|商品名稱：||Lixoft Monolix Suite 2019 R1 x64 MacOS-非常實用的關於藥物方面的建模和模擬軟件|
- 回上一頁您可能感興趣：Lixoft Monolix Suite 2019 R1 x64 MacOS-非常實用的關於藥物方面的建模和模擬軟件Monolix 2019 mac是用於藥物學的非線性混合效應建模（NLME）的最先進和最簡單的解決方案。它基於SAEM算法，即使對於複雜的PK / PD模型也能提供強大的全局收斂。Monolix用於臨床前和臨床人群PK / PD建模和系統藥理學。Monolix擁有龐大的用戶社區。Monolix被學術界，製藥業以及美國監管機構廣泛使用。
Monolix is the most advanced and simple solution for non-linear mixed effects modeling (NLME) for pharmacometrics. It is based on the SAEM algorithm and provides robust, global convergence even for complex PK/PD models. Monolix is used for preclinical and clinical population PK/PD modeling and for Systems Pharmacology. Monolix enjoys a large user community. Monolix is widely used by academia, the pharmaceutical industry as well as the US regulatory agencies.
Advanced Statistical Methodologies
Reliable convergence for all type of data is a centerpiece in population PKPD modeling, which is why Lixoft pioneered in collaboration with Inria the SAEM algorithm.
Automated generation of diagnostic tests
Monolix automatically generates a full set of diagnostic plots even for complex PK/PD models. For example, you can instantaneously create the Visual Predictive Check, split by any patient subgroup you would want to investigate.
Increased productivity and quality
Efficient C++ solver package, standardized model language with Mlxtran, PK/PD model library and integrated software all contribute to better productivity and quality.
Very easy to use with its GUI
Our solutions are designed for ease of use. Monolix can be used via a graphical interface or command lines for powerful scripting. This means less programming for you and more focus on exploring models and pharmacology to deliver in time to your customers.
Support of all relevant data types and statistical features
Monolix covers a wide range of data types and statistical features for population PK/PD modeling. For all cases the right statistical methodology has been developed and published for reference.
Continuous, categorical, count and repeated time to event data.
Mixture models and mixtures of models.
Inter-occasion variability with any number of levels.
Proper handling of BLQ data.
Normal, lognormal, logit, probit and user defined distributions for the individual parameters.