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The ability of PGAMs to estimate the log-baseline hazard rate, endows them with the capability to be used as smooth alternatives to the Kaplan Meier curve.
From the above definition it is obvious that the value of the survival distribution at any given time point is a non-linear function of the PGAM estimate.
The function makes use of another function, Survdataset, that expands internally the vector of time points into a survival dataset. To illustrate the use of these functions we revisit the PBC example from the 2nd part of this blog series. Subsequently, we fit the log-hazard rate to the coarsely (5 nodes) and more finely discretized (using a 10 point Gauss Lobatto rule) versions of the PBC dataset, created in Part 2.
In all cases 1000 Monte Carlo samples were obtained for the calculation of survival probability estimates and their pointwise 95% confidence intervals. Survival analysis is used when the researcher is interested in whether or not an event happens, and also when it happens. Many social-science phenomena are about causal relationships, that is, that they have a progress over time. The common denominator for the above mentioned examples is that they possess the same logical structure.
Survival analysis is a collection of statistical procedures for data analysis for which the outcome variable of interest is time until an event occurs. Many popular P2P historical tools provide an instantaneous ROI based on filter parameters with a discount factor for late loans. Standard practice for classification based machine learning involves feeding a model loan characteristics with its final outcome (fully paid or charged off).
The questions of interest in survival analysis are questions like: What is the probability that a participant survives 5 years? In the first instance, the participants observed time is less than the length of the follow-up and in the second, the participant's observed time is equal to the length of the follow-up period. A small prospective study is run and follows ten participants for the development of myocardial infarction (MI, or heart attack) over a period of 10 years. During the study period, three participants suffer myocardial infarction (MI), one dies, two drop out of the study (for unknown reasons), and four complete the 10-year follow-up without suffering MI. Based on this data, what is the likelihood that a participant will suffer an MI over 10 years? This is called non-informative censoring and essentially assumes that the participants whose data are censored would have the same distribution of failure times (or times to event) if they were actually observed. Notice here that, once again, three participants suffer MI, one dies, two drop out of the study, and four complete the 10-year follow-up without suffering MI. In survival analysis we analyze not only the numbers of participants who suffer the event of interest (a dichotomous indicator of event status), but also the times at which the events occur. Time zero, or the time origin, is the time at which participants are considered at-risk for the outcome of interest. In survival analysis, we use information on event status and follow up time to estimate a survival function. The horizontal axis represents time in years, and the vertical axis shows the probability of surviving or the proportion of people surviving. The figure below shows Kaplan-Meier curves for the cumulative risk of dementia among elderly persons who frequently played board games such as chess, checkers, backgammon, or cards at baseline as compared with subjects who rarely played such games. We focus here on two nonparametric methods, which make no assumptions about how the probability that a person develops the event changes over time.
One way of summarizing the experiences of the participants is with a life table, or an actuarial table. To construct a life table, we first organize the follow-up times into equally spaced intervals. For the first interval, 0-4 years: At time 0, the start of the first interval (0-4 years), there are 20 participants alive or at risk.
This table uses the actuarial method to construct the follow-up life table where the time is divided into equally spaced intervals. An issue with the life table approach shown above is that the survival probabilities can change depending on how the intervals are organized, particularly with small samples. Appropriate use of the Kaplan-Meier approach rests on the assumption that censoring is independent of the likelihood of developing the event of interest and that survival probabilities are comparable in participants who are recruited early and later into the study. In the survival curve shown above, the symbols represent each event time, either a death or a censored time.
These estimates of survival probabilities at specific times and the median survival time are point estimates and should be interpreted as such. Some investigators prefer to generate cumulative incidence curves, as opposed to survival curves which show the cumulative probabilities of experiencing the event of interest. From this figure we can estimate the likelihood that a participant dies by a certain time point. We are often interested in assessing whether there are differences in survival (or cumulative incidence of event) among different groups of participants.
The log rank test is a popular test to test the null hypothesis of no difference in survival between two or more independent groups. A small clinical trial is run to compare two combination treatments in patients with advanced gastric cancer.
Six participants in the chemotherapy before surgery group die over the course of follow-up as compared to three participants in the chemotherapy after surgery group.
The survival probabilities for the chemotherapy after surgery group are higher than the survival probabilities for the chemotherapy before surgery group, suggesting a survival benefit. The sums of the observed and expected numbers of events are computed for each event time and summed for each comparison group. To compute the test statistic we need the observed and expected number of events at each event time.
To generate the expected numbers of events we organize the data into a life table with rows representing each event time, regardless of the group in which the event occurred.
If we assume for the shake of simplicity that there are no proportional co-variates in the PGAM regression, then the quantity modeledĀ  corresponds to the log-hazard of theĀ  survival function. Consequently, the predicted survival value, , cannot be derived in closed form; as with all non-linear PGAM estimates, a simple Monte Carlo simulation algorithm may be used to derive both the expected value of and its uncertainty.



This dataset is used to obtain predictions of the log-hazard function by calling the predict function from the mgcv package. A common tool for the medicine, the social scientist community is starting to see the opportunities provided by this tool. The best non-experimental technique to study these processes of causality is by the use of survival analysis.
A sample of subjects is considered a group at risk where events, like getting married, giving birth, change job, or die from an illness, can happen in a period of risk. Are there differences in survival between groups (e.g., between those assigned to a new versus a standard drug in a clinical trial)? True survival time (sometimes called failure time) is not known because the study ends or because a participant drops out of the study before experiencing the event.
The most common is called right censoring and occurs when a participant does not have the event of interest during the study and thus their last observed follow-up time is less than their time to event.
Participants are recruited into the study over a period of two years and are followed for up to 10 years.
Three of 10 participants suffer MI over the course of follow-up, but 30% is probably an underestimate of the true percentage as two participants dropped out and might have suffered an MI had they been observed for the full 10 years. The fact that all participants are often not observed over the entire follow-up period makes survival data unique. Specifically, we assume that censoring is independent or unrelated to the likelihood of developing the event of interest.
However, the events (MIs) occur much earlier, and the drop outs and death occur later in the course of follow-up. Consider a 20 year prospective study of patient survival following a myocardial infarction. There are a number of popular parametric methods that are used to model survival data, and they differ in terms of the assumptions that are made about the distribution of survival times in the population. Using nonparametric methods, we estimate and plot the survival distribution or the survival curve. The study involves 20 participants who are 65 years of age and older; they are enrolled over a 5 year period and are followed for up to 24 years until they die, the study ends, or they drop out of the study (lost to follow-up). Life tables are often used in the insurance industry to estimate life expectancy and to set premiums. In the table above we have a maximum follow-up of 24 years, and we consider 5-year intervals (0-4, 5-9, 10-14, 15-19 and 20-24 years). The proportion surviving past each subsequent interval is computed using principles of conditional probability introduced in the module on Probability. The Kaplan-Meier approach, also called the product-limit approach, is a popular approach which addresses this issue by re-estimating the survival probability each time an event occurs. When comparing several groups, it is also important that these assumptions are satisfied in each comparison group and that for example, censoring is not more likely in one group than another. At Time=0 (baseline, or the start of the study), all participants are at risk and the survival probability is 1 (or 100%). From the survival curve, we can also estimate the probability that a participant survives past 10 years by locating 10 years on the X axis and reading up and over to the Y axis. There are formulas to produce standard errors and confidence interval estimates of survival probabilities that can be generated with many statistical computing packages. The Kaplan-Meier survival curve is shown as a solid line, and the 95% confidence limits are shown as dotted lines. Cumulative incidence, or cumulative failure probability, is computed as 1-St and can be computed easily from the life table using the Kaplan-Meier approach. For example, in a clinical trial with a survival outcome, we might be interested in comparing survival between participants receiving a new drug as compared to a placebo (or standard therapy). The test compares the entire survival experience between groups and can be thought of as a test of whether the survival curves are identical (overlapping) or not. Twenty participants with stage IV gastric cancer who consent to participate in the trial are randomly assigned to receive chemotherapy before surgery or chemotherapy after surgery.
Other participants in each group are followed for varying numbers of months, some to the end of the study at 48 months (in the chemotherapy after surgery group).
There are several forms of the test statistic, and they vary in terms of how they are computed.
The log rank statistic has degrees of freedom equal to k-1, where k represents the number of comparison groups.
The table below contains the information needed to conduct the log rank test to compare the survival curves above. Note that the only assumptions made by the PGAM is that the a) log-hazard is a smooth function, with b) a given maximum complexity (number of degrees of freedom) and c) continuous second derivatives. For the case of the survival function, the simulation steps are provided in Appendix (Section A3) of our paper.
Furthermore, the 95% confidence interval of each estimator (dashed lines) contain the expected value of the other estimator. The strength of survival analysis is that the observations over time enable us to estimate the chain of causality with a large degree of confidence.
In sociology one can mention studies of unemployment, careers, marriage, divorce, and child birth. For each subject one records whether or not the event took place within the time scope of the study, and also how long it took from when that subject entered into the risk phase.
Statistical analysis of time to event variables requires different techniques than those described thus far for other types of outcomes because of the unique features of time to event variables. How do certain personal, behavioral or clinical characteristics affect participants' chances of survival? For example, in a study assessing time to relapse in high risk patients, the majority of events (relapses) may occur early in the follow up with very few occurring later.
What we know is that the participants survival time is greater than their last observed follow-up time. This can occur when a participant drops out before the study ends or when a participant is event free at the end of the observation period. The graphic below indicates when they enrolled and what subsequently happened to them during the observation period.


In this small example, participant 4 is observed for 4 years and over that period does not have an MI. Should these differences in participants experiences affect the estimate of the likelihood that a participant suffers an MI over 10 years? In a prospective cohort study evaluating time to incident stroke, investigators may recruit participants who are 55 years of age and older as the risk for stroke prior to that age is very low. In this study, the outcome is all-cause mortality and the survival function (or survival curve) might be as depicted in the figure below. Some popular distributions include the exponential, Weibull, Gompertz and log-normal distributions.2 Perhaps the most popular is the exponential distribution, which assumes that a participant's likelihood of suffering the event of interest is independent of how long that person has been event-free. We focus on a particular type of life table used widely in biostatistical analysis called a cohort life table or a follow-up life table.
The proportion of participants surviving past 10 years is 84%, and the proportion of participants surviving past 20 years is 68%. Survival curves are estimated for each group, considered separately, using the Kaplan-Meier method and compared statistically using the log rank test. The primary outcome is death and participants are followed for up to 48 months (4 years) following enrollment into the trial. Using the procedures outlined above, we first construct life tables for each treatment group using the Kaplan-Meier approach. Group 1 represents the chemotherapy before surgery group, and group 2 represents the chemotherapy after surgery group. The following R function can be used to predict the survival function and an associated confidence interval at a grid of points. This suggests that there is no systematic difference between the KM and the PGAM survival estimators.
Statistical analysis of these variables is called time to event analysis or survival analysis even though the outcome is not always death.
On the other hand, in a study of time to death in a community based sample, the majority of events (deaths) may occur later in the follow up. In a prospective cohort study evaluating time to incident cardiovascular disease, investigators may recruit participants who are 35 years of age and older. Time is shown on the X-axis and survival (proportion of people at risk) is shown on the Y-axis. The follow-up life table summarizes the experiences of participants over a pre-defined follow-up period in a cohort study or in a clinical trial until the time of the event of interest or the end of the study, whichever comes first.
The probability that a participant survives past interval 2 means that they had to survive past interval 1 and through interval 2: S2 = P(survive past interval 2) = P(survive through interval 2)*P(survive past interval 1), or S2 = p2*S1. Note that the calculations using the Kaplan-Meier approach are similar to those using the actuarial life table approach.
The median survival is estimated by locating 0.5 on the Y axis and reading over and down to the X axis.
The null hypothesis is that there is no difference in survival between the two groups or that there is no difference between the populations in the probability of death at any point. We multiply these estimates by the number of participants at risk at that time in each of the comparison groups (N1t and N2t for groups 1 and 2 respectively). These can be used to predict the value of the survival function, , by approximating the integral appearing in the definition of by numerical quadrature. It accepts as arguments a) the vector of time points, b) a PGAM object for the fitted log-hazard function, c) a list with the nodes and weights of a Gauss-Lobatto rule for the integration of the predicted survival, d) the number of Monte Carlo samples to obtain and optionally e) the seed of the random number generation. In each of these studies, a minimum age might be specified as a criterion for inclusion in the study. More details on parametric methods for survival analysis can be found in Hosmer and Lemeshow and Lee and Wang1,3.
Note that the percentage of participants surviving does not always represent the percentage who are alive (which assumes that the outcome of interest is death). The remaining 11 have fewer than 24 years of follow-up due to enrolling late or loss to follow-up. The probability that a participant survives past 4 years, or past the first interval (using the upper limit of the interval to define the time) is S4 = p4 = 0.897.
We present one version here that is linked closely to the chi-square test statistic and compares observed to expected numbers of events at each time point over the follow-up period.
The log rank test is a non-parametric test and makes no assumptions about the survival distributions. Of note, the order of the quadrature used to predict the survival function is not necessarily the same as the order used to fit the log-hazard function.
Nonparametric procedures could be invoked except for the fact that there are additional issues. Survival analysis techniques make use of this information in the estimate of the probability of event. Follow up time is measured from time zero (the start of the study or from the point at which the participant is considered to be at risk) until the event occurs, the study ends or the participant is lost, whichever comes first.
The calculations of the survival probabilities are detailed in the first few rows of the table.
Specifically, complete data (actual time to event data) is not always available on each participant in a study. In many studies, participants are enrolled over a period of time (months or years) and the study ends on a specific calendar date. Patients often enter or are recruited into cohort studies and clinical trials over a period of several calendar months or years. Thus, participants who enroll later are followed for a shorter period than participants who enroll early.
Thus, it is important to record the entry time so that the follow up time is accurately measured.
For participants who do not suffer the event of interest we measure follow up time which is less than time to event, and these follow up times are censored.




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