Protocol Template for Clinical Trials

For any clinical trial, the study protocol is the most critical document and is the blueprint of the entire study. There are clinical studies with very high quality of the study protocol. There are also clinical studies with sub-optimal quality of the study protocol. It would be nice if there is a protocol template so that all clinical trial protocols are written in a consistent way no matter whether the clinical trial sponsors are industry, academic, or government agencies.  

Usually, people follow the ICH E6 (Good Clinical Practice) as the guidance for developing the clinical study protocol. ICH E6 has a specific section about "Clinical Trial Protocol and Protocol Amendment(s)". The outline of the clinical trial protocol is listed as below in ICE E6:

CLINICAL TRIAL PROTOCOL AND PROTOCOL AMENDMENT(S)
1 General Information
2 Background Information
3 Trial Objectives and Purpose
4 Trial Design
5 Selection and Withdrawal of Subjects
6 Treatment of Subjects
7 Assessment of Efficacy
8 Assessment of Safety
9 Statistics
10 Direct Access to Source Data/Documents
11 Quality Control and Quality Assurance
12 Ethics
13 Data Handling and Record Keeping
14 Financing and Insurance
15 Publication Policy
16 Supplements

Another way people write the clinical study protocol is to follow the ICH E3 (Structure and Contents of Clinical Study Report). The idea is that sections 7 to 9 of the study report will describe the study protocol and delineate how the clinical study is conducted. Following the ICH E3, the clinical study protocol can be organized according to the outline below:

7. INTRODUCTION
8. STUDY OBJECTIVES
9. INVESTIGATIONAL PLAN
9.1 OVERALL STUDY DESIGN AND PLAN - DESCRIPTION
9.2 DISCUSSION OF STUDY DESIGN, INCLUDING THE CHOICE OF CONTROL GROUPS
9.3 SELECTION OF STUDY POPULATION
9.3.1 Inclusion Criteria
9.3.2 Exclusion Criteria
9.3.3 Removal of Patients from Therapy or Assessment
9.4 TREATMENTS
9.4.1 Treatments Administered
9.4.2 Identity of Investigational Product(s)
9.4.3 Method of Assigning Patients to Treatment Groups
9.4.4 Selection of Doses in the Study
9.4.5 Selection and Timing of Dose for each Patient
9.4.6 Blinding
9.4.7 Prior and Concomitant Therapy
9.4.8 Treatment Compliance
9.5 EFFICACY AND SAFETY VARIABLES
9.5.1 Efficacy and Safety Measurements Assessed and Flow Chart
9.5.2 Appropriateness of Measurements
9.5.3 Primary Efficacy Variable(s)
9.5.4 Drug Concentration Measurements
9.6 DATA QUALITY ASSURANCE
9.7 STATISTICAL METHODS PLANNED IN THE PROTOCOL AND DETERMINATION OF SAMPLE SIZE
9.7.1 Statistical and Analytical Plans
9.7.2 Determination of Sample Size

It will be desirable to have a protocol template so that all clinical trial protocols are written in a consistent way. While there are no universal protocol templates across the industry, academic, and governmental agencies, for efficiency and consistency, there should be a protocol template within each company or organization.

In an effort to increase the efficiency of clinical trial protocol reviews, the National Institutes of Health (NIH) has released a draft protocol template developed in collaboration with the US Food and Drug Administration (FDA). As indicated in the preface of the draft protocol template,

"This Clinical Trial Protocol Template is a suggested format for Phase 2 or 3 clinical trials supported by the National Institutes of Health (NIH) that are being conducted under a Food and Drug Administration (FDA) Investigational New Drug Application (IND) or Investigational Device Exemption (IDE). Investigators for such trials are strongly encouraged to use this template when developing protocols for NIH supported clinical trial(s). However, others may also find this template beneficial for other clinical trials not named here.

This template is provided to aid the investigator in writing a comprehensive clinical trial protocol that meets the standard outlined in the International Conference on Harmonisation (ICH) Guidance for Industry, E6 Good Clinical Practice: Consolidated Guidance (ICH-E6). In order to facilitate review by NIH and FDA, investigators should retain the sections in the order provided."

In the meantime, FDA is also making the collaborative efforts to develop so-called the common protocol template developed by TransCelerate Biopharma to help ensure consistency for the medical product development community. CDISC is also making efforts to develop or modelize the clinical study protocol - The protocol representation model (PRM). The common protocol template and PRM (once developed and accepted by clinical research community) can also help with the downstream activities: standardized study protocol - standardized data collection/case report forms - standardized data structure - standardized software - standardized data presentations.

Reference:

• Guide Notice for Clinical Trial Protocol Template for Phase 2 and 3 IND/IDE Studies
• NIH Clinical Trial Protocol Template
• NCI Protocol Template and Guidelines
• NIH Clinical Trials Protocol Template - NIH Office of Science Policy
• NIH/FDA Protocol Template
• Michael Mezher (2016) NIH, FDA Release Streamlined Clinical Trial Protocol Template 

Targeted or Selective Safety Data Collection in Late Pre-authorisation and Post-authorisation Clinical Trials.

Last month, FDA released its final guidance "Guidance for Industry: Determining the Extent of Safety Data Collection Needed in Late-Stage Premarket and postapproval Clinical Investigations. One thing we noticed is that in the final version, the FDA changed its terminology from "Targeted Safety Data Collection" to "Selective Safety Data Collection".

“This guidance provides recommendations on when to consider selective safety data collection and how to do so to maintain a balance between eliminating the collection of data that will not be useful and collecting sufficient data to allow adequate characterization of the safety profile of a drug,” FDA says,

The final guidance significantly revised and finalized guidance originally released in 2012. In response to public comments requesting more detail and examples, FDA says the draft guidance was revised and reorganized to clarify what types of safety data and what circumstances may be appropriate for the selective collection, in addition to more detail on the draft guidance topics and additional information on safety data reporting issues.

Following the release of the draft guidance, FDA conducted the webinar to explain the main points of this guidance. The webinar and the slides can be found here.

While FDA has the explicit guidance on the targeted/selective safety data collection, EMA's position is less clear. EMA’s clinical trial directive does not explicitly require a complete collection of all AEs and other non-critical safety data. The communications with EMA suggest that it allows sponsors to target the collection of nonserious AEs and other non-critical safety data when appropriate in post-authorization studies.

The US Food and Drug Administration (FDA) on Thursday significantly revised and finalized guidance originally released in 2012 that will help the industry understand what types of safety data needs to be collected in late-stage premarket and postapproval clinical investigations.

In response to public comments requesting more detail and examples, FDA says the draft guidance was revised and reorganized to clarify what types of safety data and what circumstances may be appropriate for the selective collection, in addition to more detail on the draft guidance topics and additional information on safety data reporting issues.

Combining with the FDA guidance "Oversight of Clinical Investigations —A Risk-Based Approach to Monitoring", we see an effort from FDA to ease the burden in conducting the clinical trial and cut the cost of drug development. Over the years, the clinical trial protocol has become so complicated, a lot of data collected during the trial has little or no value to the objective of the study, and on-site monitoring and 100% source data verification has limited improvement in data quality, but are always implemented.

Fully adopting this two guidance may be quick in the government and academic sponsored clinical trials, but it will take some time for the clinical trials sponsored by the industry for the licensure purpose.

References:

Zachary Brenan 2016 "FDA Finalizes Guidance on Safety Data Collection for Late-Stage and Postapproval Trials"

One-sided versus Two-sided test

For vast majority of clinical trials, two-sided tests are performed and two-sided p-values are presented. Once a while, we will see some study results presented with one-sided p-value. 

 

In a previous post "One-Sided Test in A Superiority Trial", an example from the RAPID study was given and the purpose of presenting the one-sided p-value seemed to be for looking better to the readers. Since then, the RAPID study results have been officially published in LANCET. However, in LANCET publication, the one-sided p-value was replaced with two-sided p-value. I can only guess that LANCET did not like the trick of presenting the one-sided p-value. Below is the comparison. Notice that for a one-sided p value of 0.017 (significance level is 0.025), the two sided p value is supposed to be 0.034 (significance level is 0.05).

In early presentation by the company	In official publication in LANCET
The annual rate of lung density loss was significantly less in augmentation-treated patients (-1.45 +/- 0.24 units vs. -2.19 +/-0.25 units; p = 0.017, one-sided).	However, the annual rate of lung density loss at TLC alone was significantly less in patients in the A1PI group (–1·45 g/L per year [SE 0·23]) than in the placebo group (–2·19 g/L per year [0·25]; difference 0·74 g/L per year [95% CI 0·06–1·42], p=0·03)

In a recent paper by Sitbon et al "Selexipag for the Treatment of Pulmonary Arterial Hypertension", ones-sided p-value was presented. Here is what the paper says:

•  Sample size calculation is based on at a one-sided type 1 error rate of 0.005.
• Because of the alpha spending for one interim analysis, The final analysis used a one-sided significance level of 0.00499 instead of 0.005.
• P values were calculated with the use of a one-sided log-rank test. 

 

Ironically, even though the one-side p-value was presented, when it came to the confidence interval, the two-sided confidence interval were presented.

 

FDA's statistical review has more details about how the statistical analyses were performed and the results were presented. In this study, one of the reasons for using the one-sided p-value could be the nature of the group sequential design. In group sequential design and the adaptive design, one-sided significant level is often used because it is easier for calculation.
 

In both cases, the statistical test was essentially the two-sided test even though the one-sided p-values were presented. The significance level was α/2 instead of α. I can only guess that the reason for presenting the one-sided p-value is to make the p-value look smaller (more impressive).

Surrogate Biomarkers, Diagnostic Biomarkers, Prognostic Biomarkers, and Predictive Biomarkers

A biomarker is a biologic molecule, such as a protein or gene, that is measureable in tissue, blood, or other body fluids, and is an indicator of some clinically significant condition. Biomarkers can be diagnostic, surrogate, prognostic, or predictive. Biomarkers can be very useful in clinical trials. They can be used as the inclusion criteria to identify the right patient population for the clinical study. They can be used as the efficacy endpoint (specifically the predictive biomarkers).

Surrogate Biomarkers: Biomarkers are easier to measure and can be used as screening or surrogate measures for more sophisticated, more accurate, but more cumbersome measures. For example, while the gold standard of diagnosis in oncology is a pathologic tissue review, a highly elevated prostate-specific antigen (PSA) level in the right clinical setting can be diagnostic of prostate cancer. Although the value of PSA level as a diagnostic biomarker is limited by its sensitivity and specificity, it can be an excellent surrogate biomarker for monitoring prostate cancer response to treatment. Surrogate biomarkers can be diagnostic, prognostic, or predictive. In clinical trials, the term 'surrogate endpoints' are also used. Surrogate biomarkers can be surrogate endpoints, but not all surrogate endpoints are surrogate biomarkers. For example, the imaging endpoints (e.g. MRI, CT Scan) may be used as surrogate endpoints, but they are not surrogate biomarkers.

Diagnostic biomarkers indicate if a disease already exists. They are often used for screening for diseases such as cancer. If diagnostic biomarkers are used for screening, they must have good sensitivity and specificity and also must be sufficiently noninvasive and inexpensive to allow widespread applicability.

Prognostic biomarkers indicate how a disease may develop in an individual case regardless of the type of treatment and show the progression of disease with or without treatment. In other words, prognostic biomarkers refer to markers that correlate with the natural progression or aggressiveness of a disease. In oncology, prognostic biomarkers are useful for informing patients about the risk of recurrence or median survival for their particular type of malignancy and for minimizing confounding factors when analyzing clinical trial cohorts or when prospectively stratifying patients in randomized clinical trials.

Predictive biomarkers are defined by their role in predicting a response to a given treatment. Therefore, these are most useful if they can be assessed before the initiation of treatment. Predictive biomarkers help to assess the most likely response to a particular treatment type. If we are looking surrogate endpoints for efficacy measure in clinical trials, predictive biomarkers are most useful.

When we discuss the biomarkers, it is necessary to distinguish whether or not they are diagnostic biomarkers, prognostic biomarkers, or predictive biomarkers. 

Medscape has an article by Tezak, Kondratovich, and Mansfield "US FDA and Personalized Medicine: In vitro Diagnostic Regulatory Perspective". The article included the following diagram to distinguish the differences between prognostic biomarkers and predictive biomarkers.

Predictive versus prognostic biomarkers.
Marker-positive population is marked in red, and marker-negative population is marked in blue. The figures only illustrate a few simple ways in which biomarker–therapy–outcome interactions might occur. Other factors (such as risk:benefit ratio, safety concerns, availability of other treatment and so on) that may affect assessment of the biomarker and therapeutic effect are not taken into account. (A) No biomarker effect tested. The effect of T versus S is assessed. T shows improved outcome (green arrow) compared with the S in all comers. (B) Prognostic biomarker. Only S is used to assess the effect of biomarker; the effect of therapy is not assessed. When the same type of care is used (regardless of whether there is treatment or no treatment), marker-positive population (dashed red line) shows better outcome than the marker-negative population (dashed blue line). Biomarker shows prognostic effect (yellow arrow). (C) Prognostic biomarker. The effect of S versus T is assessed in both biomarker-positive (red) and biomarker-negative population (blue). Similar therapy versus standard-of-care effect size is observed (green arrows), regardless of biomarker status. For the purposes of the point described, the therapeutic effect is the same, for example, in an 'absolute' survival sense (the green arrows are the same length). Biomarker-positive population has better outcome than biomarker-negative population (yellow arrows) regardless of whether the S or T is used. The biomarker shows prognostic effect, and there is no predictive biomarker effect (i.e., treatment effect is independent of marker status). (D) Predictive biomarker. The effect of S versus T is assessed, in both biomarker-positive (red) and biomarker-negative population (blue). T does not appear to improve patient outcomes over S in the marker-negative population (circled green arrow between blue lines T and S). T shows large improvement in patient outcomes when compared with S in marker-positive population (green arrow between T and S red lines). Biomarker shows predictive effect. (E) No biomarker effect. The effect of S versus T is assessed, in both biomarker-positive (red) and biomarker-negative population (blue). Similar therapy versus standard-of-care effect size is observed (green arrow), regardless of biomarker status, and T shows improved patient outcomes when compared with S. There appears to be no biomarker effect on patient outcomes in either S or T arm (marked by yellow circles). There is no predictive or prognostic biomarker effect.
Figures are simplified illustrations of the relevant points, and not depictions of biological data.
S: Standard of care; T: New therapy.

One of the slides from Roche is also a good summary for the differences among three type of biomarkers.

References:

• FDA Biomarkers and Surrogate Endpoints
• Prognostic and Predictive Biomarkers: Tools in Personalized Oncology

Estimating the average treatment effects at two different visits and its implementation using MMRM model

Orkambi is a combination drug including Ivacaftor and Lumacaftor and is approved by FDA for the treatment of cystic fibrosis in patients less than 12 years with homozygous F508del mutation. The approval was based on two large pivotal studies with identical study design. The treatment effect is trivial, but statistically significant. One thing that is interesting to me is that the primary efficacy endpoint and the key secondary efficacy endpoint are based on the average of two different visits (visits 16 and 24).

• The primary endpoint was absolute change in ppFEV1 from baseline at week 24 (assessed as the average treatment effects at week 16 and 24).
• Average relative change from baseline in ppFEV1 at Week 16 and at Week 24

Typically, for a clinical trial with fixed treatment duration, the treatment effect will be assessed at one specified time point. In Orkambi pivotal studies, the primary efficacy endpoint was measured at post-baseline visits Day 15, Weeks 4, 8, 16, and 24. The treatment effect would usually be estimated at Week 16 or at Week 24.

In Vertex’s briefing book for FDA advisory committee meeting on May 12, 2015, the rationale of using the average of two visits was indicated as below:

Change in ppFEV1 at Week 24 was assessed as the average of the treatment effects at Week 16 and at Week 24 to provide a more precise estimate of the treatment effect at the end of the treatment period, given the inherent variability in ppFEV1. 

The average treatment effects at Weeks 16 and 24 was obtained from the mixed model with repeated measures (MMRM). It would be naïve if one thinks that the average value of week 16 and week 24 is calculated for each individual subject. According to FDA’s statistical review, the statistical methods were described as below:

For the primary efficacy endpoint, absolute change from baseline in ppFEV1 at Week 24, the primary analysis was to test the difference between each active combination treatment group versus placebo using a mixed model with repeated measures (MMRM). Both on-treatment measurements and measurements after treatment discontinuation (for subjects who discontinued dosing early) were included in primary analyses. The MMRM analysis included subject as a random effect, treatment, visit, and treatment-by-visit interaction as fixed effects, with adjustment for sex, age group at baseline, and ppFEV1 severity at screening. An unstructured covariance structure was assumed to model the within-subject errors. A Kenward-Roger approximation was used for the denominator degrees of freedom. The primary result obtained from the model was the average of the treatment effects at Week 16 and at Week 24.

The summary results were depicted at the following graph.

Based on the description above, the following SAS codes can be used to calculate the average treatment effect at weeks 16 and 24. Notice that in MMRM model, all observations for the change from baseline to all visits (day 15, weeks 4, 8, 16, 24) are used. The last two estimate statements are to calculate the treatment effect at week 16 or at week 24 separately. 

proc mixed data=FEV1;
     class subject treat visit sex agegrp ppFEV1_severity;
      model Chg_ppFEV1 = treat visit treat*visit sex 

                         agegrp ppFEV1_severity/ddfm=kr;
       repeated window / sub = subject type = un;
       estimate 'Average Treatment Effect at Weeks 16 and 24'
                             treat -1 1
                             treat*visit 0 0 0 -0.5 -0.5
                                         0 0 0  0.5  0.5/cl;
       estimate 'Treatment Difference at Week 16'
                             treat -1 1
                             treat*visit 0 0 0 -1 0
                                         0 0 0  1 0/cl;
       estimate 'Treatment Difference at Week 24'
                             treat -1 1
                             treat*visit 0 0 0 0 -1
                                         0 0 0 0  1 /cl;
run;

Demonstrating the disease modifying effect through delayed start study design or delayed start analyses

Previously, I described a study designed called ‘randomized start design (RSD)’. One special case of the RSD, delayed start design, has been specifically gaining popularity in studies for identifying whether or not the efficacy improvement is due to the symptomatic change or due to the disease modifier. A disease modifier is desired.

Several studies with delayed start design or delayed start analysis have been published. The study design seems to be more popular in neurology disease area such as Parkison’s disease and Alzheimer’s disease.

• Liu-Seifert et al 2015 Delayed-start analysis: Mild Alzheimer’s disease patients in solanezumab trials, 3.5 years
• Olanow et al 2008 A Randomized, Double-Blind, Placebo-Controlled, Delayed Start Study to Assess Rasagiline as a Disease Modifying Therapy in Parkinson’s Disease (The ADAGIO Study): Rationale, Design, and Baseline Characteristics
• Olanow et al 2009 A Double-Blind, Delayed-Start Trialof Rasagiline in Parkinson’s Disease
• Schapira et al 2013 Pramipexole in patients with early Parkinson’s disease (PROUD): a randomised delayed-start trial
• Chapman et al 2015 Intravenous augmentation treatment and lung density in severe α1 antitrypsin defi ciency (RAPID): a randomised,double-blind, placebo-controlled trial

The concept of the delayed start study was proposed by Dr Ralph B. D'Agostino in his NEJM article "The Delayed-Start Study Design". In a web-based article, Dr Hauser described the details about the delayed start study design.

The delayed-start trial design is one approach to separating symptomatic improvement from a true effect on disease progression. In this design, one group receives active treatment and another group receives placebo during the first period of the trial, and both groups receive active treatment during the second period of the trial. The results in the second period may show whether an effect is long term and disease modifying or short term and symptomatic. Figure 1 presents a schematic of the delayed start trial.

The delayed-start trial has the potential to demonstrate disease modification based on the following logic: improvements in the active treatment group during the first trial period could be due to disease modification or symptomatic improvement. During the second trial period, however, when both groups are receiving the active treatment, a sustained benefit in the early-start group as compared with the delayed-start group would represent evidence for disease modification. If the effect of the experimental treatment was due solely to symptomatic improvement, then both groups would show similar improvement from baseline during the second stage of the trial when both are receiving the same treatment. 

 
Figure 1. Schematic of the Delayed-Start Clinical Trial Design 

In some studies, the stage 1 and stage 2 are separated as two different studies. The initial study design is not strictly the delayed start design. However, during the statistical analyses, the data from two stages are pooled and analyzed – this approach is called ‘delayed start analysis’ even though the stage 2 (extension study) may be originally designed for other purpose. This approach has been used in above papers by Liu-Seifert et al 2015 and Chapman et al 2015.

While the patients are randomized into the stage 1 of the study, patients are not blinded during the stage 2 of the study since all patients will receive the active treatment. This may cause the biases in the efficacy assessment during the stage 2 of the study.

In studies with delayed start design or delayed start analyses, the treatment effect is symptomatic improvement or disease modifier can be judged by the sustainable treatment effect in the stage 2. The diagram below illustrated the results indicating the symptomatic treatment improvement versus disease modifier.

The delayed start study starts to gain popularity in studying the disease modifying agents. However, it must be pointed out that the study design requires the reasonable lack of equipoise so that it is adequate to switch all patients from the double-blinded phase (stage 1) to the open label phase (stage 2) where all patients receive the active treatments. 

Customizing the Kaplan-Meier Survival Plot in SAS

Kaplan-Meier plot is very commonly used in analyzing the clinical trial data. If the study endpoint is time to event such as progression free survival, overall survival in oncology trials, time to first exacerbation in COPD trials, the Kaplan-Meier curve must be presented.

In a previous article, I described Using SAS ODS Graphics with Example for Generating Kaplan-Meier Curves. The graph template was utilized for modifying the features of the Kaplan Meier curve. However, the process is very cumbersome since we have to make sure that we identify the correct template and we have to copy and paste the lengthy template into the SAS program. The graph template language (GTL) was not easy to read and modify.

Luckily, SAS has now provided detail instructions for customizing the Kaplan-Meier plot. It also developed several macros to let users to modify the relevant sections of the graph template without actually copying and pasting the lengthy graph template languages into the program.

Here are the User’s guide for SAS version 9.4 and 9.3.

• SAS/Stat 14.1 User’s Guide Customizing the Kaplan-Meier Survival Plot
• SAS/STAT® 13.1 User’s Guide Customizing the Kaplan-Meier Survival Plot

The graph template language are located on the SAS website and can be read into the SAS session through the following program.

data _null_;
    %let url = //support.sas.com/documentation/onlinedoc/stat/ex_code/141; 
              *for SAS 9.4;
   *%let url = //support.sas.com/documentation/onlinedoc/stat/ex_code/131; 
              *for SAS 9.3;
   infile "http:&url/templft.html" device=url;
   file 'macros.tmp';
   retain pre 0;
   input;
   if index(_infile_, '') then pre = 0;
   if pre then put _infile_;
   if index(_infile_, '') then pre = 1;
run;
%inc 'macros.tmp' / nosource;

User’s Guide describes two macros ProvideSurvivalMacros and CompileSurvivalTemplates. With these two macros, the Kaplan-Meier plot can be customized with several easily understood statements. Below is an example program to further modifying the Kaplan-Meier plot that was generated in the previous blog. Some notes are added to indicate the purpose of each statement. 

%ProvideSurvivalMacros                                     

%let TitleText0 = "Kaplan-Meier Plot of Disease Free Time";   
*Change the title; 

%let TitleText1 = &titletext0 " for " STRATUMID;

%let TitleText2 = &titletext0;

%let ntitles=1;                       *suppressing the second title;

%macro StmtsBeginGraph;                                       *add footnote;

entryfootnote halign=left "ABC Pharmaceuticals %sysfunc(date(),worddate.)" /

textattrs=GraphDataText;

%mend;

%let GraphOpts = attrpriority=none DataLinePatterns=(solid ShortDash LongDash); 

                              *change line pattern;

%let yOptions = label="Patients without an event (%)"

     linearopts=(viewmin=0 viewmax=1

     tickvaluelist=(0 .25 .50 .75 1) tickdisplaylist=('0' '25' '50' '75' '100')) ;    

     *modify y-axis label and display as percentage instead of fraction;

%let xOptions   = label="Disease Free Time (days)" offsetmin=0

         linearopts=(viewmin=0 viewmax=2500 
tickvaluelist=(0 500 1000 1500 2000 2500) 
tickvaluefitpolicy=XTICKVALFITPOL);      *modify x-axis;

%let InsetOpts = ;         *Remove the legend for censored value;

%let LegendOpts = title='' location=inside across=1 autoalign=(BottomRight);   

         *change the legend for treatment group;

%CompileSurvivalTemplates  /* Compile the templates with upated information*/

ods rtf file="c:\temp\test.doc" style=journal;

ods graphics on/noborder;     *remove the border of the graph area;    

ods noptitle;                *remove the default title; 

title;                *remove the default title 'SAS System';

ods trace on;

proc lifetest data=BMT plot=survival(nocensor atrisk(outside maxlen=13 
atrisktick)=0 to 2500 by 500); 
         *Place the atrisk outside;

      ods select SurvivalPlot;

      time T * Status(0);

      strata Group / test=logrank adjust=sidak;

      run;

run;

ods trace off;

ods graphics off;

ods rtf close;

proc template;

   delete Stat.Lifetest.Graphics.ProductLimitSurvival;

   delete Stat.Lifetest.Graphics.ProductLimitSurvival2;

run;

The output graph is attached here. 

Additional Resources:

• SAS Graph Line Patterns
• SAS Proc Lifetest Documentation
• SAS 9.3 Customizing the Survival Plot
• Creating and Customizing the Kaplan-Meier Survival Plot in PROCLIFETEST in the SAS/STAT® 13.1 Release 

Median of Differences versus Difference in Medians

Both mean and median are used as location parameters to measure the central tendency of the data. If there is an intervention (such as drug treatment in a clinical trial), the mean and the median for the change from baseline can be used as the point estimate for measuring the magnitude of the effect by the intervention. The statistical test will then be performed to see if the point estimate of the effect is statistically significant or not. 

One mistake people can make is to calculate the difference in medians while the correct way should be to calculate the median of differences. The example below is a typical data presentation for a pre-post study design. The change from baseline will be calculated for each subject. The mean and median will be calculated for change from baseline values across all subjects. One temptation is to calculate the difference in medians as the median for postbaseline - the median for baseline. However, the median of differences and the difference of medians can be very different especially when data is skewed. 

Subject	Baseline	Post Baseline	Change From Baseline
1	50.6	38	-12.6
2	39.2	18.6	-20.6
3	35.2	23.2	-12
4	17	19	2
5	11.2	6.6	-4.6
6	14.2	16.4	2.2
7	24.2	14.4	-9.8
8	37.4	37.6	0.2
9	35.2	24.4	-10.8
Mean	29.36	22.02	-7.33
Median	35.2	19	-9.8

The median of differences is calculated as the 50th percentile of all individual differences (change from baseline). The Median of differences (the last column) is -9.8. However, the difference in medians = Median of Postbaseline Measures – Median of Baseline Measures = 19 – 35.2 = 16.2

The median of differences (-9.8) and the difference in medians (-16.2) are quite different especially for skewed data.

The median of differences is the correct number to be used and is the number that corresponding to the signed rank test.

It would be ok if we do this for mean. The mean of differences is equal to the difference in means, i.e., -7.33 = 22.02 (mean for postbaseline) – 29.36 (mean for baseline). However, if we need to perform a statistical test such as the paired t-test, the numbers in the last column for change from baseline should be the basis. 

Suppose we have "change from baseline" for two treatment groups, we would need to calculate the median for each treatment group in the same way as above. For treatment comparison, we may use the non-parametric Wilcoxon rank-sum test and calculate the magnitude of the difference in medians using the Hodges-Lehmann estimator.  Hodges Lehmann's estimation of location shift can be calculated in SAS using Proc NPAR1WAY.

Dose Response Modeling: calculating EC50, ED50 by Fitting Emax Model using SAS Proc NLIN

Dose response data can come from the laboratory test, pre-clinical, and clinical. The responses can be the assay results, fluorescence output, cell counts, hormone concentrations, efficacy measures.

This type of dose response data can be analyzed using model-based approach - assuming a functional relationship between the response and the dose following a pre-specified parametric model. There are many different models used to characterize a dose-response: linear, quadratic, orthogonal polynomials, exponential, linear in log-dose, Emax. If the response is discreet or dichotomous (success/failure, survival/death,...), it is called quantal response. The different set of models will need to be used such as probit, logit, ordinal logistic, and extreme value (or gompit) regression models that can be easily fit using SAS Proc Probit. 

For continuous response data, one of the most common parametric model is Emax model. There is a 3-parameter Emax model by fitting the dose response function g(D)

where E0 is the response Y at baseline (absence of dose), Emax is the asymptotic maximum dose effect (maximum effect attributable to the drug) and ED50 is the dose which produces 50% of the maximal effect. A generalization is the 4-parameter Emax model for

where l 

is the 4th parameter which is sometimes called the Hill parameter or Hill factor, or slope factor. The Hill parameter affects the shape of the curve and is in some cases very difficult to estimate.

The Emax model may be referred as three-parameter logistic model and four-parameter logistic model, or simply three-parameter model and four-parameter model. 

SAS Proc NLIN can be used to fit the Emax model (three-parameter or four-parameter). We can use the data in an online paper "How can I generate a dose response curve in SAS?". The concentration-response is similar to the dose-response from the modeling purpose. In the data below, for some concentration level, there are two duplicates.  

Concentration	Response
0.1	21.125
0.1	20.575
0.25	40.525
0.5	26.15
0.75	26.35
0.75	44.275
1	49.725
1	63.6
10	49.35
10	68.875
100	58.025
100	58.075
1000	68.025
1000	52.3

We can read the data into SAS data set as following:

data dr;
input concentration response;
datalines;
.1 21.125
.1 20.575
.25 40.525
.5 26.15
.75 26.35
.75 44.275
1 49.725
1 63.6
10 49.35
10 68.875
100 58.025
100 58.075
1000 68.025
1000 52.3
;

In order to fit the four parameter Emax model above, we will need to provide the initial values for all four parameters. The initial values provided do not need to be precise. Usually, the same results can be obtained with different initial values. However, we want to provide the initial values close to the data. For example, from the data set above, we can choose the min as the E0 (minimum response),  max as Emax (maximum response), a median dose as ED50 (dose corresponding to 50% of Emax). For the fourth parameter (Hill slope), we can run a simple linear regression to obtain an initial slope. It is not critical if the concentration response really follows the linear relationship. The purpose here is just to obtain an initial Hill slope value for the non linear model.

If we run the following simple regression, we will get a slope of 0.01831 and we can use this value as the initial value for the fourth parameter.

proc reg data=dr;
  model response=concentration;
run;   

From the data set, we now have the initial values for all four parameters: E0 = 20.575, Emax = 68.875, ED50 = 1, and slope factor = 0.01831. Again, these initial values do not have to be very accurate.

We can then run the following SAS program to fit the non linear (four parameter Emax) model described above:

proc nlin data = dr method=marquardt;
  parms E0 = 20.575 Emax = 68.875 ED50 = 1 hill = 0.01831;
  model response = Emax + (E0 * concentration**hill) / (ED50**hill + concentration**hill);
run;

From the outputs, we will get an estimate of ED50 = 0.8171.

Notice that in online paper "How can I generate a dose response curve in SAS?", a different four parameter model was presented. However, if we fit the nonlinear model, we will get the same estimate of ED50. The four-parameter model was written as:

The SAS program can be written as:

proc nlin data = dr method=marquardt;
  parms E0 = 20.575 Emax = 68.875 ED50 = 1 hill = 0.01831;
  model response = E0 + (Emax - E0) / (1 + (concentration / ED50)**hill);
run;

Reference/Further reading:

• Felmlee, Morris, and Mager (2012) Mechanism-Based Pharmacodynamic Modeling
• James Macdougall Analysis of Dose–Response Studies—Emax Model
• Dayneka, Garg, and Jusko (1993) Comparison of Four Basic Models of Indirect PharmacodynamicResponses Boomer: Pharmacodynamic Models
• Sigmoidal Emax Model

Pediatric Study Plan (PSP) and Paediatric Investigation Plan (PIP)

Pharmaceutical companies usually put their efforts into the adult population when they develop a new compound. There is usually low rates of pediatric testing that resulted in a paucity of information regarding the safe use of pharmaceutical products in children. While a common refrain heard from regulators is that "children are not simply little adults," physicians had little to inform with which to inform their prescribing habits. To encourage drug development in the pediatric population, the regulatory agencies have come up with different requirements and incentives. Pediatric Study Plan (PSP) and Paediatric Investigation Plan (PIP) are requirements in the US and EU respectively.

PSP in the US is the result of PREA (Pediatric Research Equity Act) and FDASIA (The Food and Drug Administration Safety and Innovation Act). Under FDASIA, signed into law on July 9, 2012, for the first time PREA includes a provision that requires manufacturers of drugs subject to PREA to submit a PSP early in the drug development process. The intent of the PSP is to identify needed pediatric studies early in drug development and begin planning for these studies. The timing and content of the submission of an initial PSP are described below. FDASIA requires the FDA to promulgate regulations and issue guidance to implement these and other provisions.

PIP (paediatric investigation plan) in EU is a development plan aimed at ensuring that the necessary data are obtained through studies in children, to support the authorisation of a medicine for children. All applications for marketing authorisation for new medicines have to include the results of studies as described in an agreed PIP, unless the medicine is exempt because of a deferral or waiver. This requirement also applies when a marketing-authorisation holder wants to add a new indication, pharmaceutical form or route of administration for a medicine that is already authorised and covered by intellectual property rights.

Questions: Are PSP and PIP mandated or voluntary?

PSP and PIP are mandated unless it is waivered or deferred. Waiver means that the pediatric study/investigation plan is not needed. Deferral means that the pediatric study/investigation plan can be deferred to the post-marketing stage.

Question: How to obtain the waiver or deferral for PSP and PIP?

Under some circumstances, pediatric assessment may be unnecessary, undesirable, impractical, or delayed. In the US, the legislation authorizes FDA to grant waivers or deferrals to the pediatric assessments required under the Act. If the applicant requests a waiver or deferral, either full or partial, appropriate and sufficient supporting evidence must be provided. The criteria for granting waivers or deferrals center on safety, the nature of the drug product, and the practicability of the requisite studies. FDA has provided some specific information in a draft guidance, that describes how to comply with PREA.

In the EU, the Class waivers will be granted by the regulatory authority. "The need for a paediatric development may be waived for classes of medicines that are likely unsafe or ineffective in children, that lack benefit for paediatric patients or are for diseases and conditions that only affect the adult population. This page lists the class waivers granted by the European Medicines Agency (EMA)."

Question: what is the age cut point for defining the pediatric population

In the EU, pediatric population refers to children aged less than 18 years old.

In the US, pediatric population refers to children ages less than and equal to 16 years old. FDA further classify the pediatric population into the following categories:

NAME	DEFINITION	FDA CODE
NEONATES	NEWBORNS UP TO ONE MONTH	NEO
INFANTS	ONE MONTH TO TWO YEARS	INF
CHILDREN	TWO YEARS TO TWELVE YEARS	CHI
ADOLESCENTS	TWELVE YEARS TO SIXTEEN YEARS	ADO

 An example of a partial waiver would be that PSP/PIP is not required for children less than or equal to two years old, but required for children greater than 2 years old. 

Question: What are the incentives for doing pediatric studies?

In the US, As an incentive to industry to conduct studies requested by the Agency, Section 505(A) provides for a 6-month period of marketing exclusivity (pediatric exclusivity). in addition, FDA also has a Rare Pediatric Disease Priority Review Voucher Program that can issue a priority review voucher for companies who has drug development in rare pediatric disease. A priority review voucher can be worth millions of dollars.

In the EU, Rewards and incentives for paediatric medicines include the following:

·         Medicines authorised across the EU with the results of studies from a paediatric investigation plan included in the product information is eligible for an extension of their supplementary protection certificate by six months. This is the case even when the studies' results are negative.

·         For orphan medicines, the incentive is an additional two years of market exclusivity.

·           Scientific advice and protocol assistance at the Agency are free of charge for questions relating to the development of paediatric medicines.

·         Medicines developed specifically for children that are already authorised but are not protected by a patent or supplementary protection certificate are eligible for a paediatric-use marketing authorisation (PUMA). If a PUMA is granted, the product will benefit from 10 years of market protection as an incentive.

Question: Who will assess the PSP and PIP?

In the US, the PSP will be reviewed by the Pediatric Review Committee (PeRC).

In the EU, The Paediatric Committee (PDCO) is the committee at the European Medicines Agency that is responsible for assessing the content of paediatric investigation plan and adopting opinions on them. This includes assessing applications for full or partial waivers and assessing applications for deferrals.

References:

·         US FDA (July 2013) Pediatric Study Plans: Content of and Process for Submitting Initial Pediatric Study Plans and Amended Pediatric Study Plans

·         US FDA (July 2005) How to Comply with the Pediatric Research Equity Act

      ·         US FDA Pediatric Product Development Webpage

·         EMA Paediatric investigation plans

·         EMA Paediatric investigation plans: Templates, forms and submission dates

·         Current FDA Perspective & Future of JAS Testing to SupportPediatric Development Programs

•    Pediatric Medicine Development: An Overview and Comparison of Regulatory Processes in     the European Union and United States
•  Emilie Desfontaine (2012) PIP assessment procedure
• Thomson (2019) Global Pediatric Development Plan
• Wable (2018) Navigating the development of pediatric plans