjects are particularly prone to the “halo effect” in which subjects judge a return to a “baseline” appearance (after resolution of the swelling, redness and tenderness associated with implantation) to be a significant improvement. “


Hawthorne Effect

When people are under study, observation or investigation, this very fact can have an effect on them and on the results of the study. This is known as the Hawthorne effect.

In a FDA presentation, the Hawthorne Effect is described as one of the main contributor to the placebo effect.

In clinical trial auditing/inspection, the observer effect needs to be minimized. “When you observe the inspection, you change the dynamics of the situation. When people are aware they are being observed, they act differently than they would if they were not being observed. The problem is not unique to this program. It has sometimes been referred to as the experimenter effect, Hawthorne effect, restaurant manners effect, and job interview effect.”
Reference: De Amici D, Klersy C, Ramajoli F, Brustia L and Politi P (2000) impact of the Hawthorne effect in a longitudinal clinical study. The case of anesthesia. Control Clin Trials. 21: 103-14

Hello-goodbye effect

This is part of the psychodynamics of some people, whereby they initially present themselves in the worst possible light in order to become eligible for treatment, and then after treatment present themselves in the best possible light in an attempt to signal substantial gain in their condition. If uncorrected, the health gain of the patient will be overestimated. The hello-goodbye effect is a serious threat to the validity of clinical studies, meta-analyses and policy making.


John Henry effect

The John Henry effect is an experimental bias introduced into social experiments by reactive behavior by the control group.

In a controlled social experiment if a control is aware of their status as members of the control group and is able to compare their performance with that of the treatment group, members of the control group may actively work harder to overcome the "disadvantage" of being in the control group.

For example, if in an educational trial where the school classes who are in the treatment receive an extra support teacher, students who are in the control group may be induced to work harder to overcome that disadvantage.

The term was first used by Gary Saretsky (1972) to describe the behavior of a legendary American steel driver in the 1870s who, when he heard his output was being compared with that of a steam drill, worked so hard to outperform the machine he died in the process.

Pygmalion effect

The Pygmalion effect, or Rosenthal effect, is the phenomenon whereby the greater the expectation placed upon people, the better they perform. The effect is named after the Greek myth of Pygmalion, a sculptor who fell in love with a statue he had carved.
 

Wednesday, April 01, 2015

Standardized MedDRA Queries (SMQs) and Standardized Drug Groupings (SDGs)

Clinical trial data collection usually includes the collection of adverse events, concomitant medications, and medical histories. The data is usually collected as the verbatim term in free text fields. The data collected with free text fields is not possible for meaningful summary and analysis. Therefore, the first step will be to perform the medical coding to group the same type or similar type of events or drugs together.

The industry standard for medical coding in clinical trials is to use MedDRA (Medical Dictionary for Regulatory Activities) for adverse events and medical histories and to use WHO-DD (World Health Organization – Drug Dictionary) for concomitant medications.

MedDRA hierarchy includes five layers from top to bottom:
  • System Organ Class (SOC)
  • High Level Group Term (HLGT)
  • High Level Term (HLT)
  • Preferred Term (PT)
  • Lowest Level Term (LLT)
During the coding, verbatim or reported term will be mapped to LLT ->  PT -> HLT -> HLGT -> SOC. The summary and statistical analysis will typically be based on Preferred Term and / or System Organ Class.

WHO-DD adopts Anatomical Therapeutic Chemical (ATC) Classification System and the hierarchy includes five ATC levels from top to bottom:

  • ATC Level 1: The anatomical main group
  • ATC Level 2: The therapeutic main group
  • ATC Level 3: The therapeutic/pharmacological subgroup
  • ATC Level 4: The chemical/therapeutic/pharmacological subgroup
  • ATC Level 5: The chemical substance
The ATC classifications are often used as starting points in the development of protocol violation lists and other medication of interest lists in clinical trials. The classes are also used to identify class effects in safety signal detection.

During the coding, verbatim or reported drug names will be mapped to ATC Level 5 -> ATC Level 4 -> ATC Level 3 -> ATC Level 2 -> ATC Level 1. Summary and statisitcal analyses will typically be based on ATC level 1 and ATC level 3, but can also be based on ATC level 2 and ATC level 4.

ATC Level is not as obvious as MedDRA hierarchy, however, both dictionaries use the tree structure. From top to bottom, the choice of terms expands.

In certain situations, the typical coding based on these hierarchy levels is not sufficient. Additional grouping may be needed. For example, if we are specifically interested in any pulmonary hypertension adverse event, we will need to group any of the following terms together:

Acute right ventricular failure
Chronic right ventricular failure
Cor pulmonale
Cor pulmonale acute
Cor pulmonale chronic
Portopulmonary hypertension
Pulmonary arterial hypertension
Pulmonary arterial pressure abnormal
……
Pulmonary artery wall hypertrophy
Pulmonary endarterectomy
Pulmonary hypertension
Pulmonary hypertensive crisis
Pulmonary vascular resistance abnormality
Pulmonary vein occlusion
Pulmonary vein stenosis
Right atrial dilatation
Right ventricular hypertrophy
Right ventricular systolic pressure increased
Vascular resistance pulmonary increased
Angiogram pulmonary abnormal
……
Portal hypertension
Pulmonary infarction
Pulmonary microemboli
Pulmonary vascular disorder

 
Similarly, we may be specifically interested in certain type of medications to define the inclusion/exclusion criteria or to identify the protocol deviations for subjects who take the prohibited medications.  In a diabetes study, we could utilize ‘Drugs used in diabetes’ to find patients potentially having diabetes, then prohibit the drugs affecting the insulin level. In a study in menopausal women, we may need to define a broad or narrow scope of Estrogens. The narrow scope will include substances classified as estrogens based on their therapeutic and chemical properties and the broad scope include substances having estrogen like effects.

 
Fortunately, both  MSSO (the organization for maintaining MedDRA) and UMC (the organization for maintaining WHO-DD) are working on providing these additional groupings. For MedDRA, the additional groupings are called SMQ which stands for Standardized MedDRA Queries; for WHO-DD, the additional groupings are called SDGs which stands for Standardized Drug Groupings.


Standardised MedDRA Queries (SMQs) are developed to facilitate retrieval of MedDRA-coded data as a first step in investigating drug safety issues in pharmacovigilance and clinical development. SMQs are validated, pre-determined sets of MedDRA terms grouped together after extensive review, testing, analysis, and expert discussion. SMQs are a unique feature of MedDRA and provide a strong tool to support safety analysis and reporting. The SMQ topics are intended to address the important pharmacovigilance topics needed by regulatory and industry users. SMQs have been developed with the CIOMS Working Group on Standardised MedDRA Queries that provides pharmacovigilance expertise and validation of SMQs. The SMQs are maintained with each release of MedDRA dictionary by the MSSO.

SMQs are included in each release of the MedDRA dictionary with no additional charge.


The Standardized Drug Queries (SDQs) are new additional classification tools in the WHO Drug Dictionary Enhanced. They have been developed together with a group of expert dictionary users and cover different types of classifications e.g. NSAID, cardiac drugs, Old form, CYP etc.

A SDG is a grouping of medicines having one or several properties in common. The individual grouping can be based on indication, chemical properties, pharmacodynamic properties or pharmacokinetic properties as well as any other property of interest. SDGs can be used in pre-qualification of patients, prohibited medicines, or safety- and subgroup analysis. Standardized Drug Groupings simplify creation of protocol violation lists,  medications of interest, or other relevant
medication groupings.

SDGs are available per request (not automatically provided with WHO-DD release) and are free of charge to all users of the WHO Drug Dictionary EnhancedTM (WHO DDE).  The list of currently available SDGs can be found at SDG product leaflet for drug safety.pdf

 
There are many presentations describing the use of SDGs.

Monday, March 16, 2015

Commonly Used Clinical Trial Terms in English and Their Chinese Translations; List of Regulatory Guidance for Clinical Trials in China

Friends sometimes ask me about the Chinese translations of the clinical trial terms. Some of the terms are new in Chinese. They did not exist in Chinese until recent years when the drug development and clinical trials had been rapidly expanding in China. It would be useful to list the commonly used terms in clinical trials and provide the corresponding translations. These translations are based on the regulatory documents issued by SFDA (China Food and Drug Administration). Some of the translations are different between the mainland China and the Taiwan.
English
Chinese (中文)
Active Control
阳性对照
Adaptive Design
自适应设计
Absorption, Distribution, Metabolization, and Elimination (ADME)
吸收、分布、代谢、清除
Adverse Event
不良反应
Alpha-spending function
α消耗函数
Alternative Hypothesis
备择假设
Area Under Curve (AUC)
曲线下面积
Audit
稽查
Audit Trail
稽查踪迹
Baseline Value
基线值
Bias
偏倚
Bioavailability
生物利用率
Bioequivalence
生物等效性
Biomarker
生物标志物
Biosimilar
生物类似药
Blind Review
盲态审核
Blinding/Masking
设盲
Candidate Product (Proposed Product)
候选药
Case Report Form
病例报告表
CIOMS
国际医学科学组织委员会
Clinical Trial
临床试验
Maximum Concentration (Cmax)
达峰浓度
Minimum Concentration (Cmin)
谷浓度
Coding Dictionary
术语表
Company Core Data Sheet (CCDS)
公司核心数据表
Common Technical Document (ICH-CTD)
通用技术文件
Common Toxicity Criteria (CTC)
常见毒性反应标准
Comparative Study
比对试验
Confidence Interval
可信区间
Confirmatory Trial
验证性试验
Concomitant Medication
并用药品
Confidentiality
保密性
Conflict of Interest
利益冲突
Contract Research Organization
合同研究组织
Coordinating Investigator
协调研究者
co-primary endpoint
协同的主要疗效指标
Cross-over Design
交叉设计
Data and Safety Monitoring Board (DSMB)
数据与安全监查委员会
DiseaseFree Survival ,DFS
无病生存期
Progression-Free Survival ,PFS
无进展生存期
Dose Limited Toxicity, DLT
剂量限制性毒性
Double-Dummy
双模拟
Dropout
脱落
eCTD
电子化通用技术文件
Endpoint Assessment or Adjudication Committee (EAC)
终点判定委员会
Electronic Data Capture (EDC)
电子数据采集
Equivalence
等效性
Ethics Committee
伦理委员会
Exploratory Efficacy Endpoint
辅助疗效指标
Full Analysis Set
全分析集
Generic Product
仿制药
Good Clinical Practice (GCP)
临床试验质量管理规范
Half Life
半衰期
Health-related Quality of Life HRQoL
健康相关的生活质量
Highly Variable Drug
高变异性药
Hypothesis Test
假设检验
Immunogenecity
免疫原性
Independent Data Monitoring Committee (IDMC)
独立数据监察委员会
Indication
适应症
Individual Bioequivalence (IBE)
个体生物等效性
Informed Consent Form
知情同意书
Intention To Treat Principl
意向性分析原则
Interim Analysis
期中分析
Inspection
视察
Institutional Review Board
伦理委员会
Interaction
交互作用
Investigator
研究者
Investigator’s Brochure
研究者手册
Investigational Product
试验用药品
Investigational Medicinal Product (IMP)
临床试验用品药
Label
标签
last observation carried forward
结转
Lower Limit of quantitationLLOQ
定量下限
Maximal Tolerated Dose (MTD)
最大耐受剂量
Mean Residence Time (MRT)
平均滞留时间
Medical History
病史
Missing Data
缺失数据
Monitor
监查员
Multicentre Trial
多中心临床试验
Non-compliance/Violation
不依从/违背方案
No Observed Adverse Effect Level (NOAEL)
未观察到不良反应的剂量
Non-inferiority
非劣(效)性
Open Label
开放
Originator Biologic
原研产品
Overall Survival  OS
总生存期
Patient-reported Outcomes (PRO)
患者自评结果
Per Protocol Set
符合方案集
Pivotal Study
关键性临床试验
Placebo
安慰剂
Pharmacodynamics (PD)
药效动力学
Pharmacokinetics (PK)
药代动力学
Pharmacovigilence (PV)
药物警戒
Population Bioequivalence (PBE)
群体生物等效性
Population Pharmacokinetics (PPK)
群体药效动力学
post hoc
事后的
Primary Efficacy Endpoint
主要疗效指标
Primary Efficacy Variable
主要疗效变量
Protocol
试验方案
Protocol Amendment
试验方案修正案
Periodic Safety Update Reports for Marketed Drugs (PSUR)
上市药品定期安全性更新报告
PK/PD Model
药代/药效模型
RelationshipCausality
关联性
Randomization
随机
Reference Product
参照药
Sample Size
样本量
Secondary Efficacy Endpoint
次要疗效指标
Sequelae
后遗症
Serious Adverse Event
严重不良反应
Single-arm Design
单臂设计
Sponsor
申办者
Standard Operating Procedure
标准操作规程
Statistical Analysis Plan (SAP)
统计分析计划
Steady State
稳态
Superiority
优效性
Surrogate Endpoint and Surrogate Variable
替代指标
Survival analysis
生存分析
Time to Maximum Concentration (Tmax)
达峰时间
Unexpected  Adverse Event
非预期不良事件
Washout period
清洗期
In collecting the Chinese translations for the commonly used clinical trials, I have to review the regulatory guidance issued by SFDA. It is good to have the links below for references. 


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