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Think Stats by Allen B. Downey Think Stats is an introduction to Probability and Statistics for Python programmers.

This is the accompanying code for this book.

Website: http://greenteapress.com/wp/think-stats-2e/

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License: GPL3
Kernel: Unknown Kernel

This notebook contains examples related to survival analysis, based on Chapter 13 of
Think Stats, 2nd Edition
by Allen Downey
available from thinkstats2.com

from __future__ import print_function, division import nsfg import survival import thinkstats2 import thinkplot import pandas import numpy from lifelines import KaplanMeierFitter from collections import defaultdict import matplotlib.pyplot as pyplot %matplotlib inline

The first example looks at pregnancy lengths for respondents in the National Survey of Family Growth (NSFG). This is the easy case, because we can directly compute the CDF of pregnancy length; from that we can get the survival function:

preg = nsfg.ReadFemPreg() complete = preg.query('outcome in [1, 3, 4]').prglngth
cdf = thinkstats2.Cdf(complete, label='cdf') sf = survival.SurvivalFunction(cdf, label='survival') thinkplot.Plot(sf) thinkplot.Config(xlabel='duration (weeks)', ylabel='survival function') #thinkplot.Save(root='survival_talk1', formats=['png'])
{"metadata":{},"output_type":"display_data","png":"iVBORw0KGgoAAAANSUhEUgAAAYQAAAEPCAYAAABCyrPIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiKSMv4/Yt2qyoDaIv4AAAAASUVORK5CYII=\n","text":["<matplotlib.figure.Figure at 0x7f7c2b0a7f50>"]}

About 17% of pregnancies end in the first trimester, but a large majorty pregnancies that exceed 13 weeks go to full term.

Next we can use the survival function to compute the hazard function.

hf = sf.MakeHazard(label='hazard') thinkplot.Plot(hf) thinkplot.Config(xlabel='duration (weeks)', ylabel='hazard function', ylim=[0, 0.75], loc='upper left') #thinkplot.Save(root='survival_talk2', formats=['png'])
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at 0x7f7c2b062450>"]}

The hazard function shows the same pattern: the lowest hazard in the second semester, and by far the highest hazard around 30 weeks.

We can also use the survival curve to compute mean remaining lifetime as a function of how long the pregnancy has gone.

rem_life = sf.RemainingLifetime() thinkplot.Plot(rem_life) thinkplot.Config(xlabel='weeks', ylabel='mean remaining weeks', legend=False) #thinkplot.Save(root='survival_talk3', formats=['png'])
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at 0x7f7c2a243090>"]}

For 38 weeks, the finish line approaches nearly linearly. But at 39 weeks, the expected remaining time levels off abruptly. After that, each week that passes brings the finish line no closer.

I started with pregnancy lengths because they represent the easy case where the distribution of lifetimes is known. But often in observational studies we have a combination of complete cases, where the lifetime is known, and ongoing cases where we have a lower bound on the lifetime.

As an example, we'll look at the time until first marriage for women in the NSFG.

resp = survival.ReadFemResp2002() len(resp)
7643

For complete cases, we know the respondent's age at first marriage. For ongoing cases, we have the respondent's age when interviewed.

complete = resp[resp.evrmarry == 1].agemarry ongoing = resp[resp.evrmarry == 0].age

There are only a few cases with unknown marriage dates.

nan = complete[numpy.isnan(complete)] len(nan)
37

EstimateHazardFunction is an implementation of Kaplan-Meier estimation.

With an estimated hazard function, we can compute a survival function.

hf = survival.EstimateHazardFunction(complete, ongoing) sf = hf.MakeSurvival()

Here's the hazard function:

thinkplot.Plot(hf) thinkplot.Config(xlabel='age (years)', ylabel='hazard function', legend=False) #thinkplot.Save(root='survival_talk4', formats=['png'])
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wY2ycHWPjvGJRF10p34PFnJWHJnLPjv2sbGvO8muo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\nfb6Qen8zsN7HsRuBYeB3qde1BR3xPEO1BQeprOvlhP/R/jkVvs0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at 0x7f7c2b0622d0>"]}

As expected, the hazard function is highest in the mid-20s. The function increases again after 35, but that is an artifact of the estimation process and a misleading visualization. Making a better representation of the hazard function is on my TODO list.

Here's the survival function:

thinkplot.Plot(sf) thinkplot.Config(xlabel='age (years)', ylabel='prob unmarried', ylim=[0, 1], legend=False) #thinkplot.Save(root='survival_talk5', formats=['png'])
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at 0x7f7c04864a50>"]}

The survival function naturally smooths out the noisiness in the hazard function.

With the survival curve, we can also compute the probability of getting married before age 44, as a function of current age.

ss = sf.ss end_ss = ss[-1] prob_marry44 = (ss - end_ss) / ss thinkplot.Plot(sf.ts, prob_marry44) thinkplot.Config(xlabel='age (years)', ylabel='prob marry before 44', ylim=[0, 1], legend=False) #thinkplot.Save(root='survival_talk6', formats=['png'])
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WZbEnvp+uyMomg+OpM8xmJzSjWVpeMwmhAFyrKyu7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\ncYmJ9FkyxaWa8j3x/eyM7avZ0rOdKCGIiKwhX8oylbl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at 0x7f7c2af8c250>"]}

After age 20, the probability of getting married drops off nearly linearly.

We can also compute the median time until first marriage as a function of age.

func = lambda pmf: pmf.Percentile(50) rem_life = sf.RemainingLifetime(filler=numpy.inf, func=func) thinkplot.Plot(rem_life) thinkplot.Config(ylim=[0, 15], xlim=[11, 31], xlabel='age (years)', ylabel='median remaining years') #thinkplot.Save(root='survival_talk7', formats=['png'])
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at 0x7f7c2955b850>"]}

At age 11, young women are a median of 14 years away from their first marriage. At age 23, the median has fallen to 7 years. But an never-married woman at 30 is back to a median remaining time of 14 years.

I also want to demonstrate lifelines, which is a Python module that provides Kaplan-Meier estimation and other tools related to survival analysis.

To use lifelines, we have to get the data into a different format. First I'll add a column to the respondent DataFrame with "event times", meaning either age at first marriage OR age at time of interview.

resp['event_times'] = resp.age resp['event_times'][resp.evrmarry == 1] = resp.agemarry len(resp)
7643

Lifelines doesn't like NaNs, so let's get rid of them:

cleaned = resp.dropna(subset=['event_times']) len(cleaned)
7606

Now we can use the KaplanMeierFitter, passing the series of event times and a series of booleans indicating which events are complete and which are ongoing:

kmf = KaplanMeierFitter() kmf.fit(cleaned.event_times, cleaned.evrmarry)
<lifelines.KaplanMeierFitter: fitted with 7606 observations, 3517 censored>

Here are the results from my implementation compared with the results from Lifelines.

thinkplot.Plot(sf) thinkplot.Config(xlim=[0, 45], legend=False) pyplot.grid() kmf.survival_function_.plot()
<matplotlib.axes.AxesSubplot at 0x7f7c2955ba90>
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at 0x7f7c2b290f10>"]}

They are at least visually similar. Just to double check, I ran a small example:

complete = [1, 2, 3] ongoing = [2.5, 3.5]

Here's the hazard function:

hf = survival.EstimateHazardFunction(complete, ongoing) hf.series
1 0.20 2 0.25 3 0.50 dtype: float64

And the survival function.

sf = hf.MakeSurvival() sf.ts, sf.ss
(array([1, 2, 3]), array([ 0.8, 0.6, 0.3]))

My implementation only evaluate the survival function at times when a completed event occurred.

Next I'll reformat the data for lifelines:

T = pandas.Series(complete + ongoing) E = [1, 1, 1, 0, 0]

And run the KaplanMeier Fitter:

kmf = KaplanMeierFitter() kmf.fit(T, E) kmf.survival_function_
KM-estimate
timeline
0.0 1.0
1.0 0.8
2.0 0.6
2.5 0.6
3.0 0.3
3.5 0.3

The results are the same, except that the Lifelines implementation evaluates the survival function at all event times, complete or not.

Next, I'll use additional data from the NSFG to investigate "marriage curves" for successive generations of women.

Here's data from the last 4 cycles of the NSFG:

resp5 = survival.ReadFemResp1995() resp6 = survival.ReadFemResp2002() resp7 = survival.ReadFemResp2010() resp8 = survival.ReadFemResp2013()

This function takes a respondent DataFrame and estimates survival curves:

def EstimateSurvival(resp): """Estimates the survival curve. resp: DataFrame of respondents returns: pair of HazardFunction, SurvivalFunction """ complete = resp[resp.evrmarry == 1].agemarry ongoing = resp[resp.evrmarry == 0].age hf = survival.EstimateHazardFunction(complete, ongoing) sf = hf.MakeSurvival() return hf, sf

This function takes a list of respondent files, resamples them, groups by decade, optionally generates predictions, and returns a map from group name to a list of survival functions (each based on a different resampling):

def ResampleSurvivalByDecade(resps, iters=101, predict_flag=False, omit=[]): """Makes survival curves for resampled data. resps: list of DataFrames iters: number of resamples to plot predict_flag: whether to also plot predictions returns: map from group name to list of survival functions """ sf_map = defaultdict(list) # iters is the number of resampling runs to make for i in range(iters): # we have to resample the data from each cycles separately samples = [thinkstats2.ResampleRowsWeighted(resp) for resp in resps] # then join the cycles into one big sample sample = pandas.concat(samples, ignore_index=True) for decade in omit: sample = sample[sample.decade != decade] # group by decade grouped = sample.groupby('decade') # and estimate (hf, sf) for each group hf_map = grouped.apply(lambda group: EstimateSurvival(group)) if predict_flag: MakePredictionsByDecade(hf_map) # extract the sf from each pair and acculumulate the results for name, (hf, sf) in hf_map.iteritems(): sf_map[name].append(sf) return sf_map

And here's how the predictions work:

def MakePredictionsByDecade(hf_map, **options): """Extends a set of hazard functions and recomputes survival functions. For each group in hf_map, we extend hf and recompute sf. hf_map: map from group name to (HazardFunction, SurvivalFunction) """ # TODO: this only works if the names and values are in increasing order, # which is true when hf_map is a GroupBy object, but not generally # true for maps. names = hf_map.index.values hfs = [hf for (hf, sf) in hf_map.values] # extend each hazard function using data from the previous cohort, # and update the survival function for i, hf in enumerate(hfs): if i > 0: hf.Extend(hfs[i-1]) sf = hf.MakeSurvival() hf_map[names[i]] = hf, sf

This function takes a list of survival functions and returns a confidence interval:

def MakeSurvivalCI(sf_seq, percents): # find the union of all ts where the sfs are evaluated ts = set() for sf in sf_seq: ts |= set(sf.ts) ts = list(ts) ts.sort() # evaluate each sf at all times ss_seq = [sf.Probs(ts) for sf in sf_seq] # return the requested percentiles from each column rows = thinkstats2.PercentileRows(ss_seq, percents) return ts, rows

Make survival curves without predictions:

resps = [resp5, resp6, resp7, resp8] sf_map = ResampleSurvivalByDecade(resps)

Make survival curves with predictions:

resps = [resp5, resp6, resp7, resp8] sf_map_pred = ResampleSurvivalByDecade(resps, predict_flag=True)

This function plots survival curves:

def PlotSurvivalFunctionByDecade(sf_map, predict_flag=False): thinkplot.PrePlot(len(sf_map)) for name, sf_seq in sorted(sf_map.iteritems(), reverse=True): ts, rows = MakeSurvivalCI(sf_seq, [10, 50, 90]) thinkplot.FillBetween(ts, rows[0], rows[2], color='gray') if predict_flag: thinkplot.Plot(ts, rows[1], color='gray') else: thinkplot.Plot(ts, rows[1], label='%d0s'%name) thinkplot.Config(xlabel='age(years)', ylabel='prob unmarried', xlim=[15, 45], ylim=[0, 1], legend=True, loc='upper right')

Now we can plot results without predictions:

PlotSurvivalFunctionByDecade(sf_map) #thinkplot.Save(root='survival_talk8', formats=['png'])
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MkuSI4crciVj1hsMe3x+O8MD6Cj6ubCYc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at 0x7f7c2b281490>"]}

And plot again with predictions:

PlotSurvivalFunctionByDecade(sf_map_pred, predict_flag=True) PlotSurvivalFunctionByDecade(sf_map) #thinkplot.Save(root='survival_talk9', formats=['png'])
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OCuHmzClYTIa936nA2wU7TusN5Gcx88eb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at 0x7f7c2aefda50>"]}

The gray regions show the confidence intervals for the estimates and predictions.

Although the last two cohorts are lagging their predecessors, if we assume that their hazard function from here out will be similar to previous cohorts, they are on track to reach marriage rates at age 45 that are similar to previous cohorts.

Also, at the risk of overinterpreting noise, it looks like the 90s cohort might have delayed marriage in the last few years and then made up for lost time, possibly as a reaction to an improving economy. To investigate that conjecture, it would be useful to cut a different cross section of this data, with time on the x-axis, rather than age.