Adding support for Python 3.9 and 3.10
Fixing scikit-learn dependency issue
Adding font size optional argument for EffectMeasurePlot
Switched testing from Travis CI to GitHub workflows
The 0.9.x series drops support of Python 3.5.x. Only Python 3.6+ are now supported. Support has also been added for Python 3.8
Cross-fit estimators have been implemented for better causal inference with machine learning. Cross-fit estimators
include SingleCrossfitAIPTW, DoubleCrossfitAIPTW, SingleCrossfitTMLE, and DoubleCrossfitTMLE. Currently
functionality is limited to treatment and outcome nuisance models only (i.e. no model for missing data). These
estimators also do not accept weighted data (since most of sklearn does not support weights)
Super-learner functionality has been added via SuperLearner. Additions also include emprical mean (EmpiricalMeanSL),
generalized linear model (GLMSL), and step-wise backward/forward selection via AIC (StepwiseSL). These new
estimators are wrappers that are compatible with SuperLearner and mimic some of the R superlearner functionality.
Directed Acyclic Graphs have been added via DirectedAcyclicGraph. These analyze the graph for sufficient adjustment
sets, and can be used to display the graph. These rely on an optional NetworkX dependency.
AIPTW now supports the custom_model optional argument for user-input models. This is the same as TMLE now.
zipper_plot function for creating zipper plots has been added.
Housekeeping: bound has been updated to new procedure, updated how print_results displays to be uniform, created
function to check missingness of input data in causal estimators, added warning regarding ATT and ATU variance for
IPTW, and added back observation IDs for MonteCarloGFormula
Future plans: TimeFixedGFormula will be deprecated in favor of two estimators with different labels. This will more
clearly delineate ATE versus stochastic effects. The replacement estimators are to be added
IPSW and AIPSW now natively support adjusting for confounding. Both now have the treatment_model() function,
which calculates the inverse probability of treatment weights. How weights are handled in AIPSW are updated. They
are used in both the weight and the outcome models.
IPSW and AIPSW both add censoring...
TimeFixedGFormula has added support for the average treatment effect in the treated (ATT), and average treatment
effect in the untreated (ATU).
Improved warnings when the treatment/exposure variable is not included in models that it should be in (such as the outcome model or in structural nested models).
Background refactoring for IPTW. utils.py now contains a function to calculate inverse probability of treatment
weights. The function iptw_calculator is used by IPTW, AIPTW, IPSW, and AIPSW to calculate the weights now
Added support for pygam's LogisticGAM for TMLE with custom models (Thanks darrenreger!)
Removed warning for TMLE with custom models following updates to Issue #109 I plan on creating a smarter warning system that flags non-Donsker class machine learning algorithms and warns the user. I still need to think through how to do this.
IPTW had a massive overhaul. It now follows a similar structure to AIPTW and other causal inference methods.
One major change is that missing data is dropped before any calculations. Therefore, if missing data was present for
certain types of data, the weights may no longer match with previous versions. While users can still call the weights
attribute, all the calculations of the ATE are now contained within the IPTW class. Future updates with be other
instances of the IPTW calculations for other methods, like LongitudinalIPTW and SurvivalIPTW. The major advantage
of this new structure is it removes some of the burden from users on how to apply IPTW to different data structures.
Diagnostic functions have been added to TimeFixedGFormula, AIPTW, and TMLE. The diagnostics have been restructured
for functions contained within a different file rather than function instances within specific classes. This is
due to diagnostics commonly being shared across functions.
How missing data is handled byAIPTW and IPTW has been updated. Rather than dropping all missing data, they only drop
missing data for non-outcome variables. This behavior mimics TMLE. Additionally, both have gained the missing_model
function. This new function calculates inverse probability of censoring weights.
bound argument is now available to IPTW and AIPTW to truncate the predicted probabilities of the g-model. The
behavior is the same as TMLE. bound is also available for missing_model().
IPCW no longer supports late-entries into the data. The pooled logistic regression model will not correctly accrue
weights when late entries occur. This is not a problem I have seen reported in the literature, but I have seen it in
my own simulations. While you can correctly estimate IPCW with time-fixed variables, this is difficult for me to
detect. Instead, I have IPCW not allow late-entries. If users would like to allow late-entries, they would need to
"extend backwards" observations or they would need to drop the late-entries. I have update the documentation to note
this change.
S-value calculator function has been added. s_value returns the correspond transformed p-value into an s-value. See
documentation for details on s-values and how to interpret them.
I have also been moving around background functions. Most notably, the IPTW diagnostics have migrated to the
causal/utils.py branch since these diagnostics are to be used by other causal inference methods. These reformats
should have no change for users. This is merely maintenance on my end.
Labeling fix for RiskDifference summary
Adding option to extract standard errors from TMLE and AIPTW
Warning for upcoming change for IPTW in v0.8.0. To better align with other causal estimators, IPTW will no longer
only return a vector of weights. Behind the scenes, IPTW will be able to estimate the marginal structural model
and provide the results directly in v0.8.0. IPTW will still allow access to the Weight column. Other tweaks are
coming, such as IPTW estimators built for different data types. For example, SurvivalIPTW for survival data (like
SurvivalGFormula).
Stochastic treatments can be estimated with the new StochasticIPTW class. This class is different from IPTW in that
it provides the estimated mean of the outcome given the treatment plan. For comparisons, multiple versions of treatment
plans need to be specified, calculated, then compared. For confidence intervals, a bootstrap procedure should be used
G-estimation of structural nested models (for a single time point) are now available through GEstimationSNM. Psi
parameters can be calculated using a closed form solution or via a scipy optimization procedure
Survival analysis g-formula is now implemented with SurvivalGFormula. This g-formula implementation is for
time-to-event data, where the treatment/exposure is determined at baseline. This does not allow for time-varying
exposures. For time-varying exposures, MonteCarloGFormula or IterativeCondGFormula should be used instead
summary() functions have been updated to provide more information regarding the model
Added a calculator function for Rubin's Rule to merge multiple imputation results. Input is a list of point estimates
and a list of variance estimates for rubins_rules(). This function returns a summary point estimate and summary
variance
Weighted models are switched from GEE to GLM when possible. GEE takes extra computation time. GLM provides the
correct point estimates, but wrong variance. Since I don't need the variance to be correct from most models, I switched
to GLM. This improves the speed of fitting weighted models. Especially important for bootstrapping procedures
Aligned exposure and outcome references with the causal functions. All classes now use the same labels for the
exposure and the outcome column labels.
Updated ReadTheDocs website
AIPTW now supports continuous outcomes (normal or Poisson). Format is the same as TMLE.
AIPTW and IPTW now include the optional argument weights
Fixed TMLE attribute for average treatment effect confidence intervals, from average_treatment_effect_ic to
average_treatment_effect_ci
Fixed issue in IPTW assumption calculations. Depending on when positivity() was called, it changed the results of
plot_love().
MonteCarloGFormula now includes a separate censoring_model() function for informative censoring.
Additionally, I added a low memory option to reduce the memory burden during the Monte-Carlo procedure
IterativeCondGFormula has been refactored to accept only data in a wide format. This allows for me to handle more
complex treatment assignments and specify models correctly. Additional tests have been added comparing to R's ltmle
There is a new branch in zepid.causal. This is the generalize branch. It contains various tools for generalizing
or transporting estimates from a biased sample to the target population of interest. Options available are
inverse probability of sampling weights for generalizability (IPSW), inverse odds of sampling weights for
transportability (IPSW), the g-transport formula (GTransportFormula), and doubly-robust augmented inverse
probability of sampling weights (AIPSW)
RiskDifference now calculates the Frechet probability bounds
TMLE now allows for specified bounds on the Q-model predictions. Additionally, avoids error when predicted
continuous values are outside the bounded values.
AIPTW now has confidence intervals for the risk difference based on influence curves
spline now uses numpy.percentile to allow for older versions of NumPy. Additionally, new function
create_spline_transform returns a general function for splines, which can be used within other functions
Lots of documentation updates for all functions. Additionally, summary() functions are starting to be updated.
Currently, only stylistic changes
While conducting further testing, I found an error in AIPTW. I have since corrected it and added additional tests
to tests/test_doublyrobust.py. Please rerun any analyses ran that used AIPTW
Added a fix to TMLE for machine learning libraries and missing outcome data
Support for Python 3.7 has been added
AIPW has been removed. It has been replaced with AIPTW
TMLE now supports continuous outcomes (normal or Poisson) and allows for missing outcome data to be missing at
random. This matches more closely to the functionality to R's tmle
IPMW allows for monotone missing data.
MonteCarloRR for probabilistic bias analysis allows for random error to be incorporated
TimeVaryGFormula is separated into MonteCarloGFormula and IterativeCondGFormula. This change is for
maintenance of the estimators and to avoid confusion since they are sufficiently distinct. Originally, I was unaware of
the iterative conditional estimator, which is why the original name was based on time-varying g-formula. While they are
related, it is more confusing to wrap them both in the same class. TimeVaryGFormula will stick around to v0.6.0.
Going forward it will be cut. It will not be updated going forward
L'Abbe plots are now supported. These plots are useful for visualizing additive and multiplicative interactions for risk estimates. These are valid for either associations or causal effects.
IPTW.plot_love now displays the legend. I have previously not included this in the function (I should have)
TMLE refactored to estimate machine learners via an outside function. Also converts all pd.Series to np.array to
avoid some unhappiness with sklearn / supylearner models
MAJOR CHANGES:
TMLE defaults to calculate all possible measures (risk difference, risk ratio, odds ratio) rather than individual
ones
TimeFixedGFormula allows stochastic interventions for binary exposures. For a stochastic intervention, p percent
of the population is randomly treated. This process is repeated n times and mean is the marginal outcome. Stochastic
interventions may better align with real-world interventions (often you intervention will not be able to treat
everyone). Additionally, conditional probabilities are implemented for stochastic interventions. For example, those
with C=1 might be treated randomly at p, while those with C=0 are treated at q.
IPTW.standard_mean_difference and IPTW.plot_love both support categorical variables. Categorical variables must
be modeled with patsy's C(.) keyword. Otherwise, the dummy variables will be treated as binary variables
MINOR CHANGES:
Added case-control example data set. load_case_control_data()
Changed rounding in Table 1 generator
MAJOR CHANGES:
TimeFixedGFormula supports Poisson and normal distributed continuous outcomes
IPTW's plot_kde and plot_boxplot can plot either the probabilities of treatment or the log-odds
IPTW allows for sklearn or supylearner to generate predicted probabilities. Similar to TMLE
IPTW now allows for Love plot to be generated. These plots are valuable for assessing covariate balance via absolute
standardized mean differences. See Austin & Stuart 2015 for an example. In its current state IPTW.plot_love is
"dumb", in the sense that it plots all variables in the model. If you have a quadratic term in the model for a
continuous variable, it plots both the linear and quadratic terms. However, it is my understanding that you only need
to look at the linear term. These plots are not quite for publication, rather they are useful for quick diagnostics
IPTW.standardized_mean_differences now calculates for all variables automatically. This is used in the background
for the plot_love. For making publication-quality Love plots, I would recommend using the returned DataFrame from
this function and creating a plot manually. Note it only returns standardized differeneces, not absolute standardized
differences. Love plots use the standardized differences.
WARNING: standardized differences only supports binary or continuous variables. Categorical variables are NOT
supported. This will be fixed in v0.4.2 update
MINOR CHANGES:
Website updated to reflect above changes and correcting errors I had missed on last check
MAJOR CHANGES:
TMLE has been modified to estimate the custom user models now, rather than take the input. This better corresponds
to R's tmle (however, R does the entire process in the background. You must specify for this implementation). The reason
for this major change is that LTMLE requires an iterative process. The iterative process requires required fitting
based on predicted values. Therefore, for LTMLE an unfitted model must be input and repeatedly fit. TMLE matches
this process.
TimeVaryGFormula supports both Monte Carlo estimation and Sequential Regression (interative conditionals) this
added approach reduces some concern over model misspecification. It is also the process used by LTMLE to estimate
effects of interventions. Online documentation has been updated to show how the sequential regression is estimated and
demonstrates how to calculated cumulative probabilities for multiple time points
All calculator functions now return named tuples. The returned tuples can be index via returned[0] or
returned.point_estimate
Documentation has been overhauled for all functions and at ReadTheDocs
Tests have been added for all currently available functions.
Travis CI has been integrated for continuous testing
MINOR CHANGES:
AIPW drops missing data. Similar to TMLE
IPTW calculation of standardized differences is now the stabilized_difference function instead of the previously
used StandardDifference. This change is to follow PEP guidelines
The psi argument has been replaced with measure in TMLE. The print out still refers to psi. This update is
to help new users better understand what the argument is for
Better errors for IPTW and IPMW, when a unstabilized weight is requested but a numerator for the model is
specified
MAJOR CHANGES:
TMLE now allows estimation of risk ratios and odds ratios. Estimation procedure is based on tmle.R
TMLE variance formula has been modified to match tmle.R rather than other resources. This is beneficial for
future implementation of missing data adjustment. Also would allow for mediation analysis with TMLE (not a priority
for me at this time).
TMLE now includes an option to place bounds on predicted probabilities using the bound option. Default is to use
all predicted probabilities. Either symmetrical or asymmetrical truncation can be specified.
TimeFixedGFormula now allows weighted data as an input. For example, IPMW can be integrated into the time-fixed
g-formula estimation. Estimation for weighted data uses statsmodels GEE. As a result of the difference between GLM
and GEE, the check of the number of dropped data was removed.
TimeVaryGFormula now allows weighted data as an input. For example, Sampling weights can be integrated into the
time-fixed g-formula estimation. Estimation for weighted data uses statsmodels GEE.
MINOR CHANGES:
Added Sciatica Trial data set. Mertens, BJA, Jacobs, WCH, Brand, R, and Peul, WC. Assessment of patient-specific surgery effect based on weighted estimation and propensity scoring in the re-analysis of the Sciatica Trial. PLOS One 2014. Future plan is to replicate this analysis if possible.
Added data from Freireich EJ et al., "The Effect of 6-Mercaptopurine on the Duration of Steriod-induced Remissions in Acute Leukemia: A Model for Evaluation of Other Potentially Useful Therapy" Blood 1963
TMLE now allows general sklearn algorithms. Fixed issue where predict_proba() is used to generate probabilities
within sklearn rather than predict. Looking at this, I am probably going to clean up the logic behind this and
the rest of custom_model functionality in the future
AIPW object now contains risk_difference and risk_ratio to match RiskRatio and RiskDifference
classes
MINOR CHANGES:
TMLE now allows user-specified prediction models (like machine learning models). This is done by setting the option
argument custom_model to a fitted model with the predict() function. For a full tutorial (with SuPyLearner), see
the website.
Updated API for printing model results to the console. All branches have been updated to
use print_results now. (Thanks Cameron Davidson-Pilon)
Semi-Bayesian function now calculates a check on the compatibility between the prior and data. It generates a warning if a small p-value is detected (p < 0.05). The full information on this check can be read in Modern Epidemiology 3rd edition pg340.
BIG CHANGES:
To conform with PEP and for clarity, all association/effect measures on a pandas dataframe are now class statements. This makes them distinct from the summary data calculators. Additionally, it allows users to access any part of the results now, unlike the previous implementation. The SD can be pulled from the corresponds results dataframe. Please see the updated webiste for how to use the class statements.
Name changes within the calculator branch. With the shift of the dataframe calculations to classes, now these functions are given more descriptive names. Additionally, all functions now return a list of the point estimate, SD, lower CL, upper CL. Please see the website for all the new function names
Addition of Targeted Maximum Likelihood Estimator as zepid.causal.doublyrobust.TMLE
MINOR CHANGES: Added datasets from;
Glaubiger DL, Makuch R, Schwarz J, Levine AS, Johnson RE. Determination of prognostic factors and their influence on therapeutic results in patients with Ewing's sarcoma. Cancer. 1980;45(8):2213-9
Keil AP, Edwards JK, Richardson DB, Naimi AI, Cole SR. The parametric g-formula for time-to-event data: intuition and a worked example. Epidemiology. 2014;25(6):889-97
Fixed spelling error for dynamic_risk_plot that I somehow missed (previously named dyanmic_risk plot...)
Renamed func_form_plot to functional_form_plot (my abbreviations are bad, and version 0.3.0 should fix all this)
TimeVaryGFormula speed-up: some background optimization to speed up TimeVaryGFormula. Changes include: pd.concat() rather than pd.append() each loop . Shuffled around some statements to execute only once rather than multiple times. In some testing, I went from 22 seconds to run to 3.4 seconds
BIG CHANGES:
IPW all moved to zepid.causal.ipw. zepid.ipw is no longer supported
IPTW, IPCW, IPMW are now their own classes rather than functions. This was done since diagnostics are easier for IPTW and the user can access items directly from the models this way.
Addition of TimeVaryGFormula to fit the g-formula for time-varying exposures/confounders
effect_measure_plot() is now EffectMeasurePlot() to conform to PEP
ROC_curve() is now roc(). Also 'probability' was changed to 'threshold', since it now allows any continuous variable for threshold determinations
MINOR CHANGES:
Added sensitivity analysis as proposed by Fox et al. 2005 (MonteCarloRR)
Updated Sensitivity and Specificity functionality. Added Diagnostics, which calculates both sensitivity and specificity.
Updated dynamic risk plots to avoid merging warning. Input timeline is converted to a integer (x100000), merged, then back converted
Updated spline to use np.where rather than list comprehension
Summary data calculators are now within zepid.calc.utils
FUTURE CHANGES:
All pandas effect/association measure calculations will be migrating from functions to classes in a future version. This will better meet PEP syntax guidelines and allow users to extract elements/print results. Still deciding on the setup for this... No changes are coming to summary measure calculators (aside from possibly name changes). Intended as part of v0.3.0
Addition of Targeted Maximum Likelihood Estimation (TMLE). No current timeline developed
Addition of IPW for Interference settings. No current timeline but hopefully before 2018 ends
Further conforming to PEP guidelines (my bad)
Removed histogram option from IPTW in favor of kernel density. Since histograms are easy to generate with matplotlib, just dropped the entire option.
Created causal branch. IPW functions moved inside this branch
Added depreciation warning to the IPW branch, since this will be removed in 0.2 in favor of the causal branch for organization of future implemented methods
Added time-fixed g-formula
Added simple double-robust estimator (based on Funk et al 2011)
Fix to 0.1.4 and since PyPI does not allow reuse of library versions, I had to create new one. Fixes issue with ipcw_prep() that was a pandas error (tried to drop NoneType from columns)
Updates: Added dynamic risk plot
Fixes: Added user option to allow late entries for ipcw_prep()
Updates: added ROC curve generator to graphics, allows user-specification of censoring indicator to ipcw,
Original release. Previous versions (0.1.0, 0.1.1) had errors I found when trying to install via PyPI. I forgot to
include the package statement in setup