# Kaplan-Meier using SPSS Statistics

## Introduction

The Kaplan-Meier method (Kaplan & Meier, 1958), also known as the "product-limit method", is a nonparametric method used to estimate the probability of survival past given time points (i.e., it calculates a survival distribution). Furthermore, the survival distributions of two or more groups of a between-subjects factor can be compared for equality.

For example, in a study on the effect of drug dose on cancer survival in rats, you could use the Kaplan-Meier method to understand the survival distribution (based on time until death) for rats receiving one of four different drug doses: "40 mg/m^{2}/d", "80 mg/m^{2}/d", "120 mg/m^{2}/d" and "160 mg/m^{2}/d" (i.e., the survival time variable would be "time to death" and the between-subjects factor would be "drug dose"). You could then compare the survival distributions (experiences) between the four doses to determine if they are equal. If they were not equal, you could further determine where any differences between the groups of the between-subjects factor lie (e.g., whether death rates were higher in rats given the lowest drug dose – "40 mg/m^{2}/d" of the drug – compared to rats given the highest drug dose: "160 mg/m^{2}/d"). Alternately, you could use the Kaplan-Meier method to determine whether the (distribution of) time to failure of a knee replacement differs based on exercise impact amongst young patients (i.e., the survival time would be "time to knee replacement failure" and the between-subjects factor would be "exercise impact", which has three groups: "sedentary", "low impact" and "high impact"). You could then compare the survival distributions (experiences) between the three levels of exercise impact to determine if they are equal, and if not, where any differences lie (e.g., whether time to knee replacement failure was lower in the "sedentary" exercise group compared to the "high impact" exercise group).

This "quick start" guide shows you how to carry out a Kaplan-Meier analysis using SPSS Statistics, as well as interpret and report the results from this analysis. However, before we introduce you to the SPSS Statistics procedure to perform a Kaplan-Meier analysis, you need to understand the different assumptions that you must meet in order to use the Kaplan-Meier method. We discuss these assumptions next.

###### SPSS Statistics

## Assumptions

The Kaplan-Meier method has six assumptions that must be met. If these assumptions are not met, you cannot use the Kaplan-Meier method, but may be able to use another type of survival analysis instead. Therefore, before you can use the Kaplan-Meier method using SPSS Statistics, you need to check that you have met the following six assumptions:

- Assumption #1: The
**event status**should consist of**two mutually exclusive and collectively exhaustive states: "censored" or "event"**(where the**"event"**can also be referred to as**"failure"**). The event status is**mutually exclusive**because the outcome for a case can either be censored or the event has occurred. It cannot be both. For example, imagine that we were interested in the survival times of people suffering from skin cancer, where the event is "death". If the length of the experiment was 5 years, at the end of the 5-year period, all participants would either be "censored" or "dead". Therefore, the two states should not only be mutually exclusive, but also**collectively exhaustive**(i.e., at least one of these states – censored or event – must occur). - Assumption #2: The
**time to an event or censorship**(known as the**"survival time"**) should be**clearly defined and precisely measured**. The Kaplan-Meier method, unlike some other approaches to survival analysis (e.g., the**actuarial approach**), requires the survival time to be recorded precisely (i.e., exactly when the event or censorship occurred) rather than simply recording whether the event occurred within some predefined interval (e.g., only recording when a death or censorship occurred sometime within a 1, 2, 3, 4 and 5 year follow-up). In addition, the survival time should be clearly defined, whether this is measured in days, weeks, months, years, or some other time-based measurement. - Assumption 3: Where possible,
**left-censoring**should be**minimized or avoided**. Left-censoring occurs when the starting point of an experiment is not easily identifiable. Again, imagine that we were interested in the survival times of people suffering from skin cancer. The "ideal" starting point would be to measure the survival time from the very moment that the participant developed skin cancer. However, it is more likely that the first time the participant knew they had cancer was the moment it was diagnosed, such that the "diagnosis" acts as the starting point for the experiment. Even if we isolated our sample to a "Stage 1" cancer diagnosis, there will still be differences between participants. For example, some participants may have had a suspicious mole that they did not get checked for some time, whilst other participants may have regular check-ups such that a diagnosis was made much earlier. Therefore, the time between the participant developing skin cancer and the diagnosis is "unknown" and is "not included" in the Kaplan-Meier analysis. The result is that this data – known as**left-censored data**– does not reflect the observed survival time. Instead, the survival time recorded will be less than (or equal to) the observed survival time. As such, the goal is to avoid left-censoring as much as possible. - Assumption 4: There should be
**independence of censoring and the event**. This means that the reason why cases are censored does not relate to the event. For the assumption of independent censoring to be met, we need to be confident that when we record that a participant is "censored", this is not because they were at greater risk of the event occurring (i.e., "death" being the "event" in this case). Instead, there may be many other reasons why a participant is "legitimately censored", including: (a) natural dropout or withdrawal (e.g., perhaps because the participant does not want to take part in the experiment any more or moves away from the area); and (b) the event not occurring by the end of the experiment (e.g., if the follow-up period for the experiment is 5 years, any participant still alive at this point will be recorded as "censored"). Independent censoring is important because the Kaplan-Meier method is based on observed data (i.e., observed events) and assumes that**censored data behaves in the same way as uncensored data (after the censoring)**. However, if the censored data does relate to the event (e.g., a participant that was recorded as being censored died due to the cancer or perhaps even something related to the cancer), this introduces serious bias to the results (e.g., over-estimating 5-year survival rates from skin cancer amongst participants). - Assumption 5: There should be
**no secular trends**(also known as**secular changes**). A characteristic of many studies that involve survival analysis is that: (a) there is often a long time period between the start and end of the experiment; and (b) not all cases (e.g., participants) tend to start the experiment at the same time. For example, the starting point in our hypothetical experiment was when participants were "diagnosed" with skin cancer. However, imagine that we wanted a sample of 500 participants in our experiment. It may take a number of months to recruit all of these participants, each of whom would have different starting points (i.e., the dates when they were diagnosed), but we would "pool" the starting and subsequent times (e.g., everybody's first diagnosis would be time point 0). However, if over this period of time, factors have changed that affect the likelihood of the event, this may introduce bias. For example, death rates for skin cancer may have gone down following the introduction of new drugs, improving survival rates amongst participants joining the experiment later on. Alternately, the introduction of a national skin-screening programme may have led to faster diagnoses, increasing the perception of worse survival rates amongst participants who started the study before the programme was introduced (i.e., by reducing left-censoring). These factors (e.g., new drugs or better screening) are examples of secular trends that can bias the results. - Assumption 6: There should be a
**similar amount and pattern of censorship per group**. One of the assumptions of the Kaplan-Meier method and the statistical tests for differences between group survival distributions (e.g., the log rank test, which we discuss later in the guide) is that**censoring**is similar in all groups tested. This includes a similar**"amount"**of censorship per group and similar**"patterns"**of censorship per group. Failure to meet the assumption can lead to false conclusions being drawn about differences in group survival distributions (i.e., rejection or not of the null hypothesis), based on these statistical tests "confusing" differences in censoring patterns with actual group differences in survival distributions (Bland & Altman, 2004; Hosmer et al., 2008; Norušis, 2012).

To detect censoring, you can use SPSS Statistics: (a) to calculate the percentage of censored cases (e.g., participants) per intervention group to determine whether there is a similar "amount" of censorship per group; and (b) to produce a scatterplot illustrating the "pattern" of censoring. We show you how to generate a table of the percentage of censored cases using SPSS Statistics, as well as how to generate the scatterplot of censored cases to determine the "pattern" of censorship per group in our enhanced Kaplan-Meier guide. You can access this enhanced guide by subscribing to Laerd Statistics.

If your study design does not meet these six assumptions, you might not be able to use the Kaplan-Meier method. If you would like to know more about the characteristics of the Kaplan-Meier method, including the null and alternative hypotheses it is testing, see our enhanced Kaplan-Meier guide. In the section, Test Procedure in SPSS Statistics, we show you how to analyse your data using the Kaplan-Meier method in SPSS Statistics. First, we introduce you to the example we use in this guide.