{"product_id":"dynamical-biostatistical-models-advanced-longitudinal-methods","title":"Dynamical Biostatistical Models - Advanced Longitudinal Methods","description":"\u003cp\u003eIn this review of Dynamical Biostatistical Models the authors deliver a focused, research-oriented treatment of longitudinal analysis that will most appeal to statisticians and quantitative biologists. The single biggest reason to buy is its coherent presentation of time-aware regression frameworks, particularly its emphasis on \u003cstrong\u003emultistate and joint models\u003c\/strong\u003e and how they connect through a stochastic process viewpoint. This is a book for readers who need rigorous methodology and a path to implementation rather than an introductory statistics primer.\u003c\/p\u003e\u003ch2\u003eKey Features\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eComprehensive longitudinal focus:\u003c\/strong\u003e The text systematically covers models for repeated measures, qualitative variables and event history, making it easier to handle mixed data types in a single study.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAdvanced regression models:\u003c\/strong\u003e Readers get detailed discussion of mixed-effect models, survival models and multistate models that include the time dimension for richer inference.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eJoint modeling explained:\u003c\/strong\u003e The book describes joint models for repeated measures and time-to-event data so readers can analyze linked outcomes without ad hoc compromises.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eStochastic process viewpoint:\u003c\/strong\u003e Presenting models through a stochastic process lens helps unify different approaches and supports more coherent causal reasoning.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSoftware applicability:\u003c\/strong\u003e Most advanced methods are explained with practical applicability in SAS or R, enabling readers to implement techniques on real datasets.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eWho It's For\u003c\/h2\u003e\u003cp\u003eThe book is best for applied statisticians, biostatisticians, epidemiologists and quantitative researchers who already have a grounding in regression and survival analysis and want to extend that knowledge to longitudinal, multistate and joint modeling frameworks. It suits readers who plan to implement methods in R or SAS and value theoretical motivation alongside applied examples.\u003c\/p\u003e\u003cp\u003eThose seeking an introductory text or a cookbook of elementary tutorials should look elsewhere; the presentation assumes familiarity with core statistical concepts and focuses on advanced methods and unifying perspectives rather than step-by-step beginner instruction.\u003c\/p\u003e\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003ePros\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eOffers an integrated view of \u003cstrong\u003emultistate and joint models\u003c\/strong\u003e that clarifies connections across methods.\u003c\/li\u003e\n\u003cli\u003eIncludes practical notes on implementing methods in SAS and R so theory can be applied to data.\u003c\/li\u003e\n\u003cli\u003eExplores causal inference from a dynamic viewpoint, useful for longitudinal causal questions.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cstrong\u003eCons\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\u003cli\u003eNot designed as an introductory text; readers without prior regression and survival knowledge will find it challenging.\u003c\/li\u003e\u003c\/ul\u003e\u003ch2\u003eSpecifications\u003c\/h2\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003eTitle\u003c\/td\u003e\n\u003ctd\u003eDynamical Biostatistical Models (Chapman \u0026amp; Hall\/CRC Biostatistics Series)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAuthors\u003c\/td\u003e\n\u003ctd\u003eDaniel Commenges, Helene Jacqmin-Gadda\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePrimary topics\u003c\/td\u003e\n\u003ctd\u003eLongitudinal data, repeated measures, event history, survival, multistate models\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAdvanced content\u003c\/td\u003e\n\u003ctd\u003eMixed-effect models, joint models, stochastic process viewpoint\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSoftware applicability\u003c\/td\u003e\n\u003ctd\u003eMethods applicable with SAS or R\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eApproach\u003c\/td\u003e\n\u003ctd\u003eMethodological with applied implementation guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\u003ch2\u003eOur Verdict\u003c\/h2\u003e\u003cp\u003eDynamical Biostatistical Models is a strong choice for researchers and biostatisticians who need a rigorous, integrated treatment of longitudinal and event-history modeling with direct paths to implementation. Its focus on \u003cstrong\u003ejoint and multistate methods\u003c\/strong\u003e and on a stochastic process unification makes it good value for those who will use these techniques in applied research.\u003c\/p\u003e\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003eDoes the book include implementation guidance in statistical software?\u003c\/strong\u003e\u003cbr\u003eYes, the authors note that most advanced methods discussed can be applied using SAS or R and provide guidance to help translate models to code.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eIs this suitable for beginners?\u003c\/strong\u003e\u003cbr\u003eNo, the text assumes familiarity with regression and survival analysis and is aimed at readers seeking advanced methodological detail.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat kinds of models are covered?\u003c\/strong\u003e\u003cbr\u003eThe book covers mixed-effect models, survival and multistate models, joint models for repeated measures and time-to-event data, and discusses a stochastic process perspective.\u003c\/p\u003e","brand":"Daniel Commenges, Helene Jacqmin-Gadda","offers":[{"title":"Default Title","offer_id":48184913461467,"sku":"1498729673","price":139.93,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0724\/1043\/1707\/files\/41UPp1hwsXL.jpg?v=1769696786","url":"https:\/\/gearmusthave.com\/products\/dynamical-biostatistical-models-advanced-longitudinal-methods","provider":"GearMustHave","version":"1.0","type":"link"}