comparison lifelines_tool/plotlykm.py @ 2:dd5e65893cb8 draft default tip

add survival and collapsed life table outputs suggested by Wolfgang

1:232b874046a7

    kmf.fit(df[args.time], df[args.status])
    kmf.plot_survival_function(ax=ax)
    ax.set_title(args.title)
    fig.savefig(os.path.join(args.image_dir,'KM_%s.png' % args.title))
    print('#### No grouping variable, so no log rank or other Kaplan-Meier statistical output is available')
if len(args.cphcols) > 0:
    fig, ax = plt.subplots()
    ax.set_title('Cox-PH model: %s' % args.title)
    cphcols = args.cphcols.strip().split(',')
    cphcols = [x.strip() for x in cphcols]

2:dd5e65893cb8

    kmf.fit(df[args.time], df[args.status])
    kmf.plot_survival_function(ax=ax)
    ax.set_title(args.title)
    fig.savefig(os.path.join(args.image_dir,'KM_%s.png' % args.title))
    print('#### No grouping variable, so no log rank or other Kaplan-Meier statistical output is available')
survdf = lifelines.utils.survival_table_from_events(df[args.time], df[args.status])
lifedf = lifelines.utils.survival_table_from_events(df[args.time], df[args.status], collapse=True)
print("Survival table using time %s and event %s" % (args.time, args.status))
with pd.option_context('display.max_rows', None,
                       'display.max_columns', None,
                       'display.precision', 3,
                       ):
    print(survdf)
print("Life table using time %s and event %s" % (args.time, args.status))
with pd.option_context('display.max_rows', None,
                       'display.max_columns', None,
                       'display.precision', 3,
                       ):
    print(lifedf)
outpath = os.path.join(args.image_dir,'survival_table.tabular')
survdf.to_csv(outpath, sep='\t')
outpath = os.path.join(args.image_dir,'life_table.tabular')
lifedf.to_csv(outpath, sep='\t')
if len(args.cphcols) > 0:
    fig, ax = plt.subplots()
    ax.set_title('Cox-PH model: %s' % args.title)
    cphcols = args.cphcols.strip().split(',')
    cphcols = [x.strip() for x in cphcols]