Patent Description:
When a drug (medicament) is developed for medical treatment of human patients having a particular disease, it is important to measure the treatment effect of the drug before applying it at a large scale to patients to ensure that effective treatment will be applied to said patients so that the disease can be cured or at least the progression of the disease can be positively influenced. To measure the treatment effect of the drug, clinical studies (clinical trials) are conducted in multiple stages with the last stage typically involving a large number of participants in the respective clinical trial.

A clinical target trial for measuring the treatment effect of the drug always involves at least one so-called treatment arm with human patients receiving experimental treatment with said drug, and a control arm with patients not receiving experimental treatment but instead are treated with standard-of-care (SOC) while using placebos. It may occur that the target trial is unbalanced with regard to the participants in that not all relevant characteristics of patients are equally represented in the target trial. There may be an imbalance in age, sex, in the number of participants with particular known previous illnesses, etc..

Such imbalances can lead to lower reliability of the measurement of the treatment effect based on the results obtained from the target trial.

Further, it is to be noted that the participation in a target trial typically involves a burden for the participants which goes beyond SOC. Typically, patients in such a trial have to undergo certain tests, receive injections or the like, etc. In particular, patients which belong to the control arm of the target trial do not even receive any experimental treatment but only receive placebos instead of the drug. In other words, patients in the control arm may experience an additional psychological as well as physical burden although their health state is not positively affected by their trial participation.

<CIT> discloses simulator engines to simulate patient populations, disease progressions, and/or predicted responses to various treatments using a sampling engine for drawing samples from generative models that simulate the probability distribution of the data. A Boltzmann machine is trained to simulate patients with a disease. This model can then be used to create a cohort of simulated patients that match the inclusion criteria of new trial. Data from simulated patients can be used to supplement data from a concurrent placebo arm using standard statistical methods.

In "<NPL> et al. disclose a method for simulated patient population where the patient population for the simulated active treatment arm was selected <NUM> Crohn's patients enrolled in earlier phase II trials. To provide a placebo control arm with realistic clinical responses, a bootstrap approach is used.

<CIT> discloses a method for enrolling patient candidates in a clinical trial for a treatment where the clinical trial for a treatment where the clinical trial data includes measures of patient condition associated with the conduct of the clinical trial and addresses the problem of extraordinary challenges for drug discovery and drug development companies trying to conduct human clinical tries due to the disease progression heterogeneity.

<CIT> discloses a method of conducting a randomized clinical trial by detecting a genotype of a placebo-associated polymorphism in a subpopulation of human subjects, wherein the human subjects are candidates for a clinical trial, thereby providing a first sub-group of subjects comprising a first genotype of the polymorphism and a second sub-group of subjects comprising a second genotype of the polymorphism, wherein the first and second genotypes are not the same, and distributing the first sub-group evenly, unevenly and/or randomly into at least a first study group and a second study group, wherein at least the first study group is administered a first treatment and the second study group is administered a placebo treatment.

There is therefore a need to provide system and method for improving the measurement of the treatment effect of a medicament (medical drug) while at the same time providing some relief in particular with regard to the suffering of patients in the control arm of the respective target trial. It is thereby advantageous to achieve such an improvement in particular in situations where the number of participants in a target trial is lower than desired.

This technical problem is solved by the embodiments - a computer-implemented method, a computer program product, and a computer system - for measuring the treatment effect of a drug as claimed in the independent claims.

In one embodiment, a computer system is provided for measuring the treatment effect of a medical drug for a disease. A medical drug, as used herein, is a substance used in the diagnosis, treatment, or prevention of a disease or as a component of a medication. In general, the treatment effect of a drug is measured as the difference in change from baseline of a measured clinical outcome between the respective treatment arm and control arm. The system has one or more interfaces which are configured to establish a communication with other computer systems, such as for example, remote data storage systems, remote data analytics systems, or the like. Via a respective interface, the system obtains a disease progression model of the disease which is targeted by the experimental drug.

The disease progression model is based on historical covariates and clinical outcome data reflecting the progression of the disease for a plurality of patients affected by said disease, and quantitively describes the time course of disease progression by one or more corresponding covariates. A clinical outcome is a measure referring to the occurrence of a disease, symptom, sign or laboratory abnormality, which constitutes the target outcome of clinical trials. That is, the term "clinical outcome" refers to the measured variable (e.g., peak volume of oxygen, PROMIS Fatigue score, etc.) or an event such as death, hospitalization, disease progression, etc..

For instance, the clinical outcome in oncology trials may be progression free survival, defined as the time since the treatment started until the disease progressed or the patient died, or it may be overall survival, defined as the time since the treatment started until the patient died. Another example may be in a disease affecting the lungs, the clinical outcome may be the loss of functional capacity of the lungs measured as the difference in volume that a patient can exhale at the beginning of the trial and at the end of the trial. Examples of further clinical outcomes currently accepted by the EMA (cf. European Medicines Agency. Clinical efficacy and safety guidelines. Available from:.

https://www. eu/en/human-regulatory/research-development/scientific-guidelines/clinical-efficacy-safety-guidelines) or FDA (cf. Food & Drug Administration. Clinical Outcome Assessment Compendium. Available from
https://www. gov/drugs/development-resources/clinical-outcome-assessment-compendium) are given in the following Table <NUM>:.

Covariates may include one or more of the following: one or more trends of the development of disease symptoms over time, the variability in disease progression between patients, and a quantitative description of the relationship between patient characteristics, disease characteristics, treatment characteristics and the disease progression dynamics.

In general, a disease progression model is developed using available historical data. Disease progression models are mathematical functions used to quantitatively describe the time course of disease progression (e.g., size of tumor over time, progression of disability scores over time, functional markers evolution over time). In the publication <NPL>), disease modeling techniques are disclosed involving the use of mathematical functions to describe quantitatively the time course of disease progression, Disease progression models can be more or less comprehensive, and can describe aspects such as:.

The aspects included in the disease progression model are referred to as the relevant covariates herein.

The historical data used to develop such a model may be data coming from a previously conducted clinical trial, multiple clinical trials, electronic health records, data registries, or other sources.

Disease progression models are available for a plurality of diseases. For example, "<NPL>" describe a disease progression model for asthma with the clinical outcome "forced expiratory volume in <NUM>" (FEV1) and the relevant covariates "age" and "height" of the patients. Another example is a disease progression model for melanoma described by <NPL>" with the clinical outcome "tumor size" and the relevant covariates "number of target lesions" and "ECOG status" of the patients. Yet another example for a Schizophrenia disease progression model is given by <NPL>". The clinical outcome in this example is "PANSS total score" and the relevant covariates are "treatment" and "placebo effect". Yet another example for a disease progression model to predict tumor size over time (clinical outcome) depending on the treatment (covariate) taken by the patients is disclosed by<NPL>.

Further examples of disease progression models are described in the following papers. <NPL>) a disease progression model for wAMD, where the clinical outcome is visual acuity and the covariate is exposure to drug.

A disease progression model for CRC with the clinical outcome being tumor size and survival and the covariate being exposure to drug is described by <NPL>.

A disease progression model for AMD or DME with visual acuity as clinical outcome and exposure to drug as the predictive covariate is described by <NPL>".

A person skilled in the art is able to identify or develop further disease progression models which are suitable in the context of the herein disclosed approach in the literature.

Further, the system has an interface to receive a target trial dataset (also referred to as the trial data set herein) with trial covariates and clinical outcome data obtained from a plurality of human patients participating in a target trial. The target trial includes one or more treatment arms with human patients receiving experimental treatment, and a control arm with patients not receiving experimental treatment.

Further, the system has an artificial patient generator module to create an artificial patient dataset. There are two alternative ways how the artificial patient generator can create artificial patient data records.

In one implementation, it uses distributions of one or more corresponding covariates. Relevant covariates are identified from the disease progression model that was developed based on historical data (e.g., the result from earlier trial). Then, a mathematical description of the distribution of relevant covariates, and the correlation between them is built based on the target trial. For instance, if the disease progression model considers height and weight to be the covariates relevant to how the disease will progress over time in a particular population, a mathematical description of the distribution of these two covariates (e.g., mean, median, standard deviation) and their correlation (for the covariates age and height most likely a positive correlation is found, meaning the taller a patient is the heavier he/she will be) can be used to generate a dataset describing these covariates. This mathematical description is then used to sample a large artificial patient dataset (e.g., <NUM> patients), with artificial patients which are virtually impossible to distinguish from the real patients. Each data record in the generated artificial patient dataset corresponds to a simulated patient. Then, the artificial patient dataset is filtered in accordance with inclusion-exclusion criteria of the target trial so that data records associated with artificial patients which do not meet the inclusion-exclusion criteria of the target trial are removed from the artificial patient dataset. That is, simulated patients which do not meet the inclusion criteria, or which meet the exclusion criteria of the target trial, are then removed from the artificial patient dataset, trying to replicate a screening process (e.g., the target trial does not recruit patients with weights above <NUM> and, therefore, such artificial patient data records are removed from the artificial patient dataset. ) Finally, this process results in a dataset where each data record represents an artificial patient with the characteristics of the patient, the characteristics of the patient's disease and the treatments the patient is receiving - to the extent they are considered relevant in the used disease progression model. It is to be noted that a person skilled in the art will understand the term inclusion-exclusion criteria of a target trial as comprising criteria which qualify a patient to participate in the target trial (inclusion criteria) as well as comprising criteria which disqualify a patient from participating in the target trial (exclusion criteria).

In an alternative implementation, the artificial patient dataset is generated by using a bootstrapping approach which includes random sampling with replacement. If the target trial dataset is large enough, this sampling with replacement method may be used. Inherent to this method is that only artificial patients meeting the inclusion/exclusion criteria of the target trial are generated because only such patients are included in the target trial data. Therefore, a filtering step as in the first implementation is not necessary here (although it would not harm because no data would be filtered from the resampled dataset). For example, patients may be randomly sampled from the trial dataset, obtaining paired values of covariates (e.g., age and height), to generate a dataset of covariates for the artificial patients. A large dataset may be generated (e.g., <NUM> patients). In order to obtain such a big dataset out of a smaller set of patients sampling with replacement is performed (patients can be sampled more than once).

The artificial patient dataset is then provided as an input to the disease progression model to generate a simulated dataset with simulated covariates and simulated clinical outcome data for simulated artificial patients not receiving experimental treatment. In other words, the simulated artificial patients reflect patients which only receive SOC in the control arm of a target trial. That is, such a simulated patient can play the role of a patient in the control arm of the target trial to relieve a real human patient from the above-mentioned burden of trial participation while not receiving experimental treatment.

At this point the system has: <NUM>) real data for the treatment and control arms of the target trial, and <NUM>) simulated data for artificial patients in the control arm with matching characteristics to those recruited in the target trial obtained by using the disease progression model created based on historical data. These two sources are now brought together into a single analysis. This is performed by a trial analyzer module of the system which determines the treatment effect of the drug by analyzing the trial dataset and the simulated dataset together. To do this, the trial analyzer uses a power prior module to incorporate the simulated dataset as prior information. For example, the power prior approach as described by Ibrahim JG, Chen MH, Gwon Y, Chen F. The power prior: theory and applications. <NPL> may be used to incorporate the simulated patient data as prior information. Thereby, the simulated dataset is given a weight in comparison to the control arm of the trial dataset to avoid overweighting the influence of the artificial patients on the overall result of the analysis. In other words, choosing an appropriate weight to the simulated data is important given that the number of simulated patients can be disproportionally large (e.g., <NUM> patients) as compared to the actual number of patients in the target trial (e.g., <NUM> in treatment arm and <NUM> in control arm. This weight is based on at least a maximum weight value received by the system from a user. That is, a user of the system provides a limitation for the impact of the artificial patient data on the computation of the treatment effect of the drug. The weight can be interpreted as the number of real patients that would have to be added to the trial to provide the same amount of information as the virtual (artificial) patients will do. For example, if <NUM> simulated patients were created but only <NUM> real patients would have to be added to the control arm, then each simulated patient would only be taken into account with a <NUM>% weight so that the entire weight of the <NUM> simulated patients corresponds to a total number of <NUM> patients.

The maximum weight value received from the user may be based on any of the following:.

A person skilled in the art will acknowledge that the maximum weight value received from the user may be further based the following criteria:.

The clinical outcomes for the control arm in the target trial are then updated using the power prior by taking into account the maximum weight value for the artificial patient data. By including the simulated patient data, the distribution of the control arm becomes narrower and higher than the original distribution of the control arm of the target trial. That is, the estimated measure of the treatment effect (i.e., the difference in the clinical outcome progression rate between treatment and control arm) based on the herein disclosed method is of higher reliability than an estimate which is merely based on the target trial data alone. It is to be noted that this improvement is achieved without involving additional real patients in the control arm which would lead to the above-mentioned disadvantages for humans participating in the control arm.

In one embodiment, the weight is further based on the similarity between the clinical outcome data for artificial patients not receiving experimental treatment and the clinical outcome data obtained from the control arm of the target trial. This similarity may be obtained by using a dynamic borrowing method with a higher similarity leading to a higher dynamic borrowing weight value. The weight used for the analysis is then the lower of the received maximum weight value and the obtained dynamic borrowing weight value. In other words, in this embodiment an automatic quantitative algorithm is used together with the maximum weight value to select the weight finally used for measuring the treatment effect. An example of such an algorithm is the dynamic borrowing for power prior analysis described by <NPL>. This dynamic borrowing algorithm is adapted to the treatment effect analysis by implementing a cost function considering the difference between the observed outcomes in the patients enrolled in the trial and the simulated outcomes of the patients in the simulated patient dataset, and the amount of information borrowed from the simulated dataset. That is, the method uses the already available real patient data and borrows additional information from the simulated patient data. In doing this, a balance is reached between the risk of introducing bias into the analysis and the improvement of the estimate precision.

Further aspects of the invention will be realized and attained by means of the elements and combinations particularly depicted in the appended claims. It is to be understood that both, the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as described.

<FIG> includes a block diagram of a computer system <NUM> for measuring the treatment effect of a medical drug 10d for a disease. <FIG> is a simplified flow chart of a computer-implemented method <NUM> for measuring the treatment effect of the drug 10d for said disease. The method <NUM> can be executed by the system <NUM>. For this reason, <FIG> is described in view of <FIG> and the following description of <FIG> also refers to reference numbers used in <FIG>.

The system <NUM> has one or more interfaces <NUM> to communicate with other computer systems (e.g., simulation systems, data storage systems, etc.) for data retrieval, and further to communicate with a human user <NUM> of the system. Via the interface <NUM>, the system <NUM> obtains <NUM> a disease progression model <NUM> of the disease. The obtained disease progression model <NUM> is based on historical covariates and clinical outcome data reflecting the progression of the disease for a plurality of patients affected by said disease. The disease progression model quantitively describes the time course <NUM> of disease progression by one or more corresponding covariates. In the herein described example, a disease progression model is used which characterizes lung function change over time in patients with asthma. In this example, the progression of the disease is monitored through "Forced Expiratory Volume in <NUM> second" (FEV1), which increases with age and is decreased in asthmatic patients. Thereby, the loss of functional capacity of the lungs is measured as the difference in volume that the human patient can exhale at the beginning of the trial and at the end of the trial. The model describes how this marker evolves over time in patients receiving standard-of-care and how covariates influence this. In this example, the relevant covariates are Age and Height (as described in the above cited paper by Wu et al. It is to be noted that a person skilled in the art may also use other/further covariates when monitoring FEV1 progression. It is further to be noted that a person skilled in the art can apply the approach disclosed herein also to other diseases using other appropriate disease progression models. With regard to the FEV1 example, "Equation <NUM>" of the Wu paper describes that the best prediction by the disease progression model for FEV1 was achieved by the following exponential function of age and height: <MAT> with theta<NUM> and theta<NUM> being the rate of change in FEV1 associated with age and height, respectively. The theta<NUM> refers to a baseline level for FEV1 (i.e., the FEV1 level at birth assuming the model is applicable to that age range). <FIG> reproduces table <NUM> of the Wu paper as table <NUM> showing the estimates of the parameters from the final model reflected by "Equation <NUM>".

With this model at hand, the system <NUM> can predict how patients with a particular set of covariates (age and height) are expected to progress in terms of FEV1 over time, considering the variability between patients and the FEV1 measurement noise. It is to be noted that, in the herein described example, theta<NUM> is not considered as a relevant covariate of the disease progression model. This has the effect that, when using the disease progression model for simulating artificial patient data, the simulation relates to reference patients (not associated with a particular ethnic group). The variability between patients (interindividual variability) is described by equation <NUM> in Wu et al:
"Interindividual variability on FEV<NUM> was evaluated using an additive error model: <MAT> where Pij is the true value of the jth parameter for the ith subject. PTVj is the population typical value (TV) for the jth parameter, and ηij is an interindividual random effect, which quantifies the deviation of Pij from PTvj and is assumed to follow a normal distribution with mean of <NUM> and variance of ω2j.

The FEV1 measurement noise (intraindividual variability or residual variability) is described by equation <NUM> in Wu et al:
"Intraindividual variability was evaluated using a combined proportional and additive error model as follows: <MAT> where FEV1obs are the observations and FEV1pred is the corresponding model prediction. ε<NUM> and ε<NUM> are independent and normally distributed random variables with zero mean and variance of σ21 and σ22, respectively, that account for the residual unexplained variability. The final model was evaluated by a nonparametric bootstrap and the visual predictive check to assess the predictive performance and robustness.

The system <NUM> further receives <NUM> a trial dataset <NUM> with trial covariates tc and clinical outcome data tco obtained from a plurality of human patients <NUM> participating in a target trial. The target trial comprises at least one treatment arm <NUM> with human patients receiving experimental treatment 11t (indicated by black heads in <FIG>), and a control arm <NUM> with patients not receiving experimental treatment lint (indicated white heads in <FIG>). The patients in the control arm <NUM> receive a placebo 10p instead of the experimental drug 10d. The considerations about the target trial and the clinical outcome regarding this example include an observation period of one year with the patients enrolled being randomly assigned to treatment or control arm, and then receiving the corresponding treatment (experimental drug or standard of care 10p) for a year. Enrollment of <NUM> patients per arm was intended. The patients were screened before randomization and their baseline FEV1 was measured. After the <NUM>-year follow-up period, their FEV1 was measured again. The change in FEV1 during this period (FEV1 change from baseline) is the clinical outcome. As children grow, FEV1 is expected to increase. Therefore, change from baseline for patients not receiving the drug is expected to increase. Change from baseline for patients receiving the experimental drug is expected to be greater if the drug really works as it is intended to. An artificial patient generator module <NUM> (APG) of the system <NUM> generates <NUM> an artificial patient dataset <NUM> based on the data obtained from the target trial. In a first implementation, APG <NUM> uses distributions of the one or more corresponding covariates (e.g., age and height) and their correlations derived from the set of trial covariates tc to simulate artificial patient data based on variance-covariance matrices. This can be done by using the variance-covariance matrix of the covariates of interest obtained from the real patient's data of the target trial. In this example, the variance-covariance matrix for age and height had the form shown in the following Table <NUM>:.

This variance-covariance matrix is then used to simulate the new cohort of the artificial patient dataset <NUM>. In this implementation, the APG <NUM> has a filtering module APF <NUM> to filter the artificial patient dataset <NUM> in accordance with inclusion-exclusion criteria of the target trial so that data records associated with artificial patients which do not meet the inclusion criteria or which meet the exclusion criteria of the target trial are removed from the artificial patient dataset <NUM>. Inclusion criteria are characteristics that the target trial participants must have if they are to be included in the clinical trial. Exclusion criteria are those characteristics that disqualify prospective participants from inclusion in the clinical trial. In the target trial, an exclusion criterion was that no patients above the age of <NUM> participated in the target trial. Therefore, artificial patients <NUM>-<NUM> and <NUM>-<NUM> which fall under such exclusion criterion (i.e., above <NUM> years-old) are filtered out and removed from the artificial patient dataset <NUM>. <FIG> illustrates correlations of the covariates "Age" and "Height" (considered as relevant for the disease progression model) for the observed data <NUM> which is used to build the variance covariance matrix, and for the simulated dataset <NUM> which is created by using the variance-covariance matrix. The output of APG <NUM> is a dataset with simulated covariates sc' for the artificial patients of the artificial patient dataset <NUM>.

In an alternative implementation, bootstrapping is used, where patients are randomly sampled from the target trial dataset, obtaining their paired values of age and height, to generate the simulated covariates sc' for the artificial patients. A large dataset can be generated (e.g., <NUM> patients). In order to obtain such a big dataset out of a smaller set of real patients in the target trial sampling with replacement may be performed (meaning patients can be sampled more than once). In other words, by random sampling the trial covariates tc APG <NUM> generates an artificial patient data record for each patient of the artificial patient dataset <NUM> and stores the simulated covariates sc' of the respective artificial patient with said data record. Because sampling patients from the target trial automatically results in an artificial patient dataset which complies with the exclusion/inclusion criteria of the target trial, no further filtering is needed in this implementation of the APG <NUM>.

The generated artificial patient dataset <NUM> with the simulated covariates sc' is now provided as input to the disease progression model <NUM>. With this input, the disease progression model <NUM> generates <NUM> a simulated dataset <NUM> with simulated covariates sc and simulated clinical outcome data sco for artificial patients not receiving experimental treatment. That is, the disease progression model simulates covariates and clinical outcomes only for artificial patients that would receive the standard-of-care, and therefore would belong to the control arm of the target trial. It is clear that the simulated patient data cannot relate to the treatment arm of the trial because it would be impossible to simulate the treatment effect of the drug. It is exactly the goal of the target trial to measure such treatment effect. In the example, clinical outcomes for <NUM> artificial control patients were simulated. That is, the simulated dataset <NUM> now includes information about how each of the <NUM> artificial patients evolves over time in terms of FEV1 progression.

A trial analyzer module (TA) <NUM> of system <NUM> finally determines <NUM> the treatment effect 10d-te of the drug 10d. In a classic treatment effect analysis, only the trial dataset <NUM> (tc/tco) would have been used for such analysis. However, the herein disclosed method analyzes the trial dataset <NUM> and the simulated dataset <NUM> together using a power prior module <NUM> to incorporate the simulated dataset <NUM> as prior information. Thereby, the simulated dataset <NUM> is given a weight in comparison to the control arm of the trial dataset <NUM>. The weight for the simulated dataset allows to acknowledge that the information gain obtained from an artificial patient is less than the information obtained from a real patient of the trail control arm <NUM>. In other words, the weight is to be interpreted as the number of real patients that would have to be added to the trial control arm to provide the same amount of information as the simulated patients can contribute. The weight is a constraint (for TA <NUM> and its power prior function <NUM>) which is limited by a user <NUM> to a maximum weight value <NUM>-<NUM> through a corresponding input to TA <NUM>. The user <NUM> is setting this maximum weight value to be used by the treatment effect analysis algorithm based on medical considerations taking into account respective statistics.

For example, the maximum weight value <NUM>-<NUM> may be the number of patients used to develop the disease progression model <NUM>. This reflects that the reliability of the disease progression model predictions is limited by the number of input datasets (of real patients) which were available for the model development. It would make no sense to give the total outcome of simulated patients a higher weight in the analysis than justifiable by the model which was used to generate the simulated clinical outcomes <NUM>. In that sense, the number of patients used to develop the disease progression model provides an upper limit for the maximum weight value input <NUM>-<NUM>. If for example, the disease progression model <NUM> was developed using a dataset of <NUM> real patients, the received maximum weight value <NUM>-<NUM> in this implementation (corresponding to an objective meaningful upper limit for the weight) is <NUM>. That is, the <NUM> simulated patients are only given a weight in the analysis which corresponds to <NUM> patients in the control arm.

In another implementation, the maximum weight value <NUM>-<NUM> may be based on the similarity of patients used to develop the disease progression model <NUM> and target trial patients <NUM>, and assumptions made to extrapolate from one to another population. In the FEV1 example, the disease progression model <NUM> was developed using data from adult trial patients. A multidisciplinary team of FEV1 experts reached an agreement that <NUM> adult patients are not as valuable to inform a pediatric trial as pediatric patients would be. Therefore, a <NUM> correction factor may be applied to account for the assumption made that the relation between age, height and FEV1 holds in pediatric patients, thus setting the maximum number of patients to <NUM>% of the patients used to develop the model. This results in a maximum weight value of <NUM> patients. A reduction of the maximum weight value always results in a treatment effect 10d-te estimate which gets closer to the treatment effect that would be measured based on the trial dataset <NUM> alone. That is, the risk of introducing errors from the simulation is reduced, but at the same time the reliability of the measured treatment effect is reduced which will be shown further below.

In a third implementation, the user <NUM> may use a sensitivity analysis (SA) tool <NUM> to determine a maximum weight value which reduces the probability of concluding that the drug 10d works when it does not. In other words, the worst-case scenario is considered as a too high "false positive results probability" (i.e., the probability of concluding that the drugs works when it does not). In the FEV1 example, the simulated dataset <NUM> shows a lower FEV1 change from baseline than the patients <NUM> recruited in the trial. When pooling together the simulated data <NUM> of the artificial patients <NUM> and the real data <NUM> of the real patients <NUM>, the FEV1 change from baseline in control patients could be estimated to be lower than the true FEV1 change from baseline. The consequence, when comparing control and treatment arms, would be an increased probability of concluding the drug increases FEV1 change from baseline, when it actually does not.

Under this worst-case scenario, a range of weights is evaluated through clinical trial simulation and analyzed using the power prior. <FIG> shows an example with six weight values in the range between <NUM> and <NUM> (units equivalent to number of patients) which was used for the FEV1 example. In this example, the user has decided for a threshold of <NUM>% for the probability of false positives. That is, the maximum weight value to be used as input for TA <NUM> should keep the probability of a false positive outcome (Type I Error Rate) below <NUM>%. By interpolation using linear regression, a weight of <NUM> was identified as the weight value which corresponds to a <NUM>% Type I Error rate (<NUM>). In this case, the maximum weight value was set to <NUM> patients. As a result, in this example, the artificial patients cannot have a weight higher than <NUM> real patients in the analysis.

Other factors which can influence the selection of an appropriate maximum weight value input are: the precision of the estimates of the disease progression model, the results of model validation, and the size of the target trial. The skilled person knows how to assess these factors before entering the maximum weight value as input to TA <NUM>. In general, if there is good concordance between observed and simulated control patients, a higher weight is desired, and when discrepancies occur, a lower weight is desired to prevent a biased analysis.

If the received maximum weight value <NUM>-<NUM> is the only consideration for setting the weight in the treatment effect analysis, then it would automatically become the predefined weight to be used by TA <NUM> in the analysis of the treatment effect 10d-te for said drug. In one embodiment, an automatic quantitative algorithm is used together with this maximum weight value to select the weight used for the analysis. In this embodiment, the used weight is further based on the similarity between the clinical outcome data sco for artificial patients not receiving experimental treatment and the clinical outcome data tco obtained from the control arm of the target trial. The similarity is obtained by using a dynamic borrowing method with a higher similarity leading to a higher dynamic borrowing weight value. The finally used weight value is the lower of the received maximum weight value and the obtained dynamic borrowing weight value. An example of an algorithm implementing such a dynamic borrowing method is the dynamic borrowing for power prior analysis described <NPL>. This algorithm implements a cost function considering the difference between the clinical outcomes observed for the patients enrolled in the trial and the clinical outcomes of the patients in an external dataset. In the herein described method, the simulated dataset is used instead of the external dataset. Further, the algorithm uses the amount of information borrowed from the simulated dataset. This allows to reach a balance between the risk of introducing bias into the treatment effect analysis and the improvement of the estimate precision. Following Ibrahim et al. <NUM>, section <NUM>, the cost function takes the following form: <MAT> Where: <MAT> Where: <MAT> Where:.

<FIG> illustrates the relationship of this cost-function <NUM> over a range of weights. For the FEV1 example, a minimum value MIN was found at the weight <NUM> (patients). As this weight was below the maximum weight value received from the user, the weight value determined by the dynamic borrowing is now used as the weight of simulated patients in the clinical trial analysis when using the power prior function PP <NUM> of TA <NUM>. In case the dynamic borrowing had pointed to a weight above <NUM> patients, and the maximum weight value had been determined based on the third implementation as described in <FIG>, the maximum weight value would have been used as the weight value given to the simulated patients for PP <NUM> because TA <NUM> always uses the lower weight value in this embodiment.

Turning back to <FIG>, it is to be noted that the reliability of the determined treatment effect 10d-te is improved by using the PP <NUM> function of TA <NUM> to incorporate the clinical outcomes of the simulated patients with the appropriate weight in comparison to the classic clinical trial analysis which is only based on real patient data <NUM> from the clinical trial <NUM>. This improvement effect is illustrated in <FIG>, <FIG> by way of example for the FEV1 scenario.

Once the weight is given to the artificial patient data, the FEV1 change from baseline for the control arm in the trial is updated using the power prior <NUM> and the simulated FEV1 change from baselines. <FIG> shows the posterior distributions of FEV1 change from baseline estimate for the control arm (solid line) and the treatment arm (dashed line). The fact that the distribution of the treatment arm differs from the distribution of the control arm already indicates that there is a treatment effect associated with the medical drug. In the left chart <NUM>, the distributions obtained with the classical treatment effect analysis without using the artificial patients is shown. The distribution <NUM> of the control arm is slightly lower and broader than the distribution <NUM> of the treatment arm. In the right chart <NUM>, the distributions <NUM>, <NUM> are obtained with the simulated patient data using PP <NUM> with the given weight value <NUM>. That is, the control arm is enhanced by <NUM> simulated patients which results in more patients in the control arm than in the treatment arm. The distribution <NUM> of the control arm is now higher and narrower than the distribution <NUM> of the treatment arm (which remains of course unchanged vs. distribution <NUM>). That is, the distribution <NUM> of the control arm with using the artificial patients with a weight of <NUM> leads to a better precision of the FEV1 change estimate.

<FIG> illustrates a chart <NUM> with the distributions <NUM>, <NUM> of the treatment effects derived from the charts <NUM> and <NUM> of <FIG>. The distribution of the treatment effect is computed as the difference in FEV1 change from baseline between the respective treatment arm and control arm. The distribution <NUM> is derived from chart <NUM> (including <NUM> artificial patients). The distribution <NUM> is derived from chart <NUM> without artificial patients. <FIG> clearly demonstrates how the estimate of the treatment effect is improved by the herein disclosed approach using artificial patients in the control arm. The distribution <NUM> of the treatment effect measured by including artificial patients is narrower and has a higher peak - in short provides a more precise estimate of the treatment effect - than distribution <NUM> showing the distribution of the treatment effect derived from real patient data of the clinical trial alone. Hence, the proposed method not only improves the measurement of the treatment effect of a drug based on a clinical trial but further allows to achieve this improved result without adding additional patients to the control arm. This avoids unnecessary burden for such patients who would only receive a placebo and could not even profit from the treatment effect of the drug.

<FIG> is a diagram that shows an example of a generic computer device <NUM> and a generic mobile computer device <NUM>, which may be used with the techniques described here. Computing device <NUM> is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Generic computer device <NUM> may correspond to the computer system <NUM> of <FIG>. Computing device <NUM> is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, and other similar computing devices. For example, computing device <NUM> may be used as a GUI frontend for a user to input the maximum weight value to the computer device <NUM>, and in turn, receive from the computer device <NUM>, the predicted treatment effect of the drug.

Computing device <NUM> includes a processor <NUM>, memory <NUM>, a storage device <NUM>, a high-speed interface <NUM> connecting to memory <NUM> and high-speed expansion ports <NUM>, and a low-speed interface <NUM> connecting to low-speed bus <NUM> and storage device <NUM>. The processor <NUM> can process instructions for execution within the computing device <NUM>, including instructions stored in the memory <NUM> or on the storage device <NUM> to display graphical information for a GUI on an external input/output device, such as display <NUM> coupled to high-speed interface <NUM>. In other implementations, multiple processing units and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices <NUM> may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a processing device).

The high-speed controller <NUM> manages bandwidth-intensive operations for the computing device <NUM>, while the low-speed controller <NUM> manages lower bandwidth-intensive operations.

The processor may be implemented as a chipset of chips that include separate and multiple analog and digital processing units.

Thus, for example, expansion memory <NUM> may act as a security module for device <NUM>, and may be programmed with instructions that permit secure use of device <NUM>. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing the identifying information on the SIMM card in a non-hackable manner.

As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal.

The systems and techniques described here can be implemented in a computing device that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components.

The computing device can include clients and servers.

A number of embodiments have been described. Nevertheless, it will be understood that various modifications may be made without departing from the scope of the invention.

Claim 1:
A computer-implemented method (<NUM>) for measuring the treatment effect of a medical drug (10d) for a disease, the method comprising:
obtaining (<NUM>) a disease progression model (<NUM>) of the disease, wherein the disease progression model (<NUM>) is based on historical covariates and clinical outcome data reflecting the progression of the disease for a plurality of patients affected by said disease, and wherein the disease progression model quantitively describes the time course (<NUM>) of disease progression by one or more corresponding covariates;
receiving (<NUM>) a target trial dataset (<NUM>) with trial covariates (tc) and clinical outcome data (tco) obtained from a plurality of human patients (<NUM>) participating in a target trial, wherein the target trial comprises at least one treatment arm (<NUM>) with human patients receiving experimental treatment (11t), and a control arm (<NUM>) with patients not receiving experimental treatment (lint);
characterized in that
generating (<NUM>) an artificial patient dataset (<NUM>) by
using distributions of one or more corresponding covariates and their correlations derived from the set of trial covariates (tc), and filtering the artificial patient dataset (<NUM>) in accordance with inclusion-exclusion criteria of the target trial so that data records associated with artificial patients (<NUM>-<NUM>, <NUM>-<NUM>) which do not meet the inclusion criteria, or which meet the exclusion criteria of the target trial, are removed from the artificial patient dataset (<NUM>); or
by random sampling the trial covariates (tc) with replacement, with each data record of the artificial patient dataset (<NUM>) storing covariates of a respective artificial patient;
with the artificial patient dataset (<NUM>) as input for the disease progression model (<NUM>), generating (<NUM>) a simulated dataset (<NUM>) with simulated covariates (sc) and simulated clinical outcome data (sco) for artificial patients not receiving experimental treatment;
determining (<NUM>) the treatment effect (10d-te) of the drug (10d) by analyzing the trial dataset (<NUM>) and the simulated dataset (<NUM>) together using a power prior approach (<NUM>) to incorporate the simulated dataset (<NUM>) as prior information, wherein the simulated dataset (<NUM>) is given a weight in comparison to the control arm of the trial dataset (<NUM>), with the weight based on at least a maximum weight value (<NUM>-<NUM>) received from a user (<NUM>).