Sample Size and Power Calculation Using SAS Proc Power and twosamplesurvival Statement

For sample size and power calculations, several commercially available software can be used. The commonly used ones are EAST, PASS, and NQuery Advisor. SAS has a procedure (PROC POWER) that can be used for sample size and power calculations for many types of the study designs / study endpoints. One of the statements (twosamplesurvival) in Proc Power is for comparing two survival curves and calculating the sample size/power for time to event variable.

The syntax and descriptions for Twosamplesurvival statement in PROC POWER can be found on SAS website. It can be used to calculate:

• the total number of events needed (EVENTSTOTAL = .  Option)
• the total number of subjects needed (NTOTAL = . Option)
• the number of subjects needed per treatment group (NPERGROUP=. Option)
• the statistical power (POWER=. Option)

Notice that only one option designated as the result is allowed. If we want to get both the total number of events and the total number of subjects, we would need to run the program two times: one for solving the total number of events and one for solving the total number of subjects.

Here are some of the example applications of using twosamplesurvival statement.

EXAMPLE #1:

In a SUGI paper "Proc Power in SAS 9.1" by Bauer, Lavery, and Ford, an example was provided to calculate the sample size for log-rank test with 2:1 randomization ratio and with drop out.

The example assumes 30% of placebo patients are sustained responders (exponential hazard =0.3567) compared to 45 or 50% for the treatment group (exp. hazard = 0.5978 or 0.6931). Twice as many patients are on treatment as placebo, and all patients are enrolled at the beginning of the study with a 30% drop-out rate.

Prior to the sample size calculation, the event rates were converted to hazards. Exponential hazard in Placebo group = - ln(1 - event rate) = -ln(1-0.3) = 0.3567.  Similarly, Exponential hazards corresponding to 45% or 50% event rate were 0.5978 and 0.6931.

The dropout rate were also converted to group loss hazards in the same way. Therefore, the 30% dropout rate was corresponding to the group loss hazard of -ln(1-dropout rate)=-ln(1-0.3)=0.3567.

Groupweights statement was used to indicate the 2:1 randomization ratio. 

proc power;

       twosamplesurvival test=logrank
       gexphs= 0.3567 | 0.5978 .6931
       grouplossexphazards=(0.3567 0.3567)
       accrualtime = 1
       followuptime = 1
       groupweights = (1 2)
       power = .
       ntotal=225;
run;


EXAMPLE #2:

Dr Hudgens from UNC had a nice posting about the power and sample size calculations for log-rank test. He gave an example as following:

Clinical trial to assess new treatment for patients with chronic active hepatitis. Under standard treatment, 41% of patients survive beyond 5 years. Expect new treatment to increase survival beyond 5 years to 60%.

In order to calculate the sample size, we will need to calculate some parameters.

Event rate for standard treatment (Ec) = 1-0.41 = 0.59
Event rate for new treatment (Et) = 1-0.60 = 0.4
Since event rate E = 1 - exp(-t*HAZARD), we have HAZARD = -ln((1-E)/t   
The Hazard for standard treatment is HAZARDc=-ln(1-Ec)/t = -ln(1-0.59)/t = -ln(0.41)/t
The Hazard for new treatment is HAZARDt = -ln(1-Et)/t = -ln(1-0.40)/t = -ln(0.60)/t
The hazard ratio  = HAZARDt/HAZARDc = ln((0.6)/ln(0.41)=0.5729
T=5, the hazard for standard treatment is HAZARDc = -ln(0.41)/5 = 0.178

After these calculation, the following SAS codes can be used to calculate the sample size:

proc power;

    twosamplesurvival test=logrank
    hazardratio = 0.57
    refsurvexphazard=0.178
    followuptime = 5
    totalTIME = 5
    power = 0.90
    ntotal = . ;
run;

EXAMPLE #3: Sample Size Calculation with piecewise linear survival curve

SAS has a GUI desktop application PSS (the Power and Sample Size Application) that provides easy access to power analysis and sample size determination techniques. Anything implemented in PSS desktop application can also be realized using Proc Power. Here is a link to an example from using PSS desktop application. The calculation can be realized using Proc Power Twosamplesurvival.

Suppose you want to compare survival rates for an existing cancer treatment and a new treatment. You intend to use a log-rank test to compare the overall survival curves for the two treatments. You want to determine a sample size to achieve a power of 0.8 for a two-sided test using a balanced design, with a significance level of 0.05.

The survival curve of patients for the existing treatment is known to be approximately exponential with a median survival time of five years. You think that the proposed treatment will yield a survival curve described by the times and probabilities listed in Table 69.9. Patients are to be accrued uniformly over two years and followed for three years.

Table 69.9 Survival Probabilities for Proposed Treatment
Time	Probability
1	0.95
2	0.90
3	0.75
4	0.70
5	0.60

The descriptions for using PSS desktop application for this example can be found on SAS website. The following program will do exactly the same.

proc power;

      twosamplesurvival test=logrank
       curve("Existing Treatment") = 5 : 0.5
      curve("Proposed Treatment") = 1 : 0.95 2 : 0.90 3:0.75  4:0.70 5:0.60
      groupsurvival = "Existing Treatment" | "Proposed Treatment"
      accrualtime = 2
      FOLLOWUPTIME = 3
      power = 0.80
      alpha=0.05
      npergroup = . ;
run;

EXAMPLE #4:

twosamplesurvival statement embedded in PROC SEQDESIGN can be used to estimate the sample size for group sequential design with interim analyses.

EXAMPLE #5:

In the following SAS program to calculate the sample size, the survival probability at 12 months are for standard and proposed groups are specified and the statement of grouplossexphazards is used to account for the dropout rate.

proc power;

      twosamplesurvival test=logrank

      curve(“Standard”) = 12 : 0.8781

      curve(“Proposed”) = 12 : 0.9012

      groupsurvival = “Standard” | “Proposed”

      accrualtime = 18

      Totaltime = 24

      GROUPLOSSEXPHAZARDS = (0.0012 0.0012)

      NSUBINTERVAL = 1

      power = 0.85

      ntotal = . ;

run;

Some Explanations about Survival Analysis or Time to Event Analysis

The concept of Survival analysis came from the old days for cancer study where the measurement is the length of the survival or time to death. The concept of survival analysis has since been generalized to ‘time to event’ analysis. ‘Time to death’ is just one type of time to event variables. There are a lot of events that can be analyzed in the same way. For time to event measures, the event can be positive and negative. For example, in a clinical trial for recurrent herpes labialis (or cold sores), the event (healing of the primary lesion complex) is positive. The time to event is measured as time to healing of the primary lesion complex. Perhaps, more clinical trials have the negative event (event is bad). In oncology studies, we measure survival (or time to death). In COPD studies, we may measure time to exacerbation. In a PAH study, we may measure time to clinical worsening.

In short, in Time to Event analysis, the analysis relates not just to whether an event occurs but also when.

The planning (for example sample size estimation) and analysis of the time to event study, several important concepts and their relationship are important. These concepts and their relationships are explained below:

Time to Event is just the measure from the start of an intervention to the time when an event occurs. The start of an intervention could be the randomization, start of the treatment, date of surgery,…

Event Rate is the proportion of subjects or patients in a group in whom an event is observed.  Event rate is usually measured for a period of the time from t to t + Dt. For example, if the Dt = 12 months, the event rate will be for one year. Event rate is also given as the event rate for the entire study period.

Hazard Rate is the probability of an event occurring given that it hasn’t occurred up to the current point in time. Hazard rate is the instantaneous risk of a patient experiencing a particular event at each specified time. The instantaneous rate with which an event occurs at a single point in time. It is the probability that the event occurs between time t and time t+delta given that it has not yet occurred by time t, divided by delta, as delta becomes vanishingly small. Note that rates, unlike probabilities, can exceed 1.0 because they are quotients.

Hazard Ratio is a measure of effect produced by a survival analysis. This represents the increased risk with which one group is likely to experience the outcome of interest.  For example, if the hazard ratio for death for a treatment is 0.5, then we can say that treated patients are likely to die at half the rate of untreated patients.

Hazard ratio is calculated as the ratio of hazard rates at a single time t, for two groups of subjects (treatment versus control group). Hazard ratios are in the interval [0, infinity), and they are frequently good ways to summarize the relative effects of two treatments at a specific time t. Like odds ratios, hazard ratios can apply to any level of outcome probability for the reference group. Note that a hazard ratio is distinct from a risk ratio, the latter being the ratio of two simple probabilities and not the ratio of two rates.

The Median Event Time is calculated as the smallest even time for which the event function is less or equal to 0.5.

When the event is death, the median event time is called the median survival time. The median survival time is calculated as the smallest survival time for which the survivor function is less than or equal to 0.5. In oncology study, median survival time the time from either diagnosis or treatment at which half of the patients with a given disease are found to be, or expected to be, still alive. In a clinical trial, median survival time is one way to measure the effectiveness of a treatment to see how well a new treatment works. Median survival time may be called median overall survival or simply median survival.  

Censoring is a form of missing data problem which is common in survival analysis and time to event analysis. In clinical trials, we usually have to deal with the right censoring. In the situation of the right censoring, the event did not occur when subjects are lost to follow-up or when the study ends. A patient might be known not to have had the event only up to a particular point in time, so ‘time to event’ or ‘survival time’ is censored at this point.

Lost to Follow-up refers to patients who at one point in time were actively participating in a clinical trial, but have become lost (either by error in a computer tracking system or by being unreachable) at the point of follow-up in the trial. These patients can become lost for many reasons. Without properly informing the investigator associated with the clinical trial, they may have opted to withdraw from the clinical trial, moved away from the particular study site during the clinical trial, or become ill and unable to communicate or are deceased.

Attrition: The loss of participants during the course of a study. Participants that are lost during the study are often call dropouts.

Accrual time or accrual period is recruitment period during which subjects are being enrolled (recruited) into a study.

Follow-up time or follow-up period is the period after the last subject entered the study until the end of the study. The follow-up defines the phase of a study during which subjects are under observation and no new subjects enter the study.

If T is the total duration of a study, and R is the accrual period of the study, then follow-up period f is equal to T – R.

Event Rate =  1 -  Non Event Rate

Mortality Rate =  1 - Survival Rate

Given the MET (median event time), we can calculate the hazard rate and the event rate, and hazard ratio.

If METc is the Median event time for control group and METt is the Median event time for treatment group, HAZARDc and HAZARDt are Hazard rates for control group and treatment groups, we will have:

        HAZARDc = log(2)/METc       
        HAZARDt = log(2)/METt
        METc  = log(2)/HAZARDc       
        METt = log(2)/HAZARDt

    Event rate at month 12 for control group is

      Ec = 1 - exp(-12*HAZARDc) 

    Event rate at month 12 for treatment group is  

      Et = 1 - exp(-12*HAZARDt);   

    HAZARD rate can be calculated from Event rate (for example event rate at month 12)

      HAZARDc = -ln(1-Ec) / t   (for example t=12)
      HAZARDt  = -ln(1-Et) / t   (for example t=12)  
   
The hazard ratio is:
      HAZARDt/HAZARDc

If the given parameter is event rate over the entire course of the study (for example, 5 years), the event rate for one year can be calculated using the following formula:

        1 - (1 - event rate)^(1/t)

        where t=5

 If the given parameter is event rate over one year period, the event rate for five year study period can be calculated using the formula below:


       1 - exp(t*ln(1 - annual event rate))

        where t=5

 
The formula above can also be used to convert the loss follow up rate from the entire treatment period to one year or vice versa.

References:

•   Clinical trial glossary 1
•   Clinical trial glossary 2
•   Janet Wittes (2002) Sample Size Calculations for Randomized Controlled Trials
•    Lakatos (1988) Sample Sizes Based on the Log-Rank Statistic in Complex Clinical Trial
•    Lachin and Foulkes (1986) Evaluation of Sample Size and Power for analyses of survival with allowance for nonuniform patient entry, losses to follow-up, noncompliance, and stratification
•    Lakatos and Lan (1992) A comparison of sample size methods for the log-rank statistic
•    An introduction to survival analysis by Maarten Buis
•    Survival models

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             

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.

 

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.

In a previous article, I discussed “Adverse Event of Special Interest (AESI), Standardized MedDRA Query (SMQs), Customer Queries (CQs), and SAS Programming “.

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.

STANDARDISED DRUG GROUPINGS (SDGs)

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.

• SDQs – Standardized Drug Queries by Sydow & Singh
• The WHO Drug Dictionary Types, Formats and Loading Considerations in TMS
• Strategies for Coding Oncology Trial Data in Today’s World
• Terminologies for Coding of Adverse Reactions and Drug Development (part 1 of 2)
• Terminologies for Coding of Adverse Reactions and Drug Development (part 2 of 2)
• Terminologies for Coding of Adverse Reactions and Drug Information Hand-outshttp://media.medfarm.uu.se/play/attachmentfile/video/3519/Handouts.pdf
• http://media.medfarm.uu.se/play/attachmentfile/video/3519/Handouts.pdf
• Using the WHO Drug Dictionary for Reporting Clinical Trials