Cervical cancer #1287
Cervical cancer #1287
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…ses with proper structured manner as mixin, also remove unecessary input parameters other than self and person_id
…sed on other adult cancers
… the the chemo used
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per @mmsuarezcosta, this is now ready for review. |
| .when(2, p['rr_hpv_vaccinated']), | ||
| Predictor('age_years', conditions_are_mutually_exclusive=True) | ||
| .when('.between(0,15)', 0.0) | ||
| .when('.between(50,110)', p['rr_hpv_age50plus']), |
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Is there a reason why this was reverted to .when('.between(50,110)', p['rr_hpv_age50plus']),? It was suggested to have it as .when('>50'),p['rr_hpv_age50plus']) earlier over here
| # Current counts, total | ||
| out.update({ | ||
| f'total_{k}': v for k, v in df.loc[df.is_alive & (df['sex'] == 'F') & | ||
| (df['age_years'] > p['min_age_hpv'])].ce_hpv_cc_status.value_counts().items()}) |
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| (df['age_years'] > p['min_age_hpv'])].ce_hpv_cc_status.value_counts().items()}) | |
| df_alive_females = df.loc[df.is_alive & (df['sex'] == 'F') & (df['age_years'] > p['min_age_hpv'])] | |
| out.update({f'total_{k}': v for k, v in df_alive_females.ce_hpv_cc_status.value_counts().items()}) |
There is a repetition of this line of code here df.loc[df.is_alive & (df['sex'] == 'F') & (df['age_years'] > p['min_age_hpv'])] as well as at lines 1581 and 1586. Would probably be more efficient if these conditions are not repeated
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Thanks @mmsuarezcosta and Andrew for this great work. Find below my comments for your consideration. I have only managed to look into cervical cancer main module. I hope to provide my feedback on the tests tomorrow. Thanks
| "prob_cure_stage1": Parameter( | ||
| Types.REAL, | ||
| "probability of cure if treated in stage 1 cervical cancer", | ||
| ), | ||
| "prob_cure_stage2a": Parameter( | ||
| Types.REAL, | ||
| "probability of cure if treated in stage 1 cervical cancer", | ||
| ), | ||
| "prob_cure_stage2b": Parameter( | ||
| Types.REAL, | ||
| "probability of cure if treated in stage 1 cervical cancer", | ||
| ), | ||
| "prob_cure_stage3": Parameter( | ||
| Types.REAL, | ||
| "probability of cure if treated in stage 1 cervical cancer", | ||
| ), |
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description looks the same. should it be like that?
| Types.DATE, | ||
| "date of first receiving palliative care (pd.NaT is never had palliative care)" | ||
| ), | ||
| "ce_ever_diagnosed": Property( |
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This has been used in the code as a bool. I suggest changing the TYPE here from date to Bool
| "ce_date_death": Property( | ||
| Types.DATE, | ||
| "date of cervical cancer death" | ||
| ), |
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we can remove this property here and capture date of death in demography.
| df.loc[df.is_alive, "ce_selected_for_xpert_this_month"] = False | ||
| df.loc[df.is_alive, "ce_biopsy"] = False | ||
| df.loc[df.is_alive, "ce_ever_screened"] = False | ||
| df.loc[df.is_alive, "ce_ever_diagnosed"] = False |
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I also had a feeling that this should be a bool. Can you please update ce_ever_diagnosed in properties section?. It should be of type bool not date?
| # this was not assigned here at outset because baseline value of hv_inf was not accessible - it is assigned | ||
| # at start of main polling event below |
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do we still have this issue? Does adding as a dependency HIV module or initialising it before cervical cancer still not making available baseline value of hv_inf?
| out.update({ | ||
| 'total_males': len(df[df.is_alive & (df['sex'] == 'M')])}) | ||
| out.update({ | ||
| 'total_dead': len(df[df['is_alive'].eq(False)])}) |
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| 'total_dead': len(df[df['is_alive'].eq(False)])}) | |
| 'total_dead': len(~df['is_alive'])}) |
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| df['rounded_decimal_year'] = rounded_decimal_year | ||
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| date_1_year_ago = self.sim.date - pd.DateOffset(days=365) |
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| date_1_year_ago = self.sim.date - pd.DateOffset(days=365) | |
| date_1_year_ago = self.sim.date - pd.DateOffset(years=1) |
I think years/months is better since it takes into account leap years?
| out.update({"n_xpert_past_year": n_xpert_past_year}) | ||
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| pop = len(df[df.is_alive]) |
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| pop = len(df[df.is_alive]) | |
| pop = len(df.is_alive) |
| # todo: | ||
| # ? move to using the logger: | ||
| # i.e. logger.info(key='cervical_cancer_stats_every_month', description='XX', data=out) |
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if this is done then the below commented code should be deleted?
| # selected_columns = ['sex', 'age_years', 'is_alive'] | ||
| # pd.set_option('display.max_rows', None) | ||
| # print(df[selected_columns]) |
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delete this code if its not need anymore?
Thanks for the detailed look and feedback, will review and update |
Here's a first draft of this module. I need to do another search to try to find additional data for calibration.
I couldn't upload the draft write as it was asking me to use the command line and Git LFS.