Cox proportional hazards regression model: univariate, multivariate, adjusted, stratified

Cox proportional hazards regression model has been called different names (Cox model, Cox regression model, Proportional hazards model, ... can be used interchangeably). The original paper by D.R. Cox "Regression models and life tables" is one of the most cited papers. Paired with the Kaplan-Meier method (and the log-rank test), the Cox proportional hazards model is the cornerstone for the survival analyses or all analyses with time to event endpoints.

With Cox regression model, we are now able to analyze the time to event data just like the linear regressions and logistic regressions to compare the treatment difference and to investigate the explanatory variables. The explanatory variables may be called independent variables, covariates,  confounding factors,...

Depending on whether the explanatory variables are continuous or categorical and depending on how the explanatory variables are used in the Cox regression model (as covariates, as stratification factors), Cox regression model can be fitted in different ways:

Univariate Cox regression	Unstratified and unadjusted
Multivariate Cox regression	Unstratified and adjusted
Stratified Cox regression	Stratified and unadjusted
Stratified Multivariate Cox regression	Stratified and adjusted

The variables used in adjusted Cox regression can be categorical or continuous, but the variables used in stratified Cox regression should be categorical.

The following are compiled from various sources listed below:

• What is a Cox model?
• Introduction to Cox Model
• Introduction to survival analysis in SAS
• SAS/STAT® 15.1 User’sGuide The PHREG Procedure
• Cox Proportional Hazards Model Using SAS 
• Statistical Analysis Plan for a NEJM article
• Cox proportional hazards model by STHDA
• A plain man's guide to the proportional hazards model

Univariate Cox Proportional Hazards Regression Model (Unstratified Unadjusted Proportional Hazards Regression Model)

The unstratified, unadjusted proportional hazards regression model is more commonly called Univariate Cox proportional hazards regression model and its assumptions are illustrated by:

h (.) hazard function as a function of time (relative to the start date), the patient’s

treatment and the unknown regression parameter

h0(t) unspecified baseline hazard function at time t

x_j1 treatment for patient j coded as 0 (placebo group) or 1 (treatment group); this coding leads to a hazard ratio less than 1 if the estimated effect suggests a lower hazard in the treatment group relative to the placebo group

β₁ unknown regression parameter for the treatment effect on the log-scale (the log hazard ratio).

The hazard ratio (HR) is a multiplicative constant λ1 = exp(β₁) comparing the hazard function in the treatment group relative to the hazard function in the placebo group (identical to the baseline hazard function). A HR less than 1 indicates decreased hazard for the event of interest in the treatment group compared to the placebo group.

SAS PROC PHREG (with the TIES=EXACT option for the “exact” handling of ties) is used to estimate the hazard ratio based on the partial maximum likelihood function; a Wald-test based two-sided CI is requested by the RISKLIMITS option (additional options controlling the output may be added):

PROC PHREG DATA=dataset;
MODEL tte*cnsr(1)=treat / TIES=EXACT RISKLIMITS ALPHA=;
RUN;

* tte represents variable containing event/censoring times;
* cnsr represents censoring variable (0=event, 1=censored);
* treat represents treatment group variable (0=placebo, 1=treatment);

Stratified (Unadjusted) Proportional Hazards Regression

The stratified unadjusted proportional hazards regression model and its assumptions are described by:

h (.) hazard function as a function of time (relative to the start date), the patient’s

stratum and treatment and the unknown regression parameter

h0k(t) unspecified baseline hazard function for stratum k at time t

xjk1 treatment for patient j in stratum k coded as 0 (placebo group) or 1 (treatment group); this coding leads to a hazard ratio less than 1 if the estimated effect suggests a lower hazard in the treatment group relative to the placebo group

β₁ unknown regression parameter for the treatment effect on the log-scale (the log hazard ratio).

The hazard ratio is a multiplicative constant λ1 = exp(β₁) comparing the hazard functions in the treatment group relative to the hazard functions in the placebo group. The latter are allowed varying across strata. However, the hazard ratio is assumed to be common across strata. An HR less than 1 indicates decreased hazard for the event of interest in the treatment group compared to the placebo group.

SAS PROC PHREG (with the TIES=EXACT option for the “exact” handling of ties) is

used to estimate the hazard ratio based on the partial maximum likelihood function; a Wald-test based two-sided CI is requested by the RISKLIMITS option (additional options controlling the output may be added):

PROC PHREG DATA=dataset;
MODEL tte*cnsr(1)=treat / TIES=EXACT RISKLIMITS ALPHA=;
STRATA strat1 .. stratj;
RUN;

* tte represents variable containing event/censoring times;
* cnsr represents censoring variable (0=event, 1=censored);
* treat represents treatment group variable (0=placebo, 1=treatment);
* strat1 to stratj represent stratification variables;

Adjusted Cox Proportional Hazards Regression Model (including Univariate Cox Proportional Hazards Regression Model and Multivariate Cox Proportional Hazards Regression Model)

The purpose of the model is to evaluate the effect of a single factor (univariate) or simultaneously the effect of several factors (multivariate) on survival. In other words, it allows us to examine how specified factor(s) influence the rate of a particular event happening (e.g., infection, death) at a particular point in time. This rate is commonly referred as the hazard rate. Predictor variable(s) (or factor(s)) are usually termed covariates in the survival-analysis literature.

The Cox model is expressed by the hazard function denoted by h(t). Briefly, the hazard function can be interpreted as the risk of dying at time t. It can be estimated as follow:

where,

·         t represents the survival time

·         h(t) is the hazard function determined by a set of p covariates (x₁,x₂,...,x_p)

·         the coefficients (b₁,b₂,...,b_p) measure the impact (i.e., the effect size) of covariates.

·         the term h₀ is called the baseline hazard. It corresponds to the value of the hazard if all the x_i are equal to zero (the quantity exp(0) equals 1). The ‘t’ in h(t) reminds us that the hazard may vary over time.

The Cox model can be written as a multiple linear regression of the logarithm of the hazard on the variables x_i, with the baseline hazard being an ‘intercept’ term that varies with time.

The quantities exp(b_i) are called hazard ratios (HR). A value of b_i greater than zero, or equivalently a hazard ratio greater than one, indicates that as the value of the ith covariate increases, the event hazard increases and thus the length of survival decreases.

Put another way, a hazard ratio above 1 indicates a covariate that is positively associated with the event probability, and thus negatively associated with the length of survival.

      Univariate Cox proportional hazards regression model

PROC PHREG DATA=dataset;
MODEL tte*cnsr(1)=treat /
TIES=EXACT RISKLIMITS ALPHA;RUN;

      Multivariate Cox proportional hazards regression model

PROC PHREG DATA=dataset;
MODEL tte*cnsr(1)=treat cov1 .. covk /
TIES=EXACT RISKLIMITS ALPHA;
RUN;

* tte represents variable containing event/censoring times;
* cnsr represents censoring variable (0=event, 1=censored);
* treat represents treatment group variable (0=placebo, 1=treatment);
* cov1 to covk represent covariates other than treatment group;

Stratified Multivariate Cox Regression (Stratified Adjusted Proportional Hazards Regression)

The stratified adjusted proportional hazards regression model and its assumptions are illustrated by:

h (.) hazard function as a function of time (relative to the start date), the patient’s

stratum and covariates, and the unknown vector of regression parameters

h_0k(t) unspecified baseline hazard function for patients with covariate vector (0, .., 0) for stratum k at time t

x_jk vector of Baseline covariates for patient j in stratum k, with xjk1 representing

treatment coded as 0 (placebo) or 1 (treatment)

β: unknown regression parameter vector on the log-scale (β1 represents the log hazard ratio for treatment).

The hazard ratio for the i-th covariate is a multiplicative constant exp(βi) comparing the hazard functions between the levels of the i-th covariate. Baseline hazard functions are allowed varying across strata without any restrictions. However, the hazard ratio comparing a covariate (including treatment) is assumed to be common across strata. The estimates of βi are adjusted for the other covariates in the model.

SAS PROC PHREG (with the TIES=EXACT option for the “exact” handling of ties) is

used to estimate the hazard ratios based on the partial maximum likelihood function; a Wald-test based two-sided CI is requested by the RISKLIMITS option (additional options controlling the output may be added):

PROC PHREG DATA=dataset;
MODEL tte*cnsr(1)=treat cov1 .. covk /
TIES=EXACT RISKLIMITS ALPHA;

STRATA strat1 .. stratj;

RUN;

* tte represents variable containing event/censoring times;
* cnsr represents censoring variable (0=event, 1=censored);
* treat represents treatment group variable (0=placebo, 1=treatment);
* cov1 to covk represent covariates other than treatment group;
* strat1 to stratj represent stratification variables;

Unstratified log-rank test and stratified log-rank test

The logrank test, or log-rank test, is a hypothesis test to compare the survival distributions of two samples. It is a nonparametric test and appropriate to use when the data are right skewed and censored (technically, the censoring must be non-informative). It is widely used in clinical trials to establish the efficacy of a new treatment in comparison with a control treatment when the measurement is the time to event (such as the time from initial treatment to a heart attack).

There are unstratified log-rank test and stratified log-rank test (as described below). In a clinical trial with stratified randomization, the stratified log-rank test is commonly used and the stratification factors used for randomization will be included in the log-rank test. 

Even for a study with time to event primary efficacy endpoint, the sample size calculation is usually based on the unstratifed log-rank test (i.e., the stratification factors are not considered). 

Log-rank test can provide a p-value for comparing the survival distributions of two samples (between two treatment groups), however, as described in an early post, "Splitting p-value and estimate of the treatment difference", the magnitude of the treatment difference (hazard ratio) needs to be calculated using Cox proportional regression - a semi-parametric method.

Unstratified log-rank test

Let S_T(t) and Sp(t) denote the survival functions for the treatment group and placebo group, respectively.

The null hypothesis

  H0: S_T(t) = Sp(t) for all t ≥ 0 “identical survival functions for both treatment groups”

is tested against the one-sided alternative hypothesis

  HA: ST(t) ≥ Sp(t) for all t ≥ 0 and S_T(t) > Sp(t) for at least some t > 0

        “survival function in the treatment group superior to the survival function in the placebo group”

The unstratified log-rank test can be conducted by SAS PROC LIFETEST where the STRATA statement includes only the treatment group variable (treat). The TIME statement includes a variable with times to event (TTE) and an indicator variable for right censoring (cnsr) with 1 representing censoring (additional options controlling the output may be added):

PROC LIFETEST DATA=dataset METHOD=KM;
TIME tte*cnsr(1);
STRATA treat;RUN;

As an output of the procedure, the rank statistic S and variance Var(S) are obtained. Under the null hypothesis, the test statistic Z = S/sqrt[Var(S)] is approximately normally distributed. The one-sided p-value is therefore obtained from normally distributed Z statistic and H0 is rejected if the p-value does not exceed the (nominal) significance level assigned to the test.

Stratified log-rank test

Let ST,k(t) and Sp,k(t) denote the survival functions for the treatment group and placebo group in stratum k, k=1,..,K, K = m1 x m2 x .. x mj where mi denotes the number of categories for stratification

factor i, 1≤ i ≤ j, respectively. The null hypothesis

  H0: ST,k(t) = Sp,k(t) for all t ≥ 0 and all k (“identical survival functions for both treatment groups in each stratum”) is tested against the one-sided alternative hypothesis

  HA: ST,k(t) ≥ Sp,k(t) for all t ≥ 0 and all k=1,..,K and ST,m(t) > Sp,m(t) for at least some t > 0 and some m (“survival function in the treatment group superior to the survival in the placebo group in at least one stratum”)

Log-rank tests are performed for each of the strata separately, obtaining the rank statistic Sk and variance Var(Sk) where k=1, 2, …, K. The stratified log-rank test statistic is constructed as Z = [S1 +…+ SK] / sqrt[Var(S1) +…+ Var(SK)]. Under the null hypothesis, Z is approximately normally distributed. The one-sided stratified log-rank p-value is therefore obtained from normally distributed Z statistic and H0 is rejected if p does not exceed the (nominal) significance level assigned to the test.

The stratified log-rank test can be conducted with SAS PROC LIFETEST where the STRATA

statement includes the j strata variables (strat_1, .., strat_j) and the GROUP option

includes the treatment variable (treat). The TIME statement includes a variable with times to event (TTE) and an indicator variable for right censoring (cnsr) with 1 representing censoring (additional options controlling the output may be added):

PROC LIFETEST DATA=dataset METHOD=KM;
TIME tte*cnsr(1);
STRATA strat_1 .. strat_j GROUP=treat;
RUN;

ICH E19 Optimisation of Safety Data Collection and FDA Guidance on Collecting Less Safety Data

The U.S. Food and Drug Administration is announcing the availability of a draft guidance entitled “E19 Optimisation of Safety Data Collection.” The guidance was prepared under the auspices of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) and provides recommendations regarding appropriate use of a selective approach to safety data collection in some late-stage pre- or postmarketing studies of drugs where the safety profile, with respect to commonly occurring adverse events, is well understood and documented. The agency intends for the draft guidance to advance important clinical research questions through clinical investigations that collect relevant patient data. This will enable an adequate benefit-risk assessment of the drug for its intended use, while reducing the burden to patients from unnecessary tests that may yield limited additional information. Interested parties may submit comments to the docket until August 26, 2019.

FDA publishes ICH guidelines as FDA guidance. This guidance reflects just one element in the FDA’s work with regulatory authorities and industry associations from around the world to promote international harmonization of regulatory requirements under the ICH. One of the goals of harmonization is to identify and reduce differences in technical requirements for drug development among regulatory agencies. FDA is committed to seeking scientifically based harmonized technical procedures for the development and manufacture of pharmaceuticals.

• ICH (2019) E19 Optimisation of Safety Data Collection
• FDA Guidance for Industry (2016) "Determining the Extent of Safety Data Collection Needed in Late-Stage Premarket and Post approval Clinical Investigations"

Duke-Margolis Center for Health Policy organized a two-day workshop "Leveraging Randomized Clinical Trials to Generate Real-World Evidence for Regulatory Purposes"

Dr. Ellis Unger from FDA discussed the topic "Safety Monitoring in Clinical Trials for Generation of RWE" (see the video @1:10)

"Once a drug has been approved, its risks/harms are well-characterized; safety monitoring in studies for new indications can be less intensive (in may circumstances)"

"...about the defensive research, which is really a huge problem, which is that let's collect everything because some regulators might ask us in a year, ho, well, what were the CBCs? all these nitpicking questions that require mountains of data.

We put on a guidance in 2016. it's called, 'Determining the Extent of Safety Data Collection Needed in Late-Stage Premarket and Post approval Clinical Investigations'  What it basically talks about collecting less than usual safety data in those situations where the safety data are well characterized. My particular office oversees the Division of Cardiovascular and Renal products, and we talk with many sponsors. Look, don't collect all this stuff in your next trial. You want to do a cardiovascular outcome trial, that's great. But we already know how many headaches and hangnails a drug causes.  Just collect serious adverse events. and they thank us and they shake their heads and they walk out of the door. and then when they send in the final protocol, they are collecting everything. This happens time and time again and it dawned on us. well, maybe the problem is regulators across the pond and so about 4 years ago, I think, we proposed this topic to ICH collecting less than full safety data collection and it was adopted and I'm glad to say that the step 2 guideline is out and open for public comment. it is called "optimization of safety data collection".

And the interesting thing was that a lot of the resistance to this approach was not coming from the regulators across the pond or in Asian. It was coming from companies who were just firmly entrenched in the idea that they better collet it because they may need it. I would like someone who works for a company that has done one of these outcome studies for an anti-diabetic drug could tell me exactly what they learned by getting vital signs every, lab everything, every nonserious adverse event. Tell me what you learned and tell me what it costed you. I suspect that you didn't get much bang for your buck.

So this guideline is out there and I really do hope most of the people who have a vested interest take a look at it and there's a docket in the US and other countries where you can submit your comments Through official Pathways to get them considered."

Further Readings:

• FDA Seeks Comment on ICH Guideline on Optimizing Safety Data Collection
• FDA published ICH E19 for comments