We estimated hazard ratios (HRs) using a standard Cox proportional hazards model as well as with a nested, spatial random-effects Cox model (Krewski et al.
What does CM stand for?
CM stands for Cox Model (biostatistics)
This definition appears very frequently and is found in the following Acronym Finder categories:
- Science, medicine, engineering, etc.
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We have 176 other meanings of CM in our Acronym Attic
- Counseling Ministries
- Country Manager (various locations)
- Coupling Multiplier (NIOSH)
- Cours Moyen (French)
- Cow's Milk
- Crane Maintenance
- Crane, Monorail
- Cras Mane (Latin: tomorrow morning; medical prescription instructions)
- Creative Memories
- Credentialed Manager (International City/County Management Association)
- Credit Memo (accounting)
- Crédit Mutuel (French bank)
- Crew Manager (fire-fighter rank)
- Crew Module
- Criminal Minds (TV series)
Samples in periodicals archive:
A Cox survival model is to be used for modeling TOM One distinctive feature which separates the Cox model from other survival models is that, in a Cox model, the baseline hazard function is left unspecified.
Part 2 presents different extensions of the Cox model, for use when the proportional-hazards assumption of the Cox model is violated.
In the former, we use weibull, gompertz and exponentiall functions (these models may present bias problems; nevertheless the estimation is based on two points to have a reference to compare the Cox model and because the risk function is not monotonic).
Among the topics are parametric methods, inferential methods for categorical time-to-event endpoints, an efficient alternative to the Cox model for small trials, the design and analysis of analgesic trials, antiviral trials, parametric likelihoods for multiple nonfatal competing risks and death, and chronic carcinogenicity studies of pharmaceuticals in rodents.
Although Cox proportional hazard model is a well known method to examine the relationship between survival and clinical or demographic covariates (12), certain parametric models can also estimate the parameter more efficiently than the Cox model (13).
Mixed linear or logistic regression was used to examine the association of each patient characteristic with patient level of systolic blood pressure (110-129 mm Hg, 130-159 mm Hg, or 160 mm Hg and higher), and proportional hazard Cox models were used to look at all-cause mortality for patients according to both patient and facility levels of blood pressure control.