Multiple hypothesis testing is a fundamental problem in statistical inference. The failure to manage the multiple testing problem has been highlighted as one of the elements contributing to the replicability crisis in science (Ioannidis 2015). Methodologies have been developed for a family of hypotheses to adjust the significance levels to manage the multiple testing situation by controlling error metrics such as the familywise error rate (FWER) or the false discovery rate (FDR).

Frequently, modern data analysis problems have a further complexity that the hypothesis arrive sequentially in a stream. This introduces the challenge that at each step the investigator must decide whether to reject the current null hypothesis without having access to the future p-values or the total number of hypothesis to be tested, but does have knowledge of the historic decisions to date. The International Mouse Phenotyping Consortium (Koscielny *et al*, 2013), provides a concrete example of such a scenario. Here the dataset is constantly growing as new knockout mice lines are generated and phenotyping data uploaded to a database.

Javanmard and Montanari have proposed two procedures, LOND and LORD, to control FDR in an online manner (Javanmard and Montanari (2015, 2018)). The LOND procedure sets the significance level \(\alpha_i\) based on the number of discoveries made so far, while LORD sets them according to the time of the most recent discovery. This package, onlineFDR, implements these procedures and provides wrapper functions to apply them to a historic dataset or a growing dataset. As a comparison, we have also provided a wrapper function for implementation of a method based on the Bonferroni approach adapted to an online scenario. This vignette explains the use of the package and demonstrates a typical workflow.

A dataset with three columns (an identifier (‘id’), date (‘date’) and p-value (‘pval’)) is passed to one of the onlineFDR wrapper functions.

The function orders the information by date. If there are multiple p-values with the same date (i.e. the same batch), the order of the p-values within each batch is randomised by default. In order for the randomisation of the p-values to be reproducible, it is necessary to set a seed (via the

`set.seed`

function) before calling the wrapper function (see also step 6).For each hypothesis test, an adjusted test level (

`alphai`

) is calculated, which gives the threshold at which the corresponding p-value would be declared significant.Using the p-values provided and the alphai, an indicator of discoveries (

`R`

) is calculated, where`R[i] = 1`

corresponds to hypothesis i being rejected (and`R[i] = 0`

otherwise).A dataframe is returned, reordered by batch, with the original data and the newly calculated

`alphai`

and`R`

.For simplicity, as the dataset grows the new larger dataset should be passed to the wrapper function and the values recalculated repeating steps 1 to 5. In order for the randomisation of the data within the previous batches to remain the same (and hence to allow for reproducibility of the results),

*the same seed should be used for all analyses*.

####1: LOND Implements the LOND procedure for online FDR control, where LOND stands for (significance) Levels based On Number of Discoveries, as presented by Javanmard and Montanari (2015). The procedure controls the FDR for independent p-values, with an option (dep = TRUE) which guarantees control for dependent p-values.

####2: LORD Implements the LORD procedure for online FDR control where LORD stands for (significance) Levels based On Recent Discovery, as presented by Javanmard and Montanari (2018). The function provides three different versions of the procedure for independent p-values, see ‘Theory behind onlineFDR’.

####3: LORDdep Implements the LORD procedure for online FDR control for dependent p-values, where LORD stands for (significance) Levels based On Recent Discovery, as presented by Javanmard and Montanari (2018).

####4: BonfInfinite Implements online FDR control using a Bonferroni-like test.

Here we show the steps to achieve online FDR control of a growing dataset. First you load a dataframe with the three columns: an identifier (‘id’), date (‘date’) and p-value (‘pval’), and then call the wrapper function of interest. In order for the results to be reproducible, we also set a seed using the `set.seed`

function.

```
library(onlineFDR)
sample.df <- data.frame(
id = c('A15432', 'B90969', 'C18705', 'B49731', 'E99902',
'C38292', 'A30619', 'D46627', 'E29198', 'A41418',
'D51456', 'C88669', 'E03673', 'A63155', 'B66033'),
date = as.Date(c(rep("2014-12-01",3),
rep("2015-09-21",5),
rep("2016-05-19",2),
"2016-11-12",
rep("2017-03-27",4))),
pval = c(2.90e-14, 0.06743, 0.01514, 0.08174, 0.00171,
3.61e-05, 0.79149, 0.27201, 0.28295, 7.59e-08,
0.69274, 0.30443, 0.000487, 0.72342, 0.54757))
set.seed(1)
results <- LORD(sample.df)
results
#> id date pval alphai R
#> 1 A15432 2014-12-01 2.9000e-14 0.0002675839 1
#> 2 C18705 2014-12-01 1.5140e-02 0.0026615183 0
#> 3 B90969 2014-12-01 6.7430e-02 0.0005787961 0
#> 4 D46627 2015-09-21 2.7201e-01 0.0004929725 0
#> 5 B49731 2015-09-21 8.1740e-02 0.0004099744 0
#> 6 C38292 2015-09-21 3.6100e-05 0.0003475734 1
#> 7 E99902 2015-09-21 1.7100e-03 0.0048294380 1
#> 8 A30619 2015-09-21 7.9149e-01 0.0069792368 0
#> 9 A41418 2016-05-19 7.5900e-08 0.0015177634 1
#> 10 E29198 2016-05-19 2.8295e-01 0.0089327595 0
#> 11 D51456 2016-11-12 6.9274e-01 0.0019425928 0
#> 12 C88669 2017-03-27 3.0443e-01 0.0016545462 0
#> 13 A63155 2017-03-27 7.2342e-01 0.0013759827 0
#> 14 B66033 2017-03-27 5.4757e-01 0.0011665482 0
#> 15 E03673 2017-03-27 4.8700e-04 0.0010091523 1
```

A dataset with three columns (an identifier (‘id’), date (‘date’) and p-value (‘pval’)). All p values generated should be passed to the function (and not just the significant p-values). An exception to this would be if you have implemented an orthogonal filter to reduce the dataset size, such as discussed in (Burgon *et al.*, 2010).

For each hypothesis test, the functions calculate the adjusted test level (`alphai`

) at which the corresponding p-value would be declared significant.

Also calculated is an indicator function of discoveries (`R`

), where `R[i] = 1`

corresponds to hypothesis i being rejected, otherwise `R[i] = 0`

.

A dataframe is returned, reordered by batch, with the original data and the newly calculated `alphai`

and `R`

.

All questions regarding onlineFDR should be posted to the **Bioconductor support site**, which serves as a searchable knowledge base of questions and answers:

https://support.bioconductor.org

Posting a question and tagging with “onlineFDR” will automatically send an alert to the package authors to respond on the support site.

Consider a sequence of hypotheses \(H_1, H_2, H_3, \ldots\) that arrive sequentially in a stream, with corresponding \(p\)-values \((p_1, p_2, p_3, \ldots)\). A testing procedure provides a sequence of test levels \(\alpha_i\), with corresponding decision rule: \[ R_i = \begin{cases} 1 & \text{if } p_i \leq \alpha_i & (\text{reject } H_i)\\ 0 & \text{otherwise} & (\text{accept } H_i) \end{cases} \]

In *online* testing, the test levels can only be functions of the prior decisions, i.e. \(\alpha_i = \alpha_i(R_1, R_2, \ldots, R_{i-1})\).

Javanmard and Montanari (2015, 2018) proposed two procedures for online control. The first is LOND, which stands for (significance) Levels based On Number of Discoveries. The second is LORD, which stands for (significance) Levels based On Recent Discovery.

The LOND procedure controls the FDR for independent \(p\)-values. Given an overall significance level \(\alpha\), we choose an infinite sequence of non-negative numbers \(\beta = (\beta_i)_{i \in \mathbb{N}}\) such that they sum to \(\alpha\). The values of the test levels \(\alpha_i\) are chosen as follows: \[ \alpha_i = \beta_i (D(i-1) + 1) \] where \(D(n) = \sum_{i=1}^n R_i\) denotes the number of discoveries (i.e. rejections) in the first \(n\) hypotheses tested.

LOND can be adjusted to also control FDR under *dependent* \(p\)-values. To do so, it is modified with \(\tilde{\beta}_i = \beta_i/H(i)\) in place of \(\beta_i\), where \(H(i) = \sum_{j=1}^i \frac{1}{j}\) is the \(i\)-th harmonic number. Note that this leads to a substantial loss in power compared to the unadjusted LOND procedure.

The default sequence of \(\beta\) is given by \[\beta_j = C \alpha \frac{\log(\max(j, 2))}{j e^{\sqrt{\log j}}}\] where \(C \approx 0.07720838\), as proposed by Javanmard and Montanari (2018) equation 31.

The LORD procedure controls the FDR for independent \(p\)-values. We first fix a sequence of non-negative numbers \(\gamma = (\gamma_i)_{i \in \mathbb{N}}\) such that \(\gamma_i \geq \gamma_j\) for \(i \leq j\) and \(\sum_{i=1}^{\infty} \gamma_i = 1\). At each time \(i\), let \(\tau_i\) be the last time a discovery was made before \(i\): \[ \tau_i = \max \left\{ l \in \{1, \ldots, i-1\} : R_l = 1\right\} \] Also let \(T_i\) be the set of discovery times up to time \(i\): \[ T(i) = \left\{ l \in \{1, \ldots, i-1 \} : R_l = 1 \right\}\]

LORD depends on constants \(w_0\) and \(b_0\), where \(w_0 \geq 0\) represents the initial ‘wealth’ of the procedure and \(b_0 > 0\) represents the ‘payout’ for rejecting a hypothesis. We require \(w_0+b_0 \leq \alpha\) for FDR control to hold.

Javanmard and Montanari (2018) presented three different versions of LORD, which have different definitions of the test levels \(\alpha_i\).

**LORD 1**: The test levels are based on the time of the last discovery, with \[ \alpha_i = \begin{cases} \gamma_i w_0 & \text{ if } i \leq t_1 \\ \gamma_{i - \tau_i} b_0 & \text{ if } i > t_1 \end{cases} \] where \(t_1\) denotes the time of the first discovery.**LORD 2**: The test levels are based on all previous discovery times, with \[ \alpha_i = \gamma_i w_0 + \left(\sum_{l \in T(i)} \gamma_{i-l} \right) b_0 \]**LORD 3**: The test levels depend on the time of the last discovery time and the wealth accumulated at that time, with \[ \alpha_i = \gamma_{i - \tau_i} W(\tau_i) \] where \(\tau_1 = 0\). Here \(\{W(j)\}_{j \geq 0}\) represents the ‘wealth’ available at time \(j\), and is defined recursively: \[ \begin{align} W(0) & = w_0 \nonumber \\ W(j) & = W(j-1) - \alpha_{j-1} + b_0 R_j \end{align} \] where \(w_0\) represents the initial ‘wealth’, and \(b_0\) represents the amount earned for rejecting a hypothesis.

LORD 1 and LORD 2 are instances of monotone generalised alpha investing rules, and hence provably control the FDR for independent p-values provided \(w_0 + b_0 \leq \alpha\). In Javanmard and Montanari (2018), the authors also demonstrate empirically that LORD 3 controls the FDR.

Note that LORD 1 will always have a lower power than LORD 2 and LORD 3, and is included for completeness. In many scenarios LORD 3 will have a higher power than LORD 2, and so we would recommend using LORD 3 (the default) unless there is a need for a method for a provable guarantee of FDR control in which case we would recommend using LORD 2.

In all versions, the default sequence of \(\gamma\) is given by \[\gamma_j = C \frac{\log(\max(j, 2))}{j e^{\sqrt{\log j}}}\] where \(C \approx 0.07720838\), as proposed by Javanmard and Montanari (2018) equation 31.

Javanmard and Montanari (2018) also presented an adjusted version of LORD that is valid for *dependent* p-values. Similarly to LORD 3, the adjusted test levels are set equal to \[ \alpha_i = \xi_i W(\tau_i)\] where (assuming \(w_0 \leq b_0\)), \(\sum_{j=1}^{\infty} \xi_i (1 + \log(j)) \leq \alpha / b_0\)

The default sequence of \(\xi\) is given by \[ \xi_j = \frac{C \alpha }{b_0 j \log(\max(j, 2))^3}\] where \(C \approx 0.139307\).

Note that allowing for dependent p-values can lead to a substantial loss in power compared with the LORD procedures described above.

The procedure controls the FDR (as well as the FWER) for a potentially infinite stream of p-values using a Bonferroni-like test. Given an overall significance level \(\alpha\), the test levels are chosen as \[\alpha_i = \alpha \gamma_i\] where \(\sum_{i=1}^{\infty} \gamma_i = 1\) and \(\gamma_i \geq 0\). Note that the procedure is also valid for dependent p-values.

The default sequence of \(\gamma\) is the same as that for \(\xi\) for LORD given here.

As noted above, the LOND procedure and LORD procedures (1, 2 and 3) assumes *independent* p-values, with adjusted versions of LOND and LORD available for *dependent* p-values (where the dependencies may be arbitrary). The Bonferroni-like procedure also controls the FDR for dependent p-values.

By way of comparison, the usual Benjamini and Hochberg method for controlling the FDR assumes that the p-values are positively dependent (for example, for multivariate normal test statistics with a positive correlation matrix). See Benjamini and Yekutieli (2001) for further technical details.

In the following section, we consider the arguments that a typical user might consider amending for their analysis.

As a default, the `alpha`

argument is set to 0.05, where `alpha`

sets the overall significance level of the FDR procedure. As a community the standard significance level utilised is the 5%. However, there are applications where an alternate threshold could be considered. For example, a more stringent threshold might be appropriate when there are limited resources to follow up significant findings. A less stringent threshold might be appropriate when the downstream analysis is a global analysis which can tolerate a higher proportion of false positives.

To ensure correct interpretation of the dates provided there is a date.format argument. As a default, the date format is set to receive dates as year-month(00-12)-day(number). The following website provides clear guidance on symbols used to interpret the date information: https://www.statmethods.net/input/dates.html

As a default, the `random`

argument is set to `TRUE`

. In this situation, the order of p-values in each batch (i.e. with the same date) are randomised. This is to avoid the risk of p-values being ordered post-hoc, which can lead to an inflation of the FDR. As the dataset grows the data is reprocessed. To ensure the consistency of the output (with the randomisation within the previous batches remaining the same), it is necessary to set the same `seed`

for all analyses.

The user also has the option to turn off the randomisation step, by setting the `random`

argument to `FALSE`

. This approach would be appropriate if the user has both a date *and* a time stamp for the p-values, in which case the data should be ordered by date and time beforehand and then passed to a wrapper function. Another scenario would be when p-values within the batches are ordered using *independent* side information, so that hypotheses most likely to be rejected come first, which would potentially increase the power of the procedure (see Javanmard and Montanari (2018) and Li and Barber (2017)).

As a default, the `dep`

argument is set to `FALSE`

. Alternatively, this could be set to `TRUE`

and will implement the LOND procedure to guarantee FDR control for dependent p-values. This method will in general be more conservative.

```
set.seed(1); results.indep <- LOND(sample.df) # for independent p-values
set.seed(1); results.dep <- LOND(sample.df, dep=TRUE) # for dependent p-values
# compare adjusted test levels
cbind(independent = results.indep$alphai, dependent = results.dep$alphai)
#> independent dependent
#> [1,] 0.0026758385 0.0026758385
#> [2,] 0.0011638206 0.0007758804
#> [3,] 0.0009912499 0.0005406818
#> [4,] 0.0008243606 0.0003956931
#> [5,] 0.0006988870 0.0003060819
#> [6,] 0.0006045900 0.0002467714
#> [7,] 0.0007979166 0.0003077364
#> [8,] 0.0007117838 0.0002618915
#> [9,] 0.0006421423 0.0002269882
#> [10,] 0.0007796504 0.0002661860
#> [11,] 0.0007155186 0.0002369363
#> [12,] 0.0006610273 0.0002130140
#> [13,] 0.0006141682 0.0001931265
#> [14,] 0.0005734509 0.0001763616
#> [15,] 0.0005377472 0.0001620585
```

The vector `beta`

is supplied by default, but can optionally be specified by the user (as described above, see the formula for \(\beta_j\) here).

The default version of LORD used is version 3, but the user can optionally specify versions 1 and 2 using the `version`

argument (see here for further details about the different versions).

```
set.seed(1); results.LORD1 <- LORD(sample.df, version=1)
set.seed(1); results.LORD2 <- LORD(sample.df, version=2)
set.seed(1); results.LORD3 <- LORD(sample.df) # default version = 3
# compare adjusted test levels
cbind(LORD1 = results.LORD1$alphai,
LORD2 = results.LORD2$alphai,
LORD3 = results.LORD3$alphai)
#> LORD1 LORD2 LORD3
#> [1,] 0.0002675839 0.0002675839 0.0002675839
#> [2,] 0.0024082547 0.0024664457 0.0026615183
#> [3,] 0.0005237193 0.0005732818 0.0005787961
#> [4,] 0.0004460624 0.0004872805 0.0004929725
#> [5,] 0.0003709623 0.0004059066 0.0004099744
#> [6,] 0.0003144991 0.0003447286 0.0003475734
#> [7,] 0.0024082547 0.0027069174 0.0048294380
#> [8,] 0.0024082547 0.0031950751 0.0069792368
#> [9,] 0.0005237193 0.0012047216 0.0015177634
#> [10,] 0.0024082547 0.0034374134 0.0089327595
#> [11,] 0.0005237193 0.0014024900 0.0019425928
#> [12,] 0.0004460624 0.0012101445 0.0016545462
#> [13,] 0.0003709623 0.0010464881 0.0013759827
#> [14,] 0.0003144991 0.0009199334 0.0011665482
#> [15,] 0.0002720655 0.0008207135 0.0010091523
```

By default `w0 = alpha/10`

and `b0 = alpha - w0`

, but can optionally be specified by the user subject to the requirements that \(w_0 \geq 0\), \(b_0 > 0\) and \(w_0+b_0 \leq \alpha\).

The value of `gammai`

is also supplied by default, but can optionally be specified by the user (as described above, see the formula for \(\gamma_j\) here).

By default `w0 = alpha/10`

and `b0 = alpha - w0`

, but can optionally be specified by the user subject to the requirements that \(w_0 \geq 0\), \(b_0 > 0\) and \(w_0+b_0 \leq \alpha\).

The value of `xi`

is also supplied by default, but can optionally be specified by the user (as described above, see the formula for \(\xi_j\) here).

The test levels `alphai`

are supplied by default, but can optionally be specified by the user (as described above, see here).

We would like to thank the IMPC team (via Jeremy Mason and Hamed Haseli) for useful discussions during the development of the package.

Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. *The Annals of Statistics*, 29(4):1165-1188.

Bourgon, R., Gentleman, R., and Huber, W. (2010). Independent filtering increases detection power for high-throughput experiments. *Proceedings of the National Academy of Sciences*, 107(21), 9546-9551.

Ioannidis, JPA. (2005). Why most published research findings are false. *PLoS Medicine*, 2.8:e124.

Javanmard, A., and Montanari, A. (2015). On Online Control of False Discovery Rate. *arXiv preprint*, https://arxiv.org/abs/1502.06197.

Javanmard, A., and Montanari, A. (2018). Online Rules for Control of False Discovery Rate and False Discovery Exceedance. *Annals of Statistics*, 46(2):526-554.

Li, A., and Barber, FG. (2017). Accumulation Tests for FDR Control in Ordered Hypothesis Testing. *Journal of the American Statistical Association*, 112(518):837-849.

Koscielny, G., *et al*. (2013). The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data. *Nucleic Acids Research*, 42.D1:D802-D809.